Rapid COVID-19 Modeling Support for Regional Health Systems in England
40 Pages Posted: 19 Oct 2020
Date Written: September 18, 2020
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.
Methodology: Since March 2020, we have been studying four research questions that have allowed us to address the pandemic's current and near-future rapidly evolving epidemiological state, as well as the bed capacity demand in the short (a few weeks) and medium (several months) term. 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, Bayesian estimation, 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 academics to support COVID-19 planning activities in the East of England, 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 rapid response, our portfolio of scratch models aims to achieve the right balance between rigor and speed in the face of an uncertain and 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 East of England. In addition, the learnings from our collaborative experiences may present 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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