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Deep recurrent reinforced learning model to compare the efficacy of targeted local versus national measures on the spread of COVID-19 in the UK.
Dong, Tim; Benedetto, Umberto; Sinha, Shubhra; Fudulu, Daniel; Dimagli, Arnaldo; Chan, Jeremy; Caputo, Massimo; Angelini, Gianni.
  • Dong T; Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK.
  • Benedetto U; Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK umberto.benedetto@bristol.ac.uk.
  • Sinha S; Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK.
  • Fudulu D; Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK.
  • Dimagli A; Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK.
  • Chan J; Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK.
  • Caputo M; Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK.
  • Angelini G; Bristol Heart Institute, Bristol Medical School, University of Bristol, Bristol, UK.
BMJ Open ; 12(2): e048279, 2022 02 21.
Article in English | MEDLINE | ID: covidwho-1707181
ABSTRACT

OBJECTIVES:

To prevent the emergence of new waves of COVID-19 caseload and associated mortalities, it is imperative to understand better the efficacy of various control measures on the national and local development of this pandemic in space-time, characterise hotspot regions of high risk, quantify the impact of under-reported measures such as international travel and project the likely effect of control measures in the coming weeks.

METHODS:

We applied a deep recurrent reinforced learning based model to evaluate and predict the spatiotemporal effect of a combination of control measures on COVID-19 cases and mortality at the local authority (LA) and national scale in England, using data from week 5 to 46 of 2020, including an expert curated control measure matrix, official statistics/government data and a secure web dashboard to vary magnitude of control measures.

RESULTS:

Model predictions of the number of cases and mortality of COVID-19 in the upcoming 5 weeks closely matched the actual values (cases root mean squared error (RMSE) 700.88, mean absolute error (MAE) 453.05, mean absolute percentage error (MAPE) 0.46, correlation coefficient 0.42; mortality RMSE 14.91, MAE 10.05, MAPE 0.39, correlation coefficient 0.68). Local lockdown with social distancing (LD_SD) (overall rank 3) was found to be ineffective in preventing outbreak rebound following lockdown easing compared with national lockdown (overall rank 2), based on prediction using simulated control measures. The ranking of the effectiveness of adjunctive measures for LD_SD were found to be consistent across hotspot and non-hotspot regions. Adjunctive measures found to be most effective were international travel and quarantine restrictions.

CONCLUSIONS:

This study highlights the importance of using adjunctive measures in addition to LD_SD following lockdown easing and suggests the potential importance of controlling international travel and applying travel quarantines. Further work is required to assess the effect of variant strains and vaccination measures.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines / Variants Limits: Humans Country/Region as subject: Europa Language: English Journal: BMJ Open Year: 2022 Document Type: Article Affiliation country: Bmjopen-2020-048279

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study Topics: Vaccines / Variants Limits: Humans Country/Region as subject: Europa Language: English Journal: BMJ Open Year: 2022 Document Type: Article Affiliation country: Bmjopen-2020-048279