31 March 2015

Research Articles for the Week of March 30, 2015

Articles from March_30_2015

Research Committee Selected Articles for the Week of March_30_2015

Transmission of Haemorrhagic Fever with Renal Syndrome in China and the Role of Climate Factors: A Review

Haemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease that poses a serious public health threat in China. HFRS is caused by hantaviruses, mainly Seoul virus in urban areas and Hantaan virus in agricultural areas. Although preventive measures including vaccination programs and rodent control measures have resulted in a decline in cases in recent years, there has been an increase in incidence in some areas and new endemic areas have emerged. This review summarises the recent literature relating to the effects of climatic factors on the incidence of HFRS in China and discusses future research directions. Temperature, precipitation and humidity affect crop yields, rodent breeding patterns and disease transmission, and these can be influenced by a changing climate. Detailed surveillance of infections caused by Hantaan and Seoul viruses and further research on the viral agents will aid in interpretation of spatiotemporal patterns and a better understanding of the environmental and ecological drivers of HFRS amid China's rapidly urbanising landscape and changing climate. © 2015 The Authors.

Information seeking regarding tobacco and lung cancer: Effects of seasonality

This paper conducted one of the first comprehensive international Internet analyses of seasonal patterns in information seeking concerning tobacco and lung cancer. Search query data for the terms "tobacco" and "lung cancer" from January 2004 to January 2014 was collected from Google Trends. The relevant countries included the USA, Canada, the UK, Australia, and China. Two statistical approaches including periodogram and cross-correlation were applied to analyze seasonal patterns in the collected search trends and their associations. For these countries except China, four out of six cross-correlations of seasonal components of the search trends regarding tobacco were above 0.600. For these English-speaking countries, similar patterns existed in the data concerning lung cancer, and all cross-correlations between seasonal components of the search trends regarding tobacco and that regarding lung cancer were also above 0.700. Seasonal patterns widely exist in information seeking concerning tobacco and lung cancer on an international scale. The findings provide a piece of novel Internet-based evidence for the seasonality and health effects of tobacco use. © 2015 Zhang et al.

Clostridium difficile infection seasonality: Patterns across hemispheres and continents - A systematic review

Background: Studies have demonstrated seasonal variability in rates of Clostridium difficile infection (CDI). Synthesising all available information on seasonality is a necessary step in identifying large-scale epidemiological patterns and elucidating underlying causes. Methods: Three medical and life sciences publication databases were searched from inception to October 2014 for longitudinal epidemiological studies written in English, Spanish or Portuguese that reported the incidence of CDI. The monthly frequency of CDI were extracted, standardized and weighted according to the number of follow-up months. Cross correlation coefficients (XCORR) were calculated to examine the correlation and lag between the yearmonth frequencies of reported CDI across hemispheres and continents. Results: The search identified 13, 5 and 2 studies from North America, Europe, and Oceania, respectively that met the inclusion criteria. CDI had a similar seasonal pattern in the Northern and Southern Hemisphere characterized by a peak in spring and lower frequencies of CDI in summer/autumn with a lag of 8 months (XCORR = 0.60) between hemispheres. There was no difference between the seasonal patterns across European and North American countries. Conclusion: CDI demonstrates a distinct seasonal pattern that is consistent across North America, Europe and Oceania. Further studies are required to identify the driving factors of the observed seasonality. © 2015 Furuya-Kanamori et al.

Levels of alarm thresholds of meningitis outbreaks in Hamadan Province, West of Iran

Background: Few studies have focused on syndromic data to determine levels of alarm thresholds to detection of meningitis outbreaks. The purpose of this study was to determine threshold levels of meningitis outbreak in Hamadan Province, west of Iran. Methods: Data on both confirmed and suspected cases of meningitis (fever and neurological symptom) from 21 March 2010 to 20 March 2012 were used in Hamadan Province, Iran. Alarm threshold levels of meningitis outbreak were determined using four different methods including absolute values or standard method, relative increase, statistical cutoff points and upper control limit of exponentially weighted moving average (EWMA) algorithm. Results: Among 723 reported cases, 41 were diagnosed to have meningitis. Standard level of alarm thresholds for meningitis outbreak was determined as incidence of 5/100000 persons. Increasing 1.5 to two times in reported cases of suspected meningitis per week was known as the threshold levels according to relative increase method. An occurrence four cases of suspected meningitis per week that equals to 90th percentile was chosen as alarm thresholds by statistical cut off point method. The corresponding value according to EWMA algorithm was 2.57 i.e. three cases. Conclusions: Policy makers and staff of syndromic surveillance systems are highly recommended to apply the above different methods to determine the levels of alarm threshold. © 2015 Health Hamadan University of Medical Sciences. All rights reserved.

A study of waterborne diseases during flooding using Radarsat-2 imagery and a back propagation neural network algorithm

Flood disasters are closely associated with an increased risk of infection, particularly from waterborne diseases. Most studies of waterborne diseases have relied on the direct determination of pathogens in contaminated water to assess disease risk. In contrast, this study aims to use an indirect assessment that employs a back propagation neural network (BPNN) for modelling diarrheal outbreaks using data from remote sensing and dissolved-oxygen (DO) measurements to reduce cost and time. Our study area is in Ayutthaya province, which was very severely affected by the catastrophic 2011 Thailand flood. BPNN was used to model the relationships among the parameters of the flood and the water quality and the risk of people becoming infected. Radarsat-2 scenes were utilized to estimate flood area and duration, while the flood water quality was derived from the interpolation of DO samples. The risk-ratio function was applied to the diarrheal morbidity to define the level of outbreak detection and the outbreak periods. Tests of the BPNN prediction model produced high prediction accuracy of diarrheal-outbreak risk with low prediction error and a high degree of correlation. With the promising accuracy of our approach, decision-makers can plan rapid and comprehensively preventive measures and countermeasures in advance. © 2013, Taylor & Francis.

Community outbreak of legionellosis and an environmental investigation into a community water system

During two legionellosis outbreak investigations, one at a geriatric centre and the other in high-rise housing for seniors, it was observed that additional cases of legionellosis occurred in nearby smaller residential settings. This apparent geographical cluster of legionellosis occurred in the same general area of a community water storage tank. No potential airborne sources in or near the area could be identified, but a community water system storage tank that was centrally located among case residences spurred an investigation of water-quality factors in the identified investigation area. Conditions conducive for Legionella growth, particularly low chlorine residuals, were found. The rate of legionellosis among residents aged ?50 years in the investigation areas (61.0 and 64.1/100 000) was eight times higher than in the rest of the service area (9.0/100 000) and almost 20 times higher than the statewide annual average incidence rate (3.2/100 000). A water mains flushing programme in the area was launched by the water utility, and water samples taken before and during flushing found L. pneumophila. © Cambridge University Press 2014.

Evaluating vaccination strategies to control foot-and-mouth disease: A model comparison study

Simulation models can offer valuable insights into the effectiveness of different control strategies and act as important decision support tools when comparing and evaluating outbreak scenarios and control strategies. An international modelling study was performed to compare a range of vaccination strategies in the control of foot-and-mouth disease (FMD). Modelling groups from five countries (Australia, New Zealand, USA, UK, The Netherlands) participated in the study. Vaccination is increasingly being recognized as a potentially important tool in the control of FMD, although there is considerable uncertainty as to how and when it should be used. We sought to compare model outputs and assess the effectiveness of different vaccination strategies in the control of FMD. Using a standardized outbreak scenario based on data from an FMD exercise in the UK in 2010, the study showed general agreement between respective models in terms of the effectiveness of vaccination. Under the scenario assumptions, all models demonstrated that vaccination with 'stamping-out' of infected premises led to a significant reduction in predicted epidemic size and duration compared to the 'stamping-out' strategy alone. For all models there were advantages in vaccinating cattle-only rather than all species, using 3-km vaccination rings immediately around infected premises, and starting vaccination earlier in the control programme. This study has shown that certain vaccination strategies are robust even to substantial differences in model configurations. This result should increase end-user confidence in conclusions drawn from model outputs. These results can be used to support and develop effective policies for FMD control. © Cambridge University Press 2014.

Fairness versus efficiency of vaccine allocation strategies

Objectives To develop a framework to objectively measure the degree of fairness of any allocation rule aimed at distributing a limited stockpile of vaccines to contain the spread of influenza. Methods The trade-off between the efficiency and fairness of allocation strategies was demonstrated through an illustrative simulation study of an influenza epidemic in Southwestern Virginia. A Susceptible-Exposed-Infectious-Recovered model was used to represent the disease progression within the host. Results Our findings showed that among all the criteria considered here, the household size (largest first) combined with age (youngest first)-based strategy leads to the best outcome. At 80% fairness, highest efficiency can be achieved but in order to be 100% fair, disease prevalence will have to rise by approximately 1.5%. Conclusions This research provides a framework to objectively determine the degree of fairness of vaccine allocation strategies. © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR).

Modeling avian influenza using Filippov systems to determine culling of infected birds and quarantine

The growing number of reported avian influenza cases has prompted awareness of the effectiveness of pharmaceutical or/and non-pharmaceutical interventions that aim to suppress the transmission rate. We propose two Filippov models with threshold policy: the avian-only model with culling of infected birds and the SIIR (Susceptible-Infected-Infected-Recovered) model with quarantine. The dynamical systems of these two models are governed by nonlinear ordinary differential equations with discontinuous right-hand sides. The solutions of these two models will converge to either one of the two endemic equilibria or the sliding equilibrium on the discontinuous surface. We prove that the avian-only model achieves global stability. Moreover, by choosing an appropriate quarantine threshold level Ic in the SIIR model, this model converges to an equilibrium in the region below Ic or a sliding equilibrium, suggesting the outbreak can be controlled. Therefore a well-defined threshold policy is important for us to combat the influenza outbreak efficiently. © 2015 Elsevier Ltd. All rights reserved.

Spatiotemporal prediction of fine particulate matter during the 2008 Northern California wildfires using machine learning

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event. © 2015 American Chemical Society.

Use of linked electronic health records to assess mortality and length of stay associated with pandemic influenza A(H1N1)pdm09 at a UK teaching hospital

Effective use of data linkage is becoming an increasingly important focus in the new healthcare system in England. We linked data from the results of a multiplex PCR assay for respiratory viruses for a population of 230 inpatients at a UK teaching hospital with their patient administrative system records in order to compare the mortality and length of stay of patients who tested positive for influenza A(H1N1)pdm09 with those positive for another influenza A virus. The results indicated a reduced risk of death among influenza A(H1N1)pdm09 patients compared to other influenza A strains, with an adjusted risk ratio of 0.25 (95% confidence interval 0.08-0.75, P = 0.01), while no significant differences were found between the lengths of stay in the hospital for these two groups. Further development of such methods to link hospital data in a routine fashion could provide a rapid means of gaining epidemiological insights into emerging infectious diseases. © Cambridge University Press 2014.

Social contact patterns of school-age children in Taiwan: Comparison of the term time and holiday periods

School closure is one of the most common interventions in the early weeks of an influenza pandemic. Few studies have investigated social contact patterns and compared individual student contact characteristics during the school term and holiday periods in Taiwan. Here, we conducted a well-used questionnaire survey in a junior high school (grades 7-8) in June 2013. All 150 diary-based effective questionnaires covering conversation and skin-to-skin contact behaviour were surveyed. Two questionnaires for each participant were designed to investigate the individual-level difference of contact numbers per day during the two periods. The questionnaire response rate was 44%. The average number of contacts during term time (20.0 contacts per day) and holiday periods (12.6 contacts per day) were significantly different (P<0.05). The dominant contact frequencies and duration were everyday contact (89.10%) and contacts lasting less than 5 minutes (37.09%). The greatest differences occurred within the 13-19 years age groups. The result presented in this study provide an indication of the likely reduction in daily contact frequency that might occur if a school closure policy was adopted in the event of an influenza pandemic in Taiwan. Comparing contact patterns during term time and holiday periods, the number of contacts decreased by 40%. This study is the first research to investigate the contact numbers and contact characteristics for school-age children during the school term and a holiday period in Taiwan. With regard to public health, this study could provide the basic contact information and database for modelling influenza epidemics for minimizing the spread of influenza that depends on personal contacts for transmission. © Cambridge University Press 2014.

Statistical foundations for model-based adjustments

Most epidemiology textbooks that discuss models are vague on details of model selection. This lack of detail may be understandable since selection should be strongly influenced by features of the particular study, including contextual (prior) information about covariates that may confound, modify, or mediate the effect under study. It is thus important that authors document their modeling goals and strategies and understand the contextual interpretation of model parameters and model selection criteria. To illustrate this point, we review several established strategies for selecting model covariates, describe their shortcomings, and point to refinements, assuming that the main goal is to derive the most accurate effect estimates obtainable from the data and available resources. This goal shifts the focus to prediction of exposure or potential outcomes (or both) to adjust for confounding; it thus differs from the goal of ordinary statistical modeling, which is to passively predict outcomes. Nonetheless, methods and software for passive prediction can be used for causal inference as well, provided that the target parameters are shifted appropriately. Copyright © 2015 by Annual Reviews. All rights reserved.

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Research Articles for the Week of March 23, 2015

Articles from March_23_2015

Research Committee Selected Articles for the Week of March 23, 2015

    Highlights for the week’s selected surveillance-related articles:
  • Two overview articles in the Journal Science on disease modelling with authors including numerous pioneers in the field
  • Several articles on the modeling of incidence of parasitic diseases from a special issue of Advances in Parasitology
  • Several policy articles from current Disaster Medicine and Public Health Preparedness and other journals on global post-Ebola surveillance, communication, and response

How environmental conditions impact mosquito ecology and Japanese encephalitis: An eco-epidemiological approach

Japanese encephalitis (JE) is one of the major vector-borne diseases in Southeast Asia and the Western Pacific region, posing a threat to human health. In rural and suburban areas, traditional rice farming and intensive pig breeding provide an ideal environment for both mosquito development and the transmission of JEV among human beings. Combining surveillance data for mosquito vectors, human JE cases, and environmental conditions in Changsha, China, 2004-2009, generalized threshold models were constructed to project the mosquito and JE dynamics. Temperature and rainfall were found to be closely associated with mosquito density at 1, and 4month lag, respectively. The two thresholds, maximum temperature of 22-23°C for mosquito development and minimum temperature of 25-26°C for JEV transmission, play key roles in the ecology of JEV. The model predicts that, in the upper regime, a 1g/m3 increase in absolute humidity would on average increase human cases by 68-84%. A shift in mosquito species composition in 2007 was observed, and possibly caused by a drought. Effective predictive models could be used in risk management to provide early warnings for potential JE transmission.

Modeling infectious disease dynamics in the complex landscape of global health

Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.

Reduced vaccination and the risk of measles and other childhood infections post-Ebola

The Ebola epidemic in West Africa has caused substantial morbidity and mortality. The outbreak has also disrupted health care services, including childhood vaccinations, creating a second public health crisis. We project that after 6 to 18 months of disruptions, a large connected cluster of children unvaccinated for measles will accumulate across Guinea, Liberia, and Sierra Leone. This pool of susceptibility increases the expected size of a regional measles outbreak from 127,000 to 227,000 cases after 18 months, resulting in 2000 to 16,000 additional deaths (comparable to the numbers of Ebola deaths reported thus far). There is a clear path to avoiding outbreaks of childhood vaccine-preventable diseases once the threat of Ebola begins to recede: an aggressive regional vaccination campaign aimed at age groups left unprotected because of health care disruptions.

Multi-scale modeling for the transmission of influenza and the evaluation of interventions toward it

Mathematical modeling of influenza epidemic is important for analyzing the main cause of the epidemic and finding effective interventions towards it. The epidemic is a dynamic process. In this process, daily infections are caused by people's contacts, and the frequency of contacts can be mainly influenced by their cognition to the disease. The cognition is in turn influenced by daily illness attack rate, climate, and other environment factors. Few existing methods considered the dynamic process in their models. Therefore, their prediction results can hardly be explained by the mechanisms of epidemic spreading. In this paper, we developed a heterogeneous graph modeling approach (HGM) to describe the dynamic process of influenza virus transmission by taking advantage of our unique clinical data. We built social network of studied region and embedded an Agent-Based Model (ABM) in the HGM to describe the dynamic change of an epidemic. Our simulations have a good agreement with clinical data. Parameter sensitivity analysis showed that temperature influences the dynamic of epidemic significantly and system behavior analysis showed social network degree is a critical factor determining the size of an epidemic. Finally, multiple scenarios for vaccination and school closure strategies were simulated and their performance was analyzed.

Using an adjusted serfling regression model to improve the early warning at the arrival of peak timing of influenza in beijing

Serfling-type periodic regression models have been widely used to identify and analyse epidemic of influenza. In these approaches, the baseline is traditionally determined using cleaned historical non-epidemic data. However, we found that the previous exclusion of epidemic seasons was empirical, since year-year variations in the seasonal pattern of activity had been ignored. Therefore, excluding fixed 'epidemic' months did not seem reasonable. We made some adjustments in the rule of epidemic-period removal to avoid potentially subjective definition of the start and end of epidemic periods. We fitted the baseline iteratively. Firstly, we established a Serfling regression model based on the actual observations without any removals. After that, instead of manually excluding a predefined 'epidemic ' period (the traditional method), we excluded observations which exceeded a calculated boundary. We then established Serfling regression once more using the cleaned data and excluded observations which exceeded a calculated boundary.We repeated this process until the R2 value stopped to increase. In addition, the definitions of the onset of influenza epidemic were heterogeneous, which might make it impossible to accurately evaluate the performance of alternative approaches. We then used this modified model to detect the peak timing of influenza instead of the onset of epidemic and compared this model with traditional Serfling models using observed weekly case counts of influenza-like illness (ILIs), in terms of sensitivity, specificity and lead time. A better performance was observed. In summary, we provide an adjusted Serfling model which may have improved performance over traditional models in early warning at arrival of peak timing of influenza.

Using Mobile Phone Data to Predict the Spatial Spread of Cholera

Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p < 0.05). Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains.

Temporal and spatial monitoring and prediction of epidemic outbreaks

This paper introduces a nonlinear dynamic model to study spatial and temporal dynamics of epidemics of susceptible-infected-removed type. It involves modeling the respective collections of epidemic states and syndromic observations as random finite sets. Each epidemic state consists of the number of infected individuals in an isolated population system and the corresponding partially known parameters of the epidemic model. The infectious disease could spread between population systems with known probabilities based on prior knowledge of ecological and biological features of the environment. The problem is then formulated in the context of Bayesian framework and estimated via a probability hypothesis density filter. Each population system under surveillance is assumed to be homogenous and fixed, with daily reports on the number of infected people available for monitoring and prediction. When model parameters are partially known, results of numerical studies indicate that the proposed approach can help early prediction of the epidemic in terms of peak and duration.

Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis b in china

Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.

Effectiveness of traveller screening for emerging pathogens is shaped by epidemiology and natural history of infection

During outbreaks of high-consequence pathogens, airport screening programs have been deployed to curtail geographic spread of infection. The effectiveness of screening depends on several factors, including pathogen natural history and epidemiology, human behavior, and characteristics of the source epidemic. We developed a mathematical model to understand how these factors combine to influence screening outcomes. We analyzed screening programs for six emerging pathogens in the early and late stages of an epidemic. We show that the effectiveness of different screening tools depends strongly on pathogen natural history and epidemiological features, as well as human factors in implementation and compliance. For pathogens with longer incubation periods, exposure risk detection dominates in growing epidemics, while fever becomes a better target in stable or declining epidemics. For pathogens with short incubation, fever screening drives detection in any epidemic stage. However, even in the most optimistic scenario arrival screening will miss the majority of cases.

Evaluation of the integrated disease surveillance and response system for infectious diseases control in northern Ghana

Background: Well-functioning surveillance systems are crucial for effective disease control programs. The Integrated Disease Surveillance and Response (IDSR) strategy was developed and adopted in 1998 for Africa as a comprehensive public health approach and subsequently, Ghana adopted the IDSR technical guidelines in 2002. Since 2012, the IDSR data is reported through the new District Health Information Management System II (DHIMS2) network. The objective was to evaluate the Integrated Disease Surveillance and Response (IDSR) system in northern Ghana. Methods: This was an observational study using mixed methods. Weekly and monthly IDSR data on selected infectious diseases were downloaded and analyzed for 2011, 2012 and 2013 (the years before, of and after DHIMS2 implementation) from the DHIMS2 databank for the Upper East Region (UER) and for two districts of UER. In addition, key informant interviews were conducted among local and regional health officers on the functioning of the IDSR. Results: Clinically diagnosed malaria was the most prevalent disease in UER, with an annual incidence rate close to 1. Around 500 suspected HIV/AIDS cases were reported each year. The highest incidence of cholera and meningitis was reported in 2012 (257 and 392 cases respectively). Three suspected cases of polio and one suspected case of guinea worm were reported in 2013. None of the polio and guinea worm cases and only a fraction of the reported cases of the other diseases were confirmed. A major observation was the large and inconclusive difference in reported cases when comparing weekly and monthly reports. This can be explained by the different reporting practice for the sub-systems. Other challenges were low priority for surveillance, ill-equipped laboratories, rare supervision and missing feedback. Conclusions: The DHIMS2 has improved the availability of IDSR reports, but the quality of data reported is not sufficient. Particularly the inconsistencies between weekly and monthly

A method for detecting and characterizing outbreaks of infectious disease from clinical reports

Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising.

Quantifying the determinants of outbreak detection performance through simulation and machine learning

Objective: To develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks. Materials and methods: We used an existing software platform to simulate waterborne disease outbreaks of varying duration and magnitude. The simulated data were overlaid on real data from visits to emergency department in Montreal for gastroenteritis. We analyzed the combined data using biosurveillance algorithms, varying their parameters over a wide range. We then applied structure and parameter learning algorithms to the resulting data set to build a Bayesian network model for predicting detection performance as a function of outbreak characteristics and surveillance system parameters. We evaluated the predictions of this model through 5-fold cross-validation. Results: The model predicted performance metrics of commonly used outbreak detection methods with an accuracy greater than 0.80. The model also quantified the influence of different outbreak characteristics and parameters of biosurveillance algorithms on detection performance in practically relevant surveillance scenarios. In addition to identifying characteristics expected a priori to have a strong influence on detection performance, such as the alerting threshold and the peak size of the outbreak, the model suggested an important role for other algorithm features, such as adjustment for weekly patterns. Conclusion: We developed a model that accurately predicts how characteristics of disease outbreaks and detection methods will influence on detection. This model can be used to compare the performance of detection methods under different surveillance scenarios, to gain insight into which characteristics of outbreaks and biosurveillance algorithms drive detection performance, and to guide the configuration of surveillance systems.

Mass medication distribution for disease outbreak: Comparison of personal digital assistant and paper-based decision support

In a disease outbreak, medication must be rapidly yet safely distributed to a population. Are there significant differences in efficiency (time) and error rates in drug dissemination to a population using algorithm-driven paper and personal digital assistant (PDA) methodology? In this study, conducted at the University of Hawaii, mock citizens were sent through points of dispensing with volunteer clerks processing them during two sessions (alternating modes for Session 2). No significant differences were found in time or number of errors with PDA vs. paper. However, the mode and order of testing affected time. Clerks doing the paper method second were slower than those doing paper first (significant at p < 0.0001 level). The PDA was consistent in time whether clerks used it first or second. This may indicate the presence of a fatigue factor from using the paper method and may indicate that during an outbreak, when clerks are tired, using an algorithm-driven PDA may help sustain efficiency.

Extracting transmission networks from phylogeographic data for epidemic and endemic diseases: Ebola virus in Sierra Leone, 2009 H1N1 pandemic influenza and polio in Nigeria

Background: Phylogeography improves our understanding of spatial epidemiology. However, application to practical problems requires choices among computational tools to balance statistical rigor, computational complexity, sensitivity to sampling strategy and interpretability. Methods: We introduce a fast, heuristic algorithm to reconstruct partially-observed transmission networks (POTN) that combines features of phylogenetic and transmission tree approaches. We compare the transmission network generated by POTN with existing algorithms (BEAST and SeqTrack), and discuss the benefits and challenges of phylogeographic analysis on examples of epidemic and endemic diseases: Ebola virus, H1N1 pandemic influenza and polio. Results: For the 2014 Sierra Leone Ebola virus outbreak and the 2009 H1N1 outbreak, all three methods provide similarly plausible transmission histories but differ in detail. For polio in northern Nigeria, we discuss performance trade-offs between the POTN and discrete phylogeography in BEASTand conclude that spatial history reconstruction is limited by under-sampling. Conclusions: POTN is complementary to available tools on densely-sampled data, fails gracefully on undersampled data and is scalable to accommodate larger datasets. We provide further evidence for the utility of phylogeography for understanding transmission networks of rapidly evolving epidemics. We propose simple heuristic criteria to identify how sampling rates and disease dynamics interact to determine fundamental limitations of phylogeographic inference.

Ecology, Evolution and Control of Chagas Disease: A Century of Neglected Modelling and a Promising Future

More than 100years after its formal description, Chagas disease remains a major public health concern in Latin America with a yearly burden of 430,000 Disability-Adjusted Life Years (DALYs). The aetiological agent, a protozoan named Trypanosoma cruzi, is mainly transmitted to mammalian hosts by triatomine vectors. Multiple species of mammals and triatomines can harbour and transmit the parasite, and the feeding range of triatomine species typically includes many noncompetent hosts. Furthermore, the transmission of the pathogen can occur via several routes including the typical vector's faeces, but also oral, congenital and blood transfusion routes. These ecological and epidemiological complexities of the disease have hindered many control initiatives. In such a context, mathematical models provide invaluable tools to explore and understand the dynamics of T. cruzi transmission, and to design, optimize and monitor the efficacy of control interventions. We intend here to provide the first review of the mathematical models of Chagas disease, focussing on how they have contributed to our understanding of (1) the population dynamics and control of triatomine vectors, and (2) the epidemiology of T. cruzi infections. We also aim at suggesting promising lines of modelling that could further improve our understanding of the ecology, evolution, and control of the disease.

Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study

Challenges with alternative data sources for disease surveillance include differentiating the signal from the noise, and obtaining information from data constrained settings. For the latter, events such as increases in hospital traffic could serve as early indicators of social disruption resulting from disease. In this study, we evaluate the feasibility of using hospital parking lot traffic data extracted from high-resolution satellite imagery to augment public health disease surveillance in Chile, Argentina and Mexico. We used archived satellite imagery collected from January 2010 to May 2013 and data on the incidence of respiratory virus illnesses from the Pan American Health Organization as a reference. We developed dynamical Elastic Net multivariable linear regression models to estimate the incidence of respiratory virus illnesses using hospital traffic and assessed how to minimize the effects of noise on the models. We noted that predictions based on models fitted using a sample of observations were better. The results were consistent across countries with selected models having reasonably low normalized root-mean-squared errors and high correlations for both the fits and predictions. The observations from this study suggest that if properly procured and combined with other information, this data source could be useful for monitoring disease trends.

Calculation of incubation period and serial interval from multiple outbreaks of Marburg virus disease

Background: Marburg viruses have been responsible for a number of outbreaks throughout sub-Saharan Africa, as well as a number of laboratory infections. Despite many years of experience with the viruses, little is known about several important epidemiologic parameters relating to the development of Marburg virus disease.The analysis uses pooled data from all Marburg cases between 1967 and 2008 to develop estimates for the incubation period and the clinical onset serial interval (COSI). Methods: Data were obtained from original outbreak investigation forms (n = 406) and from published data (n = 45). Incubation periods were calculated for person-to-person exposure, for laboratory-acquired infections, and for presumed zoonotic exposures. Similar analysis was conducted for COSI, using only cases with unambiguous person-to-person transmission where both the primary and the secondary case patients had well-defined illness onsets. Results: Seventy-six cases were retained for the incubation period analysis. Incubation periods ranged from a minimum of 2 days in the case of two laboratory workers to a maximum of at least 26 days for a person-to-person household transmission. Thirty-eight cases were retained for COSI analysis.The median COSI was 11 days, with an interquartile range of 8 to 15. Conclusions: This study extends the maximum known incubation period of Marburg virus disease to 26 days.The analysis was severely hampered by a lack of completeness in epidemiologic data. It is necessary to prioritize obtaining more accurate epidemiologic data in future outbreaks; greater use of COSI may facilitate an improved understanding of outbreak dynamics in Marburg and other diseases.

Dengue: Need an entomological and virological surveillance for disease control

Dengue infection is the most important arbo-virus infection of humans and the most important tropical infectious disease after malaria. This disease is characterized by headache, arthralgia, myalgia, rash, nausea, vomiting. Four serotypes of DENV exist, and severe illness is usually associated with secondary infection by a different serotype. Aedes aegypti is the vector for transmission of this disease and the only effective way to prevent epidemic dengue fever/dengue hemorrhagic fever (DF/DHF) is to control growth of the mosquito vector i.e Aedes aegypti. A global approach aimed at increasing the capacity for surveillance and outbreak response, changing behaviors and reducing the disease burden using included vector management in conjunction with early and precise diagnosis has been advocated. This review confers how effectively the Virological-entomological laboratory-based surveillance systems in endemic areas furnish information for effective vector control measures.

Optimal staffing strategies for points of dispensing

We present a heuristic-based multi-objective optimization approach for minimizing staff and maximizing throughput at Points-of-Dispensing (PODs). PODs are sites quickly set up by local health departments to rapidly dispense life-saving medical countermeasures during large-scale public health emergencies. Current modeling tools require decision makers to modify their models and re-run them for each "what if" scenario they are charged with preparing for, e.g. what happens if more/less staff are available. The exploration of these "what if" scenarios becomes tedious if there are many variables to change and the decision space quickly becomes too large to analyze effectively. Currently, to understand the trade-offs between throughput and staffing levels, public health emergency managers must maximize throughput subject to a specified staffing level. Then, they must repeatedly change the constraint (altering the maximum staff allowed) and re-run the model. In contrast, by approaching the problem from a multi-objective perspective and integrating discrete event and optimization tools, we automate of the exploration of the decision space. This approach allows public health emergency planners to examine far more potential solutions and to focus tangible planning resources on areas that show theoretical promise. Such an approach can also expose previously unidentified constraints in existing plans.

Journalists and Public Health Professionals: Challenges of a Symbiotic Relationship

Journalists and health professionals share a symbiotic relationship during a disease outbreak as both professions play an important role in informing the public's perceptions and the decisions of policy makers. Although critics in the United States have focused on US reporters and media outlets whose coverage has been sensationalist and alarmist, the discussion in this article is based on the ideal - gold standard - for US journalists. Journalists perform three primary functions during times of health crises: disseminating accurate information to the public, medical professionals, and policy makers; acting as the go-between for the public and decision makers and health and science experts; and monitoring the performance of institutions responsible for the public health response. A journalist's goal is to responsibly inform the public in order to optimize the public health goals of prevention while minimizing panic. The struggle to strike a balance between humanizing a story and protecting the dignity of patients while also capturing the severity of an epidemic is harder in the era of the 24-7 news cycle. Journalists grapple with dueling pressures: confirming that their information is correct while meeting the demand for rapid updates. Just as health care professionals triage patients, journalists triage information. The challenge going forward will be how to get ahead of the story from the onset, racing against the pace of digital dissemination of misinformation by continuing to refine the media-science relationship.

Ethical Challenges of Big Data in Public Health

[No abstract available]

Evaluation of an Unplanned School Closure in a Colorado School District: Implications for Pandemic Influenza Preparedness

Objective: From January 29 through February 5, 2013, a school district outside metropolitan Denver, Colorado, was closed because of absenteeism related to influenza-like illness (ILI) among students and staff. We evaluated the consequences and acceptability of the closure among affected households. Methods: We conducted a household survey regarding parent or guardian employment and income interruptions, alternative child care arrangements, interruption of noneducational school services, ILI symptoms, student re-congregation, and communication preferences during the closure. Results: Of the 35 (31%) of 113 households surveyed, the majority (28 [80%]) reported that the closure was not challenging. Seven (20%) households reported challenges: 5 (14%) reported that 1 or more adults missed work, 3 (9%) reported lost pay, and 1 (3%) reported challenges because of missed subsidized school meals. The majority (22 [63%]) of households reported that a hypothetical 1-month closure would not represent a problem; 6 of 8 households that did anticipate challenges reported that all adults worked outside the home. The majority (58%) of students visited at least 1 outside venue during the closure. Conclusions: A brief school closure did not pose a major problem for the majority of the affected households surveyed. School and public health officials should consider the needs of families in which all adults work outside the home when creating school closure contingency plans.

Modelling Lymphatic Filariasis Transmission and Control: Modelling Frameworks, Lessons Learned and Future Directions

Mathematical modelling provides a useful tool for policy making and planning in lymphatic filariasis control programmes, by providing trend forecasts based on sound scientific knowledge and principles. This is now especially true, in view of the ambitious target to eliminate lymphatic filariasis as a public health problem globally by the year 2020 and the short remaining timeline to achieve this. To meet this target, elimination programmes need to be accelerated, requiring further optimization of strategies and tailoring to local circumstances. Insights from epidemiological transmission models provide a useful basis. Two general models of lymphatic filariasis transmission and control are nowadays in use to support decision-making, namely a population-based deterministic model (EPIFIL) and an individual-based stochastic model (LYMFASIM). Model predictions confirm that lymphatic filariasis transmission can be interrupted by annual mass drug administration (MDA), but this may need to be continued much longer than the initially suggested 4-6years in areas with high transmission intensity or poor treatment coverage. However, the models have not been validated against longitudinal data describing the impact of MDA programmes. Some critical issues remain to be incorporated in one or both of the models to make predictions on elimination more realistic, including the possible occurrence of systematic noncompliance, the risk of emerging parasite resistance to anthelmintic drugs, and spatial heterogeneities. Rapid advances are needed to maximize the utility of models in decision-making for the ongoing ambitious lymphatic filariasis elimination programmes.

Advancing digital methods in the fight against communicable diseases

Important advances are being made in the fight against communicable diseases by using new digital tools. While they can be a challenge to deploy at-scale, GPS-enabled smartphones, electronic dashboards and computermodels have multiple benefits. They can facilitate programoperations, lead to new insights about the disease transmission and support strategic planning. Today, tools such as these are used to vaccinate more children against polio in Nigeria, reduce the malaria burden in Zambia and help predict the spread of the Ebola epidemic in West Africa.

Operationalizing Public Health Skills to Resource Poor Settings: Is This the Achilles Heel in the Ebola Epidemic Campaign?

Sustainable approaches to crises, especially non-trauma-related public health emergencies, are severely lacking. At present, the Ebola crisis is defining the operational public health skill sets for infectious disease epidemics that are not widely known or appreciated. Indigenous and foreign medical teams will need to adapt to build competency-based curriculum and standards of care for the future that concentrate on public health emergencies. Only by adjusting and adapting specific operational public health skill sets to resource poor environments will it be possible to provide sustainable prevention and preparedness initiatives that work well across cultures and borders.

Modelling the Effects of Mass Drug Administration on the Molecular Epidemiology of Schistosomes

As national governments scale up mass drug administration (MDA) programs aimed to combat neglected tropical diseases (NTDs), novel selection pressures on these parasites increase. To understand how parasite populations are affected by MDA and how to maximize the success of control programmes, it is imperative for epidemiological, molecular and mathematical modelling approaches to be combined. Modelling of parasite population genetic and genomic structure, particularly of the NTDs, has been limited through the availability of only a few molecular markers to date. The landscape of infectious disease research is being dramatically reshaped by next-generation sequencing technologies and our understanding of how repeated selective pressures are shaping parasite populations is radically altering. Genomics can provide high-resolution data on parasite population structure, and identify how loci may contribute to key phenotypes such as virulence and/or drug resistance. We discuss the incorporation of genetic and genomic data, focussing on the recently sequenced Schistosoma spp., into novel mathematical transmission models to inform our understanding of the impact of MDA and other control methods. We summarize what is known to date, the models that exist and how population genetics has given us an understanding of the effects of MDA on the parasites. We consider how genetic and genomic data have the potential to shape future research, highlighting key areas where data are lacking, and how future molecular epidemiology knowledge can aid understanding of transmission dynamics and the effects of MDA, ultimately informing public health policy makers of the best interventions for NTDs.

Triage Management, Survival, and the Law in the Age of Ebola

Liberia, Sierra Leone, and Guinea lack the public health infrastructure, economic stability, and overall governance to stem the spread of Ebola. Even with robust outside assistance, the epidemiological data have not improved. Vital resource management is haphazard and left to the discretion of individual Ebola treatment units. Only recently has the International Health Regulations (IHR) and World Health Organization (WHO) declared Ebola a Public Health Emergency of International Concern, making this crisis their fifth ongoing level 3 emergency. In particular, the WHO has been severely compromised by post-2003 severe acute respiratory syndrome (SARS) staffing, budget cuts, a weakened IHR treaty, and no unambiguous legal mandate. Population-based triage management under a central authority is indicated to control the transmission and ensure fair and decisive resource allocation across all triage categories. The shared responsibilities critical to global health solutions must be realized and the rightful attention, sustained resources, and properly placed legal authority be assured within the WHO, the IHR, and the vulnerable nations.

Assessing dengue infection risk in the southern region of Taiwan: Implications for control

Dengue, one of the most important mosquito-borne diseases, is a major international public health concern. This study aimed to assess potential dengue infection risk from Aedes aegypti in Kaohsiung and the implications for vector control. Here we investigated the impact of dengue transmission on human infection risk using a well-established dengue-mosquito-human transmission dynamics model. A basic reproduction number (R 0)-based probabilistic risk model was also developed to estimate dengue infection risk. Our findings confirm that the effect of biting rate plays a crucial role in shaping R 0 estimates. We demonstrated that there was 50% risk probability for increased dengue incidence rates exceeding 0·5-0·8 wk-1 for temperatures ranging from 26°C to 32°C. We further demonstrated that the weekly increased dengue incidence rate can be decreased to zero if vector control efficiencies reach 30-80% at temperatures of 19-32°C. We conclude that our analysis on dengue infection risk and control implications in Kaohsiung provide crucial information for policy-making on disease control.

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16 March 2015

Research Committee Articles, March 16, 2015

Devising an indicator to detect mid-term abortions in dairy cattle: A first step towards syndromic surveillance of abortive diseases

Bovine abortion surveillance is essential for human and animal health because it plays an important role in the early warning of several diseases. Due to the limited sensitivity of traditional surveillance systems, there is a growing interest for the devel opment of syndromic surveillance. Our objective was to assess whether, routinely collected, artificial insemination (AI) data could be used, as part of a syndromic surveillance system, to devise an indicator of mid-term abortions in dairy cattle herds in F rance. A mid-term abortion incidence rate (MAIR) was computed as the ratio of the number of mid-term abortions to the number of female- weeks at risk. A mid-term abortion was defined as a return-to-service (i.e. a new AI) taking place 90 to 180 days after the previous AI. Weekly variations in the MAIR in heifers and parous cows were modeled with a time-dependent Poisson model at the d├ępartement level (French administrative division) during the period of 2004 to 2010. The usefulness of monitoring this indica tor to detect a disease-related increase in mid-term abortions was evaluated using data from the 2007-2008 episode of bluetongue serotype 8 (BT8) in France. An increase in the MAIR was identified in heifers and parous cows in 47% (n = 24) and 71% (n = 39) of the d├ępartements. On average, the weekly MAIR among heifers increased by 3.8% (min-max: 0.02-57.9%) when the mean number of BT8 cases that occurred in the previous 8 to 13 weeks increased by one. The weekly MAIR among parous cows increased by 1.4% (0.01 -8.5%) when the mean number of BT8 cases occurring in the previous 6 to 12 weeks increased by one. These results underline the potential of the MAIR to identify an increase in mid-term abortions and suggest that it is a good candidate for the implementatio n of a syndromic surveillance system for bovine abortions.

Inference of seasonal and pandemic influenza transmission dynamics

The inference of key infectious disease epidemiological parameters is critical for characterizing disease spread and devising prevention and containment measures. The recent emergence of surveillance records mined from big data such as health-related onlin e queries and social media, as well as model inference methods, permits the development of new methodologies for more comprehensive estimation of these parameters. We use such data in conjunction with Bayesian inference methods to study the transmission dy namics of influenza. We simultaneously estimate key epidemiological parameters, including population susceptibility, the basic reproductive number, attack rate, and infectious period, for 115 cities during the 2003-2004 through 2012-2013 seasons, including the 2009 pandemic. These estimates discriminate key differences in the epidemiological characteristics of these outbreaks across 10 y, as well as spatial variations of influenza transmission dynamics among subpopulations in the United States. In addition, the inference methods appear to compensate for observational biases and underreporting inherent in the surveillance data.

Time series analyses of hand, foot and mouth disease integrating weather variables

Background: The past decade witnessed an increment in the incidence of hand foot mouth disease (HFMD) in the Pacific Asian region; specifically, in Guangzhou China. This emphasized the requirement of an early warning system designed to allow the medical co mmunity to better prepare for outbreaks and thus minimize the number of fatalities. Methods: Samples from 1,556 inpatients (hospitalized) and 11,004 outpatients (non-admitted) diagnosed with HFMD were collected in this study from January 2009 to October 20 13. Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish high predictive model for inpatients and outpatient as well as three viral serotypes (EV71, Pan-EV and CA16). To integrate climate variables in the data analyses, data from eight climate variables were simultaneously obtained during this period. Significant climate variable identified by correlation analyses was executed to improve time series modeling as external repressors. Results: Among inpatients with HFMD, 24 8 (15.9%) were affected by EV71, 137 (8.8%) were affected by Pan-EV+, and 436 (28.0%) were affected by CA16. Optimal Univariate SARIMA model was identified: (2,0,3)(1,0,0)52 for inpatients, (0,1,0)(0,0,2)52 for outpatients as well as three serotypes (EV71, (1,0,1)(0,0,1)52; CA16, (1,0,1)(0,0,0)52; Pan-EV, (1,0,1) (0,0,0)52). Using climate as our independent variable, precipitation (PP) was first identified to be associated with inpatients (r = 0.211, P = 0.001), CA16-serotype (r = 0.171, P = 0.007) and outp atients (r = 0.214, P = 0.01) in partial correlation analyses, and was then shown a significant lag in cross-autocorrelation analyses. However, inclusion of PP [lag -3 week] as external repressor showed a moderate impact on the predictive performance of th e SARIMA model described here-in. Conclusion: Climate patterns and HFMD incidences have been shown to be strongly correlated. The SARIMA model developed here can be a helpful tool in developing an early warni

Spatial, temporal and genetic dynamics of highly pathogenic avian influenza A (H5N1) virus in China

Background: The spatial spread of H5N1 avian influenza, significant ongoing mutations, and long-term persistence of the virus in some geographic regions has had an enormous impact on the poultry industry and presents a serious threat to human health. Metho ds: We applied phylogenetic analysis, geospatial techniques, and time series models to investigate the spatiotemporal pattern of H5N1 outbreaks in China and the effect of vaccination on virus evolution. Results: Results showed obvious spatial and temporal clusters of H5N1 outbreaks on different scales, which may have been associated with poultry and wild-bird transmission modes of H5N1 viruses. Lead-lag relationships were found among poultry and wild-bird outbreaks and human cases. Human cases were preceded by poultry outbreaks, and wild-bird outbreaks were led by human cases. Each clade has gained its own unique spatiotemporal and genetic dominance. Genetic diversity of the H5N1 virus decreased significantly between 1996 and 2011; presumably under strong se lective pressure of vaccination. Mean evolutionary rates of H5N1 virus increased after vaccination was adopted in China. A clear signature of positively selected sites in the clade 2.3.2 virus was discovered and this may have resulted in the emergence of c lade Conclusions: Our study revealed two different transmission modes of H5N1 viruses in China, and indicated a significant role of poultry in virus dissemination. Furthermore, selective pressure posed by vaccination was found in virus evolution i n the country.

Human brucellosis occurrences in inner mongolia, China: A spatio-temporal distribution and ecological niche modeling approach

Background: Brucellosis is a common zoonotic disease and remains a major burden in both human and domesticated animal populations worldwide. Few geographic studies of human Brucellosis have been conducted, especially in China. Inner Mongolia of China is co nsidered an appropriate area for the study of human Brucellosis due to its provision of a suitable environment for animals most responsible for human Brucellosis outbreaks. Methods: The aggregated numbers of human Brucellosis cases from 1951 to 2005 at the municipality level, and the yearly numbers and incidence rates of human Brucellosis cases from 2006 to 2010 at the county level were collected. Geographic Information Systems (GIS), remote sensing (RS) and ecological niche modeling (ENM) were integrated t o study the distribution of human Brucellosis cases over 1951-2010. Results: Results indicate that areas of central and eastern Inner Mongolia provide a long-term suitable environment where human Brucellosis outbreaks have occurred and can be expected to p ersist. Other areas of northeast China and central Mongolia also contain similar environments. Conclusions: This study is the first to combine advanced spatial statistical analysis with environmental modeling techniques when examining human Brucellosis out breaks and will help to inform decision-making in the field of public health.

Surveillance for severe acute respiratory infections (SARI) in hospitals in the WHO european region - an exploratory analysis of risk factors for a severe outcome in influenza-positive SARI cases

Background: The 2009 H1N1 pandemic highlighted the need to routinely monitor severe influenza, which lead to the establishment of sentinel hospital-based surveillance of severe acute respiratory infections (SARI) in several countries in Europe. The objecti ve of this study is to describe characteristics of SARI patients and to explore risk factors for a severe outcome in influenza-positive SARI patients. Methods: Data on hospitalised patients meeting a syndromic SARI case definition between 2009 and 2012 fro m nine countries in Eastern Europe (Albania, Armenia, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Romania, Russian Federation and Ukraine) were included in this study. An exploratory analysis was performed to assess the association between risk factors and a severe (ICU, fatal) outcome in influenza-positive SARI patients using a multivariate logistic regression analysis. Results: Nine countries reported a total of 13,275 SARI patients. The majority of SARI patients reported in these countries were young child ren. A total of 12,673 SARI cases (95%) were tested for influenza virus and 3377 (27%) were laboratory confirmed. The majority of tested SARI cases were from Georgia, the Russian Federation and Ukraine and the least were from Kyrgyzstan. The proportion pos itive varied by country, season and age group, with a tendency to a higher proportion positive in the 15+ yrs age group in six of the countries. ICU admission and fatal outcome were most often recorded for influenza-positive SARI cases aged > 15 yrs. An ex ploratory analysis using pooled data from influenza-positive SARI cases in three countries showed that age > 15yrs, having lung, heart, kidney or liver disease, and being pregnant were independently associated with a fatal outcome. Conclusions: Countries i n Eastern Europe have been able to collect data through routine monitoring of severe influenza and results on risk factors for a severe outcome in influenza-positive SARI cases have identified several risk gr

Meeting the International Health Regulations (2005) surveillance core capacity requirements at the subnational level in Europe: The added value of syndromic surveillance

Background: The revised World Health Organization's International Health Regulations (2005) request a timely and all-hazard approach towards surveillance, especially at the subnational level. We discuss three questions of syndromic surveillance application in the European context for assessing public health emergencies of international concern: (i) can syndromic surveillance support countries, especially the subnational level, to meet the International Health Regulations (2005) core surveillance capacity re quirements, (ii) are European syndromic surveillance systems comparable to enable cross-border surveillance, and (iii) at which administrative level should syndromic surveillance best be applied? Discussion: Despite the ongoing criticism on the usefulness of syndromic surveillance which is related to its clinically nonspecific output, we demonstrate that it was a suitable supplement for timely assessment of the impact of three different public health emergencies affecting Europe. Subnational syndromic surve illance analysis in some cases proved to be of advantage for detecting an event earlier compared to national level analysis. However, in many cases, syndromic surveillance did not detect local events with only a small number of cases. The European Commissi on envisions comparability of surveillance output to enable cross-border surveillance. Evaluated against European infectious disease case definitions, syndromic surveillance can contribute to identify cases that might fulfil the clinical case definition bu t the approach is too unspecific to comply to complete clinical definitions. Syndromic surveillance results still seem feasible for comparable cross-border surveillance as similarly defined syndromes are analysed. We suggest a new model of implementing syn dromic surveillance at the subnational level. In this model, syndromic surveillance systems are fine-tuned to their local context and integrated into the existing subnational surveillance and reporting struct

Influenza surveillance and forecast with smartphone sensors

In this paper we introduce an influenza surveillance and forecast system (ISFS) that can track the proliferation of influenza and predict potential infections by analyzing smartphone sensor readings. While previous studies investigate social connectivity t o deduce proliferation paths, we focus on the physical contacts of each individual that are the dominant cause of influenza infections. To estimate the probability of an infection through each physical contact we measure the surrounding features of each co ntact including the staying time of a contact, the human density and the openness of the space, and the infection status of each individual. By using a smartphone equipped with various sensors we can estimate the infection status of its owner by analyzing both the envelope of incoming sound and the surrounding features of the contact. A surveillance server, which aggregates the information from multiple smartphones, monitors the infection status of influenza and ranks both high risk persons and influential persons that have to be vaccinated promptly. To evaluate the forecast accuracy of ISFS we have implemented a full ISFS including an Android ISFS client and compare the forecast accuracy of ISFS against that of the traditional forecast system based on socia l connectivity. Our evaluation results suggest that influenza surveillance and forecast should be performed based on human activity rather than social connectivity. This would not only improve the forecast accuracy but it can also improve the cost efficien cy and the suppression effect of vaccinations by finding the most influential persons in the proliferation paths.

2014 MERS-CoV outbreak in Jeddah - A link to health care facilities

Background: A marked increase in the number of cases of Middle East respiratory syndrome coronavirus (MERS-CoV) infection occurred in Jeddah, Saudi Arabia, in early 2014. We evaluated patients with MERS-CoV infection in Jeddah to explore reasons for this i ncrease and to assess the epidemiologic and clinical features of this disease. Methods: We identified all cases of laboratory-confirmed MERS-CoV infection in Jeddah that were reported to the Saudi Arabian Ministry of Health from January 1 through May 16, 2 014. We conducted telephone interviews with symptomatic patients who were not health care personnel, and we reviewed hospital records. We identified patients who were reported as being asymptomatic and interviewed them regarding a history of symptoms in th e month before testing. Descriptive analyses were performed. Results: Of 255 patients with laboratory-confirmed MERS-CoV infection, 93 died (case fatality rate, 36.5%). The median age of all patients was 45 years (interquartile range, 30 to 59), and 174 pa tients (68.2%) were male. A total of 64 patients (25.1%) were reported to be asymptomatic. Of the 191 symptomatic patients, 40 (20.9%) were health care personnel. Among the 151 symptomatic patients who were not health care personnel, 112 (74.2%) had data t hat could be assessed, and 109 (97.3%) of these patients had had contact with a health care facility, a person with a confirmed case of MERS-CoV infection, or someone with severe respiratory illness in the 14 days before the onset of illness. The remaining 3 patients (2.7%) reported no such contacts. Of the 64 patients who had been reported as asymptomatic, 33 (52%) were interviewed, and 26 of these 33 (79%) reported at least one symptom that was consistent with a viral respiratory illness. Conclusions: The majority of patients in the Jeddah MERS-CoV outbreak had contact with a health care facility, other patients, or both. This highlights the role of health care-associated transmission.

Environmental Drivers of the Spatiotemporal Dynamics of Respiratory Syncytial Virus in the United States

Epidemics of respiratory syncytial virus (RSV) are known to occur in wintertime in temperate countries including the United States, but there is a limited understanding of the importance of climatic drivers in determining the seasonality of RSV. In the Uni ted States, RSV activity is highly spatially structured, with seasonal peaks beginning in Florida in November through December and ending in the upper Midwest in February-March, and prolonged disease activity in the southeastern US. Using data on both age- specific hospitalizations and laboratory reports of RSV in the US, and employing a combination of statistical and mechanistic epidemic modeling, we examined the association between environmental variables and state-specific measures of RSV seasonality. Tem perature, vapor pressure, precipitation, and potential evapotranspiration (PET) were significantly associated with the timing of RSV activity across states in univariate exploratory analyses. The amplitude and timing of seasonality in the transmission rate was significantly correlated with seasonal fluctuations in PET, and negatively correlated with mean vapor pressure, minimum temperature, and precipitation. States with low mean vapor pressure and the largest seasonal variation in PET tended to experience biennial patterns of RSV activity, with alternating years of “early-big” and “late-small” epidemics. Our model for the transmission dynamics of RSV was able to replicate these biennial transitions at higher amplitudes of seasonality in the transmission rat e. This successfully connects environmental drivers to the epidemic dynamics of RSV; however, it does not fully explain why RSV activity begins in Florida, one of the warmest states, when RSV is a winter-seasonal pathogen. Understanding and predicting the seasonality of RSV is essential in determining the optimal timing of immunoprophylaxis.

Development of a time-trend model for analyzing and predicting case-pattern of Lassa fever epidemics in Liberia, 2013-2017

Objective: The objective was to develop a case-pattern model for Lassa fever (LF) among humans and derive predictors of time-trend point distribution of LF cases in Liberia in view of the prevailing under-reporting and public health challenge posed by the disease in the country.

The Spatiotemporal Expansion of Human Rabies and Its Probable Explanation in Mainland China, 2004-2013

Background Human rabies is a significant public health concern in mainland China. However, the neglect of rabies expansion and scarce analyses of the dynamics have made the spatiotemporal spread pattern of human rabies and its determinants being poorly und erstood. Methods We collected geographic locations and timeline of reported human rabies cases, rabies sequences and socioeconomic variables for the years 2004-2013, and integrated multidisciplinary approaches, including epidemiological characterization, h otspots identification, risk factors analysis and phylogeographic inference, to explore the spread pattern of human rabies in mainland China during the last decade. Results The results show that human rabies distribution and hotspots were expanding from so utheastern regions to north or west regions, which could be associated with the evolution of the virus, especially the clade I-G. A Panel Poisson Regression analysis reveals that human rabies incidences had significant correlation with the education level, GDP per capita, temperature at one-month lag and canine rabies outbreak at two-month lag. Conclusions The reduction in the overall human rabies incidence was accompanied by a westward and northward expansion of the circulating region in mainland China. Hi gher risk of human rabies was associated with lower level of education and economic status. New clades of rabies, especial Clade I-G, played an important role in recent spread. Our findings provide valuable information for rabies control and prevention in the future.

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11 March 2015

Research Committee Articles, March 9, 2015

Articles from March 9, 2015

Research Committee Selected Articles for the Week of March 9th, 2015

Zoonotic disease emergence is not a purely biological process mediated only by ecologic factors; opportunities for transmission of zoonoses from animals to humans also depend on how people interact with animals. While exposure is conditioned by the type of animal and the location in which interactions occur, these in turn are influenced by human activity. The activities people engage in are determined by social as well as contextual factors including gender, age, socio-economic status, occupation, social norms, settlement patterns and livelihood systems, family and community dynamics, as well as national and global influences. This paper proposes an expanded "One Health" conceptual model for human-animal exposure that accounts for social as well as epidemiologic factors. The expanded model informed a new study approach to document the extent of human exposure to animals and explore the interplay of social and environmental factors that influence risk of transmission at the individual and community level. The approach includes a formative phase using qualitative and participatory methods, and a representative, random sample survey to quantify exposure to animals in a variety of settings. The paper discusses the different factors that were considered in developing the approach, including the range of animals asked about and the parameters of exposure that are included, as well as factors to be considered in local adaptation of the generic instruments. Illustrative results from research using this approach in Lao PDR are presented to demonstrate the effect of social factors on how people interact with animals. We believe that the expanded model can be similarly operationalized to explore the interactions of other social and policy-level determinants that may influence transmission of zoonoses.

The One Health approach integrates health investigations across the tree of life, including, but not limited to, wildlife, livestock, crops, and humans. It redresses an epistemological alienation at the heart of much modern population health, which has long segregated studies by species. Up to this point, however, One Health research has also omitted addressing fundamental structural causes underlying collapsing health ecologies. In this critical review we unpack the relationship between One Health science and its political economy, particularly the conceptual and methodological trajectories by which it fails to incorporate social determinants of epizootic spillover. We also introduce a Structural One Health that addresses the research gap. The new science, open to incorporating developments across the social sciences, addresses foundational processes underlying multispecies health, including the place-specific deep-time histories, cultural infrastructure, and economic geographies driving disease emergence. We introduce an ongoing project on avian influenza to illustrate Structural One Health's scope and ambition. For the first time researchers are quantifying the relationships among transnational circuits of capital, associated shifts in agroecological landscapes, and the genetic evolution and spatial spread of a xenospecific pathogen.

'One World One Health' (OWOH), 'One Medicine' and 'One Health' are all injunctions to work across the domains of veterinary, human and environmental health. In large part they are institutional responses to growing concerns regarding shared health risks at the human, animal and environmental interfaces. Although these efforts to work across disciplinary boundaries are welcome, there are also risks in seeking unity, not least the tendency of one health visions to reduce diversity and to under-value the local, contingent and practical engagements that make health possible. This paper uses insights from Geography and Science and Technology Studies along with multi-sited and multi-species qualitative fieldwork on animal livestock and zoonotic influenzas in the UK, to highlight the importance of those practical engagements. After an introduction to one health, I argue that there is a tendency in OWOH visions to focus on contamination and transmission of pathogens rather than the socio-economic configuration of disease and health, and this tendency conforms to or performs what sociologist John Law calls a one world metaphysics. Following this, three related field cases are used to demonstrate that health is dependent upon a patchwork of practices, and is configured in practice by skilled people, animals, micro-organisms and their social relations. From surveillance for influenza viruses to tending animals, good health it turns out is dependent on an ability to construct common sense from a complex of signs, responses and actions. It takes, in other words, more than one world to make healthy outcomes. In light of this, the paper aims to, first, loosen any association between OWOH and a one world-ist metaphysics, and, second, to radicalize the inter-disciplinary foundations of OWOH by both widening the scope of disciplinarity as well as attending to how different knowledgesare brought together.

Among the most relevant elements contributing to define the One World One Health programme we find epidemics. The reason is that in recent decades, infectious diseases such as HIV/SIDA, SARS and Influenza have shown that we need new approaches and concepts in order to understand how biological emergencies and health alerts deploy new scales of action. Especially relevant has been the case of A(H1N1) influenza. This reached the status of global threat virtually from its onset, triggering an international response with a diffusion, visibility and rapidity unparalleled in previous health alerts. This article maintains that this global condition cannot be explained solely by the epidemiologic characteristics of the disease, such as mortality rate, severe cases, propagation capacity, etc. Resorting to the approach proposed by the Actor-Network Theory (ANT), this paper suggests that the action of certain socio-technical operators was what built a heterogeneous network of ideas, concepts and materials that turned the A (H1N1) influenza into a global-scale phenomenon with unprecedented speed. Among these operators, the most important ones were: the speaking position, a discourse about threat, the protocols and guidelines that were used and, lastly, the maps that allowed a real-time monitoring of the influenza. The paper ends with the notion of panorama, as defined by Bruno Latour: a suggestion to describe the common denominator of the aforementioned operators, and a means to foresee the development of global scales for certain health alerts. The paper will conclude by proposing that this type of analysis would allow the One World One Health to understand with greater precision the dynamic of epidemics and thus make its principles of action much more specific as well as its definition of what global health should be.

This paper traces the emergence and tensions of an internationally constructed and framed One World-One Health (OWOH) approach to control and attempt to eliminate African Trypanosomiasis in Uganda. In many respects Trypanosomiasis is a disease that an OWOH approach is perfectly designed to treat, requiring an integrated approach built on effective surveillance in animals and humans, quick diagnosis and targeting of the vector. The reality appears to be that the translation of global notions of OWOH down to national and district levels generates problems, primarily due to interactions between: a) international, external actors not engaging with the Ugandan state; b) actors setting up structures and activities parallel to those of the state; c) actors deciding when emergencies begin and end without consultation; d) weak Ugandan state capacity to coordinate its own integrated response to disease; e) limited collaboration between core Ugandan planning activities and a weak, increasingly devolved district health system. These interrelated dynamics result in the global, international interventionalist mode of OWOH undermining the Coordinating Office for Control of Trypanosomiasis in Uganda (COCTU), the body within the Ugandan state mandated expressly with managing a sustainable One Health response to trypanosomiasis outbreaks in Uganda. This does two things, firstly it suggests we need a more grounded, national perspective of OWOH, where states and health systems are acknowledged and engaged with by international actors and initiatives. Secondly, it suggests that more support needs to be given to core coordinating capacity in resource-poor contexts. Supporting national coordinating bodies, focused around One Health, and ensuring that external actors engage with and through those bodies can help develop a sustained, effective OWOH presence in resource-poor countries, where after all most zoonotic disease burden remains.

The development of the One World, One Health agenda coincides in time with the appearance of a different model for the management of human-animal relations: the genetic manipulation of animal species in order to curtail their ability as carriers of human pathogens. In this paper we examine two examples of this emergent transgenic approach to disease control: the development of transgenic chickens incapable of shedding avian flu viruses, and the creation of transgenic mosquitoes refractory to dengue or malaria infection. Our analysis elaborates three distinctions between the One World, One Health agenda and its transgenic counterpoint. The first concerns the conceptualization of outbreaks and the forms of surveillance that support disease control efforts. The second addresses the nature of the interspecies interface, and the relative role of humans and animals in preventing pathogen transmission. The third axis of comparison considers the proprietary dimensions of transgenic animals and their implications for the assumed public health ethos of One Health programs. We argue that the fundamental difference between these two approaches to infectious disease control can be summarized as one between strategies of containment and strategies of competition. While One World, One Health programs seek to establish an equilibrium in the human-animal interface in order to contain the circulation of pathogens across species, transgenic strategies deliberately trigger a new ecological dynamic by introducing novel animal varieties designed to out-compete pathogen-carrying hosts and vectors. In other words, while One World, One Health policies focus on introducing measures of inter-species containment, transgenic approaches derive their prophylactic benefit from provoking new cycles of intra-species competition between GM animals and their wild-type counterparts. The coexistence of these divergent health protection strategies, we suggest, helps to elucidate enduring tensions and con

Emerging infectious diseases from animals pose significant and increasing threats to human health; places of risk are simultaneously viewed as conservation and emerging disease 'hotspots'. The One World/One Health paradigm is an 'assemblage' discipline. Extensive research from the natural and social sciences, as well as public health have contributed to designing surveillance and response policy within the One World/One Health framework. However, little research has been undertaken that considers the lives of those who experience risk in hotspots on a daily basis. As a result, policymakers and practitioners are unable to fully comprehend the social and ecological processes that catalyze cross-species pathogen exchange. This study examined local populations' comprehension of zoonotic disease. From October 2008-May 2009 we collected data from people living on the periphery of Kibale National Park, in western Uganda. We administered a survey to 72 individuals and conducted semi-structured, in-depth interviews with 14 individuals. Results from the survey showed respondents had statistically significant awareness that transmission of diseases from animals was possible compared to those who did not think such transmission was possible (x2=30.68, df=1, p<0.05). However, individual characteristics such as gender, occupation, location, and age were not significantly predictive of awareness. Both quantitative and qualitative data show local people are aware of zoonoses and provided biomedically accurate examples of possible infections and corresponding animal sources (e.g., worm infection from pigs and Ebola from primates). Qualitative data also revealed expectations about the role of the State in managing the prevention of zoonoses from wildlife. As a result of this research, we recommend meaningful discourse with people living at the frontlines of animal contact in emerging disease and conservation hotspots in order to develop informed and relevant zoonoses prevention pr

The social environment has changed rapidly as technology has facilitated communication among individuals and groups in ways not imagined 20 years ago. Communication technology increasingly plays a role in decision-making about health and environmental behaviors and is being leveraged to influence that process. But at its root is the fundamental need to understand human cognition, communication, and behavior. The concept of 'One Health' has emerged as a framework for interdisciplinary work that cuts across human, animal, and ecosystem health in recognition of their interdependence and the value of an integrated perspective. Yet, the science of communication, information studies, social psychology, and other social sciences have remained marginalized in this emergence. Based on an interdisciplinary collaboration, this paper reports on a nascent conceptual framework for the role of social science in 'One Health' issues and identifies a series of recommendations for research directions that bear additional scrutiny and development.

This article contributes to the literature on One Health and public health ethics by expanding the principle of solidarity. We conceptualise solidarity to encompass not only practices intended to assist other people, but also practices intended to assist non-human others, including animals, plants, or places. To illustrate how manifestations of humanist and more-than-human solidarity may selectively complement one another, or collide, recent responses to Hendra virus in Australia and Rabies virus in Canada serve as case examples. Given that caring relationships are foundational to health promotion, people's efforts to care for non-human others are highly relevant to public health, even when these efforts conflict with edicts issued in the name of public health. In its most optimistic explication, One Health aims to attain optimal health for humans, non-human animals and their shared environments. As a field, public health ethics needs to move beyond an exclusive preoccupation with humans, so as to account for moral complexity arising from people's diverse connections with places, plants, and non-human animals.

Background: Influenza is a contagious disease with high transmissibility to spread around the world with considerable morbidity and mortality and presents an enormous burden on worldwide public health. Few mathematical models can be used because influenza incidence data are generally not normally distributed. We developed a mathematical model using Extreme Value Theory (EVT) to forecast the probability of outbreak of highly pathogenic influenza. Methods: The incidence data of highly pathogenic influenza in Zhejiang province from April 2009 to November 2013 were retrieved from the website of Health and Family Planning Commission of Zhejiang Province. MATLAB "VIEM" toolbox was used to analyze data and modelling. In the present work, we used the Peak Over Threshold (POT) model, assuming the frequency as a Poisson process and the intensity to be Pareto distributed, to characterize the temporal variability of the long-term extreme incidence of highly pathogenic influenza in Zhejiang, China. Results: The skewness and kurtosis of the incidence of highly pathogenic influenza in Zhejiang between April 2009 and November 2013 were 4.49 and 21.12, which indicated a "fat tail" distribution. A QQ plot and a mean excess plot were used to further validate the features of the distribution. After determining the threshold, we modeled the extremes and estimated the shape parameter and scale parameter by the maximum likelihood method. The results showed that months in which the incidence of highly pathogenic influenza is about 4462/2286/1311/487 are predicted to occur once every five/three/two/one year, respectively. Conclusions: Despite the simplicity, the present study successfully offers the sound modeling strategy and a methodological avenue to implement forecasting of an epidemic in the midst of its course.

Timely outbreak investigations are central in containing communicable disease outbreaks; despite this, no guidance currently exists on expectations of timeliness for investigations. A literature review was conducted to assess the length of epidemiological outbreak investigations in Europe in peer-reviewed publications. We determined time intervals between outbreak declaration to hypothesis generation, and hypothesis generation to availability of results from an analytical study. Outbreaks were classified into two groups: those with a public health impact across regions within a country and requiring national coordination (level 3) and those with a severe or catastrophic impact requiring direction at national level (levels 4 and 5). Investigations in Europe published between 2003 and 2013 were reviewed. We identified 86 papers for review: 63 level 3 and 23 level 4 and 5 investigations. Time intervals were ascertained from 55 papers. The median period for completion of an analytical study was 15 days (range: 4–32) for levels 4 and 5 and 31 days (range: 9–213) for level 3 investigations. Key factors influencing the speed of completing analytical studies were outbreak level, severity of infection and study design. Our findings suggest that guidance for completing analytical studies could usefully be provided, with different time intervals according to outbreak severity.

objective. To identify clinical signs and symptoms (ie, “terms”) that accurately predict laboratory-confirmed influenza cases and thereafter generate and evaluate various influenza-like illness (ILI) case definitions for detecting influenza. A secondary objective explored whether surveillance of data beyond the chief complaint improves the accuracy of predicting influenza. design. Retrospective, cross-sectional study. setting. Large urban academic medical center hospital. participants. A total of 1,581 emergency department (ED) patients who received a nasopharyngeal swab followed by rRT-PCR testing between August 30, 2009, and January 2, 2010, and between November 28, 2010, and March 26, 2011. methods. An electronic surveillance system (GUARDIAN) scanned the entire electronic medical record (EMR) and identified cases containing 29 clinical terms relevant to influenza. Analyses were conducted using logistic regressions, diagnostic odds ratio (DOR), sensitivity, and specificity. results. The best predictive model for identifying influenza for all ages consisted of cough (DOR =5.87), fever (DOR = 4.49), rhinorrhea (DOR = 1.98), and myalgias (DOR =1.44). The 3 best case definitions that included combinations of some or all of these 4 symptoms had comparable performance (ie, sensitivity =89%–92% and specificity= 38%–44%). For children <5 data-blogger-escaped-0="" data-blogger-escaped-37.1="" data-blogger-escaped-a="" data-blogger-escaped-achieved="" data-blogger-escaped-addition="" data-blogger-escaped-age="" data-blogger-escaped-ages="" data-blogger-escaped-all="" data-blogger-escaped-and="" data-blogger-escaped-balance="" data-blogger-escaped-based="" data-blogger-escaped-be="" data-blogger-escaped-better="" data-blogger-escaped-between="" data-blogger-escaped-case="" data-blogger-escaped-cases="" data-blogger-escaped-chief="" data-blogger-escaped-complaint="" data-blogger-escaped-conclusions.="" data-blogger-escaped-cough="" data-blogger-escaped-data.="" data-blogger-escaped-definition="" data-blogger-escaped-detection="" data-blogger-escaped-did="" data-blogger-escaped-emr="" data-blogger-escaped-entire="" data-blogger-escaped-fever="" data-blogger-escaped-finally="" data-blogger-escaped-for="" data-blogger-escaped-further="" data-blogger-escaped-group.="" data-blogger-escaped-guardian="" data-blogger-escaped-identified="" data-blogger-escaped-ili="" data-blogger-escaped-implementation="" data-blogger-escaped-improve="" data-blogger-escaped-inclusion="" data-blogger-escaped-influenza="" data-blogger-escaped-is="" data-blogger-escaped-it="" data-blogger-escaped-may="" data-blogger-escaped-more="" data-blogger-escaped-of="" data-blogger-escaped-on="" data-blogger-escaped-only="" data-blogger-escaped-recommended.="" data-blogger-escaped-rhinorrhea="" data-blogger-escaped-sensitivity="" data-blogger-escaped-simplified="" data-blogger-escaped-specificity="" data-blogger-escaped-suitable="" data-blogger-escaped-surveillance="" data-blogger-escaped-than="" data-blogger-escaped-the="" data-blogger-escaped-to="" data-blogger-escaped-using="" data-blogger-escaped-while="" data-blogger-escaped-year-old="" data-blogger-escaped-years="">

objective. Contact patterns and microbiological data contribute to a detailed understanding of infectious disease transmission. We explored the automated collection of high-resolution contact data by wearable sensors combined with virological data to investigate influenza transmission among patients and healthcare workers in a geriatric unit. design. Proof-of-concept observational study. Detailed information on contact patterns were collected by wearable sensors over 12 days. Systematic nasopharyngeal swabs were taken, analyzed for influenza A and B viruses by real-time polymerase chain reaction, and cultured for phylogenetic analysis. setting. An acute-care geriatric unit in a tertiary care hospital. participants. Patients, nurses, and medical doctors. results. A total of 18,765 contacts were recorded among 37 patients, 32 nurses, and 15 medical doctors. Most contacts occurred between nurses or between a nurse and a patient. Fifteen individuals had influenza A (H3N2). Among these, 11 study participants were positive at the beginning of the study or at admission, and 3 patients and 1 nurse acquired laboratory-confirmed influenza during the study. Infectious medical doctors and nurses were identified as potential sources of hospital-acquired influenza (HA-Flu) for patients, and infectious patients were identified as likely sources for nurses. Only 1 potential transmission between nurses was observed. conclusions. Combining high-resolution contact data and virological data allowed us to identify a potential transmission route in each possible case of HA-Flu. This promising method should be applied for longer periods in larger populations, with more complete use of phylogenetic analyses, for a better understanding of influenza transmission dynamics in a hospital setting.

During a foodborne crisis, risk assessors are often scrambling to assemble data needed to trace suspected foods along very complex supply chains. Although traceability systems ensure that stakeholders in the supply chain record lot-specific trace-back and trace-forward data, there are few databases available that describe in detail the flow of product in the complex web of supply chains. This paper presents the methodological approach used to design and assemble a relational database of nation-wide trade data for packaged ready-to-eat lettuce and leafy greens. The database was used in the development of an integrated simulation tool (Canadian GIS-based Risk Assessment, Simulation and Planning for food safety tool, i.e. CanGRASP) that can predict the spatial distribution and public health risk associated with contaminated food. The database includes the geographical coordinates of 5 domestic processors, 28 produce distribution centres and 2946 retail outlets from five of the top ten retail chains in Canada. It also includes other critical information to predict the fate of pathogens during distribution of contaminated product through the supply chain including: (a) product volumes handled by each stakeholder, (b) flow of product between stakeholders, (c) temperatures of product each season, and (d) times products spend in each step or during transit between steps, for each season. The database is used by both the simulation and mapping components of the integrated simulation tool during risk assessment exercises associated with emergency preparedness planning and training. Using the database, CanGRASP was able to assess the spread of the population at risk during a simulation of a hypothetical outbreak caused by fresh-cut leafy vegetables contaminated with Escherichia coli O157:H7 in the Canadian food distribution systems during both summer and winter seasons.

Purpose: To enhance speedy communication between the patient and the doctor through newly proposed routing protocol at the mobile node. Materials and Methods: The proposed model is applied for a telemedicine application during disaster recovery management. In this paper, Energy Efficient Link Stability Routing Protocol (EELSRP) has been developed by simulation and real time. This framework is designed for the immediate healing of affected persons in remote areas, especially at the time of the disaster where there is no hospital proximity. In case of disasters, there might be an outbreak of infectious diseases. In such cases, the patient's medical record is also transferred by the field operator from disaster place to the hospital to facilitate the identification of the disease-causing agent and to prescribe the necessary medication. The heterogeneous networking framework provides reliable, energy efficientand speedy communication between the patient and the doctor using the proposed routing protocol at the mobile node. Results: The performance of the simulation and real time versions of the Energy Efficient Link Stability Routing Protocol (EELSRP) protocol has been analyzed. Experimental results prove the efficiency of the real-time version of EESLRP protocol. Conclusion: The packet delivery ratio and throughput of the real time version of EELSRP protocol is increased by 3% and 10%, respectively, when compared to the simulated version of EELSRP. The end-to-end delay and energy consumption are reduced by 10% and 2% in the real time version of EELSRP.

[No abstract available]

objective. While incidence, mortality, morbidity, and recurrence rates of C. difficile infection (CDI) among the critically ill have been investigated, the impact of its recurrence on 30-day rehospitalization (ReAd), an important policy focus, has not been examined. design. Secondary analysis of a multicenter retrospective cohort study patients. Adult critically ill patients who survived their index hospitalization complicated by CDI methods. CDI was defined by diarrhea or pseudomembranous colitis and a positive assay for C. difficile toxins A and/or B. CDI recurrence (rCDI) was defined as diarrhea, positive C. difficile toxin and need for retreatment after cessation of therapy. Descriptive statistics and a logistic regression examined ReAd rates and characteristics, and factors that impact it. results. Among 287 hospital survivors, 76 (26.5%) required ReAd (ReAd+). At baseline, the ReAd+ group did not differ significantly from the ReAd– group based on demographics, comorbidities, APACHE II scores, or ICU type. ReAd+ patients were more likely to have hypotension at CDI onset (48.7% vs 34.1%, P=.025) and to require vasopressors (40.0% vs 27.1%, P=.038); they were less likely to require mechanical ventilation (56.0% vs 77.3%, P<.001). A far greater proportion of ReAd+ than ReAd– had developed a recurrence either during the index hospitalization or within 30 days after discharge (32.89% vs 2.84%, P<.001). In a logistic regression, rCDI was a strong predictor of ReAd+ (adjusted odd ratio, 15.33, 95% confidence interval, 5.68–41.40). conclusions. Greater than 25% of all survivors of critical illness complicated by CDI require readmission within 30 days of discharge. CDI recurrence is a strong predictor of such rehospitalizations.