Identification of high-risk contact areas between feral pigs and outdoor-raised pig operations in California: Implications for disease transmission in the wildlife-livestock interface.

The US is currently experiencing a return to raising domestic pigs outdoors, due to consumer demand for sustainably-raised animal products. A challenge in raising pigs outdoors is the possibility of these animals interacting with feral pigs and an associated risk of pathogen transmission. California has one of the largest and widest geographic distributions of feral pigs. Locations at greatest risk for increased contact between both swine populations are those regions that contain feral pig suitable habitat located near outdoor-raised domestic pigs. The main aim of this study entailed identifying potential high-risk areas of disease transmission between these two swine populations. Aims were achieved by predicting suitable feral pig habitat using Maximum Entropy (MaxEnt); mapping the spatial distribution of outdoor-raised pig operations (OPO); and identifying high-risk regions where there is overlap between feral pig suitable habitat and OPO. A MaxEnt prediction map with estimates of the relative probability of suitable feral pig habitat was built, using hunting tags as presence-only points. Predictor layers were included in variable selection steps for model building. Five variables were identified as important in predicting suitable feral pig habitat in the final model, including the annual maximum green vegetation fraction, elevation, the minimum temperature of the coldest month, precipitation of the wettest month and the coefficient of variation for seasonal precipitation. For the risk map, the final MaxEnt model was overlapped with the location of OPOs to categorize areas at greatest risk for contact between feral swine and domestic pigs raised outdoors and subsequent potential disease transmission. Since raising pigs outdoors is a remerging trend, feral pig numbers are increasing nationwide, and both groups are reservoirs for various pathogens, the contact between these two swine populations has important implications for disease transmission in the wildlife-livestock interface.

Spatiotemporal risk assessment for avian influenza outbreak based on the dynamics of habitat suitability for wild birds.

Highly pathogenic avian influenza (HPAI) has predominantly damaged the poultry industry worldwide. The fundamental prevention and control strategy for HPAI includes early detection and timely intervention enforcement through a systematic surveillance system for wild birds based on the ecological understanding of the dynamics of wild birds' movements. Our study aimed to develop a spatiotemporal risk assessment model for avian influenza (AI) infection in wild birds to empower surveillance information for a contingency strategy. For this purpose, first, we predicted the monthly habitat suitability of seven waterfowl species, using 227,671 Global Positioning System (GPS) tracking records of 562 birds from 2014 to 2018 in the Republic of Korea (ROK). Then, that predicted habitat suitability and 421 coordinates of AI detection sites in wild birds were used to build the risk assessment model. Subsequently, we compared the monthly predicted risk of avian influenza virus (AIv) identification in wild birds between case and non-case poultry farms with HPAI H5N6 outbreak in the ROK between 2016 and 2017. The results reported considerable variation of monthly habitat suitability of seven waterfowls and the impact of predicting AI occurrences in wild birds. The high habitat suitability for spot-billed ducks (contribution rate in November = 40.9%) and mallards (contribution rate in January = 34.3%) significantly contributed to predicting the average risk of AIv identification in wild birds, with high predictive performance [the monthly mean of area under the curve (AUC) = 0.978]. Moreover, our model showed that the averaged risk of identification AI in wild birds was significantly higher in HPAI infected premises, with infected domestic duck holdings exhibiting a significantly higher risk than the chicken farms in November. This study suggests that animal health authority establishes a risk-based HPAI surveillance system grounded on the ecological nature of wild birds to improve the effectiveness of prevention and preparedness of emerging epidemics.

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

BACKGROUND: Discovered 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 FINDINGS: In 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/SIGNIFICANCE: The 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.

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

Tunisia 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.

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.

Rift Valley fever in northern Senegal: A modelling approach to analyse the processes underlying virus circulation recurrence.

Rift Valley fever (RVF) is endemic in northern Senegal, a Sahelian area characterized by a temporary pond network that drive both RVF mosquito population dynamics and nomadic herd movements. To investigate the mechanisms that explain RVF recurrent circulation, we modelled a realistic epidemiological system at the pond level integrating vector population dynamics, resident and nomadic ruminant herd population dynamics, and nomadic herd movements recorded in Younoufere area. To calibrate the model, serological surveys were performed in 2015-2016 on both resident and nomadic domestic herds in the same area. Mosquito population dynamics were obtained from a published model trained in the same region. Model comparison techniques were used to compare five different scenarios of virus introduction by nomadic herds associated or not with vertical transmission in Aedes vexans. Our serological results confirmed a long lasting RVF endemicity in resident herds (IgG seroprevalence rate of 15.3%, n = 222), and provided the first estimation of RVF IgG seroprevalence in nomadic herds in West Africa (12.4%, n = 660). Multivariate analysis of serological data suggested an amplification of the transmission cycle during the rainy season with a peak of circulation at the end of that season. The best scenario of virus introduction combined yearly introductions of RVFV from 2008 to 2015 (the study period) by nomadic herds, with a proportion of viraemic individuals predicted to be larger in animals arriving during the 2nd half of the rainy season (3.4%). This result is coherent with the IgM prevalence rate (4%) found in nomadic herds sampled during the 2nd half of the rainy season. Although the existence of a vertical transmission mechanism in Aedes cannot be ruled out, our model demonstrates that nomadic movements are sufficient to account for this endemic circulation in northern Senegal.

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

Understanding 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.

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

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.

Prevalence and Potential Risk Factors for Bartonella Infection in Tunisian Stray Dogs.

Bartonellae are blood-borne and vector-transmitted pathogens, some are zoonotic, which have been reported in several Mediterranean countries. Transmission from dogs to humans is suspected, but has not been clearly demonstrated. Our objectives were to determine the seroprevalence of Bartonella henselae, Bartonella vinsonii subsp. berkhoffii, Bartonella clarridgeiae, and Bartonella bovis (as a proxy for Candidatus Bartonella merieuxii) in stray dogs from Tunisia, identify the Bartonella species infecting the dogs and evaluate potential risk factors for canine infection. Blood samples were collected between January and November 2013 from 149 dogs in 10 Tunisian governorates covering several climatic zones. Dog-specific and geographic variables were analyzed as potential risk factors for Bartonella spp. seropositivity and PCR-positivity. DNA was extracted from the blood of all dogs and tested by PCR for Bartonella, targeting the ftsZ and rpoB genes. Partial sequencing was performed on PCR-positive dogs. Twenty-nine dogs (19.5%, 95% confidence interval: 14-27.4) were seropositive for one or more Bartonella species, including 17 (11.4%) for B. vinsonii subsp. berkhoffii, 14 (9.4%) for B. henselae, 13 (8.4%) for B. clarridgeiae, and 7 (4.7%) for B. bovis. Statistical analysis revealed a few potential risk factors, mainly dog's age and breed, latitude and average winter temperature. Twenty-two (14.8%) dogs, including 8 of the 29 seropositive dogs, were PCR-positive for Bartonella based on the ftsZ gene, with 18 (81.8%) of these 22 dogs also positive for the rpoB gene. Partial sequencing showed that all PCR-positive dogs were infected with Candidatus B. merieuxii. Dogs from arid regions and regions with cold average winter temperatures were less likely to be PCR-positive than dogs from other climatic zones. The widespread presence of Bartonella spp. infection in Tunisian dogs suggests a role for stray dogs as potential reservoirs of Bartonella species in Tunisia.

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.