Online detection and quantification of epidemics

<p>Abstract</p> <p>Background</p> <p>Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses.</p> <p>Results</p> <p>We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at <url>http://www.u707.jussieu.fr/periodic_regression/</url>. The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea).</p> <p>Conclusion</p> <p>The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners.</p

A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data

Epidemic outbreaks are an important healthcare challenge, especially in developing countries where they represent one of the major causes of mortality. Approaches that can rapidly target subpopulations for surveillance and control are critical for enhancing containment processes during epidemics. Using a real-world dataset from Ivory Coast, this work presents an attempt to unveil the socio-geographical heterogeneity of disease transmission dynamics. By employing a spatially explicit meta-population epidemic model derived from mobile phone Call Detail Records (CDRs), we investigate how the differences in mobility patterns may affect the course of a realistic infectious disease outbreak. We consider different existing measures of the spatial dimension of human mobility and interactions, and we analyse their relevance in identifying the highest risk sub-population of individuals, as the best candidates for isolation countermeasures. The approaches presented in this paper provide further evidence that mobile phone data can be effectively exploited to facilitate our understanding of individuals' spatial behaviour and its relationship with the risk of infectious diseases' contagion. In particular, we show that CDRs-based indicators of individuals' spatial activities and interactions hold promise for gaining insight of contagion heterogeneity and thus for developing containment strategies to support decision-making during country-level pandemics

Quantification of Temporal and Spatial Dynamics of Bean pod mottle virus at Different Spatial Scales

Bean pod mottle virus (BPMV) is the most prevalent virus infecting soybean (Glycine max) in the United States; however, the temporal and spatial dynamics in BPMV at varying spatial scales has not been elucidated. To quantify the temporal and spatial dynamics of BPMV at a field scale, a quadrat-based method was developed in which six soybean rows, each consisting of 30-cm-long quadrats, were established within soybean cv. NE3001 field plots (i.e., 150 quadrats per plot) in BPMV-inoculated and non-inoculated plots. Quadrats were sampled by selecting the youngest fully expanded leaflet from each of four plants within each quadrat beginning 25 days after planting, and continued at 8- to 11- day intervals until crop senescence. Leaf sap was extracted from each 4-leaflet (bulked) sample (from each quadrat), and tested for presence of the BPMV by ELISA. Quadrat position (plot, row, and quadrat number) and the date of sampling that each quadrat first tested positive for BPMV was recorded and mapped. The rate of BPMV incidence in 2006 ranged from 0.09 to 0.12 logits/day, indicating that BPMV incidence was doubling every 5.3 to 7.7 days in 2006. Doubling times for BPMV incidence in 2007 were slower, ranging from 17.3 to 34.7 days. Analysis of spatial patterns using ordinary runs revealed that BPMV-infected quadrats were predominantly clustered within both BPMV-inoculated and non-inoculated plots throughout both growing seasons. In addition to within field plot studies, a threeyear statewide disease survey (2005-2007) was conducted in Iowa to quantify county and field scale BPMV prevalence and incidence by systematically selecting 30 plants/soybean field (8 to 16 soybean fields per county). Leaf samples were then tested for BPMV by ELISA and county-level BPMV incidence maps were generated using ArcGIS software. End-of-season BPMV prevalence was 39/96 counties in 2005 (40%), 90/99 counties in 2006 (90.1%), and 74/99 counties in 2007 (74.7%). The incidence of BPMV within Iowa counties ranged from 0 to 100% and BPMV incidence significantly increased statewide from north to south. Spatial autocorrelation (dependence) analysis using Moran’s I revealed clustering for BPMV incidence among Iowa counties, indicating that BPMV incidence among counties was not random. The elucidation of the within-field temporal and spatial dynamics of BPMV and the statewide geographic distribution of BPMV in Iowa has important implications with regards to sampling, plant disease forensics, BPMV management, and risk prediction of BPMV

Gametocytes: insights gained during a decade of molecular monitoring

In vertebrate hosts, malaria parasites produce specialized male and female sexual stages (gametocytes). Soon after being taken up by a mosquito, gametocytes rapidly produce gametes and, once mated, they infect their vector and can be transmitted to new hosts. Despite being the parasite stages that were first identified (over a century ago), gametocytes have remained elusive, and basic questions remain concerning their biology. However, the postgenomic era has substantiated information on the specialized molecular machinery of gametocytogenesis and expedited the development of molecular tools to detect and quantify gametocytes. The application of such highly sensitive and specific tools has opened up novel approaches and provided new insights into gametocyte biology. Here, we review the discoveries made during the past decade, highlight unanswered questions and suggest new directions

The Association between Virus Prevalence and Intercolonial Aggression Levels in the Yellow Crazy Ant, Anoplolepis Gracilipes (Jerdon).

The recent discovery of multiple viruses in ants, along with the widespread infection of their hosts across geographic ranges, provides an excellent opportunity to test whether viral prevalence in the field is associated with the complexity of social interactions in the ant population. In this study, we examined whether the association exists between the field prevalence of a virus and the intercolonial aggression of its ant host, using the yellow crazy ant (Anoplolepis gracilipes) and its natural viral pathogen (TR44839 virus) as a model system. We delimitated the colony boundary and composition of A. gracilipes in a total of 12 study sites in Japan (Okinawa), Taiwan, and Malaysia (Penang), through intercolonial aggression assay. The spatial distribution and prevalence level of the virus was then mapped for each site. The virus occurred at a high prevalence in the surveyed colonies of Okinawa and Taiwan (100% infection rate across all sites), whereas virus prevalence was variable (30%-100%) or none (0%) at the sites in Penang. Coincidentally, colonies in Okinawa and Taiwan displayed a weak intercolonial boundary, as aggression between colonies is generally low or moderate. Contrastingly, sites in Penang were found to harbor a high proportion of mutually aggressive colonies, a pattern potentially indicative of complex colony composition. Our statistical analyses further confirmed the observed correlation, implying that intercolonial interactions likely contribute as one of the effective facilitators of/barriers to virus prevalence in the field population of this ant species