Speaker
Description
Epidemiological models are often expressed through systems of differential equations of varying levels of complexity. In multi-strain models, complexity arises from the interaction of different serotypes as a result of cross-reactivity with the host's immune system. This study proposes a novel framework for representing multi-strain disease transmission models, with some mapping of sets and integers. By mapping infection histories into indicator matrices, it eliminates explicit iteration over strain combinations while preserving key biological processes, including sequential infection and partial cross-immunity. Benchmark comparisons of numerical solutions of the proposed matrix formulation and a direct set-indexed implementation demonstrate substantial performance gains. Across increasing strain dimensions, the matrix formulation consistently reduced runtime, with speed-up exceeding two orders of magnitude for systems with 11 strains. We will present an application of this approach to the case of COVID-19 in Indonesia, focusing on the replacement of the Delta variant by Omicron. The matrix formulation approach represents a robust and powerful method to automatically generate large multi-strain epidemic models that can be solved in a computationally efficient manner.
Bibliography
- Andreasen V, Lin J, Levin SA. The dynamics of cocirculating influenza strains conferring partial cross-immunity. Journal of mathematical biology. 1997;35:825–842.
