tetano
Editor, Senior Moderator
Can Public Policy
. 2022 Mar 1;48(1):144-161.
doi: 10.3138/cpp.2021-022.
Estimating the Effects of Non-Pharmaceutical Interventions and Population Mobility on Daily COVID-19 Cases: Evidence from Ontario
Nathaniel T Stevens[SUP] 1 [/SUP], Anindya Sen[SUP] 2 [/SUP], Francis Kiwon[SUP] 1 [/SUP], Plinio P Morita[SUP] 3 [/SUP], Stefan H Steiner[SUP] 1 [/SUP], Qihuang Zhang[SUP] 4 [/SUP]
Affiliations
Abstract
in English, French
This study uses coronavirus disease 2019 (COVID-19) case counts and Google mobility data for 12 of Ontario's largest Public Health Units from Spring 2020 until the end of January 2021 to evaluate the effects of non-pharmaceutical interventions (NPIs; policy restrictions on business operations and social gatherings) and population mobility on daily cases. Instrumental variables (IV) estimation is used to account for potential simultaneity bias, because both daily COVID-19 cases and NPIs are dependent on lagged case numbers. IV estimates based on differences in lag lengths to infer causal estimates imply that the implementation of stricter NPIs and indoor mask mandates are associated with reductions in COVID-19 cases. Moreover, estimates based on Google mobility data suggest that increases in workplace attendance are correlated with higher case counts. Finally, from October 2020 to January 2021, daily Ontario forecasts from Box-Jenkins time-series models are more accurate than official forecasts and forecasts from a susceptible-infected-removed epidemiology model.
Keywords: COVID-19; Google data; Ontario; SIR; forecasts; population mobility; time-series modelling.
. 2022 Mar 1;48(1):144-161.
doi: 10.3138/cpp.2021-022.
Estimating the Effects of Non-Pharmaceutical Interventions and Population Mobility on Daily COVID-19 Cases: Evidence from Ontario
Nathaniel T Stevens[SUP] 1 [/SUP], Anindya Sen[SUP] 2 [/SUP], Francis Kiwon[SUP] 1 [/SUP], Plinio P Morita[SUP] 3 [/SUP], Stefan H Steiner[SUP] 1 [/SUP], Qihuang Zhang[SUP] 4 [/SUP]
Affiliations
- PMID: 36039068
- PMCID: PMC9395157
- DOI: 10.3138/cpp.2021-022
Abstract
in English, French
This study uses coronavirus disease 2019 (COVID-19) case counts and Google mobility data for 12 of Ontario's largest Public Health Units from Spring 2020 until the end of January 2021 to evaluate the effects of non-pharmaceutical interventions (NPIs; policy restrictions on business operations and social gatherings) and population mobility on daily cases. Instrumental variables (IV) estimation is used to account for potential simultaneity bias, because both daily COVID-19 cases and NPIs are dependent on lagged case numbers. IV estimates based on differences in lag lengths to infer causal estimates imply that the implementation of stricter NPIs and indoor mask mandates are associated with reductions in COVID-19 cases. Moreover, estimates based on Google mobility data suggest that increases in workplace attendance are correlated with higher case counts. Finally, from October 2020 to January 2021, daily Ontario forecasts from Box-Jenkins time-series models are more accurate than official forecasts and forecasts from a susceptible-infected-removed epidemiology model.
Keywords: COVID-19; Google data; Ontario; SIR; forecasts; population mobility; time-series modelling.