Disentangling spillover and onward transmission of zoonotic pathogens: Using stochastic mechanistic models to overcome surveillance data limitations

Background/Question/Methods: Understanding and quantifying a zoonotic pathogen’s transmission dynamics is essential for directing public health responses, especially for pathogens capable of transmission between humans. However, our knowledge about zoonotic pathogens' transmission dynamics is often limited to what we can infer from available surveillance data, which may only specify the date and general location of cases. Here we use a stochastic mechanistic model with a closed-form likelihood expression to overcome a particular set of challenges often inherent to zoonotic disease surveillance, including unobserved sources of transmission (both human and zoonotic), limited spatial information, and unknown scope of surveillance. After demonstrating the robustness of the method in simulation studies, we apply the method to a dataset of human monkeypox cases detected during an active surveillance program from 1982-1986 in the Democratic Republic of the Congo (DRC).

Results/Conclusions: Our results provide estimates of the reproductive number and spillover rate of human monkeypox during this surveillance period and suggest that most human-to-human transmissions occur over distances of 30km or less. Taking advantage of the contact-tracing data available for a subset of monkeypox cases, we found that around 80% of contact-traced links could be correctly recovered from transmission trees inferred using only date and location. Our results highlight the importance of identifying the appropriate spatial scale of transmission, and show how spatiotemporal data can be incorporated into models to obtain reliable estimates of transmission patterns.