An adaptive research approach to COVID-19 forecasting for regional health systems in England

Abstract

We describe the real-time participatory modeling work that our team of academics, public health officials, and clinical decision-makers undertook to support the regional efforts to tackle COVID-19 in the East of England (EoE). Our team studied questions to address the pandemic's rapidly evolving current and near-future epidemiological state, as well as short-term (a few weeks) and medium-term (several months) bed capacity demand. Frequent data input from and consultations with our public health and clinical partners allowed our academic team to apply dynamic data-driven approaches using time series and system dynamics modeling. Our portfolio of models provided decision-makers with the ability to ask nuanced questions. It allowed them to explore and explain different aspects of the pandemic and make more informed capacity plans in the EoE and its subregions. Our novel time series models have already been applied to India in collaboration with Indian health authorities, and the system dynamics model has been used in the canton of Ticino in Switzerland. Therefore, our work may address future epidemiological crises beyond the EoE, especially when used in conjunction with other methods as an ensemble. Additionally, the knowledge gained through our experiences and documented in this paper may guide academic-practitioner collaborations in rapid response to future disasters.