Understanding the Role of Mathematical Models in the Dynamics and Control of COVID-19 in California: A Scoping Review

This scoping review examines how mathematical models helped explain and manage COVID-19 in California. Twelve studies published between 2020 and 2024 were identified through Web of Science and analyzed thematically. Overall, the models were powerful and useful tools for forecasting cases and hospitalizations, estimating the size of the epidemic, identifying thresholds for school reopening, and evaluating vaccination and decarceration strategies in state prisons. Models that represented interventions as discrete, named parameters produced the most policy-relevant findings, while models that folded policy effects into a changing transmission rate were less specific about why transmission changed. At the same time, common limitations included undercounted case data, limited attention to demographic differences, and reliance on retrospective validation. Future work should incorporate demographic and spatial stratification and use prospective validation to better support public health decision-making in California.