Associations of Stay-at-Home Order and Face-Masking Recommendation with Trends in Daily New Cases and Deaths of Laboratory-Confirmed COVID-19 in the United States

Xu, Jie; Hussain, Sabiha; Lu, Guanzhu; Zheng, Kai; Shi, Wanliang; Bao, Wei; Zhang, Lanjing

doi:10.14218/erhm.2020.00045

Cited by 24 publications

(23 citation statements)

References 33 publications

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“…As of January 2021, the United States had the highest absolute number of coronavirus disease 2019 (COVID- 19) cases and deaths in the world. 1 Within the US, disease incidence has varied substantially among states with different policy interventions and adherence to public health guidance, [2][3][4] and there is also signi cant variability within states. 5 For example in Massachusetts, during the two-week period from January 10-23, 2021, COVID-19 average daily incidence exceeded 100 con rmed cases per 100,000 persons in multiple urban communities (including Chelsea, Lawrence and New Bedford), with a low of zero in a number of more rural communities.…”

Section: Introductionmentioning

confidence: 99%

Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts

Tieskens

Patil

Levy

et al. 2021

Preprint

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Background: Associations between community-level risk factors and COVID-19 incidence are used to identify vulnerable subpopulations and target interventions, but the variability of these associations over time remains largely unknown. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020. Methods: Using publicly available sociodemographic, occupational, environmental, and mobility datasets, we developed mixed-effect, adjusted Poisson regression models to depict associations between these variables and town-level COVID-19 case incidence data across five distinct time periods. We examined town-level demographic variables, including z-scores of percent Black, Latinx, over 80 years and undergraduate students, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PM2.5), and institutional facilities.Results: Associations between key predictor variables and town-level incidence varied across the five time periods. We observed reductions over time in the association with percentage Black residents (IRR=1.12 CI=(1.12-1.13) in spring, IRR=1.01 CI=(1.00-1.01) in fall). The association with number of long-term care facility beds per capita also decreased over time (IRR=1.28 CI=(1.26-1.31) in spring, IRR=1.07 CI=(1.05-1.09)in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidence of COVID-19 throughout the pandemic (e.g., IRR=1.30 CI=(1.27-1.33) in spring, IRR=1.20, CI=(1.17-1.22) in fall). Towns with higher percentages of Latinx residents also had sustained elevated incidence over time (e.g., IRR=1.19 CI=(1.18-1.21) in spring, IRR=1.14 CI=(1.13-1.15) in fall). Conclusions: Town-level COVID-19 risk factors vary with time. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence have decreased over time, perhaps indicating greater success in risk mitigation in selected communities. Our approach can be used to evaluate effectiveness of public health interventions and target specific mitigation efforts on the community level.

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Section: Introductionmentioning

confidence: 99%

Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts

Tieskens

Patil

Levy

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The other novel aspect of the present study lies in comparing the disease predictive value of various data in terms of differences in platforms and keywords. Previous work has explored the correlation between COVID-19 indicator terms of online content and the number of infected individuals (18)(19)(20). However, the potential of internet-driven information in providing early warning of COVID-19 outbreaks is still poorly understood.…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 Waves Using Social Media and Google Search: A Case Study of the US and Canada

Yousefinaghani

Dara

Mubareka

et al. 2021

Front. Public Health

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The ongoing COVID-19 pandemic has posed a severe threat to public health worldwide. In this study, we aimed to evaluate several digital data streams as early warning signals of COVID-19 outbreaks in Canada, the US and their provinces and states. Two types of terms including symptoms and preventive measures were used to filter Twitter and Google Trends data. We visualized and correlated the trends for each source of data against confirmed cases for all provinces and states. Subsequently, we attempted to find anomalies in indicator time-series to understand the lag between the warning signals and real-word outbreak waves. For Canada, we were able to detect a maximum of 83% of initial waves 1 week earlier using Google searches on symptoms. We divided states in the US into two categories: category I if they experienced an initial wave and category II if the states have not experienced the initial wave of the outbreak. For the first category, we found that tweets related to symptoms showed the best prediction performance by predicting 100% of first waves about 2–6 days earlier than other data streams. We were able to only detect up to 6% of second waves in category I. On the other hand, 78% of second waves in states of category II were predictable 1–2 weeks in advance. In addition, we discovered that the most important symptoms in providing early warnings are fever and cough in the US. As the COVID-19 pandemic continues to spread around the world, the work presented here is an initial effort for future COVID-19 outbreaks.

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“…3 Resource availability, political affiliation, and attitudes toward COVID-19 all differ between rural and urban areas, 4 and, as such, public health guidance and implementation may need to be adapted to the unique needs and circumstances of each setting. Thus, while emerging evidence has demonstrated an overall effectiveness of physical distancing measures as enforced by stay-athome orders, [5][6][7][8][9][10][11] potential differences in their effectiveness and durability (i.e., continued effectiveness even after the stay-at-home orders are lifted) of stay-at-home orders between rural and urban areas have not been explored. Identification of such differences would call for closer examination of barriers to optimal implementation and effectiveness with the goal of developing more effective, adaptive, and scalable public health guidelines going forward.…”

Section: Introductionmentioning

confidence: 99%

The Association of Stay-at-Home Orders and the Spread of COVID-19 in Rural and Urban United States

Jiang

Roy

Pollock

et al. 2021

Preprint

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Background: Throughout the spring of 2020, stay-at-home orders were imposed to curb the spread of COVID-19. There is limited data on the effectiveness of stay-at-home orders, particularly in rural as compared to urban areas. Objective: To examine the association between stay-at-home order implementation and the incidence of COVID-19 in rural vs. urban counties. Design: Interrupted time series analysis using a mixed effects zero-inflated Poisson model. Participants: 3,142 U.S. counties. Interventions: Stay-at-home orders. Main Measures: COVID-19 daily incidence (primary) and mobility (secondary and intermediate measure of stay-at-home effectiveness) Key Results: Stay-at-home orders were implemented later (median March 30 vs. March 28) and were shorter (median 35 vs. 54 days) in rural than urban counties. Indoor mobility was, on average, 2.6-6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared to the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by IRR 1.60 (95% CI, 1.57-1.64) in rural and 1.36 (95% CI, 1.30-1.42) in urban counties. For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI 0.981-0.982) in rural and 0.952 (95% CI, 0.951-0.953) in urban counties compared to prior to stay-at-home. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994-0.995) in rural and 0.997 (95% CI, 0.995-0.999) in urban counties compared to prior to stay-at-home. Conclusion: Stay-at-home orders decreased mobility and slowed the spread of COVID-19, but less effectively in rural than in urban counties. This necessitates a critical reevaluation of how stay-at-home orders are designed, communicated, and implemented in rural areas.

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Associations of Stay-at-Home Order and Face-Masking Recommendation with Trends in Daily New Cases and Deaths of Laboratory-Confirmed COVID-19 in the United States

Cited by 24 publications

References 33 publications

Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts

Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts

Prediction of COVID-19 Waves Using Social Media and Google Search: A Case Study of the US and Canada

The Association of Stay-at-Home Orders and the Spread of COVID-19 in Rural and Urban United States

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