Forecasting and Nowcasting with Genomic Data at the Regional Level: Bayesian Approaches for Limited Data

Music City Center 

After the SARS-COV-2 (COVID-19) pandemic, creating accurate models for forecasting and nowcasting viral pathogens has taken on an increased importance. In particular, it is important to understand the circulation of different variants, because these variants can put different strains on public health resources. The pandemic also showed that it is often impossible to obtain complete real-time data to inform these models, especially for models attempting to predict at resolutions below the country level. This underscores the need for models that can produce accurate forecasts with limited data. Abousamra et al. (2024) examined forecasting SARS-COV-2 variants at the national level; this paper builds on that work by discussing an extension of multinomial logistic regression (MLR) to account for the reduction in data at the state level. The extension uses a hierarchical structure to leverage information from states with more data to inform states with less data and performs better in testing than a comparable baseline in terms of the energy score, a proper scoring rule for probabilistic distributions.