31 March 2015

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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