Infectious disease phylodynamics with occurrence data

Phylodynamic models use pathogen genome sequence data to infer epidemiological dynamics. With the increasing genomic surveillance of pathogens, especially during SARS-CoV-2 pandemic, new practical questions about their are emerging. One such question focuses on inclusion un-sequenced case occurrence alongside sequenced improve phylodynamic analyses. This approach can be particularly valuable if sequencing efforts vary over time. Using simulations, we demonstrate that birth–death employ eliminate bias in estimates basic reproductive number due misspecification sampling process. In contrast, coalescent exponential model is robust biases, but absence a it cannot exploit data. Subsequent analysis epidemic northwest USA supports these results. We conclude source information combination with models. These should used bolster analyses infectious diseases and other rapidly spreading species future.