Frequent and unpredictable changes in COVID-19 policies and restrictions reduce the accuracy of model forecasts
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Frequent and unpredictable changes in COVID-19 policies and restrictions reduce the accuracy of model forecasts. / Houdroge, Farah; Palmer, Anna; Delport, Dominic; Walsh, Tom; Kelly, Sherrie L.; Hainsworth, Samuel W.; Abeysuriya, Romesh; Stuart, Robyn M.; Kerr, Cliff C.; Coplan, Paul; Wilson, David P.; Scott, Nick.
I: Scientific Reports, Bind 13, Nr. 1, 1398, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Frequent and unpredictable changes in COVID-19 policies and restrictions reduce the accuracy of model forecasts
AU - Houdroge, Farah
AU - Palmer, Anna
AU - Delport, Dominic
AU - Walsh, Tom
AU - Kelly, Sherrie L.
AU - Hainsworth, Samuel W.
AU - Abeysuriya, Romesh
AU - Stuart, Robyn M.
AU - Kerr, Cliff C.
AU - Coplan, Paul
AU - Wilson, David P.
AU - Scott, Nick
N1 - Publisher Copyright: © 2023, The Author(s).
PY - 2023
Y1 - 2023
N2 - Between June and August 2020, an agent-based model was used to project rates of COVID-19 infection incidence and cases diagnosed as positive from 15 September to 31 October 2020 for 72 geographic settings. Five scenarios were modelled: a baseline scenario where no future changes were made to existing restrictions, and four scenarios representing small or moderate changes in restrictions at two intervals. Post hoc, upper and lower bounds for number of diagnosed Covid-19 cases were compared with actual data collected during the prediction window. A regression analysis with 17 covariates was performed to determine correlates of accurate projections. It was found that the actual data fell within the lower and upper bounds in 27 settings and out of bounds in 45 settings. The only statistically significant predictor of actual data within the predicted bounds was correct assumptions about future policy changes (OR 15.04; 95% CI 2.20–208.70; p = 0.016). Frequent changes in restrictions implemented by governments, which the modelling team was not always able to predict, in part explains why the majority of model projections were inaccurate compared with actual outcomes and supports revision of projections when policies are changed as well as the importance of modelling teams collaborating with policy experts.
AB - Between June and August 2020, an agent-based model was used to project rates of COVID-19 infection incidence and cases diagnosed as positive from 15 September to 31 October 2020 for 72 geographic settings. Five scenarios were modelled: a baseline scenario where no future changes were made to existing restrictions, and four scenarios representing small or moderate changes in restrictions at two intervals. Post hoc, upper and lower bounds for number of diagnosed Covid-19 cases were compared with actual data collected during the prediction window. A regression analysis with 17 covariates was performed to determine correlates of accurate projections. It was found that the actual data fell within the lower and upper bounds in 27 settings and out of bounds in 45 settings. The only statistically significant predictor of actual data within the predicted bounds was correct assumptions about future policy changes (OR 15.04; 95% CI 2.20–208.70; p = 0.016). Frequent changes in restrictions implemented by governments, which the modelling team was not always able to predict, in part explains why the majority of model projections were inaccurate compared with actual outcomes and supports revision of projections when policies are changed as well as the importance of modelling teams collaborating with policy experts.
UR - http://www.scopus.com/inward/record.url?scp=85146760380&partnerID=8YFLogxK
U2 - 10.1038/s41598-023-27711-3
DO - 10.1038/s41598-023-27711-3
M3 - Journal article
C2 - 36697434
AN - SCOPUS:85146760380
VL - 13
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 1398
ER -
ID: 336291947