A Spatial Analysis of Rift Valley Fever Virus Seropositivity in Domestic Ruminants in Tanzania

Rift Valley fever (RVF) is an acute arthropod-borne viral zoonotic disease primarily occurring in Africa. Since RVF-like disease was reported in Tanzania in 1930, outbreaks of the disease have been reported mainly from the eastern ecosystem of the Great Rift Valley. This cross-sectional study was carried out to describe the variation in RVF virus (RVFV) seropositivity in domestic ruminants between selected villages in the eastern and western Rift Valley ecosystems in Tanzania, and identify potential risk factors. Three study villages were purposively selected from each of the two Rift Valley ecosystems. Serum samples from randomly selected domestic ruminants (n = 1,435) were tested for the presence of specific immunoglobulin G (IgG) and M (IgM), using RVF enzyme-linked immunosorbent assay methods. Mixed effects logistic regression modelling was used to investigate the association between potential risk factors and RVFV seropositivity. The overall RVFV seroprevalence (n = 1,435) in domestic ruminants was 25.8% and species specific seroprevalence was 29.7%, 27.7% and 22.0% in sheep (n = 148), cattle (n = 756) and goats (n = 531), respectively. The odds of seropositivity were significantly higher in animals sampled from the villages in the eastern than those in the western Rift Valley ecosystem (OR = 1.88, CI: 1.41, 2.51; p<0.001), in animals sampled from villages with soils of good than those with soils of poor water holding capacity (OR = 1.97; 95% CI: 1.58, 3.02; p< 0.001), and in animals which had been introduced than in animals born within the herd (OR = 5.08, CI: 2.74, 9.44; p< 0.001). Compared with animals aged 1-2 years, those aged 3 and 4-5 years had 3.40 (CI: 2.49, 4.64; p< 0.001) and 3.31 (CI: 2.27, 4.82, p< 0.001) times the odds of seropositivity. The findings confirm exposure to RVFV in all the study villages, but with a higher prevalence in the study villages from the eastern Rift Valley ecosystem

Drivers for Rift Valley fever emergence in Mayotte: A Bayesian modelling approach

Rift Valley fever (RVF) is a major zoonotic and arboviral hemorrhagic fever. The conditions leading to RVF epidemics are still unclear, and the relative role of climatic and anthropogenic factors may vary between ecosystems. Here, we estimate the most likely scenario that led to RVF emergence on the island of Mayotte, following the 2006–2007 African epidemic. We developed the first mathematical model for RVF that accounts for climate, animal imports and livestock susceptibility, which is fitted to a 12-years dataset. RVF emergence was found to be triggered by the import of infectious animals, whilst transmissibility was approximated as a linear or exponential function of vegetation density. Model forecasts indicated a very low probability of virus endemicity in 2017, and therefore of re-emergence in a closed system (i.e. without import of infected animals). However, the very high proportion of naive animals reached in 2016 implies that the island remains vulnerable to the import of infectious animals. We recommend reinforcing surveillance in livestock, should RVF be reported is neighbouring territories. Our model should be tested elsewhere, with ecosystem-specific data

Spatial heterogeneity of habitat suitability for Rift Valley fever occurrence in Tanzania: an ecological niche modelling approach

Despite the long history of Rift Valley fever (RVF) in Tanzania, extent of its suitable habitat in the country remains unclear. In this study we investigated potential effects of temperature, precipitation, elevation, soil type, livestock density, rainfall pattern, proximity to wild animals, protected areas and forest on the habitat suitability for RVF occurrence in Tanzania. Presence-only records of 193 RVF outbreak locations from 1930 to 2007 together with potential predictor variables were used to model and map the suitable habitats for RVF occurrence using ecological niche modelling. Ground-truthing of the model outputs was conducted by comparing the levels of RVF virus specific antibodies in cattle, sheep and goats sampled from locations in Tanzania that presented different predicted habitat suitability values. Habitat suitability values for RVF occurrence were higher in the northern and central-eastern regions of Tanzania than the rest of the regions in the country. Soil type and precipitation of the wettest quarter contributed equally to habitat suitability (32.4% each), followed by livestock density (25.9%) and rainfall pattern (9.3%). Ground-truthing of model outputs revealed that the odds of an animal being seropositive for RVFV when sampled from areas predicted to be most suitable for RVF occurrence were twice the odds of an animal sampled from areas least suitable for RVF occurrence (95% CI: 1.43, 2.76, p < 0.001). The regions in the northern and central-eastern Tanzania were more suitable for RVF occurrence than the rest of the regions in the country. The modelled suitable habitat is characterised by impermeable soils, moderate precipitation in the wettest quarter, high livestock density and a bimodal rainfall pattern. The findings of this study should provide guidance for the design of appropriate RVF surveillance, prevention and control strategies which target areas with these characteristics

Species composition, larval habitats, seasonal occurrence and distribution of potential malaria vectors and associated species of Anopheles (Diptera: Culicidae) from the Republic of Korea

<p>Abstract</p> <p>Background</p> <p>Larval mosquito habitats of potential malaria vectors and related species of <it>Anopheles </it>from three provinces (Gyeonggi, Gyeongsangbuk, Chungcheongbuk Provinces) of the Republic of Korea were surveyed in 2007. This study aimed to determine the species composition, seasonal occurrence and distributions of <it>Anopheles </it>mosquitoes. Satellite derived normalized difference vegetation index data (NDVI) was also used to study the seasonal abundance patterns of <it>Anopheles </it>mosquitoes.</p> <p>Methods</p> <p>Mosquito larvae from various habitats were collected using a standard larval dipper or a white plastic larval tray, placed in plastic bags, and were preserved in 100% ethyl alcohol for species identification by PCR and DNA sequencing. The habitats in the monthly larval surveys included artificial containers, ground depressions, irrigation ditches, drainage ditches, ground pools, ponds, rice paddies, stream margins, inlets and pools, swamps, and uncultivated fields. All field-collected specimens were identified to species, and relationships among habitats and locations based on species composition were determined using cluster statistical analysis.</p> <p>Results</p> <p>In about 10,000 specimens collected, eight species of <it>Anopheles </it>belonging to three groups were identified: Hyrcanus Group - <it>Anopheles sinensis</it>, <it>Anopheles kleini</it>, <it>Anopheles belenrae</it>, <it>Anopheles pullus</it>, <it>Anopheles lesteri</it>, <it>Anopheles sineroides</it>; Barbirostris Group - <it>Anopheles koreicus</it>; and Lindesayi Group - <it>Anopheles lindesayi japonicus</it>. Only <it>An. sinensis </it>was collected from all habitats groups, while <it>An. kleini, An. pullus </it>and <it>An. sineroides </it>were sampled from all, except artificial containers. The highest number of <it>Anopheles </it>larvae was found in the rice paddies (34.8%), followed by irrigation ditches (23.4%), ponds (17.0%), and stream margins, inlets and pools (12.0%). <it>Anopheles sinensis </it>was the dominant species, followed by <it>An. kleini, An. pullus </it>and <it>An. sineroides</it>. The monthly abundance data of the <it>Anopheles </it>species from three locations (Munsan, Jinbo and Hayang) were compared against NDVI and NDVI anomalies.</p> <p>Conclusion</p> <p>The species composition of <it>Anopheles </it>larvae varied in different habitats at various locations. <it>Anopheles </it>populations fluctuated with the seasonal dynamics of vegetation for 2007. Multi-year data of mosquito collections are required to provide a better characterization of the abundance of these insects from year to year, which can potentially provide predictive capability of their population density based on remotely sensed ecological measurements.</p

A hierarchical network approach for modeling Rift Valley fever epidemics with applications in North America

Rift Valley fever is a vector-borne zoonotic disease which causes high morbidity and mortality in livestock. In the event Rift Valley fever virus is introduced to the United States or other non-endemic areas, understanding the potential patterns of spread and the areas at risk based on disease vectors and hosts will be vital for developing mitigation strategies. Presented here is a general network-based mathematical model of Rift Valley fever. Given a lack of empirical data on disease vector species and their vector competence, this discrete time epidemic model uses stochastic parameters following several PERT distributions to model the dynamic interactions between hosts and likely North American mosquito vectors in dispersed geographic areas. Spatial effects and climate factors are also addressed in the model. The model is applied to a large directed asymmetric network of 3,621 nodes based on actual farms to examine a hypothetical introduction to some counties of Texas, an important ranching area in the United States of America (U.S.A.). The nodes of the networks represent livestock farms, livestock markets, and feedlots, and the links represent cattle movements and mosquito diffusion between different nodes. Cattle and mosquito (Aedes and Culex) populations are treated with different contact networks to assess virus propagation. Rift Valley fever virus spread is assessed under various initial infection conditions (infected mosquito eggs, adults or cattle). A surprising trend is fewer initial infectious organisms result in a longer delay before a larger and more prolonged outbreak. The delay is likely caused by a lack of herd immunity while the infections expands geographically before becoming an epidemic involving many dispersed farms and animals almost simultaneously

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

Investigation of the Climatic and Environmental Context of Hendra Virus Spillover Events 1994–2010

Hendra virus is a recently emerged bat-borne zoonotic agent with high lethality in horses and humans in Australia. This is a rare disease and the determinants of bat to horse transmission, including the factors that bring these hosts together at critical times, are poorly understood. In this cross-disciplinary study climatic and vegetation primary productivity variables are compared for the dispersed and heterogenic 1994–2010 outbreak sites. The significant occurrence of spillover events within the dry season (p =  0.013, 95% CI (0.57–0.98)) suggests seasonal forcing of transmission across species, or seasonal forcing of virus excretion by the reservoir host. We explore the evidence for both. Preliminary investigations of the spatial determinants of Hendra disease locations are also presented. We find that postal areas in the Australian state of Queensland in which pteropid fruit bat (flying fox) roosts occur are approximately forty times more likely (OR = 40.5, (95% CI (5.16, 317.52)) to be the location of Hendra spillover events. This appears to be independent of density of horses at these locations. We consider issues of scale of host resource use, land use change and limitations of existing data that challenge analysis and limit further conclusive outcomes. This investigation of a broad range of potential climatic and environmental influences provides a good base for future investigations. Further understanding of cross-species Hendra virus transmission requires better understanding of flying fox resource use in the urban-rural landscape

DNA barcoding reveals both known and novel taxa in the Albitarsis Group (Anopheles: Nyssorhynchus) of Neotropical malaria vectors

<p>Abstract</p> <p>Background</p> <p>Mosquitoes belonging to the Albitarsis Group (<it>Anopheles</it>: <it>Nyssorhynchus</it>) are of importance as malaria vectors across the Neotropics. The Group currently comprises six known species, and recent studies have indicated further hidden biodiversity within the Group. DNA barcoding has been proposed as a highly useful tool for species recognition, although its discriminatory utility has not been verified in closely related taxa across a wide geographic distribution.</p> <p>Methods</p> <p>DNA barcodes (658 bp of the mtDNA <it>Cytochrome c Oxidase </it>- <it>COI</it>) were generated for 565 <it>An. albitarsis </it>s.l. collected in Argentina, Brazil, Colombia, Paraguay, Trinidad and Venezuela over the past twenty years, including specimens from type series and type localities. Here we test the utility of currently advocated barcoding methodologies, including the Kimura-two-parameter distance model (K2P) and Neighbor-joining analysis (NJ), for determining species delineation within mosquitoes of the Neotropical Albitarsis Group of malaria vectors (<it>Anopheles</it>: <it>Nyssorhynchus</it>), and compare results with Bayesian analysis.</p> <p>Results</p> <p>Species delineation through barcoding analysis and Bayesian phylogenetic analysis, fully concur. Analysis of 565 sequences (302 unique haplotypes) resolved nine NJ tree clusters, with less than 2% intra-node variation. Mean intra-specific variation (K2P) was 0.009 (range 0.002 - 0.014), whereas mean inter-specific divergence were several-fold higher at 0.041 (0.020 - 0.056), supporting the reported "barcoding gap". These results show full support for separate species status of the six known species in the Albitarsis Group (<it>An. albitarsis </it>s.s., <it>An. albitarsis </it>F, <it>An. deaneorum</it>, <it>An. janconnae</it>, <it>An. marajoara </it>and <it>An. oryzalimnetes</it>), and also support species level status for two previously detected lineages - <it>An. albitarsis </it>G &<it>An. albitarsis </it>I (designated herein). In addition, we highlight the presence of a unique mitochondrial lineage close to <it>An. deaneorum </it>and <it>An. marajoara </it>(<it>An. albitarsis </it>H) from Rondônia and Mato Grosso in southwestern Brazil. Further integrated studies are required to confirm the status of this lineage.</p> <p>Conclusions</p> <p>DNA barcoding provides a reliable means of identifying both known and undiscovered biodiversity within the closely related taxa of the Albitarsis Group. We advocate its usage in future studies to elucidate the vector competence and respective distributions of all eight species in the Albitarsis Group and the novel mitochondrial lineage (<it>An. albitarsis </it>H) recovered in this study.</p