Assessing the epidemiological risk at the human-wild boar interface through a one health approach using an agent-based model in Barcelona, Spain.

Wild boar (WB, Sus scrofa) populations are increasing in urban areas, posing an epidemiological risk for zoonotic pathogens such as hepatitis E virus (HEV) and antimicrobial-resistant Campylobacter (AMR-CAMP), as well as non-zoonotic pathogens such as African swine fever virus (ASFV). An epidemiological extension of a validated Agent-Based Model (ABM) was developed to assess the one-year epidemiological scenarios of HEV, AMR-CAMP, and ASFV in the synurbic WB-human interface in Barcelona, Spain. The predicted citizen exposure was similar for HEV and AMR-CAMP, at 0.79% and 0.80% of the human population in Barcelona, respectively, despite AMR-CAMP being more prevalent in the WB population than HEV. This suggests a major role of faeces in pathogen transmission to humans in urban areas, resulting in a non-negligible public health risk. The ASFV model predicted that the entire WB population would be exposed to the virus through carcasses (87.6%) or direct contact (12.6%) in 51-71 days after the first case, with an outbreak lasting 71-124 days and reducing the initial WB population by 95%. The ABM predictions are useful for animal and public health risk assessments and to support risk-based decision-making. The study underscores the need for interdisciplinary cooperation among animal, public, and environmental health managers, and the implementation of the One Health approach to address the epidemiological and public health risks posed by the synurbization of WB in urban areas. The spatially explicit epidemiological predictions of the ABM can be adapted to other diseases and scenarios at the wildlife-livestock-human 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.

Testing and Vaccination to Reduce the Impact of COVID-19 in Nursing Homes: An Agent-Based Approach

BackgroundEfforts to protect residents in nursing homes involve non-pharmaceutical interventions, testing, and vaccine. We sought to quantify the effect of testing and vaccine strategies on the attack rate, length of the epidemic, and hospitalization. MethodsWe developed an agent-based model to simulate the dynamics of SARS-CoV-2 transmission in a nursing home with resident and staff agents. Interactions between 172 residents and 170 staff were assumed based on data from a nursing home in Los Angeles, CA. We simulated scenarios assuming different levels of non-pharmaceutical interventions, testing frequencies, and vaccine efficacy to block transmission. ResultsUnder the hypothetical scenario of widespread SARS-CoV-2 in the community, 3-day testing frequency minimized the attack rate and the time to eradicate an outbreak. Prioritization of vaccine among staff or staff and residents minimized the cumulative number of infections and hospitalization, particularly in the scenario of high probability of an introduction. Reducing the probability of a virus introduction reduced the demand on testing and vaccine to reduce infections and hospitalizations. ConclusionsImproving frequency of testing from 7-days to 3-days minimized the number of infections and hospitalizations, despite widespread community transmission. Vaccine prioritization of staff provides the best protection strategy, despite high risk of a virus introduction.