An Adaptive Research Approach to COVID-19 Forecasting for Regional Health Systems in England

Lidia Betcheva (), Feryal Erhun (), Antoine Feylessoufi (), Peter Fryers (), Paulo Gonçalves (), Houyuan Jiang (), Paul Kattuman (), Tom Pape (), Anees Pari (), Stefan Scholtes () and Carina Tyrrell ()
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Lidia Betcheva: Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom
Feryal Erhun: Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom
Antoine Feylessoufi: Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom; School of Management, University College London, London E14 5AA, United Kingdom
Peter Fryers: National Health Service England, Cambridge CB21 5XB, United Kingdom
Paulo Gonçalves: Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom; Faculty of Economics, Universitá della Svizzera Italiana, 6900 Lugano, Switzerland
Houyuan Jiang: Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom
Paul Kattuman: Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom
Tom Pape: Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom; Public Health England, Cambridge CB21 5XA, United Kingdom
Anees Pari: Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom; Public Health England, Cambridge CB21 5XA, United Kingdom
Stefan Scholtes: Cambridge Judge Business School, University of Cambridge, Cambridge CB2 1AG, United Kingdom
Carina Tyrrell: Public Health England, Cambridge CB21 5XA, United Kingdom

Interfaces, 2024, vol. 54, issue 6, 500-516

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.

Keywords: COVID-19; time series modeling; system dynamics; forecasting; scenario analysis; bed capacity (search for similar items in EconPapers)
Date: 2024
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