ArticleCanadian public policy. Analyse de politiques2022
Estimating the Effects of Non-Pharmaceutical Interventions and Population Mobility on Daily COVID-19 Cases: Evidence from Ontario.
Article in Canadian public policy. Analyse de politiques, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Pooled it
- Investigating the Relationships Between COVID-19 Cases, Public Health Interventions, Vaccine Coverage, and Mean Temperature in Ontario and Toronto.Diseases (Basel, Switzerland) · 2025Article
- Investigating the spatiotemporal characteristics and medical response during the initial COVID-19 epidemic in six Chinese cities.Scientific reports · 2024Article
- The effects of policy changes and human mobility on the COVID-19 epidemic in the Dominican Republic, 2020-2021.Preventive medicine reports · 2023Article
- Do more stringent policies reduce daily COVID-19 case counts? Evidence from Canadian provinces.Economic analysis and policy · 2023Article
- Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities.Frontiers in public health · 2023Article
- Does providing free internet access to low-income households affect COVID-19 spread?Health economics · 2022Article
- Face masks, public policies and slowing the spread of COVID-19: Evidence from Canada.Journal of health economics · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.