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
Date Written: April 30, 2021
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
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