***-Article is considered for Award Nomination***
Rodriguez-Martinez S., Sharaby Y., Pecellin M., Brettar I., Hofle M., Halpern M. Spatial distribution of Legionella pneumophila MLVA-genotypes in a drinking water system
Brady O.J., Smith D.L., Scott T.W., Hay S.I. Dengue disease outbreak definitions are implicitly variable
Jalalpour M., Gel Y., Levin S. Forecasting demand for health services: Development of a publicly available toolbox ★
King A.A., De Celles M.D., Magpantay F.M.G., Rohani P. Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola
Huang G.-Q. Function optimization algorithm based on SIRQV epidemic dynamic model
Chunara R., Goldstein E., Patterson-Lomba O., Brownstein J.S. Estimating influenza attack rates in the United States using a participatory cohort ★
Budgell E., Cohen A.L., McAnerney J., Walaza S., Madhi S.A., Blumberg L., Dawood H., Kahn K., Tempia Evaluation of two influenza surveillance systems in South Africa ★
Van Den Brom R., Roest H.-J., De Bruin A., Dercksen D., Santman-Berends I., Van Der Hoek W., Dinkla A probably minor role for land-applied goat manure in the transmission of Coxiella burnetii to humans in the 2007-2010 Dutch Q fever outbreak
Guzman Herrador B.R., De Blasio B.F., MacDonald E., Nichols G., Sudre B., Vold L., Semenza J.C., Nyg Analytical studies assessing the association between extreme precipitation or temperature and drinking water-related waterborne infections: A review
Obadia T., Silhol R., Opatowski L., Temime L., Legrand J., Thiebaut A.C.M., Herrmann J.-L., Fleury E Detailed Contact Data and the Dissemination of Staphylococcus aureus in Hospitals ★
Guo C., Yang L., Ou C.-Q., Li L., Zhuang Y., Yang J., Zhou Y.-X., Qian J., Chen P.-Y., Liu Q.-Y. Malaria incidence from 2005-2013 and its associations with meteorological factors in Guangdong, China
Pinchoff J., Chipeta J., Banda G.C., Miti S., Shields T., Curriero F., Moss W.J. Spatial clustering of measles cases during endemic (1998-2002) and epidemic (2010) periods in Lusaka, Zambia
Buck C., Dreger S., Pigeot I. Anonymisation of address coordinates for microlevel analyses of the built environment: A simulation study
Brooks-Pollock E., de Jong M.C.M., Keeling M.J., Klinkenberg D., Wood J.L.N. Eight challenges in modelling infectious livestock diseases ★
Funk S., Bansal S., Bauch C.T., Eames K.T.D., Edmunds W.J., Galvani A.P., Klepac P. Nine challenges in incorporating the dynamics of behaviour in infectious diseases models
Fitch J.P. Engineering a Global Response to Infectious Diseases
Amoros R., Conesa D., Martinez-Beneito M.A., Lopez-Quilez A. Statistical methods for detecting the onset of influenza outbreaks: A review ★
Torres G., Ciaravino V., Ascaso S., Flores V., Romero L., Simon F. Syndromic surveillance system based on near real-time cattle mortality monitoring ★
Nsoesie E.O., Brownstein J.S. Computational approaches to influenza surveillance: Beyond timeliness
Cenciarelli O., Pietropaoli S., Malizia A., Carestia M., D'Amico F., Sassolini A., Di Giovanni D., R Ebola virus disease 2013-2014 outbreak in West Africa: An analysis of the epidemic spread and response
Brottet E., Jaffar-Bandjee M.C., Rachou E., Polycarpe D., Ristor B., Larrieu S., Filleul L. Sentinel physician's network in Reunion Island: A tool for infectious diseases surveillance ★
Bennett S.D., Littrell K.W., Hill T.A., Mahovic M., Behravesh C.B. Multistate foodborne disease outbreaks associated with raw tomatoes, United States, 1990-2010: A recurring public health problem
Tian H., Xu B. Persistence and transmission of avian influenza A (H5N1): virus movement, risk factors and pandemic potential
Manore C.A., Hickmann K.S., Hyman J.M., Foppa I.M., Davis J.K., Wesson D.M., Mores C.N. A network-patch methodology for adapting agent-based models for directly transmitted disease to mosquito-borne disease ★
Siedner M.J., Gostin L.O., Cranmer H.H., Kraemer J.D. Strengthening the Detection of and Early Response to Public Health Emergencies: Lessons from the West African Ebola Epidemic
Jaspersen J.G., Montibeller G. Probability Elicitation Under Severe Time Pressure: A Rank-Based Method ★
Sharmin S., Rayhan M.I. A stochastic model for early identification of infectious disease epidemics with application to measles cases in Bangladesh ★
Murrieta-Flores P., Baron A., Gregory I., Hardie A., Rayson P. Automatically analyzing large texts in a GIS environment: The registrar general's reports and cholera in the 19th century ★
Liu M., Xiao Y. Optimal scheduling of logistical support for medical resource with demand information updating ★
Napoli C., Iannetti S., Rizzo C., Bella A., Di Sabatino D., Bruno R., Sauro F., Martini V., Santucci Vector borne infections in Italy: Results of the integrated surveillance system for west nile disease in 2013 ★
Osborne C.M., Montano A.C., Robinson C.C., Schultz-Cherry S., Dominguez S.R. Viral gastroenteritis in children in Colorado 2006-2009
Peterson A.T. Mapping risk of nipah virus transmission across Asia and across Bangladesh
Gale P., Kelly L., Mearns R., Duggan J., Snary E.L. Q fever through consumption of unpasteurised milk and milk products - a risk profile and exposure assessment
Robert A., Suelves J.M., Armayones M., Ashley S. Internet use and suicidal behaviors: Internet as a threat or opportunity?
Fraustino J.D., Ma L. CDC's Use of Social Media and Humor in a Risk Campaign—“Preparedness 101: Zombie Apocalypse”
Lawes T., Lopez-Lozano J.-M., Nebot C., Macartney G., Subbarao-Sharma R., Dare C.R.J., Edwards G.F.S Turning the tide or riding the waves? Impacts of antibiotic stewardship and infection control on MRSA strain dynamics in a Scottish region over 16 y ★
Bhansali A., Dhandania V.K., Deepa M., Anjana R.M., Joshi S.R., Joshi P.P., Madhu S.V., Rao P.V., Su Prevalence of and risk factors for hypertension in urban and rural India: The ICMR-INDIAB study
Spence P.R., Lachlan K.A., Lin X., del Greco M. Variability in Twitter Content Across the Stages of a Natural Disaster: Implications for Crisis Communication
Spatial distribution of Legionella pneumophila MLVA-genotypes in a drinking water system
Bacteria of the genus Legionella cause water-based infections, resulting in severe pneumonia. To improve our knowledge about Legionella spp. ecology, its prevalence and its relationships with environmental factors were studied. Seasonal samples were taken from both water and biofilm at seven sampling points of a small drinking water distribution system in Israel. Representative isolates were obtained from each sample and identified to the species level. Legionella pneumophila was further determined to the serotype and genotype level. High resolution genotyping of L. pneumophila isolates was achieved by Multiple-Locus Variable number of tandem repeat Analysis (MLVA). Within the studied water system, Legionella plate counts were higher in summer and highly variable even between adjacent sampling points. Legionella was present in six out of the seven selected sampling points, with counts ranging from 1.0×101 to 5.8×103cfu/l. Water counts were significantly higher in points where Legionella was present in biofilms. The main fraction of the isolated Legionella was L. pneumophila serogroup 1. Serogroup 3 and Legionella sainthelensis were also isolated. Legionella counts were positively correlated with heterotrophic plate counts at 37°C and negatively correlated with chlorine. Five MLVA-genotypes of L. pneumophila were identified at different buildings of the sampled area. The presence of a specific genotype, "MLVA-genotype 4", consistently co-occurred with high Legionella counts and seemed to "trigger" high Legionella counts in cold water. Our hypothesis is that both the presence of L. pneumophila in biofilm and the presence of specific genotypes, may indicate and/or even lead to high Legionella concentration in water. This observation deserves further studies in a broad range of drinking water systems to assess its potential for general use in drinking water monitoring and management. © 2015 Elsevier Ltd.
Dengue disease outbreak definitions are implicitly variable
Infectious diseases rarely exhibit simple dynamics. Outbreaks (defined as excess cases beyond response capabilities) have the potential to cause a disproportionately high burden due to overwhelming health care systems. The recommendations of international policy guidelines and research agendas are based on a perceived standardised definition of an outbreak characterised by a prolonged, high-caseload, extra-seasonal surge. In this analysis we apply multiple candidate outbreak definitions to reported dengue case data from Brazil to test this assumption. The methods identify highly heterogeneous outbreak characteristics in terms of frequency, duration and case burden. All definitions identify outbreaks with characteristics that vary over time and space. Further, definitions differ in their timeliness of outbreak onset, and thus may be more or less suitable for early intervention. This raises concerns about the application of current outbreak guidelines for early warning/identification systems. It is clear that quantitatively defining the characteristics of an outbreak is an essential prerequisite for effective reactive response. More work is needed so that definitions of disease outbreaks can take into account the baseline capacities of treatment, surveillance and control. This is essential if outbreak guidelines are to be effective and generalisable across a range of epidemiologically different settings. © 2015 The Authors.
Forecasting demand for health services: Development of a publicly available toolbox
Efficient health care delivery systems aim to match resources to demand for services over time. Resource allocation decisions must be made under stochastic uncertainty. This includes uncertainty in the number of individuals (i.e., counts) in need of services over discrete time intervals. Examples include counts of patients arriving to emergency departments and counts of prescription medications distributed by pharmacies. Accurately forecasting count data in health care systems allows decision-makers to anticipate the need for services and make informed decisions about how to manage resources and purchase supplies over time.A publicly available toolbox to forecast count data is developed in this work. The toolbox is implemented in MATLAB environment with the newly developed generalized autoregressive moving average (GARMA) models with discrete-valued distributions. GARMA models treat count data in a mathematically coherent manner compared to Gaussian models, often inappropriately applied in health care applications. GARMA models can incorporate none to many exogenous variables hypothesized to influence the predicted responses (i.e., counts forecasted). The toolbox's primary purpose is to deliver one to multiple-steps ahead forecasts, but also gives information for model inference and validation. The toolbox uses the maximum likelihood method to estimate model parameters from the data. We demonstrate toolbox application and validity on two example health care count data sets and show how using integer-valued conditional distributions as offered by GARMA models can produce forecast models that outperform the traditional Gaussian models. © 2015 Elsevier Ltd.
Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola
As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely used modelling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far overestimated, leading to the potential for large errors that mask their own presence. Fortunately, straightforward and computationally inexpensive alternatives exist that avoid these problems. Here, we first use a simulation study to demonstrate potential pitfalls of the standard practice of fitting deterministic models to cumulative incidence data. Next, we demonstrate an alternative based on stochastic models fit to raw data from an early phase of 2014 West Africa Ebola virus disease outbreak. We show not only that bias is thereby reduced, but that uncertainty in estimates and forecasts is better quantified and that, critically, lack of model fit is more readily diagnosed. We conclude with a short list of principles to guide the modelling response to future infectious disease outbreaks. © 2015 The Authors.
Function optimization algorithm based on SIRQV epidemic dynamic model
To solve some complicated function optimization problems, the SIRQV algorithm is constructed based on the SIRQV epidemic model. The algorithm supposes that some animal individuals exist in an ecosystem; each individual is characterized by a number of features; an infectious disease exists in the ecosystem and spreads among individuals, the disease attacks a part of features of an individual at each time. Each infected individual may pass through such states as susceptibility (S), infection (I), recovery (R), quarantine (Q) and vaccination (V), which can synthetically decide the physique strength of an individual. Individuals in the algorithm have 5 states such as S, I, R, Q and V, and 13 state transitions, each of which is equivalent to an operator. the 13 operators are logically organized together by the disease transmission logic of the SIRQV epidemic model so as to form a good cooperation and sufficient information exchange among individuals. The algorithm uses the activation, average, combination, reinforcement and assimilation operator to exchange feature information among individuals. The reinforcement operator transfers feature information from some strong individuals with higher individual physique index (IPI) to a weak individual with lower IPI index so as to make the latter grow better; the average operator ensures an individual to obtain average feature information from other individuals so as to reduce the probability that the individual drops into local optima; the activation operator expands an individual's search scope by increasing its vitality; the combination operator has the characteristics of both the activation operator and the average operator the assimilation operator enables the search to possess of jumping ability along dimension direction; the REINIT operator has exploration and exploitation ability to overcome sticky state of individuals and enhance precision of global optima; the growth operator enables the algorithm to converge globally.
Estimating influenza attack rates in the United States using a participatory cohort
We considered how participatory syndromic surveillance data can be used to estimate influenza attack rates during the 2012-2013 and 2013-2014 seasons in the United States. Our inference is based on assessing the difference in the rates of self-reported influenza-like illness (ILI, defined as presence of fever and cough/sore throat) among the survey participants during periods of active vs. low influenza circulation as well as estimating the probability of self-reported ILI for influenza cases. Here, we combined Flu Near You data with additional sources (Hong Kong household studies of symptoms of influenza cases and the U.S. Centers for Disease Control and Prevention estimates of vaccine coverage and effectiveness) to estimate influenza attack rates. The estimated influenza attack rate for the early vaccinated Flu Near You members (vaccination reported by week 45) aged 20-64 between calendar weeks 47-12 was 14.7%(95% CI(5.9%,24.1%)) for the 2012-2013 season and 3.6%(â '3.3%,10.3%) for the 2013-2014 season. The corresponding rates for the US population aged 20-64 were 30.5% (4.4%, 49.3%) in 2012-2013 and 7.1%(-5.1%, 32.5%) in 2013-2014. The attack rates in women and men were similar each season. Our findings demonstrate that participatory syndromic surveillance data can be used to gauge influenza attack rates during future influenza seasons.
Evaluation of two influenza surveillance systems in South Africa
Background: The World Health Organisation recommends outpatient influenza-like illness (ILI) and inpatient severe acute respiratory illness (SARI) surveillance. We evaluated two influenza surveillance systems in South Africa: one for ILI and another for SARI. Methodology: The Viral Watch (VW) programme has collected virological influenza surveillance data voluntarily from patients with ILI since 1984 in private and public clinics in all 9 South African provinces. The SARI surveillance programme has collected epidemiological and virological influenza surveillance data since 2009 in public hospitals in 4 provinces by dedicated personnel. We compared nine surveillance system attributes from 2009-2012. Results: We analysed data from 18,293 SARI patients and 9,104 ILI patients. The annual proportion of samples testing positive for influenza was higher for VW (mean 41%) than SARI (mean 8%) and generally exceeded the seasonal threshold from May to September (VW: weeks 21-40; SARI: weeks 23-39). Data quality was a major strength of SARI (most data completion measures >90%; adherence to definitions: 88-89%) and a relative weakness of the VW programme (62% of forms complete, with limited epidemiologic data collected; adherence to definitions: 65-82%). Timeliness was a relative strength of both systems (e.g. both collected >93% of all respiratory specimens within 7 days of symptom onset). ILI surveillance was more nationally representative, financially sustainable and expandable than the SARI system. Though the SARI programme is not nationally representative, the high quality and detail of SARI data collection sheds light on the local burden and epidemiology of severe influenza-associated disease. Conclusions: To best monitor influenza in South Africa, we propose that both ILI and SARI should be under surveillance. Improving ILI surveillance will require better quality and more systematic data collection, and SARI surveillance should be expanded to be more nationally represent
A probably minor role for land-applied goat manure in the transmission of Coxiella burnetii to humans in the 2007-2010 Dutch Q fever outbreak
In 2007, Q fever started to become a major public health problem in the Netherlands, with small ruminants as most probable source. In order to reduce environmental contamination, control measures for manure were implemented because of the assumption that manure was highly contaminated with Coxiella burnetii. The aims of this study were 1) to clarify the role of C. burnetii contaminated manure from dairy goat farms in the transmission of C. burnetii to humans, 2) to assess the impact of manure storage on temperature profiles in dung-hills, and 3) to calculate the decimal reduction time of the Nine Mile RSA 493 reference strain of C. burnetii under experimental conditions in different matrices. For these purposes, records on distribution of manure from case and control herds were mapped and a potential relation to incidences of human Q fever was investigated. Additionally, temperatures in two dunghills were measured and related to heat resistance of C. burnetii. Results of negative binomial regression showed no significant association between the incidence of human Q fever cases and the source of manure. Temperature measurements in the core and shell of dunghills on two farms were above 40°C for at least ten consecutive days which would result in a strong reduction of C. burnetii over time. Our findings indicate that there is no relationship between incidence of human Q fever and land applied manure from dairy goat farms with an abortion wave caused by C. burnetii. Temperature measurements in dunghills on two farms with C. burnetii shedding dairy goat herds further support the very limited role of goat manure as a transmission route during the Dutch human Q fever outbreak. It is very likely that the composting process within a dunghill will result in a clear reduction in the number of viable C. burnetii. © 2015 van den Brom et al.
Analytical studies assessing the association between extreme precipitation or temperature and drinking water-related waterborne infections: A review
Determining the role of weather in waterborne infections is a priority public health research issue as climate change is predicted to increase the frequency of extreme precipitation and temperature events. To document the current knowledge on this topic, we performed a literature review of analytical research studies that have combined epidemiological and meteorological data in order to analyze associations between extreme precipitation or temperature and waterborne disease. A search of the databases Ovid MEDLINE, EMBASE, SCOPUS and Web of Science was conducted, using search terms related to waterborne infections and precipitation or temperature. Results were limited to studies published in English between January 2001 and December 2013. Twenty-four articles were included in this review, predominantly from Asia and North-America. Four articles used waterborne outbreaks as study units, while the remaining articles used number of cases of waterborne infections. Results presented in the different articles were heterogeneous. Although most of the studies identified a positive association between increased precipitation or temperature and infection, there were several in which this association was not evidenced. A number of articles also identified an association between decreased precipitation and infections. This highlights the complex relationship between precipitation or temperature driven transmission and waterborne disease. We encourage researchers to conduct studies examining potential effect modifiers, such as the specific type of microorganism, geographical region, season, type of water supply, water source or water treatment, in order to assess how they modulate the relationship between heavy rain events or temperature and waterborne disease. Addressing these gaps is of primary importance in order to identify the areas where action is needed to minimize negative impact of climate change on health in the future. © 2015 Guzman Herrador et al.; licensee BioMed Cen
Detailed Contact Data and the Dissemination of Staphylococcus aureus in Hospitals
Close proximity interactions (CPIs) measured by wireless electronic devices are increasingly used in epidemiological models. However, no evidence supports that electronically collected CPIs inform on the contacts leading to transmission. Here, we analyzed Staphylococcus aureus carriage and CPIs recorded simultaneously in a long-term care facility for 4 months in 329 patients and 261 healthcare workers to test this hypothesis. In the broad diversity of isolated S. aureus strains, 173 transmission events were observed between participants. The joint analysis of carriage and CPIs showed that CPI paths linking incident cases to other individuals carrying the same strain (i.e. possible infectors) had fewer intermediaries than predicted by chance (P < 0.001), a feature that simulations showed to be the signature of transmission along CPIs. Additional analyses revealed a higher dissemination risk between patients via healthcare workers than via other patients. In conclusion, S. aureus transmission was consistent with contacts defined by electronically collected CPIs, illustrating their potential as a tool to control hospital-acquired infections and help direct surveillance. © 2015 Obadia et al.
Malaria incidence from 2005-2013 and its associations with meteorological factors in Guangdong, China
Background: The temporal variation of malaria incidence has been linked to meteorological factors in many studies, but key factors observed and corresponding effect estimates were not consistent. Furthermore, the potential effect modification by individual characteristics is not well documented. This study intends to examine the delayed effects of meteorological factors and the sub-population's susceptibility in Guangdong, China. Methods: The Granger causality Wald test and Spearman correlation analysis were employed to select climatic variables influencing malaria. The distributed lag non-linear model (DLNM) was used to estimate the non-linear and delayed effects of weekly temperature, duration of sunshine, and precipitation on the weekly number of malaria cases after controlling for other confounders. Stratified analyses were conducted to identify the sub-population's susceptibility to meteorological effects by malaria type, gender, and age group. Results: An incidence rate of 1.1 cases per 1,000,000 people was detected in Guangdong from 2005-2013. High temperature was associated with an observed increase in malaria incidence, with the effect lasting for four weeks and a maximum relative risk (RR) of 1.57 (95% confidence interval (CI): 1.06-2.33) by comparing 30°C to the median temperature. The effect of sunshine duration peaked at lag five and the maximum RR was 1.36 (95% CI: 1.08-1.72) by comparing 24 hours/week to 0 hours/week. A J-shaped relationship was found between malaria incidence and precipitation with a threshold of 150 mm/week. Over the threshold, precipitation increased malaria incidence after four weeks with the effect lasting for 15 weeks, and the maximum RR of 1.55 (95% CI: 1.18-2.03) occurring at lag eight by comparing 225 mm/week to 0 mm/week. Plasmodium falciparum was more sensitive to temperature and precipitation than Plasmodium vivax. Females had a higher susceptibility to the effects of sunshine and precipitation, and children and the elderl
Spatial clustering of measles cases during endemic (1998-2002) and epidemic (2010) periods in Lusaka, Zambia
Background: Measles cases may cluster in densely populated urban centers in sub-Saharan Africa as susceptible individuals share spatially dependent risk factors and may cluster among human immunodeficiency virus (HIV)-infected children despite high vaccination coverage. Methods: Children hospitalized with measles at the University Teaching Hospital (UTH) in Lusaka, Zambia were enrolled in the study. The township of residence was recorded on the questionnaire and mapped; SaTScan software was used for cluster detection. A spatial-temporal scan statistic was used to investigate clustering of measles in children hospitalized during an endemic period (1998 to 2002) and during the 2010 measles outbreak in Lusaka, Zambia. Results: Three sequential and spatially contiguous clusters of measles cases were identified during the 2010 outbreak but no clustering among HIV-infected children was identified. In contrast, a space-time cluster among HIV-infected children was identified during the endemic period. This cluster occurred prior to the introduction of intensive measles control efforts and during a period between seasonal peaks in measles incidence. Conclusions: Prediction and early identification of spatial clusters of measles will be critical to achieving measles elimination. HIV infection may contribute to spatial clustering of measles cases in some epidemiological settings. © 2015 Pinchoff et al.
Anonymisation of address coordinates for microlevel analyses of the built environment: A simulation study
Background: Data privacy is a major concern in spatial epidemiology because exact residential locations or parts of participants' addresses such as street or zip codes are used to perform geospatial analyses. To overcome this concern, different levels of aggregation such as census districts or zip code areas are mainly used, though any spatial aggregation leads to a loss of spatial variability. For the assessment of urban opportunities for physical activity that was conducted in the IDEFICS (Identification and prevention of dietary- and lifestyle-induced health effects in children and infants) study, macrolevel analyses were performed, but the use of exact residential addresses for microlevel analyses was not permitted by the responsible office for data protection. We therefore implemented a spatial blurring to anonymise address coordinates depending on the underlying population density. Methods: We added a standard Gaussian distributed error to individual address coordinates with the variance ?2 depending on the population density and on the chosen k-anonymity. 1000 random point locations were generated and repeatedly blurred 100 times to obtain anonymised locations. For each location 1 km network-dependent neighbourhoods were used to calculate walkability indices. Indices of blurred locations were compared to indices based on their sampling origins to determine the effect of spatial blurring on the assessment of the built environment. Results: Spatial blurring decreased with increasing population density. Similarly, mean differences in walkability indices also decreased with increasing population density. In particular for densely-populated areas with at least 1500 residents per km2, differences between blurred locations and their sampling origins were small and did not affect the assessment of the built environment after spatial blurring. Conclusions: This approach allowed the investigation of the built environment at a microlevel using individual network-depende
Eight challenges in modelling infectious livestock diseases
The transmission of infectious diseases of livestock does not differ in principle from disease transmission in any other animals, apart from that the aim of control is ultimately economic, with the influence of social, political and welfare constraints often poorly defined. Modelling of livestock diseases suffers simultaneously from a wealth and a lack of data. On the one hand, the ability to conduct transmission experiments, detailed within-host studies and track individual animals between geocoded locations make livestock diseases a particularly rich potential source of realistic data for illuminating biological mechanisms of transmission and conducting explicit analyses of contact networks. On the other hand, scarcity of funding, as compared to human diseases, often results in incomplete and partial data for many livestock diseases and regions of the world. In this overview of challenges in livestock disease modelling, we highlight eight areas unique to livestock that, if addressed, would mark major progress in the area. © 2014 The Authors.
Nine challenges in incorporating the dynamics of behaviour in infectious diseases models
Traditionally, the spread of infectious diseases in human populations has been modelled with static parameters. These parameters, however, can change when individuals change their behaviour. If these changes are themselves influenced by the disease dynamics, there is scope for mechanistic models of behaviour to improve our understanding of this interaction. Here, we present challenges in modelling changes in behaviour relating to disease dynamics, specifically: how to incorporate behavioural changes in models of infectious disease dynamics, how to inform measurement of relevant behaviour to parameterise such models, and how to determine the impact of behavioural changes on observed disease dynamics. © 2014 The Authors.
Engineering a Global Response to Infectious Diseases
Infectious diseases are a major cause of death and economic impact worldwide. A more robust, adaptable, and scalable infrastructure would improve the capability to respond to epidemics. Because engineers contribute to the design and implementation of infrastructure, there are opportunities for innovative solutions to infectious disease response within existing systems that have utility, and therefore resources, before a public health emergency. Examples of innovative leveraging of infrastructure, technologies to enhance existing disease management strategies, engineering approaches to accelerate the rate of discovery and application of scientific, clinical, and public health information, and ethical issues that need to be addressed for implementation are presented. © 1963-2012 IEEE.
Statistical methods for detecting the onset of influenza outbreaks: A review
This paper reviews different approaches for determining the epidemic period from influenza surveillance data. In the first approach, the process of differenced incidence rates is modeled either with a first-order autoregressive process or with a Gaussian white noise process depending on whether the system is in an epidemic or a non- epidemic phase. The second approach allows us to directly model the process of the observed cases via a Bayesian hierarchical Poisson model with Gaussian incidence rates whose parameters are modeled differently, depending on the epidemic phase of the system. In both cases transitions between both phases are modeled with a hidden Markov switching model over the epidemic state. Bayesian inference is carried out and both models provide the probability of being in epidemic state at any given moment. A comparison of both methodologies with previous approaches in terms of sensitivity, specificity and timeliness is also performed. Finally, we also review a web-based client application which implements the first methodology for obtaining the posterior probability of being in an epidemic phase. © 2015, National Statistical Institute. All rights reserved.
Syndromic surveillance system based on near real-time cattle mortality monitoring
Early detection of an infectious disease incursion will minimize the impact of outbreaks in livestock. Syndromic surveillance based on the analysis of readily available data can enhance traditional surveillance systems and allow veterinary authorities to react in a timely manner.This study was based on monitoring the number of cattle carcasses sent for rendering in the veterinary unit of Talavera de la Reina (Spain). The aim was to develop a system to detect deviations from expected values which would signal unexpected health events. Historical weekly collected dead cattle (WCDC) time series stabilized by the Box-Cox transformation and adjusted by the minimum least squares method were used to build the univariate cycling regression model based on a Fourier transformation. Three different models, according to type of production system, were built to estimate the baseline expected number of WCDC.Two types of risk signals were generated: point risk signals when the observed value was greater than the upper 95% confidence interval of the expected baseline, and cumulative risk signals, generated by a modified cumulative sum algorithm, when the cumulative sums of reported deaths were above the cumulative sum of expected deaths.Data from 2011 were used to prospectively validate the model generating seven risk signals. None of them were correlated to infectious disease events but some coincided, in time, with very high climatic temperatures recorded in the region. The harvest effect was also observed during the first week of the study year.Establishing appropriate risk signal thresholds is a limiting factor of predictive models; it needs to be adjusted based on experience gained during the use of the models. To increase the sensitivity and specificity of the predictions epidemiological interpretation of non-specific risk signals should be complemented by other sources of information.The methodology developed in this study can enhance other existing early detection surveilla
Computational approaches to influenza surveillance: Beyond timeliness
Several digital data sources and systems have been advanced for use in augmenting traditional influenza surveillance systems. Although timeliness is one of the main advantages of these tools, there are several other recognizable uses and potential impact of these systems on the public and global public health. © 2015 Elsevier Inc.
Ebola virus disease 2013-2014 outbreak in West Africa: An analysis of the epidemic spread and response
The Ebola virus epidemic burst in West Africa in late 2013, started in Guinea, reached in a few months an alarming diffusion, actually involving several countries (Liberia, Sierra Leone, Nigeria, Senegal, and Mali). Guinea and Liberia, the first nations affected by the outbreak, have put in place measures to contain the spread, supported by international organizations; then they were followed by the other nations affected. In the present EVD outbreak, the geographical spread of the virus has followed a new route: the achievement of large urban areas at an early stage of the epidemic has led to an unprecedented diffusion, featuring the largest outbreak of EVD of all time. This has caused significant concerns all over the world: the potential reaching of far countries from endemic areas, mainly through fast transports, induced several countries to issue information documents and health supervision for individuals going to or coming from the areas at risk. In this paper the geographical spread of the epidemic was analyzed, assessing the sequential appearance of cases by geographic area, considering the increase in cases and mortality according to affected nations. The measures implemented by each government and international organizations to contain the outbreak, and their effectiveness, were also evaluated. © 2015 Orlando Cenciarelli et al.
Sentinel physician's network in Reunion Island: A tool for infectious diseases surveillance
The surveillance of infectious diseases in Reunion Island is based on a sentinel network of family physicians (FPs) coordinated by the Indian Ocean regional institute for public health surveillance (French acronym OI Cire). The objectives are to identify and monitor outbreaks of influenza, gastroenteritis, and chicken pox, and to characterize circulating influenza viruses. The network can monitor other potentially epidemic diseases. Method: The Réunion sentinel network ensures a continuous and permanent surveillance. Physicians send their weekly activity data to the Cire that collects, processes, and interprets it; they also collect samples for biological surveillance of influenza. Statistical thresholds, based on historical data and the estimated numbers of incident cases, are calculated to follow the trend, detect outbreaks, and quantify their impact. Results: The network currently includes 56 FPs and pediatricians, accounting for 6.5% of FPs on the island. The network has clarified the seasonality of influenza during the austral winter and identified the seasonality of acute diarrhea with an epidemic peak when school starts in August. The sentinel FPs's reports allowed monitoring the epidemic trend and estimating the number of cases during the 2005 and 2006 chikungunya outbreaks and 2009 influenza A (H1N1) outbreaks. Conclusion: The network has proven its contribution, responsiveness, and reliability for epidemiological surveillance during outbreak. It is an essential tool for infectious diseases surveillance in Reunion Island. © 2014 Elsevier Masson SAS.
Multistate foodborne disease outbreaks associated with raw tomatoes, United States, 1990-2010: A recurring public health problem
We examined multistate outbreaks attributed to raw tomatoes in the United States from 1990 to 2010. We summarized the demographic and epidemiological characteristics of 15 outbreaks resulting in 1959 illnesses, 384 hospitalizations, and three deaths. Most (80%) outbreaks were reported during 2000-2010; 73% occurred May-September. Outbreaks commonly affected adult (median age 34 years) women (median 58% of outbreak cases). All outbreaks were caused by Salmonella [serotypes Newport (n = 6 outbreaks), Braenderup (n = 2), Baildon, Enteritidis, Javiana, Montevideo, Thompson, Typhimurium (n = 1 each); multiple serotypes (n = 1)]. Red, round (69% of outbreaks), Roma (23%), and grape (8%) tomatoes were implicated. Most (93%) outbreaks were associated with tomatoes served predominantly in restaurants. However, traceback investigations suggested that contamination occurred on farms, at packinghouses, or at fresh-cut processing facilities. Government agencies, academia, trade associations, and the fresh tomato industry should consider further efforts to identify interventions to reduce contamination of tomatoes during production and processing. © 2014 Cambridge University Press.
Persistence and transmission of avian influenza A (H5N1): virus movement, risk factors and pandemic potential
Repeated outbreaks in epidemic areas and invasion of new countries and regions expanded influence on poultry production, economics and the ecology of wild birds. Highly pathogenic avian influenza (HPAI) H5N1 has moved the world closer to a further global pandemic. An understanding of HPAI H5N1 transmission and persistence is therefore of significance for the prevention and control of epidemics. In this review we consider virus movement through poultry production systems (live bird markets, small holder farms and industry), wild bird migration and vector media (biological and mechanical); risk factors in poultry production systems (commercial, backyard and free-grazing farm); and the ecological environments (avian community, ecology and geographical isolation) of epidemic areas, and their effects on H5N1 virus transmission and persistence. We conclude that the pandemic potential, widespread transmission and sustained persistence of H5N1 is the result of conflicts between traditional production and consumption habits and the intense poultry production industry in Southeast Asia. © 2015 Taylor & Francis.
A network-patch methodology for adapting agent-based models for directly transmitted disease to mosquito-borne disease
Mosquito-borne diseases cause significant public health burden and are widely re-emerging or emerging. Understanding, predicting, and mitigating the spread of mosquito-borne disease in diverse populations and geographies are ongoing modelling challenges. We propose a hybrid network-patch model for the spread of mosquito-borne pathogens that accounts for individual movement through mosquito habitats, extending the capabilities of existing agent-based models (ABMs) to include vector-borne diseases. The ABM are coupled with differential equations representing ‘clouds’ of mosquitoes in patches accounting for mosquito ecology. We adapted an ABM for humans using this method and investigated the importance of heterogeneity in pathogen spread, motivating the utility of models of individual behaviour. We observed that the final epidemic size is greater in patch models with a high risk patch frequently visited than in a homogeneous model. Our hybrid model quantifies the importance of the heterogeneity in the spread of mosquito-borne pathogens, guiding mitigation strategies. © 2015 The Author(s). Published by Taylor & Francis.
Strengthening the Detection of and Early Response to Public Health Emergencies: Lessons from the West African Ebola Epidemic
[No abstract available]
Probability Elicitation Under Severe Time Pressure: A Rank-Based Method
Probability elicitation protocols are used to assess and incorporate subjective probabilities in risk and decision analysis. While most of these protocols use methods that have focused on the precision of the elicited probabilities, the speed of the elicitation process has often been neglected. However, speed is also important, particularly when experts need to examine a large number of events on a recurrent basis. Furthermore, most existing elicitation methods are numerical in nature, but there are various reasons why an expert would refuse to give such precise ratio-scale estimates, even if highly numerate. This may occur, for instance, when there is lack of sufficient hard evidence, when assessing very uncertain events (such as emergent threats), or when dealing with politicized topics (such as terrorism or disease outbreaks). In this article, we adopt an ordinal ranking approach from multicriteria decision analysis to provide a fast and nonnumerical probability elicitation process. Probabilities are subsequently approximated from the ranking by an algorithm based on the principle of maximum entropy, a rule compatible with the ordinal information provided by the expert. The method can elicit probabilities for a wide range of different event types, including new ways of eliciting probabilities for stochastically independent events and low-probability events. We use a Monte Carlo simulation to test the accuracy of the approximated probabilities and try the method in practice, applying it to a real-world risk analysis recently conducted for DEFRA (the U.K. Department for the Environment, Farming and Rural Affairs): the prioritization of animal health threats. © 2015 Society for Risk Analysis.
A stochastic model for early identification of infectious disease epidemics with application to measles cases in Bangladesh
In this article, a stochastic modeling approach was employed for the detection of epidemics in advance that was based on a negative binomial model with 2 components: an endemic component and an epidemic component. This study used monthly measles cases from January 2000 to August 2009 collected from the Expanded Program on Immunization, Bangladesh. General optimization routines provided the maximum likelihood estimates with corresponding standard errors. The negative binomial model with both seasonal endemic and epidemic components was shown to provide adequate fit with no measles epidemic during September 2008 to August 2009. © 2012 APJPH.
Automatically analyzing large texts in a GIS environment: The registrar general's reports and cholera in the 19th century
The aim of this article is to present new research showcasing how Geographic Information Systems in combination with Natural Language Processing and Corpus Linguistics methods can offer innovative venues of research to analyze large textual collections in the Humanities, particularly in historical research. Using as examples parts of the collection of the Registrar General's Reports that contain more than 200,000 pages of descriptions, census data and vital statistics for the UK, we introduce newly developed automated textual tools and well known spatial analyses used in combination to investigate a case study of the references made to cholera and other diseases in these historical sources, and their relationship to place-names during Victorian times. The integration of such techniques has allowed us to explore, in an automatic way, this historical source containing millions of words, to examine the geographies depicted in it, and to identify textual and geographic patterns in the corpus. © 2014 John Wiley & Sons Ltd.
Optimal scheduling of logistical support for medical resource with demand information updating
This paper presents a discrete time-space network model for a dynamic resource allocation problem following an epidemic outbreak in a region. It couples a forecasting mechanism for dynamic demand of medical resource based on an epidemic diffusion model and a multistage programming model for optimal allocation and transport of such resource. At each stage, the linear programming solves for a cost minimizing resource allocation solution subject to a time-varying demand that is forecasted by a recursion model. The rationale that the medical resource allocated in early periods will take effect in subduing the spread of epidemic and thus impact the demand in later periods has been incorporated in such recursion model. A custom genetic algorithm is adopted to solve the proposed model, and a numerical example is presented for sensitivity analysis of the parameters. We compare the proposed medical resource allocation mode with two traditional operation modes in practice and find that our model is superior to any of them in less waste of resource and less logistic cost. The results may provide some practical guidelines for a decision-maker who is in charge of medical resource allocation in an epidemics control effort. © 2015 Ming Liu and Yihong Xiao.
Vector borne infections in Italy: Results of the integrated surveillance system for west nile disease in 2013
The epidemiology of West Nile disease (WND) is influenced by multiple ecological factors and, therefore, integrated surveillance systems are needed for early detecting the infection and activating consequent control actions. As different animal species have different importance in the maintenance and in the spread of the infection, a multispecies surveillance approach is required. An integrated and comprehensive surveillance system is in place in Italy aiming at early detecting the virus introduction, monitoring the possible infection spread, and implementing preventive measures for human health. This paper describes the integrated surveillance system for WND in Italy, which incorporates data from veterinary and human side in order to evaluate the burden of infection in animals and humans and provide the public health authorities at regional and national levels with the information needed for a fine tune response. © 2015 Christian Napoli et al.
Viral gastroenteritis in children in Colorado 2006-2009
Acute gastroenteritis accounts for a significant burden of medically attended illness in children under the age of five. For this study, four multiplex reverse transcription PCR assays were used to determine the incidence of adenovirus, astrovirus, coronavirus, norovirus GI and GII, rotavirus, and sapovirus in stool samples submitted for viral electron microscopy (EM) to the Children's Hospital Colorado. Of 1105 stool samples available, viral RNA/DNA was detected in 247 (26.2%) of 941 pediatric samples (median age=2.97 years, 54% male) with 28 (3.0%) positive for more than one virus. Adenovirus, astrovirus, norovirus GI, norovirus GII, rotavirus, and sapovirus were detected in 95 (10.0%), 33 (3.5%), 8 (0.9%), 90 (9.6%), 49 (5.2%), and 2 (0.2%) of the pediatric samples, respectively. No coronaviruses were identified. Sequencing of norovirus positive samples indicated an outbreak of norovirus strain GII.4 in 2006 with evidence of numerous circulating strains. Multiple samples from the same immunocompromised patients demonstrated symptomatic shedding of norovirus for up to 32 weeks and astrovirus for 12 weeks. RT-PCR detected 99 of 111 (89%) adenovirus-positive samples versus 12 (11%) by EM, and 186 of 192 (97%) sapovirus/astrovirus/norovirus-positive samples versus 21 (11%) by EM. Noroviruses and adenoviruses are common causes of gastroenteritis in children. Immunocompromised patients can be infected with multiple viruses and shed viruses in their stools for prolonged periods. This data support the superiority of RT-PCR compared to EM for diagnosis of viral gastroenteritis. © 2015 Wiley Periodicals, Inc.
Mapping risk of nipah virus transmission across Asia and across Bangladesh
Nipah virus is a highly pathogenic but poorly known paramyxovirus from South and Southeast Asia. In spite of the risks that it poses to human health, the geography and ecology of its occurrence remain little understood - the virus is basically known from Bangladesh and peninsular Malaysia, and little in between. In this contribution, I use documented occurrences of the virus to develop ecological niche-based maps summarizing its likely broader occurrence - although rangewide maps could not be developed that had significant predictive abilities, reflecting minimal sample sizes available, maps within Bangladesh were quite successful in identifying areas in which the virus is predictably present and likely transmitted. © 2013 APJPH.
Q fever through consumption of unpasteurised milk and milk products - a risk profile and exposure assessment
Q fever is a zoonotic disease caused by the bacterium Coxiella burnetii which is endemic in cattle, sheep and goats in much of the world, including the United Kingdom (UK). There is some epidemiological evidence that a small proportion of cases in the developed world may arise from consumption of unpasteurised milk with less evidence for milk products such as cheese. Long maturation at low pH may give some inactivation in hard cheese, and viable C. burnetii are rarely detected in unpasteurised cheese compared to unpasteurised milk. Simulations presented here predict that the probability of exposure per person to one or more C. burnetii through the daily cumulative consumption of raw milk in the UK is 0·4203. For those positive exposures, the average level of exposure predicted is high at 1266 guinea pig intraperitoneal infectious dose 50% units (GP_IP_ID50) per person per day. However, in the absence of human dose-response data, the case is made that the GP_IP_ID50 unit represents a very low risk through the oral route. The available evidence suggests that the risks from C. burnetii through consumption of unpasteurised milk and milk products (including cheese) are not negligible but they are lower in comparison to transmission via inhalation of aerosols from parturient products and livestock contact. © 2015 The Society for Applied Microbiology.
Internet use and suicidal behaviors: Internet as a threat or opportunity?
Background: Suicidal behavior is a common and severe health problem around the world. Internet use has been related to an increase in suicidal behaviors, but few studies have focused on the potential benefits of Internet use for preventing self-harm and suicide. Materials and Methods: We reviewed the existing literature on the relationship between suicide and Internet use. Results: The accessibility of suicide-related information on the Internet seems to have an impact on the incidence of suicide behaviors. However, the Internet is useful for linking people who feel lonely or isolated, and it provides access to suicide prevention information and resources. The Internet can influence vulnerable people to attempt suicide, but it can also be used to prevent self-harm and suicide. Conclusions: We propose some efforts that can be made in this preventive line. © Copyright 2015, Mary Ann Liebert, Inc. 2015.
CDC's Use of Social Media and Humor in a Risk Campaign—“Preparedness 101: Zombie Apocalypse”
This is a multiple methods study that highlights the tension between awareness- and behavioral-based campaign successes, particularly when communicating using social media and pop-culture-referencing humor. To illustrate, it examines the Centers for Disease Control and Prevention's (CDC) “zombie apocalypse” all-disaster-preparedness campaign. An interview with a CDC campaign manager, campaign document analysis, and a 2 (information form: social vs. traditional media) × 2 (message strategy: humorous vs. non-humorous) experiment uncovers benefits and pitfalls of using social media and humorous messaging for risk communication. Findings show social media can quickly spread information to new publics for minimal costs; however, experiment participants who received the humorous (i.e., zombie) risk message reported significantly weaker intentions to take protective actions in comparison to those who received the traditional, non-humorous risk message. © 2015, National Communication Association.
Turning the tide or riding the waves? Impacts of antibiotic stewardship and infection control on MRSA strain dynamics in a Scottish region over 16 y
Objectives: To explore temporal associations between planned antibiotic stewardship and infection control interventions and the molecular epidemiology of methicillin-resistant Staphylococcus aureus (MRSA). Design: Retrospective ecological study and time-series analysis integrating typing data from the Scottish MRSA reference laboratory. Setting: Regional hospital and primary care in a Scottish Health Board. Participants: General adult (N=1 051 993) or intensive care (18 235) admissions and primary care registrations (460 000 inhabitants) between January 1997 and December 2012. Interventions: Hand-hygiene campaign; MRSA admission screening; antibiotic stewardship limiting use of macrolides and '4Cs' (cephalosporins, coamoxiclav, clindamycin and fluoroquinolones). Outcome measures: Prevalence density of MRSA clonal complexes CC22, CC30 and CC5/Other in hospital (isolates/1000 occupied bed days, OBDs) and community (isolates/10 000 inhabitant-days). Results: 67% of all clinical MRSA isolates (10 707/15 947) were typed. Regional MRSA population structure was dominated by hospital epidemic strains CC30, CC22 and CC45. Following declines in overall MRSA prevalence density, CC5 and other strains of community origin became increasingly important. Reductions in use of '4Cs' and macrolides anticipated declines in sublineages with higher levels of associated resistances. In multivariate time-series models (R2=0.63-0.94) introduction of the hand-hygiene campaign, reductions in mean length of stay (when >4 days) and bed occupancy (when >74 to 78%) predicted declines in CC22 and CC30, but not CC5/other strains. Lower importation pressures, expanded MRSA admission screening, and reductions in macrolide and third generation cephalosporin use (thresholds for association: 135-141, and 48-81 defined daily doses/1000 OBDs, respectively) were followed by declines in all clonal complexes. Strain-specific associations with fluoroquinolones and clindamycin reflected resistance phenot
Prevalence of and risk factors for hypertension in urban and rural India: The ICMR-INDIAB study
The aim of the study is to determine the prevalence of hypertension (HTN) and its risk factors in urban and rural India. In Phase I of the Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study, individuals aged ?20 years were surveyed using a stratified multistage sampling design, in three states (Tamil Nadu, Maharashtra and Jharkhand) and one union territory (Chandigarh) of India. Blood pressure was measured in all study subjects (n=14 059). HTN was defined as systolic blood pressure ?140 mm Hg, and/or DBP ?90 mm Hg and/or use of antihypertensive drugs. Overall age-standardized prevalence of HTN was 26.3% (self-reported: 5.5%; newly detected: 20.8%). Urban residents of Tamil Nadu, Jharkhand, Chandigarh and Maharashtra (31.5, 28.9, 30.7 and 28.1%) had significantly higher prevalence of HTN compared with rural residents (26.2, 21.7, 19.8 and 24.0%, respectively). Multivariate regression analysis showed that age, male gender, urban residence, generalized obesity, diabetes, physical inactivity and alcohol consumption were significantly associated with HTN. Salt intake ?6.5 g per day, showed significantly higher risk for HTN (odds ratio: 1.4, 95% confidence interval: 1.0-1.9, P=0.042) even after adjusting for confounding variables. In conclusion, prevalence of undiagnosed HTN is high in India and this calls for regular screening. © 2015 Macmillan Publishers Limited All rights reserved.
Variability in Twitter Content Across the Stages of a Natural Disaster: Implications for Crisis Communication
Little is known about the ways in which social media, such as Twitter, function as conduits for information related to crises and emergencies. The current study analyzed the content of over 1,500 Tweets that were sent in the days leading up to the landfall of Hurricane Sandy. Time-series analyses reveal that relevant information became less prevalent as the crisis moved from the prodromal to acute phase, and information concerning specific remedial behaviors was absent. Implications for government agencies and emergency responders are discussed. © 2015, © 2015 Eastern Communication Association.Zotero article collection 1(no login needed)
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