State-level variation of initial COVID-19 dynamics in the United States: The role of local government interventions

White, Easton R.

doi:10.1101/2020.04.14.20065318

Cited by 23 publications

(15 citation statements)

References 21 publications

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“…Compared with state-level US literature, we found less provincial-and territorial-level variation in NPI implementation in Canada. 13,14 In Canada, we consistently found that school closures were among the first NPIs implemented at the…”

Section: Date Of Npi Implementationmentioning

confidence: 97%

Characterizing early Canadian federal, provincial, territorial and municipal nonpharmaceutical interventions in response to COVID-19: a descriptive analysis

McCoy

Smith

Anchuri

et al. 2020

cmajo

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Background: Nonpharmaceutical interventions (NPIs) are the primary tools to mitigate early spread of the coronavirus disease 2019 (COVID-19) pandemic; however, such policies are implemented variably at the federal, provincial or territorial, and municipal levels without centralized documentation. We describe the development of the comprehensive open Canadian Non-Pharmaceutical Intervention (CAN-NPI) data set, which identifies and classifies all NPIs implemented in regions across Canada in response to COVID-19, and provides an accompanying description of geographic and temporal heterogeneity. Methods: We performed an environmental scan of government websites, news media and verified government social media accounts to identify NPIs implemented in Canada between Jan. 1 and Apr. 19, 2020. The CAN-NPI data set contains information about each intervention's timing, location, type, target population and alignment with a response stringency measure. We conducted descriptive analyses to characterize the temporal and geographic variation in early NPI implementation. Results: We recorded 2517 NPIs grouped in 63 distinct categories during this period. The median date of NPI implementation in Canada was Mar. 24, 2020. Most jurisdictions heightened the stringency of their response following the World Health Organization's global pandemic declaration on Mar. 11, 2020. However, there was variation among provinces or territories in the timing and stringency of NPI implementation, with 8 out of 13 provinces or territories declaring a state of emergency by Mar. 18, and all by Mar. 22, 2020. Interpretation: There was substantial geographic and temporal heterogeneity in NPI implementation across Canada, highlighting the importance of a subnational lens in evaluating the COVID-19 pandemic response. Our comprehensive open-access data set will enable researchers to conduct robust interjurisdictional analyses of NPI impact in curtailing COVID-19 transmission.

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Section: Date Of Npi Implementationmentioning

confidence: 97%

Characterizing early Canadian federal, provincial, territorial and municipal nonpharmaceutical interventions in response to COVID-19: a descriptive analysis

McCoy

Smith

Anchuri

et al. 2020

cmajo

View full text Add to dashboard Cite

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“…Additionally, while data are collected within local medical facilities during an emerging pandemic, outbreak rates are typically reported at aggregate levels. 4 In this article, we show the necessity of analyses on a lower level. This paper proceeds as follows.…”

mentioning

confidence: 90%

Variation in COVID-19 Outbreaks at U.S. State and County Levels

Messner

Payson

2020

Preprint

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Background. The COVID-19 pandemic poses an unprecedented threat to the health and economic prosperity of the world's population. Yet, some countries or regions within a country appear to be affected in different ways.Objectives. This research aims to understand whether the outbreak varies significantly between U.S. states and counties. Methods.A statistical model is estimated using publicly available outbreak data in the U.S., and regional differences are statistically analyzed.Results. There is significant variance in outbreak data between U.S. states and counties.At the state level, the outbreak rate follows a normal distribution with an average relative growth rate of 0.197 (doubling time 3.518 days). But there is a low degree of reliability between state-wide and county-specific data reported (ICC = 0.169, p < 0.001), with a bias of 0.070 (standard deviation 0.062) as shown with a Bland-Altman plot. Conclusions.The results emphasize the need for policy makers to look at the pandemic from the smallest population subdivision possible, so that countermeasures can be implemented, and critical resources provided effectively. Further research is needed to understand the reasons for these regional differences.

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“…Non-pharmaceutical interventions have therefore been implemented to ameliorate the tidal wave at the peak of the epidemic (the so-called flattening of the curve) [5][6][7] . While national or international measures such as border closures, socialdistancing, lock-downs, and furloughs have their merit in slowing the epidemic 8,9 , they also interrupt global supply chains and may thus starve health care systems from necessary equipment 10 .…”

Section: Introductionmentioning

confidence: 99%

Navigating hospitals safely through the COVID-19 epidemic tide: Predicting case load for adjusting bed capacity

Donker

Bürkin

Wolkewitz

et al. 2020

Infect. Control Hosp. Epidemiol.

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Background The pressures exerted by the pandemic of COVID-19 pose an unprecedented demand on health care services. Hospitals become rapidly overwhelmed when patients requiring life-saving support outpace available capacities. We here describe methods used by a university hospital to forecast caseloads and time to peak incidence. Methods We developed a set of models to forecast incidence among the hospital catchment population and describe the COVID-19 patient hospital care-path. The first forecast utilized data from antecedent allopatric epidemics and parameterized the care path model according to expert opinion (static model). Once sufficient local data were available, trends for the time dependent effective reproduction number were fitted and the care-path was re-parameterized using hazards for real patient admission, referrals, and discharge (dynamic model). Results The static model, deployed before the epidemic, exaggerated the bed occupancy (general wards 116 forecasted vs 66 observed, ICU 47 forecasted vs 34 observed) and predicted the peak too late (general ward forecast April 9, observed April 8, ICU forecast April 19, observed April 8). After April 5, the dynamic model could be run daily and precision improved with increasing availability of empirical local data. Conclusions The models provided data-based guidance in the preparation and allocation of critical resources of a university hospital well in advance of the epidemic surge, despite overestimating the service demand. Overestimates should resolve when population contact pattern before and during restrictions can be taken into account, but for now they may provide an acceptable safety margin for preparing during times of uncertainty.

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State-level variation of initial COVID-19 dynamics in the United States: The role of local government interventions

Cited by 23 publications

References 21 publications

Characterizing early Canadian federal, provincial, territorial and municipal nonpharmaceutical interventions in response to COVID-19: a descriptive analysis

Characterizing early Canadian federal, provincial, territorial and municipal nonpharmaceutical interventions in response to COVID-19: a descriptive analysis

Variation in COVID-19 Outbreaks at U.S. State and County Levels

Navigating hospitals safely through the COVID-19 epidemic tide: Predicting case load for adjusting bed capacity

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