Estimating the Effects of Non-Pharmaceutical Interventions and Population Mobility on Daily COVID-19 Cases: Evidence from Ontario

Investigating the spatiotemporal characteristics and medical response during the initial COVID-19 epidemic in six Chinese cities

In the future, novel and highly pathogenic viruses may re-emerge, leading to a surge in healthcare demand. It is essential for urban epidemic control to investigate different cities' spatiotemporal spread characteristics and medical carrying capacity during the early stages of COVID-19. This study employed textual analysis, mathematical statistics, and spatial analysis methods to examine the situation in six highly affected Chinese cities. The findings reveal that these cities experienced three phases during the initial outbreak of COVID-19: "unknown-origin incubation", "Wuhan-related outbreak", and "local exposure outbreak". Cities with a high number of confirmed cases exhibited a multicore pattern, while those with fewer cases displayed a single-core pattern. The cores were distributed hierarchically in the central built-up areas of cities' economic, political, or transportation centers. The radii of these cores shrank as the central built-up area's level decreased, indicating a hierarchical decay and a core-edge structure. It suggests that decentralized built environments (non-clustered economies and populations) are less likely to facilitate large-scale epidemic clusters. Additionally, the deployment of designated hospitals in these cities was consistent with the spatial distribution of the epidemic; however, their carrying capacity requires urgent improvement. Ultimately, the essence of prevention and control is the governance of human activities and the efficient management of limited resources about individuals, places, and materials through leveraging IT and GIS technologies to address supply-demand contradictions.

Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities

Introduction

There is a vast literature on the performance of different short-term forecasting models for country specific COVID-19 cases, but much less research with respect to city level cases. This paper employs daily case counts for 25 Metropolitan Statistical Areas (MSAs) in the U.S. to evaluate the efficacy of a variety of statistical forecasting models with respect to 7 and 28-day ahead predictions.

Methods

This study employed Gradient Boosted Regression Trees (GBRT), Linear Mixed Effects (LME), Susceptible, Infectious, or Recovered (SIR), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to generate daily forecasts of COVID-19 cases from November 2020 to March 2021.

Results

Consistent with other research that have employed Machine Learning (ML) based methods, we find that Median Absolute Percentage Error (MAPE) values for both 7-day ahead and 28-day ahead predictions from GBRTs are lower than corresponding values from SIR, Linear Mixed Effects (LME), and Seasonal Autoregressive Integrated Moving Average (SARIMA) specifications for the majority of MSAs during November-December 2020 and January 2021. GBRT and SARIMA models do not offer high-quality predictions for February 2021. However, SARIMA generated MAPE values for 28-day ahead predictions are slightly lower than corresponding GBRT estimates for March 2021.

Discussion

The results of this research demonstrate that basic ML models can lead to relatively accurate forecasts at the local level, which is important for resource allocation decisions and epidemiological surveillance by policymakers.

The effects of policy changes and human mobility on the COVID-19 epidemic in the Dominican Republic, 2020-2021

Recent advances in technology can be leveraged to enhance public health research and practice. This study aimed to assess the effects of mobility and policy changes on COVID-19 case growth and the effects of policy changes on mobility using data from Google Mobility Reports, information on public health policy, and COVID-19 testing results. Multiple bivariate regression analyses were conducted to address the study objectives. Policies designed to limit mobility led to decreases in mobility in public areas. These policies also decreased COVID-19 case growth. Conversely, policies that did not restrict mobility led to increases in mobility in public areas and led to increases in COVID-19 case growth. Mobility increases in public areas corresponded to increases in COVID-19 case growth, while concentration of mobility in residential areas corresponded to decreases in COVID-19 case growth. Overall, restrictive policies were effective in decreasing COVID-19 incidence in the Dominican Republic, while permissive policies led to increases in COVID-19 incidence.

Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings

Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.

Impact of Lifting Mask Mandates on COVID-19 Incidence and Mortality in Portugal: An Ecological Study

INTRODUCTION

The use of face masks in public was one of several COVID-19 non-pharmaceutical interventions adopted to mitigate the pandemic in Portugal. The aim of this study was to evaluate the impact of lifting the mask mandate on the April 22, 2022 on COVID-19 incidence and mortality in mainland Portugal and in the Azores. As a secondary objective, we aimed to evaluate the evolution of COVID-19 cases in a setting without a mask mandate (Azores islands) and in a setting with a mask mandate (Madeira islands).

MATERIAL AND METHODS

Surveillance data on laboratory-confirmed COVID-19 cases and COVID-19 deaths were used to conduct an interrupted time series analysis to estimate changes in daily incidence and deaths during a mask mandate period (28th March - 21st April 2022) and during a post-mask mandate period (22nd April - 15th May 2022), in mainland Portugal and the Azores. In a second phase, for each group of islands, we fitted a negative binomial regression model, with daily COVID-19 incident cases as the primary outcome of interest, and relative frequency of Omicron BA.5 lineage as explanatory variable.

RESULTS

Significant changes in trends were observed for the overall incidence rate and COVID-19 deaths; increasing trends were observed for COVID-19 incidence and deaths in the post mandate period [5.3% per day; incidence rate ratio (IRR): 1.053; 95% confidence interval (CI): 1.029 - 1.078] and [3.2% per day; mortality rate ratio (MRR): 1.032; 95% CI: 1.003 - 1.062], respectively. For every unit increase in the percentage of Omicron BA.5 lineage there was a 1.5% increase per day (IRR: 1.015; 95% CI: 1.006 - 1.024) in COVID-19 incidence rate in the Azores islands, while for Madeira islands an increase of 0.05% COVID-19 cases per day was observed (IRR: 1.005; 95% CI: 1.000 - 1.010).

CONCLUSION

Lifting the mask mandate in Portugal was associated with an increase in COVID-19 incidence and deaths, thus highlighting the positive effect of face mask policies in preventing respiratory virus transmission and saving lives.

Do more stringent policies reduce daily COVID-19 case counts? Evidence from Canadian provinces

The enactment of COVID-19 policies in Canada falls under provincial jurisdiction. This study exploits time-series variation across four Canadian provinces to evaluate the effects of stricter COVID-19 policies on daily case counts. Employing data from this time-period allows an evaluation of the efficacy of policies independent of vaccine impacts. While both OLS and IV results offer evidence that more stringent Non-Pharmaceutical Interventions (NPIs) can reduce daily case counts within a short time-period, IV estimates are larger in magnitude. Hence, studies that fail to control for simultaneity bias might produce confounded estimates of the efficacy of NPIs. However, IV estimates should be treated as correlations given the possibility of other unobserved determinants of COVID-19 spread and mismeasurement of daily cases. With respect to specific policies, mandatory mask usage in indoor spaces and restrictions on business operations are significantly associated with lower daily cases. We also test the efficacy of different forecasting models. Our results suggest that Gradient Boosted Regression Trees (GBRT) and Seasonal Autoregressive-Integrated Moving Average (SARIMA) models produce more accurate short-run forecasts relative to Vector Auto Regressive (VAR), and Susceptible-Infected-Removed (SIR) epidemiology models. Forecasts from SIR models are also inferior to results from basic OLS regressions. However, predictions from models that are unable to correct for endogeneity bias should be treated with caution.

Does providing free internet access to low-income households affect COVID-19 spread?

This paper evaluates whether a policy of providing free, in-home Internet for lower-income households can reduce COVID-19 case rates among those households. Using data from a pilot program in Toronto, we find that deploying free public WiFi in large apartment blocks within a low-income neighborhood leads to a 14.4% reduction in weekly cases in that neighborhood. Having in-home WiFi reduces the propensity of residents to visit businesses in the arts, entertainment, and recreation category, suggesting that WiFi benefits residents by providing in-home substitutes for leisure activities.

Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties

Background

The spread of COVID-19 at the local level is significantly impacted by population mobility. The U.S. has had extremely high per capita COVID-19 case and death rates. Efficient nonpharmaceutical interventions to control the spread of COVID-19 depend on our understanding of the determinants of public mobility.

Objective

This study used publicly available Google data and machine learning to investigate population mobility across a sample of US counties. Statistical analysis was used to examine the socioeconomic, demographic, and political determinants of mobility and the corresponding patterns of per capita COVID-19 case and death rates.

Methods

Daily Google population mobility data for 1085 US counties from March 1 to December 31, 2020, were clustered based on differences in mobility patterns using K-means clustering methods. Social mobility indicators (retail, grocery and pharmacy, workplace, and residence) were compared across clusters. Statistical differences in socioeconomic, demographic, and political variables between clusters were explored to identify determinants of mobility. Clusters were matched with daily per capita COVID-19 cases and deaths.

Results

Our results grouped US counties into 4 Google mobility clusters. Clusters with more population mobility had a higher percentage of the population aged 65 years and over, a greater population share of Whites with less than high school and college education, a larger percentage of the population with less than a college education, a lower percentage of the population using public transit to work, and a smaller share of voters who voted for Clinton during the 2016 presidential election. Furthermore, clusters with greater population mobility experienced a sharp increase in per capita COVID-19 case and death rates from November to December 2020.

Conclusions

Republican-leaning counties that are characterized by certain demographic characteristics had higher increases in social mobility and ultimately experienced a more significant incidence of COVID-19 during the latter part of 2020.

Impact of the First Wave of the COVID-19 Pandemic on Trade between Canada and the United States

We examine how the coronavirus disease 2019 (COVID-19) pandemic has affected trade between Canada and the United States, using a novel dataset on monthly bilateral trade flows between Canadian provinces and US states merged with COVID-19 health data. Our results show that a one-standard-deviation increase in COVID-19 severity (case levels, hospitalizations, deaths) in a Canadian province leads to a 3.1 percent to 4.9 percent fall in exports and a 6.7 percent to 9.1 percent fall in imports. Decomposing our analysis by industry, we determine that trade in the manufacturing industry was most negatively affected by the pandemic, and the agriculture industry had the least disruption to trade flows. Our descriptive evidence suggests that lockdowns may also have reduced Canadian exports and imports. However, although our regression coefficients are consistent with that finding, they are not statistically significant, perhaps because of the lack of variation as a result of similar timing in the imposition of restrictions across provinces.

Face masks, public policies and slowing the spread of COVID-19: Evidence from Canada

We estimate the impact of indoor face mask mandates and other non-pharmaceutical interventions (NPI) on COVID-19 case growth in Canada. Mask mandate introduction was staggered from mid-June to mid-August 2020 in the 34 public health regions in Ontario, Canada's largest province by population. Using this variation, we find that mask mandates are associated with a 22 percent weekly reduction in new COVID-19 cases, relative to the trend in absence of mandate. Province-level data provide corroborating evidence. We control for mobility behaviour using Google geo-location data and for lagged case totals and case growth as information variables. Our analysis of additional survey data shows that mask mandates led to an increase of about 27 percentage points in self-reported mask wearing in public. Counterfactual policy simulations suggest that adopting a nationwide mask mandate in June could have reduced the total number of diagnosed COVID-19 cases in Canada by over 50,000 over the period July-November 2020. Jointly, our results indicate that mandating mask wearing in indoor public places can be a powerful policy tool to slow the spread of COVID-19.