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

34 Pages Posted: 19 Oct 2020 Last revised: 3 May 2021

See all articles by Lidia Betcheva

Lidia Betcheva

University of Cambridge - Judge Business School

Feryal Erhun

University of Cambridge - Judge Business School

Antoine Feylessoufi

University of Cambridge - Judge Business School; University College London; University College London - School of Management

Peter Fryers

affiliation not provided to SSRN

Paulo Gonçalves

Università della Svizzera italiana

Houyuan Jiang

University of Cambridge

Paul Kattuman

University of Cambridge - Judge Business School

Tom Pape

Judge Business School, University of Cambridge

Anees Pari

Government of the United Kingdom - Public Health England

Stefan Scholtes

University of Cambridge - Judge Business School

Carina Tyrrell

Government of the United Kingdom - Public Health England

Date Written: April 30, 2021

Abstract

Problem definition: This paper describes the real-time participatory modeling work that our team of academics, public health officials, and clinical decision makers has been undertaking to support the regional efforts to tackle COVID-19 in the East of England (EoE).

Methodology: Since March 2020, we have been studying research questions that have allowed us 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 allow our academic team to apply dynamic data-driven approaches using time series modeling and system dynamics modeling. We thus obtain a broad view of the evolving situation.

Results: The academic team presents the model outcomes and insights during weekly joint meetings among public health services, national health services, and other academics to support COVID-19 planning activities in the EoE, contributing to the discussion of the COVID-19 response and issues beyond immediate COVID-19 planning.

Academic/practical relevance: As COVID-19 planning efforts necessitate a rapid response, our short- and medium-term forecasting models aim to achieve the right balance between rigor and speed in the face of an uncertain and constantly changing situation.

Managerial implications: Our regional and local focus enables us to better understand the pandemic's progression and to help decision makers make more informed short- and medium-term capacity plans in different localities in the EoE. In addition, the knowledge gained through our collaborative experiences may inform guidance on how academics and practitioners can successfully collaborate in rapid response to disasters such as COVID-19.

Note: Conflict of Interest: We declare no competing interests to declare.

Ethical Approval: Patient consent and ethical approval were not required as the research presented in this manuscript comprises a secondary analysis of routinely collected anonymized and aggregated clinical data.

Funding: None to declare.

Keywords: COVID-19, scratch models, Bayesian estimation, time series modeling, system dynamics, scenario analysis, bed capacity, effective reproduction number

Suggested Citation

Betcheva, Lidia and Erhun, Feryal and Feylessoufi, Antoine and Fryers, Peter and Gonçalves, Paulo and Jiang, Houyuan and Kattuman, Paul A. and Pape, Tom and Pari, Anees and Scholtes, Stefan and Tyrrell, Carina, An Adaptive Research Approach to COVID-19 Forecasting for Regional Health Systems in England (April 30, 2021). Available at SSRN: https://ssrn.com/abstract=3695258 or http://dx.doi.org/10.2139/ssrn.3695258

Lidia Betcheva

University of Cambridge - Judge Business School ( email )

Trumpington Street
Cambridge, CB2 1AG
United Kingdom

Feryal Erhun (Contact Author)

University of Cambridge - Judge Business School ( email )

Trumpington Street
Cambridge, CB2 1AG
United Kingdom

Antoine Feylessoufi

University of Cambridge - Judge Business School ( email )

Trumpington Street
Cambridge, CB2 1AG
United Kingdom

University College London ( email )

Gower Street
London, WC1E 6BT
United Kingdom

University College London - School of Management ( email )

Gower Street
London, WC1E 6BT
United Kingdom

Peter Fryers

affiliation not provided to SSRN

Paulo Gonçalves

Università della Svizzera italiana ( email )

Houyuan Jiang

University of Cambridge ( email )

Trinity Ln
Cambridge, CB2 1TN
United Kingdom

Paul A. Kattuman

University of Cambridge - Judge Business School ( email )

Trumpington Road
Cambridge CB2 1AG
United Kingdom
+44 1223 764 136 (Phone)

Tom Pape

Judge Business School, University of Cambridge ( email )

Trumpington Street
Cambridge, CB2 1AG
United Kingdom

HOME PAGE: http://https://www.jbs.cam.ac.uk/tompape

Anees Pari

Government of the United Kingdom - Public Health England ( email )

Wellington House
133-155 Waterloo Road
London, SE1 8UG
United Kingdom

Stefan Scholtes

University of Cambridge - Judge Business School ( email )

Trumpington Street
Cambridge, CB2 1AG
United Kingdom

Carina Tyrrell

Government of the United Kingdom - Public Health England ( email )

Wellington House
133-155 Waterloo Road
London, SE1 8UG
United Kingdom

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