Government Intervention, Human Mobility, and COVID-19: A Causal Pathway Analysis from 121 Countries

Effect of face-covering use on adherence to other COVID-19 protective behaviours: A systematic review

During the COVID-19 pandemic, concerns were raised that face covering use may elicit risk compensation; a false sense of security resulting in reduced adherence to other protective behaviours such as physical distancing. This systematic review aimed to investigate the effect of face covering use on adherence to other COVID-19 related protective behaviours. Medline, Embase, PsychInfo, EmCare, medRxiv preprints, Research Square and WHO COVID-19 Research Database were searched for all primary research studies published from 1st January 2020 to 17th May 2022 that investigated the effect of face covering use on adherence to other protective behaviours in public settings during the COVID-19 pandemic. Papers were selected and screened in accordance with the PRISMA framework. Backwards and forwards citation searches of included papers were also conducted on 16th September 2022, with eligible papers published between 1st January 2020 and that date being included. A quality appraisal including risk of bias was assessed using the Academy of Nutrition and Dietetics' Quality Criteria Checklist. This review is registered on PROSPERO, number CRD42022331961. 47 papers were included, with quality ranging from low to high. These papers investigated the effects of face covering use and face covering policies on adherence to six categories of behaviour: physical distancing; mobility; face-touching; hand hygiene; close contacts; and generalised protective behaviour. Results reveal no consistent evidence for or against risk compensation, with findings varying according to behaviour and across study types, and therefore confident conclusions cannot be made. Any policy decisions related to face coverings must consider the inconsistencies and caveats in this evidence base.

Did the COVID-19 vaccine rollout impact transportation demand? A case study in New York City

INTRODUCTION

This study investigates the influence of vaccination along with other pandemic-related factors on driving, transit, and walking in New York City (NYC). The results of this study help inform policymakers of the weight of their decisions in a pandemic setting as well as factors to consider when modeling transportation during a pandemic.

METHODS

In this study, ARIMAX time series analysis was performed on driving, transit, and walking data from Apple Mobility Trends Reports. The data was segmented into two categories "pre-vaccine" and post-vaccine" for both Manhattan and Brooklyn. The independent variables were primarily COVID-19 statistics (vaccination, case counts, deaths, etc.) along with additional predictors aggregated from Google Community Mobility Reports, Google Trends, Citi Bike, National Oceanic and Atmospheric Administration (NOAA), and the Oxford Covid-19 Government Response Tracker (OxCGRT).

RESULTS

Vaccination led to increases in driving, transit, and walking in Brooklyn but was not as statistically significant in Manhattan (the only effect being on walking trips). Despite this, vaccination was not the strongest influencer on transportation. The COVID-19 policy score variable had the highest standardized β in nearly every model, indicating that stricter lockdown policies were the main factor discouraging travel. Furthermore, the lifting of these policies contributed to increases in travel numbers more than vaccination.

CONCLUSIONS

In the event of future pandemics or health crises, NYC policymakers should be aware that they play a significant role in mitigating infectious diseases. The public is seemingly more responsive to policy than anything else. Similar studies should be conducted in other cities as the public response may vary based on other factors.

The Effect of the COVID-19 Pandemic on the Labor Markets of the Visegrad Countries

The COVID-19 pandemic caused a sudden and deep recession contributing, among other things, to a sharp rise in unemployment. The article addresses changes in the labor markets of the Visegrad countries (Czechia, Hungary, Poland, and Slovakia), covering the period 2018–2021. It attempts to answer the questions: how deep a slump was caused by the pandemic in these markets, how flexible forms of employment responded to it, and whether there were discriminatory phenomena (decline in employment and increase in unemployment in the most vulnerable groups in the labor market). The analysis was based on quarterly data published by Eurostat on the size and structure of the employed and unemployed population. The results of the compilations indicate a relatively small deepening of imbalances in the labor markets of the analyzed countries, a differentiated reaction of flexible forms of employment (depending on the form of employment), which was in line with expectations (they were used as a business cycle buffer). In most of the V4 countries, women were relatively less likely to lose their jobs than men during the pandemic. In a few cases, a relatively stronger decline in employment (increase in unemployment) affected young people, people aged 55–64, and people with the lowest education.

Community Mobility and COVID-19 Dynamics in Jakarta, Indonesia

In response to the COVID-19 pandemic, mobile-phone data on population movement became publicly available, including Google Community Mobility Reports (CMR). This study explored the utilization of mobility data to predict COVID-19 dynamics in Jakarta, Indonesia. We acquired aggregated and anonymized mobility data sets from 15 February to 31 December 2020. Three statistical models were explored: Poisson Regression Generalized Linear Model (GLM), Negative Binomial Regression GLM, and Multiple Linear Regression (MLR). Due to multicollinearity, three categories were reduced into one single index using Principal Component Analysis (PCA). Multiple Linear Regression with variable adjustments using PCA was the best-fit model, explaining 52% of COVID-19 cases in Jakarta (R-Square: 0.52; p < 0.05). This study found that different types of mobility were significant predictors for COVID-19 cases and have different levels of impact on COVID-19 dynamics in Jakarta, with the highest observed in “grocery and pharmacy” (4.12%). This study demonstrates the practicality of using CMR data to help policymakers in decision making and policy formulation, especially when there are limited data available, and can be used to improve health system readiness by anticipating case surge, such as in the places with a high potential for transmission risk and during seasonal events.