Research Committee Selected Articles for the Week of Apr 06, 2015
***-Article is considered for Award Nomination***
Liu Y., Wang X., Pang C., Yuan Z., Li H., Xue F. Spatio-temporal analysis of the relationship between climate and hand, foot, and mouth disease in Shandong province, China, 2008-2012
Marcantonio M., Rizzoli A., Metz M., Rosa R., Marini G., Chadwick E., Neteler M. Identifying the environmental conditions favouring West Nile Virus outbreaks in Europe
Velasco-Hernandez J.X., Nunez-Lopez M., Comas-Garcia A., Cherpitel D.E.N., Ocampo M.C. Superinfection between Influenza and RSV alternating patterns in San Luis Potosí State, México
Thumbi S.M., Njenga M.K., Marsh T.L., Noh S., Otiang E., Munyua P., Ochieng L., Ogola E., Yoder J., Linking human health and livestock health: A "one-health" platform for integrated analysis of human health, livestock health, and economic welfare in livestock dependent communities
Moser C.B., Gupta M., Archer B.N., White L.F. The impact of prior information on estimates of disease transmissibility using bayesian tools
Remschmidt C., Rieck T., Bodeker B., Wichmann O. Application of the screening method to monitor influenza vaccine effectiveness among the elderly in Germany
Holme P., Masuda N. The basic reproduction number as a predictor for epidemic outbreaks in temporal networks
Tan A.L., Virk R.K., Tambyah P.A., Inoue M., Lim E.A.-S., Chan K.-W., Chelvi C.S., Ooi S.-T., Chua C Surveillance and clinical characterization of influenza in a university cohort in Singapore
Drumright L.N., Frost S.D.W., Elliot A.J., Catchpole M., Pebody R.G., Atkins M., Harrison J., Parker Assessing the use of hospital staff influenza-like absence (ILA) for enhancing hospital preparedness and national surveillance
Huang T., Lan L., Fang X., An P., Min J., Wang F. Promises and Challenges of Big Data Computing in Health Sciences
Bakillah M., Li R.-Y., Liang S.H.L. Geo-located community detection in Twitter with enhanced fast-greedy optimization of modularity: the case study of typhoon Haiyan
Rashid H., Ridda I., King C., Begun M., Tekin H., Wood J.G., Booy R. Evidence compendium and advice on social distancing and other related measures for response to an influenza pandemic
Ranjan R., Misra R. Epidemic disease propagation detection algorithm using MapReduce for realistic social contact networks
Moore M., Dausey D.J. Local cross-border disease surveillance and control: Experiences from the Mekong Basin
Liu S., Pang L., Ruan S., Zhang X. Global dynamics of avian influenza epidemic models with psychological effect
Elliot A.J., Bermingham A., Charlett A., Lackenby A., Ellis J., Sadler C., Sebastianpillai P., Power Self-Sampling for community respiratory illness: a new tool for national virological surveillance
Hu K., Bianco S., Edlund S., Kaufman J. The impact of human behavioral changes in 2014 West Africa Ebola Outbreak
Shao Q., Jia M. Influences on influenza transmission within terminal based on hierarchical structure of personal contact network
Rosenberg R. Detecting the emergence of novel, zoonotic viruses pathogenic to humans
Bhoomiboonchoo P., Nisalak A., Chansatiporn N., Yoon I.-K., Kalayanarooj S., Thipayamongkolgul M., E Sequential dengue virus infections detected in active and passive surveillance programs in Thailand, 1994-2010 Disease epidemiology - Infectious
McIver L., Hashizume M., Kim H., Honda Y., Pretrick M., Iddings S., Pavlin B. Assessment of climate-sensitive infectious diseases in the Federated States of Micronesia
Britton T., Trapman P. Inferring global network properties from egocentric data with applications to epidemics
Spatio-temporal analysis of the relationship between climate and hand, foot, and mouth disease in Shandong province, China, 2008-2012
Background: Hand, foot, and mouth disease (HFMD) is the most common communicable disease in China. Shandong Province is one of the most seriously affected areas. The distribution of HFMD had spatial heterogeneity and seasonal characteristic in this setting. The aim of this study was to explore the associations between climate and HFMD by a Bayesian approach from spatio-temporal interactions perspective. Methods: The HFMD data of Shandong Province during 2008-2012 were derived from the China National Disease Surveillance Reporting and Management System. And six climatic indicators were obtained from the Meteorological Bureau of Shandong Province. The global spatial autocorrelation statistic (Moran's I) was used to detect the spatial autocorrelation of HFMD cases in each year. The optimal one among four Bayesian models was further adopted to estimate the relative risk of the occurrence of HFMD via Markov chain Monte Carlo. Results: The annual average incidence rate of HFMD was 104.40 per 100,000 in Shandong Province. Positive spatial autocorrelation appeared at county level (Moran's I ?0.30, P<0.001). The best fitting Spatio-temporal interactive model showed that annual average temperature, annual average pressure, annual average relative humidity, annual average wind speed and annual sunshine hours were significantly positive related to the occurrence of HFMD. The estimated relative risk of 36, 87, 91, 79, 65 out of 140 counties for 2008-2012 respectively were significantly more than 1. Conclusions: There were obvious spatio-temporal heterogeneity of HFMD in Shandong Province, and the climatic indicators were associated with the epidemic of HFMD. Bayesian approach should be recommended to capture the spatial-temporal pattern of HFMD. © Liu et al.
Identifying the environmental conditions favouring West Nile Virus outbreaks in Europe
West Nile Virus (WNV) is a globally important mosquito borne virus, with significant implications for human and animal health. The emergence and spread of new lineages, and increased pathogenicity, is the cause of escalating public health concern. Pinpointing the environmental conditions that favour WNV circulation and transmission to humans is challenging, due both to the complexity of its biological cycle, and the under-diagnosis and reporting of epidemiological data. Here, we used remote sensing and GIS to enable collation of multiple types of environmental data over a continental spatial scale, in order to model annual West Nile Fever (WNF) incidence across Europe and neighbouring countries. Multimodel selection and inference were used to gain a consensus from multiple linear mixed models. Climate and landscape were key predictors of WNF outbreaks (specifically, high precipitation in late winter/early spring, high summer temperatures, summer drought, occurrence of irrigated croplands and highly fragmented forests). Identification of the environmental conditions associated with WNF outbreaks is key to enabling public health bodies to properly focus surveillance and mitigation of West Nile virus impact, but more work needs to be done to enable accurate predictions of WNF risk. © 2015 Marcantonio et al.
Superinfection between Influenza and RSV alternating patterns in San Luis Potosí State, México
The objective of this paper is to explain through the ecological hypothesis superinfection and competitive interaction between two viral populations and niche (host) availability, the alternating patterns of Respiratory Syncytial Virus (RSV) and influenza observed in a regional hospital in San Luis Potosí State, México using a mathematical model as a methodological tool. The data analyzed consists of community-based and hospital-based Acute Respiratory Infections (ARI) consultations provided by health-care institutions reported to the State Health Service Epidemiology Department from 2003 through 2009. © 2015 Velasco-Hernández et al.
Linking human health and livestock health: A "one-health" platform for integrated analysis of human health, livestock health, and economic welfare in livestock dependent communities
Background For most rural households in sub-Saharan Africa, healthy livestock play a key role in averting the burden associated with zoonotic diseases, and in meeting household nutritional and socio-economic needs. However, there is limited understanding of the complex nutritional, socio-economic, and zoonotic pathways that link livestock health to human health and welfare. Here we describe a platform for integrated human health, animal health and economic welfare analysis designed to address this challenge. We provide baseline epidemiological data on disease syndromes in humans and the animals they keep, and provide examples of relationships between human health, animal health and household socio-economic status. Method We designed a study to obtain syndromic disease data in animals along with economic and behavioral information for 1500 rural households in Western Kenya already participating in a human syndromic disease surveillance study. Data collection started in February 2013, and each household is visited bi-weekly and data on four human syndromes (fever, jaundice, diarrhea and respiratory illness) and nine animal syndromes (death, respiratory, reproductive, musculoskeletal, nervous, urogenital, digestive, udder disorders, and skin disorders in cattle, sheep, goats and chickens) are collected. Additionally, data from a comprehensive socio-economic survey is collected every 3 months in each of the study households. Findings Data from the first year of study showed 93% of the households owned at least one form of livestock (55%, 19%, 41% and 88% own cattle, sheep, goats and chickens respectively). Digestive disorders, mainly diarrhea episodes, were the most common syndromes observed in cattle, goats and sheep, accounting for 56% of all livestock syndromes, followed by respiratory illnesses (18%). In humans, respiratory illnesses accounted for 54% of all illnesses reported, followed by acute febrile illnesses (40%) and diarrhea illnesses (5%). While controlling
The impact of prior information on estimates of disease transmissibility using bayesian tools
The basic reproductive number (R0) and the distribution of the serial interval (SI) are often used to quantify transmission during an infectious disease outbreak. In this paper, we present estimates of R0 and SI from the 2003 SARS outbreak in Hong Kong and Singapore, and the 2009 pandemic influenza A(H1N1) outbreak in South Africa using methods that expand upon an existing Bayesian framework. This expanded framework allows for the incorporation of additional information, such as contact tracing or household data, through prior distributions. The results for the R0 and the SI from the influenza outbreak in South Africa were similar regardless of the prior information (R 0 = 1.36 -1.46,? = 2.0-2.7,? = mean of the SI). The estimates of R0 and ? for the SARS outbreak ranged from 2.0-4.4 and 7.4-11.3, respectively, and were shown to vary depending on the use of contact tracing data. The impact of the contact tracing data was likely due to the small number of SARS cases relative to the size of the contact tracing sample. © 2015 Moser et al.
Application of the screening method to monitor influenza vaccine effectiveness among the elderly in Germany
Background: Elderly people are at increased risk for severe influenza illness and constitute therefore a major target-group for seasonal influenza vaccination in most industrialized countries. The aim of this study was to estimate influenza vaccine effectiveness (VE) among individuals aged 60+ years over three seasons and to assess if the screening method is a suitable tool to monitor influenza VE in this particular target-group in Germany. Methods: We identified laboratory-confirmed influenza cases aged 60+ years through the national communicable disease reporting system for seasons 2010/11, 2011/12 and 2012/13. Vaccination coverage (VC) data were retrieved from a database of health insurance claims representing ~85% of the total German population. We applied the screening method to calculate influenza subtype-specific VE and compared our results with VE estimates from other observational studies in Europe. Results: In total, 7,156 laboratory-confirmed influenza cases were included. VE against all influenza types ranged between 49% (95% confidence interval [CI]: 39-56) in 2011/12 and 80% (95% CI: 76-83%) in 2010/11. In 2010/11 subtype-specific VE against influenza A(H1N1)pdm and B was 76% and 84%, respectively. In the following seasons, VE against influenza A(H1N1)pdm, A(H3N2) and B was 87%, -9%, 74% (2011/12), and 74%, 39%, 73% (2012/13). VE was higher among hospitalized compared to non-hospitalized influenza A cases. Seventeen observational studies from Europe reporting subtype-specific VE among the elderly were identified for the respective seasons (all applying the test-negative design) and showed comparable subtype-specific VE estimates. Conclusions: According to our study, influenza vaccination provided moderate protection against laboratory-confirmed influenza A(H1N1)pdm and B in individuals aged 60+ but no or only little protection against A(H3N2). Higher VE among hospitalized cases might indicate higher protection against severe influenza disease. Based on
The basic reproduction number as a predictor for epidemic outbreaks in temporal networks
The basic reproduction number R0-the number of individuals directly infected by an infectious person in an otherwise susceptible population -is arguably the most widely used estimator of how severe an epidemic outbreak can be. This severity can be more directly measured as the fraction of people infected once the outbreak is over, ?. In traditional mathematical epidemiology and common formulations of static network epidemiology, there is a deterministic relationship between R0 and ?. However, if one considers disease spreading on a temporal contact network-where one knows when contacts happen, not only between whom-then larger R0 does not necessarily imply larger ?. In this paper, we numerically investigate the relationship between R0 and ? for a set of empirical temporal networks of human contacts. Among 31 explanatory descriptors of temporal network structure, we identify those that make R0 an imperfect predictor of ?.We find that descriptors related to both temporal and topological aspects affect the relationship between R0 and ?, but in different ways. © 2015 Holme, Masuda.
Surveillance and clinical characterization of influenza in a university cohort in Singapore
Background: Southeast Asia is a potential locus for the emergence of novel influenza strains. However, information on influenza within the region is limited. Objectives: This study was to determine the proportion of influenza-like illness (ILI) caused by influenza A and B viruses in a university cohort in Singapore, identify important distinctive clinical features of influenza infection and potential factors associated with influenza infection compared with other causes of ILI. Methodology: A surveillance study was conducted from 2007 to 2009, at the University Health and Wellness Centre, National University of Singapore (NUS). Basic demographic information and nasopharyngeal swabs were collected from consenting students and staff with ILI, with Influenza A and B identified by both culture and molecular methods. Results: Proportions of influenza A and B virus infections in subjects with ILI were 153/500 (30.6%) and 11/500 (2.2%) respectively. The predominant subtype was A/H1N1, including both the seasonal strain (20/153) and the pandemic strain (72/153). The clinical symptom of fever was more common in subjects with laboratory confirmed influenza than other ILIs. On-campus hostel residence and being a student (compared with staff) were associated with increased risk of laboratory confirmed influenza A/H1N1 2009 infection. Conclusions: This study provides a baseline prevalence of influenza infection within young adults in Singapore in a university setting. Potential risk factors, such as hostel residence, were identified, allowing for more targeted infection control measures in the event of a future influenza pandemic. © 2015 Tan et al.
Assessing the use of hospital staff influenza-like absence (ILA) for enhancing hospital preparedness and national surveillance
Background: Early warning and robust estimation of influenza burden are critical to inform hospital preparedness and operational, treatment, and vaccination policies. Methods to enhance influenza-like illness (ILI) surveillance are regularly reviewed. We investigated the use of hospital staff 'influenza-like absences' (hospital staff-ILA), i.e. absence attributed to colds and influenza, to improve capture of influenza dynamics and provide resilience for hospitals. Methods: Numbers and rates of hospital staff-ILA were compared to regional surveillance data on ILI primary-care presentations (15-64 years) and to counts of laboratory confirmed cases among hospitalised patients from April 2008 to April 2013 inclusive. Analyses were used to determine comparability of the ILI and hospital-ILA and how systems compared in early warning and estimating the burden of disease. Results: Among 20,021 reported hospital-ILA and 4661 community ILI cases, correlations in counts were high and consistency in illness measurements was observed. In time series analyses, both hospital-ILA and ILI showed similar timing of the seasonal component. Hospital-ILA data often commenced and peaked earlier than ILI according to a Bayesian prospective alarm algorithm. Hospital-ILA rates were more comparable to model-based estimates of 'true' influenza burden than ILI. Conclusions: Hospital-ILA appears to have the potential to be a robust, yet simple syndromic surveillance method that could be used to enhance estimates of disease burden and early warning, and assist with local hospital preparedness. © Drumright et al.; licensee BioMed Central.
Promises and Challenges of Big Data Computing in Health Sciences
With the development of smart devices and cloud computing, more and more public health data can be collected from various sources and can be analyzed in an unprecedented way. The huge social and academic impact of such developments caused a worldwide buzz for big data. In this review article, we summarized the latest applications of Big Data in health sciences, including the recommendation systems in healthcare, Internet-based epidemic surveillance, sensor-based health conditions and food safety monitoring, Genome-Wide Association Studies (GWAS) and expression Quantitative Trait Loci (eQTL), inferring air quality using big data and metabolomics and ionomics for nutritionists. We also reviewed the latest technologies of big data collection, storage, transferring, and the state-of-the-art analytical methods, such as Hadoop distributed file system, MapReduce, recommendation system, deep learning and network Analysis. At last, we discussed the future perspectives of health sciences in the era of Big Data. © 2015 Elsevier Inc.
Geo-located community detection in Twitter with enhanced fast-greedy optimization of modularity: the case study of typhoon Haiyan
As they increase in popularity, social media are regarded as important sources of information on geographical phenomena. Studies have also shown that people rely on social media to communicate during disasters and emergency situation, and that the exchanged messages can be used to get an insight into the situation. Spatial data mining techniques are one way to extract relevant information from social media. In this article, our aim is to contribute to this field by investigating how graph clustering can be applied to support the detection of geo-located communities in Twitter in disaster situations. For this purpose, we have enhanced the fast-greedy optimization of modularity (FGM) clustering algorithm with semantic similarity so that it can deal with the complex social graphs extracted from Twitter. Then, we have coupled the enhanced FGM with the varied density-based spatial clustering of applications with noise spatial clustering algorithm to obtain spatial clusters at different temporal snapshots. The method was experimented with a case study on typhoon Haiyan in the Philippines, and Twitter’s different interaction modes were compared to create the graph of users and to detect communities. The experiments show that communities that are relevant to identify areas where disaster-related incidents were reported can be extracted, and that the enhanced algorithm outperforms the generic one in this task. © 2014 Taylor & Francis.
Evidence compendium and advice on social distancing and other related measures for response to an influenza pandemic
The role of social distancing measures in mitigating pandemic influenza is not precisely understood. To this end, we have conducted a systematised review, particularly in light of the 2009 pandemic influenza, to better inform the role of social distancing measures against pandemic influenza. Articles were identified from relevant databases and the data were synthesised to provide evidence on the role of school or work place-based interventions, case-based distancing (self-isolation, quarantine), and restriction of mobility and mass gatherings. School closure, whether proactive or reactive, appears to be moderately effective and acceptable in reducing the transmission of influenza and in delaying the peak of an epidemic but is associated with very high secondary costs. Voluntary home isolation and quarantine are also effective and acceptable measures but there is an increased risk of intra-household transmission from index cases to contacts. Work place-related interventions like work closure and home working are also modestly effective and are acceptable, but likely to be economically disruptive. Internal mobility restriction is effective only if prohibitively high (50% of travel) restrictions are applied and mass gatherings occurring within 10 days before the epidemic peak are likely to increase the risk of transmission of influenza. © 2014 Elsevier Ltd.
Epidemic disease propagation detection algorithm using MapReduce for realistic social contact networks
The control and prevention of epidemics like influenza is a matter of high concern for the public health and decision support to the policy makers of public health. Epidemic disease propagation in a social contact network for the spread of contagion in a large real social contact having millions of individuals often becomes challenging for high performance computing. In this paper we present a novel MapReduce algorithm to detect the boundary of infectious nodes in social contact network. We used smart phone based personnel and community sensing for collecting the individual's connection, communication and interaction to others with respect to time. Using this extracted smart phone data; user's health status is predicted. © 2014 IEEE.
Local cross-border disease surveillance and control: Experiences from the Mekong Basin
Background: The Mekong Basin Disease Surveillance cooperation (MBDS) is one of several sub-regional disease surveillance networks that have emerged in recent years as an approach to transnational cooperation for infectious disease prevention and control. Since 2003 MBDS has pioneered a unique model for local cross-border cooperation. This study examines stakeholders' perspectives of these MBDS experiences, based on a survey of local managers and semi-structured interviews with MBDS leaders and the central coordinator. Results: Fifteen managers from 12 of 20 paired cross-border sites completed a written survey. They all monitor most or all of the 17 diseases agreed upon for MBDS surveillance information sharing. Fourteen agreed or strongly agreed with statements about the core MBDS values of cooperation, mutual trust, and transparency, and their own contributions to national and regional disease control (average score of 4.4 of 5.0). Respondents felt they implemented well to very well activities related to surveillance reporting (average scores 3.4 to 3.9 of 4.0), using computers for their work (3.9/4.0), and using surveillance data for action (3.8/4.0). Respondents reported that they did worst in implementing research (2.1/4.0) and somewhat poorly for local laboratory testing (2.9/4.0) and local coordination with cross-border counterparts (2.9/4.0), although all 15 maintain a list with contact information for these counterparts and many know their counterparts. Implementation of specified activities within their collective regional action plan was uneven across the cross-border sites. Most respondents reported positive lessons learned about local cooperation, information sharing and joint problem solving, based on trusting relationships with their cross-border counterparts. They recommend expansion of cross-border sites within MBDS and consideration of the cross-border cooperation model by other sub-regional networks. Conclusions: MBDS has over a decade of experienc
Global dynamics of avian influenza epidemic models with psychological effect
Cross-sectional surveys conducted in Thailand and China after the outbreaks of the avian influenza A H5N1 and H7N9 viruses show a high degree of awareness of human avian influenza in both urban and rural populations, a higher level of proper hygienic practice among urban residents, and in particular a dramatically reduced number of visits to live markets in urban population after the influenza A H7N9 outbreak in China in 2013. In this paper, taking into account the psychological effect toward avian influenza in the human population, a bird-to-human transmission model in which the avian population exhibits saturation effect is constructed. The dynamical behavior of the model is studied by using the basic reproduction number. The results demonstrate that the saturation effect within avian population and the psychological effect in human population cannot change the stability of equilibria but can affect the number of infected humans if the disease is prevalent. Numerical simulations are given to support the theoretical results and sensitivity analyses of the basic reproduction number in terms of model parameters that are performed to seek for effective control measures for avian influenza. © 2015 Sanhong Liu et al.
Self-Sampling for community respiratory illness: a new tool for national virological surveillance
This report aims to evaluate the usefulness of selfsampling as an approach for future national surveillance of emerging respiratory infections by comparing virological data from two parallel surveillance schemes in England. Nasal swabs were obtained via self-administered sampling from consenting adults (? 16 yearsold) with influenza symptoms who had contacted the National Pandemic Flu Service (NPFS) health line during the 2009 influenza pandemic. Equivalent samples submitted by sentinel general practitioners participating in the national influenza surveillance scheme run jointly by the Royal College of General Practitioners (RCGP) and Health Protection Agency were also obtained. When comparable samples were analysed there was no significant difference in results obtained from self-sampling and clinician-led sampling schemes. These results demonstrate that selfsampling can be applied in a responsive and flexible manner, to supplement sentinel clinician-based sampling, to achieve a wide spread and geographically representative way of assessing community transmission of a known organism. © 2007-2013. All rights reserved.
The impact of human behavioral changes in 2014 West Africa Ebola Outbreak
The current outbreak of Ebola virus disease (EVD) in West Africa has caused around 23000 infections by middle of February 2015, with a death rate of 40%. The cases have been imported into developed countries, e.g., Spain and US, through travelers and returning healthcare workers. It is clear that the virus is a threat to public health worldwide. Given the absence of vaccine and effective treatment, response has focused so far on containment and education for prophylaxis. In studying the effects of human behavioral response to contain the current Ebola transmission, we built an epidemiological model in Spatio- Temporal Epidemiological Modeler (STEM), an open source platform. We simulate the course of the infection under various conditions from public available data and realistic assumptions about the disease dynamics. We ran this spatially extended simulation in three hardest-hit countries (i.e., Liberia, Sierra Leone and Guinea) in West Africa. A series of sensitivity analysis was performed to get insights of the likely human behavioral response to the change of disease epidemic, which helps understand the determinants of disease control. Our analysis suggests the reproductive number for the disease can be driven below 1.0 and effective control is possible if hospitalization occurs within 60 hours and/or if proper burials are processed within 34 hours. We also calibrated our model using processive period of reference data starting from March 14 reported by the WHO. We have an observation of gradual human behavior changes in the affected countries in response to the epidemic outbreak. © Springer International Publishing Switzerland 2015.
Influences on influenza transmission within terminal based on hierarchical structure of personal contact network
Background: Since the outbreak of pandemics, influenza has caused extensive attention in the field of public health. It is actually hard to distinguish what is the most effective method to control the influenza transmission within airport terminal. The purpose of this study was to quantitatively evaluate the influences of passenger source, immunity difference and social relation structure on the influenza transmission in terminal. Methods: A method combining hierarchical structure of personal contact network with agent-based SEIR model was proposed to analyze the characteristics of influenza diffusion within terminal. Based on the spatial distance between individuals, the hierarchical structure of personal contact network was defined to construct a complex relationship of passengers in the real world. Moreover, the agent-based SEIR model was improved by considering the individual level of influenza spread characteristics. To evaluate the method, this process was fused in simulation based on the constructed personal contact network. Results: In the terminal we investigated, personal contact network was defined by following four layers: social relation structure, procedure partition, procedure area, and the whole terminal. With the growing of layer, the degree distribution curves move right. The value of degree distribution p(k) reached a peak at a specific value, and then back down. Besides, with the increase of layer ?, the clustering coefficients presented a tendency to exponential decay. Based on the influenza transmission experiments, the main infected areas were concluded when considering different factors. Moreover, partition of passenger sources was found to impact a lot in departure, while social relation structure imposed a great influence in arrival. Besides, immunity difference exerted no obvious effect on the spread of influenza in the transmission process both in departure and arrival. Conclusions: The proposed method is efficient to reproduce the evolut
Detecting the emergence of novel, zoonotic viruses pathogenic to humans
RNA viruses, with their high potential for mutation and epidemic spread, are the most common class of pathogens found as new causes of human illness. Despite great advances made in diagnostic technology since the 1950s, the annual rate at which novel virulent viruses have been found has remained at 2-3. Most emerging viruses are zoonoses; they have jumped from mammal or bird hosts to humans. An analysis of virus discovery indicates that the small number of novel viruses discovered annually is an artifact of inadequate surveillance in tropical and subtropical countries, where even established endemic pathogens are often misdiagnosed. Many of the emerging viruses of the future are already infecting humans but remain to be uncovered by a strategy of disease surveillance in selected populations. © 2014 Springer Basel (outside the USA).
Sequential dengue virus infections detected in active and passive surveillance programs in Thailand, 1994-2010 Disease epidemiology - Infectious
Background: The effect of prior dengue virus (DENV) exposure on subsequent heterologous infection can be beneficial or detrimental depending on many factors including timing of infection. We sought to evaluate this effect by examining a large database of DENV infections captured by both active and passive surveillance encompassing a wide clinical spectrum of disease. Methods: We evaluated datasets from 17 years of hospital-based passive surveillance and nine years of cohort studies, including clinical and subclinical DENV infections, to assess the outcomes of sequential heterologous infections. Chi square or Fisher's exact test was used to compare proportions of infection outcomes such as disease severity; ANOVA was used for continuous variables. Multivariate logistic regression was used to assess risk factors for infection outcomes. Results: Of 38,740 DENV infections, two or more infections were detected in 502 individuals; 14 had three infections. The mean ages at the time of the first and second detected infections were 7.6 ± 3.0 and 11.2 ± 3.0 years. The shortest time between sequential infections was 66 days. A longer time interval between sequential infections was associated with dengue hemorrhagic fever (DHF) in the second detected infection (OR 1.3, 95% CI 1.2-1.4). All possible sequential serotype pairs were observed among 201 subjects with DHF at the second detected infection, except DENV-4 followed by DENV-3. Among DENV infections detected in cohort subjects by active study surveillance and subsequent non-study hospital-based passive surveillance, hospitalization at the first detected infection increased the likelihood of hospitalization at the second detected infection. Conclusions: Increasing time between sequential DENV infections was associated with greater severity of the second detected infection, supporting the role of heterotypic immunity in both protection and enhancement. Hospitalization was positively associated between the first and second det
Assessment of climate-sensitive infectious diseases in the Federated States of Micronesia
Background: The health impacts of climate change are an issue of growing concern in the Pacific region. Prior to 2010, no formal, structured, evidence-based approach had been used to identify the most significant health risks posed by climate change in Pacific island countries. During 2010 and 2011, the World Health Organization supported the Federated States of Micronesia (FSM) in performing a climate change and health vulnerability and adaptation assessment. This paper summarizes the priority climate-sensitive health risks in FSM, with a focus on diarrheal disease, its link with climatic variables and the implications of climate change. Methods: The vulnerability and adaptation assessment process included a review of the literature, extensive stakeholder consultations, ranking of climate-sensitive health risks, and analysis of the available long-term data on climate and climate-sensitive infectious diseases in FSM, which involved examination of health information data from the four state hospitals in FSM between 2000 and 2010; along with each state’s rainfall, temperature and El Niño-Southern Oscillation data. Generalized linear Poisson regression models were used to demonstrate associations between monthly climate variables and cases of climate-sensitive diseases at differing temporal lags. Results: Infectious diseases were among the highest priority climate-sensitive health risks identified in FSM, particularly diarrheal diseases, vector-borne diseases and leptospirosis. Correlation with climate data demonstrated significant associations between monthly maximum temperature and monthly outpatient cases of diarrheal disease in Pohnpei and Kosrae at a lag of one month and 0 to 3 months, respectively; no such associations were observed in Chuuk or Yap. Significant correlations between disease incidence and El Niño-Southern Oscillation cycles were demonstrated in Kosrae state. Conclusions: Analysis of the available data demonstrated significant associations between c
Inferring global network properties from egocentric data with applications to epidemics
Social networks are often only partly observed, and it is sometimes desirable to infer global properties of the network from 'egocentric' data. In the current paper, we study different types of egocentric data, and show which global network properties are consistent with data. Two global network properties are considered: the size of the largest connected component (the giant) and the size of an epidemic outbreak taking place on the network. The main conclusion is that, in most cases, egocentric data allow for a large range of possible sizes of the giant and the outbreak, implying that egocentric data carry very little information about these global properties. The asymptotic size of the giant and the outbreak is also characterized, assuming the network is selected uniformly among networks with prescribed egocentric data. © The Authors 2013.Zotero article collection 1(no login needed)
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