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• Database of Rift Valley Fever occurrences from 46 countries over 22 years. • Predictions of Rift Valley Fever suitability for every month over 1995–2016. • Identifies areas at-risk by synthesizing time-series of environmental predictions. • We use human and livestock data to identify possible hotspots of disease spillover. • We identify places where long-term and routine preparation efforts should be focused. Rift Valley Fever (RVF) poses a threat to human and animal health throughout much of Africa and the Middle East and has been recognized as a global health security priority and a key preparedness target. We combined RVF occurrence data from a systematic literature review with animal notification data from an online database. Using boosted regression trees, we made monthly environmental suitability predictions from January 1995 to December 2016 at a 5 × 5-km resolution throughout regions of Africa, Europe, and the Middle East. We calculated the average number of months per year suitable for transmission, the mean suitability for each calendar month, and the "spillover potential," a measure incorporating suitability with human and livestock populations. Several countries where cases have not yet been reported are suitable for RVF. Areas across the region of interest are suitable for transmission at different times of the year, and some areas are suitable for multiple seasons each year. Spillover potential results show areas within countries where high populations of humans and livestock are at risk for much of the year. The widespread environmental suitability of RVF highlights the need for increased preparedness, even in countries that have not previously experienced cases. These maps can aid in prioritizing long-term RVF preparedness activities and determining optimal times for recurring preparedness activities. Given an outbreak, our results can highlight areas often at risk for subsequent transmission that month, enabling decision-makers to target responses effectively. [ABSTRACT FROM AUTHOR]

In recent decades, intensification of animal production has been occurring rapidly in transition economies to meet the growing demands of increasingly urban populations. This comes with significant environmental, health and social impacts. To assess these impacts, detailed maps of livestock distributions have been developed by downscaling census data at the pixel level (10 km or 1 km), providing estimates of the density of animals in each pixel. However, these data remain at fairly coarse scale and many epidemiological or environmental science applications would make better use of data where the distribution and size of farms are predicted rather than the number of animals per pixel. Based on detailed 2010 census data, we investigated the spatial point pattern distribution of extensive and intensive chicken farms in Thailand. We parameterized point pattern simulation models for extensive and intensive chicken farms and evaluated these models in different parts of Thailand for their capacity to reproduce the correct level of spatial clustering and the most likely locations of the farm clusters. We found that both the level of clustering and location of clusters could be simulated with reasonable accuracy by our farm distribution models. Furthermore, intensive chicken farms tended to be much more clustered than extensive farms, and their locations less easily predicted using simple spatial factors such as human populations. These point-pattern simulation models could be used to downscale coarse administrative level livestock census data into farm locations. This methodology could be of particular value in countries where farm location data are unavailable. • We built farm distribution models using point pattern simulation methods. • LGCP models with both environmental and anthropogenic variables performed best to predict chicken farm locations. • A discrete spatial representation depicts clustered intensive chicken farms better than a continuous one. • Farm distribution models could be of particular value in countries where farm location data are unavailable. [ABSTRACT FROM AUTHOR]

Abstract The Highly Pathogenic Avian Influenza (HPAI) subtype H5N1 virus persists in many countries and has been circulating in poultry, wild birds. In addition, the virus has emerged in other species and frequent zoonotic spillover events indicate that there remains a significant risk to human health. It is crucial to understand the dynamics of the disease in the poultry industry to develop a more comprehensive knowledge of the risks of transmission and to establish a better distribution of resources when implementing control. In this paper, we develop a set of mathematical models that simulate the spread of HPAI H5N1 in the poultry industry in Thailand, utilising data from the 2004 epidemic. The model that incorporates the intensity of duck farming when assessing transmision risk provides the best fit to the spatiotemporal characteristics of the observed outbreak, implying that intensive duck farming drives transmission of HPAI in Thailand. We also extend our models using a sequential model fitting approach to explore the ability of the models to be used in "real time" during novel disease outbreaks. We conclude that, whilst predictions of epidemic size are estimated poorly in the early stages of disease outbreaks, the model can infer the preferred control policy that should be deployed to minimise the impact of the disease. [ABSTRACT FROM AUTHOR]

Several epidemics caused by different bluetongue virus (BTV) serotypes occurred in European ruminants since the early 2000. Studies on the spatial distribution of these vector-borne infections and the main vector species highlighted contrasted eco-climatic regions characterized by different dominant vector species. However, little work was done regarding the factors associated with the velocity of these epidemics. In this study, we aimed to quantify and compare the velocity of BTV epidemic that have affected different European countries under contrasted eco-climatic conditions and to relate these estimates to spatial factors such as temperature and host density. We used the thin plate spline regression interpolation method in combination with trend surface analysis to quantify the local velocity of different epidemics that have affected France (BTV-8 2007–2008, BTV-1 2008–2009), Italy (BTV-1 2014), Andalusia in Spain (BTV-1 2007) and the Balkans (BTV-4 2014). We found significant differences in the local velocity of BTV spread according to the country and epidemics, ranging from 7.9 km/week (BTV-1 2014 Italy) to 24.4 km/week (BTV-1 2008 France). We quantify and discuss the effect of temperature and local host density on this velocity. [ABSTRACT FROM AUTHOR]

Highly pathogenic avian influenza (HPAI) viruses threaten human and animal health yet their emergence is poorly understood, partly because sampling of the HPAI Asian-origin H5N1 lineage immediately after its identification in 1996 was comparatively sparse. The discovery of a novel H5N8 virus in 2013 provides a new opportunity to investigate HPAI emergence in greater detail. Here we investigate the origin and transmission of H5N8 in the Republic of Korea, the second country to report the new strain. We reconstruct viral spread using phylogeographic methods and interpret the results in the context of ecological data on poultry density, overwintering wild bird numbers, and bird migration patterns. Our results indicate that wild waterfowl migration and domestic duck density were important to H5N8 epidemiology. Specifically, we infer that H5N8 entered the Republic of Korea via Jeonbuk province, then spread rapidly among western provinces where densities of overwintering waterfowl and domestic ducks are higher, yet rarely persisted in eastern regions. The common ancestor of H5N8 in the Republic of Korea was estimated to have arrived during the peak of inward migration of overwintering birds. Recent virus isolations likely represent re-introductions via bird migration from an as-yet unsampled reservoir. Based on the limited data from outside the Republic of Korea, our data suggest that H5N8 may have entered Europe at least twice, and Asia at least three times from this reservoir, most likely carried by wild migrating birds. [ABSTRACT FROM AUTHOR]

The One Health approach integrates health investigations across the tree of life, including, but not limited to, wildlife, livestock, crops, and humans. It redresses an epistemological alienation at the heart of much modern population health, which has long segregated studies by species. Up to this point, however, One Health research has also omitted addressing fundamental structural causes underlying collapsing health ecologies. In this critical review we unpack the relationship between One Health science and its political economy, particularly the conceptual and methodological trajectories by which it fails to incorporate social determinants of epizootic spillover. We also introduce a Structural One Health that addresses the research gap. The new science, open to incorporating developments across the social sciences, addresses foundational processes underlying multispecies health, including the place-specific deep-time histories, cultural infrastructure, and economic geographies driving disease emergence. We introduce an ongoing project on avian influenza to illustrate Structural One Health's scope and ambition. For the first time researchers are quantifying the relationships among transnational circuits of capital, associated shifts in agroecological landscapes, and the genetic evolution and spatial spread of a xenospecific pathogen. [ABSTRACT FROM AUTHOR]

• Intensive farms were found clustered in Nigeria, Thailand, Argentina and Belgium. • The gradient of clustering did not reflect the gradient of intensification. • We developed farm distribution models (FDM) to predict farm location and sizes. • Improvements should consider covariates explaining better farm locations and sizes. Efficient planning of measures limiting epidemic spread requires information on farm locations and sizes (number of animals per farm). However, such data are rarely available. The intensification process which is operating in most low- and middle-income countries (LMICs), comes together with a spatial clustering of farms, a characteristic epidemiological models are sensitive to. We developed farm distribution models predicting both the location and the number of animals per farm, while accounting for the spatial clustering of farms in data-poor countries, using poultry production as an example. We selected four countries, Nigeria, Thailand, Argentina and Belgium, along a gradient of intensification expressed by the per capita Gross Domestic Product (GDP). First, we investigated the distribution of chicken farms along the spectrum of intensification. Second, we built farm distribution models (FDM) based on censuses of commercial farms of each of the four countries, using point pattern and random forest models. As an external validation, we predicted farm locations and sizes in Bangladesh. The number of chicken per farm increased gradually in line with the gradient of GDP per capita in the following order: Nigeria, Thailand, Argentina and Belgium. Interestingly, we did not find such a gradient for farm clustering. Our modelling procedure could only partly reproduce the observed datasets in each of the four sample countries in internal validation. However, in the external validation, the clustering of farms could not be reproduced and the spatial predictors poorly explained the number and location of farms and farm sizes in Bangladesh. Further improvements of the methodology should explore other covariates of the intensity of farms and farm sizes, as well as improvements of the methodology. Structural transformation, economic development and environmental conditions are essential characteristics to consider for an extrapolation of our FDM procedure, as generalisation appeared challenging. We believe the FDM procedure could ultimately be used as a predictive tool in data-poor countries. [ABSTRACT FROM AUTHOR]

Avian influenza A H5N1 viruses have caused many, typically severe, human infections since the first human case was reported in 1997. However, no comprehensive epidemiological analysis of global human cases of H5N1 from 1997 to 2015 exists. Moreover, few studies have examined in detail the changing epidemiology of human H5N1 cases in Egypt, especially given the outbreaks since November, 2014, which have the highest number of cases ever reported worldwide in a similar period. Data on individual patients were collated from different sources using a systematic approach to describe the global epidemiology of 907 human H5N1 cases between May, 1997, and April, 2015. The number of affected countries rose between 2003 and 2008, with expansion from east and southeast Asia, then to west Asia and Africa. Most cases (67·2%) occurred from December to March, and the overall case-fatality risk was 483 (53·5%) of 903 cases which varied across geographical regions. Although the incidence in Egypt has increased dramatically since November, 2014, compared with the cases beforehand, there were no significant differences in the fatality risk, history of exposure to poultry, history of patient contact, and time from onset to hospital admission in the recent cases. [ABSTRACT FROM AUTHOR]