Landscape-Scale Epidemiological Dynamics of SARS-CoV-2 in White-Tailed Deer

• Published in: Transboundary and emerging diseases 2024-02, Vol.2024 (1)
• Main Authors: Hewitt, Joshua, Wilson-Henjum, Grete, Collins, Derek T., Linder, Timothy J., Lenoch, Julianna B., Heale, Jonathon D., Quintanal, Christopher A., Pleszewski, Robert, McBride, Dillon S., Bowman, Andrew S., Chandler, Jeffrey C., Shriner, Susan A., Bevins, Sarah N., Kohler, Dennis J., Chipman, Richard B., Gosser, Allen L., Bergman, David L., DeLiberto, Thomas J., Pepin, Kim M.
• Format: Article
• Language: English
• Subjects: Animal and Plant Health Inspection Service; Animal health; COVID-19; data collection; Deer; Epidemiology; Estimates; hosts; human diseases; Infections; Mathematical models; monitoring; Mortality; Natural resources; Odocoileus virginianus; Outbreaks; Pandemics; pathogens; Random variables; reproduction; risk; Risk assessment; Risk factors; Severe acute respiratory syndrome coronavirus 2; Statistical analysis; Statistical models; Surveillance; Viral diseases; Zoonoses
• ISSN: 1865-1674; 1865-1682
• DOI: 10.1155/2024/7589509

Understanding pathogen emergence in new host species is fundamental for developing prevention and response plans for human and animal health. We leveraged a large-scale surveillance dataset coordinated by United States Department of Agriculture, Animal and Plant Health Inspection Service and State Natural Resources Agencies to quantify the outbreak dynamics of SARS-CoV-2 in North American white-tailed deer (Odocoileus virginianus; WTD) throughout its range in the United States. Local epidemics in WTD were well approximated by a single-outbreak peak followed by fade out. Outbreaks peaked early in the northeast and mid-Atlantic. Local effective reproduction ratios of SARS-CoV-2 were between 1 and 2.5. Ten percent of variability in peak prevalence was explained by human infection pressure. This, together with the similar peak infection prevalence times across many counties and single-peak outbreak dynamics followed by fade out, suggest that widespread transmission via human-to-deer spillover may have been an important driver of the patterns and persistence. We provide a framework for inferring population-level epidemiological processes through joint analysis of many sparsely observed local outbreaks (landscape-scale surveillance data) and linking epidemiological parameters to ecological risk factors. The framework combines mechanistic and statistical models that can identify and track local outbreaks in long-term infection surveillance monitoring data.