Application of Species Distribution Modeling for Avian Influenza surveillance in the United States considering the North America Migratory Flyways.

Highly Pathogenic Avian Influenza (HPAI) has recently (2014-2015) re-emerged in the United States (US) causing the largest outbreak in US history with 232 outbreaks and an estimated economic impact of $950 million. This study proposes to use suitability maps for Low Pathogenic Avian Influenza (LPAI) to identify areas at high risk for HPAI outbreaks. LPAI suitability maps were based on wild bird demographics, LPAI surveillance, and poultry density in combination with environmental, climatic, and socio-economic risk factors. Species distribution modeling was used to produce high-resolution (cell size: 500m x 500m) maps for Avian Influenza (AI) suitability in each of the four North American migratory flyways (NAMF). Results reveal that AI suitability is heterogeneously distributed throughout the US with higher suitability in specific zones of the Midwest and coastal areas. The resultant suitability maps adequately predicted most of the HPAI outbreak areas during the 2014-2015 epidemic in the US (i.e. 89% of HPAI outbreaks were located in areas identified as highly suitable for LPAI). Results are potentially useful for poultry producers and stakeholders in designing risk-based surveillance, outreach and intervention strategies to better prevent and control future HPAI outbreaks in the US

Application of Species Distribution Modeling for Avian Influenza surveillance in the United States considering the North America Migratory Flyways.

Highly Pathogenic Avian Influenza (HPAI) has recently (2014-2015) re-emerged in the United States (US) causing the largest outbreak in US history with 232 outbreaks and an estimated economic impact of $950 million. This study proposes to use suitability maps for Low Pathogenic Avian Influenza (LPAI) to identify areas at high risk for HPAI outbreaks. LPAI suitability maps were based on wild bird demographics, LPAI surveillance, and poultry density in combination with environmental, climatic, and socio-economic risk factors. Species distribution modeling was used to produce high-resolution (cell size: 500m x 500m) maps for Avian Influenza (AI) suitability in each of the four North American migratory flyways (NAMF). Results reveal that AI suitability is heterogeneously distributed throughout the US with higher suitability in specific zones of the Midwest and coastal areas. The resultant suitability maps adequately predicted most of the HPAI outbreak areas during the 2014-2015 epidemic in the US (i.e. 89% of HPAI outbreaks were located in areas identified as highly suitable for LPAI). Results are potentially useful for poultry producers and stakeholders in designing risk-based surveillance, outreach and intervention strategies to better prevent and control future HPAI outbreaks in the US

Spatio-temporal evolution of canine rabies in Tunisia, 2011–2016

International audienceTunisia is an endemic country for dog mediated rabies. An increase in canine rabies cases during the last decade has been suspected. Since no studies have been conducted on rabies spatial distribution, the present work was focused on spatiotemporal evolution of rabies in Tunisia during the 2011-2016 period with a special focus on the reservoir species. Data collected concerned suspected dogs that originate from the whole country. Surveillance indicators such as positive fractions and number of suspected dogs received at the laboratory have been calculated. Spatiotemporal hotspots were then mapped, spatial and spatio-temporal analysis were carried out using discrete Poisson spatial model and space-time permutation models available in SaTScan9 software. The study revealed that an actual increase in canine rabies incidence occurred in Tunisia since 2012. Spatial and spatio-temporal analysis identified clusters centered in the North and in the Center East of the country. Spatio-temporal clusters were non overlapping, indicating that this spatial distribution is not fixed through time. A large heterogeneity in surveillance indicators such as number of suspected dogs was associated to the distance to the laboratory or to insufficient coordination between governorates

Application of exponential random graph models to determine nomadic herders' movements in Senegal

International audienceUnderstanding human and animal mobility patterns is a key to predict local and global disease spread. We analysed the nomad herds' movement network in a pilot area of northern Senegal and used exponential random graph models (ERGM) to investigate the reasons behind these movements. We interviewed 132 nomadic herders to collect information about nomad herd structures, movements, and reasons for taking specific routes or gathering in certain areas. We constructed a spatially explicit network with villages as the nodes and nomad herds' movements as the connecting edges. The final ERGM showed that node and edge attributes such as presence of cattle in the herd (odds ratio = 12, CI: 5.3, 27.3), morbidity (odds ratio = 3.6, CI: 2.3, 5.7), and lack of water (odds ratio = 2, CI: 1.3, 3.1) were important predictors of nomad herds' movements. This study not only provides valuable information for monitoring important livestock diseases such as Rift Valley Fever in Senegal, but also helps implement outreach, education, and intervention programs for other emerging and endemic diseases affecting nomadic herds

Beyond COVID-19: Conserving nature to prevent the next pandemic

The COVID-19 pandemic has unimaginably changed our lives with long-lasting consequences for our society, environment and the global economy. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the COVID-19 pandemic, is just one of the many pathogens that have already emerged in humans as a result of interactions with wildlife and is only one of the many to come if we do not reduce our impacts on natural systems. While the immediate priority is to tackle the COVID-19 public health emergency, our parallel and long-lasting response must focus on addressing the root causes of pandemics. Human and animal health are inextricably linked with the pathogens they carry and the ecosystems that are shared. The degradation of nature disturbs this delicate balance between microbes, their natural hosts, and environments—driving the emergence of disease

Beyond COVID-19: Conserving nature to prevent the next pandemic

The COVID-19 pandemic has unimaginably changed our lives with long-lasting consequences for our society, environment and the global economy. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the COVID-19 pandemic, is just one of the many pathogens that have already emerged in humans as a result of interactions with wildlife and is only one of the many to come if we do not reduce our impacts on natural systems. While the immediate priority is to tackle the COVID-19 public health emergency, our parallel and long-lasting response must focus on addressing the root causes of pandemics. Human and animal health are inextricably linked with the pathogens they carry and the ecosystems that are shared. The degradation of nature disturbs this delicate balance between microbes, their natural hosts, and environments—driving the emergence of disease

Canine leishmaniosis in Tunisia: Growing prevalence, larger zones of infection

International audienceBackgroundDiscovered by Nicolle and Comte in 1908 in Tunisia, Leishmania infantum is an intracellular protozoan responsible for zoonotic canine leishmaniosis (CanL) and zoonotic human visceral leishmaniasis (HVL). It is endemic in several regions of the world, including Tunisia, with dogs considered as the main domestic reservoir. The geographic expansion of canine leishmaniosis (CanL) has been linked to global environmental changes that have affected the density and the distribution of its sand fly vectors.Methodology/Principal findingsIn this study, a cross-sectional epidemiological survey on CanL was carried out in 8 localities in 8 bioclimatic areas of Tunisia. Blood samples were taken from 317 dogs after clinical examination. Collected sera were tested by indirect fluorescent antibody test (IFAT; 1:80) for the presence of anti-Leishmania infantum antibodies. The overall seroprevalence was 58.3% (185/317). Among positive dogs, only 16.7% showed clinical signs suggestive of leishmaniosis. Seroprevalence rates varied from 6.8% to 84.6% and from 28% to 66% by bioclimatic zone and age group, respectively. Serological positivity was not statistically associated with gender. The presence of Leishmania DNA in blood, using PCR, revealed 21.2% (64/302) prevalence in dogs, which varied by bioclimatic zone (7.3% to 31%) and age group (7% to 25%). The entomological survey carried out in the studied localities showed 16 species of the two genera (Phlebotomus and Sergentomyia). P. perniciosus, P. papatasi, and P. perfiliewi were the most dominant species with relative abundances of 34.7%, 25% and 20.4%, respectively.Conclusions/SignificanceThe present report suggests a significant increase of CanL in all bioclimatic areas in Tunisia and confirms the ongoing spread of the infection of dogs to the country’s arid zone. Such an expansion of infection in dog population could be attributed to ecological, agronomic, social and climatic factors that affect the presence and density of the phlebotomine vectors

Identification of high risk areas for avian influenza outbreaks in California using disease distribution models

<div><p>The coexistence of different types of poultry operations such as free range and backyard flocks, large commercial indoor farms and live bird markets, as well as the presence of many areas where wild and domestic birds co-exist, make California susceptible to avian influenza outbreaks. The 2014–2015 highly pathogenic Avian Influenza (HPAI) outbreaks affecting California and other states in the United States have underscored the need for solutions to protect the US poultry industry against this devastating disease. We applied disease distribution models to predict where Avian influenza is likely to occur and the risk for HPAI outbreaks is highest. We used observations on the presence of Low Pathogenic Avian influenza virus (LPAI) in waterfowl or water samples at 355 locations throughout the state and environmental variables relevant to the disease epidemiology. We used two algorithms, Random Forest and MaxEnt, and two data-sets Presence-Background and Presence-Absence data. The models performed well (AUCc > 0.7 for testing data), particularly those using Presence-Background data (AUCc > 0.85). Spatial predictions were similar between algorithms, but there were large differences between the predictions with Presence-Absence and Presence-Background data. Overall, predictors that contributed most to the models included land cover, distance to coast, and broiler farm density. Models successfully identified several counties as high-to-intermediate risk out of the 8 counties with observed outbreaks during the 2014–2015 HPAI epizootics. This study provides further insights into the spatial epidemiology of AI in California, and the high spatial resolution maps may be useful to guide risk-based surveillance and outreach efforts.</p></div

COVID-19 pandemics modeling with modified determinist SEIR, social distancing, and age stratification. The effect of vertical confinement and release in Brazil.

The ongoing COVID-19 epidemics poses a particular challenge to low and middle income countries, making some of them consider the strategy of "vertical confinement". In this strategy, contact is reduced only to specific groups (e.g. age groups) that are at increased risk of severe disease following SARS-CoV-2 infection. We aim to assess the feasibility of this scenario as an exit strategy for the current lockdown in terms of its ability to keep the number of cases under the health care system capacity. We developed a modified SEIR model, including confinement, asymptomatic transmission, quarantine and hospitalization. The population is subdivided into 9 age groups, resulting in a system of 72 coupled nonlinear differential equations. The rate of transmission is dynamic and derived from the observed delayed fatality rate; the parameters of the epidemics are derived with a Markov chain Monte Carlo algorithm. We used Brazil as an example of middle income country, but the results are easily generalizable to other countries considering a similar strategy. We find that starting from 60% horizontal confinement, an exit strategy on May 1st of confinement of individuals older than 60 years old and full release of the younger population results in 400 000 hospitalizations, 50 000 ICU cases, and 120 000 deaths in the 50-60 years old age group alone. Sensitivity analysis shows the 95% confidence interval brackets a order of magnitude in cases or three weeks in time. The health care system avoids collapse if the 50-60 years old are also confined, but our model assumes an idealized lockdown where the confined are perfectly insulated from contamination, so our numbers are a conservative lower bound. Our results discourage confinement by age as an exit strategy