COVID-19 Case Growth Research Articles

JUE Insight: Were urban cowboys enough to control COVID-19? Local shelter-in-place orders and coronavirus case growth

One of the most common policy prescriptions to reduce the spread of COVID-19 has been to legally enforce social distancing through shelter-in-place orders (SIPOs). This study examines the role of localized urban SIPO policy in curbing COVID-19 cases. Specifically, we explore (i) the comparative effectiveness of county-level SIPOs in urbanized as compared to non-urbanized areas, (ii) the mechanisms through which SIPO adoption in urban counties yields COVID-related health benefits, and (iii) whether late adoption of a statewide SIPO yields health benefits beyond those achieved from early adopting counties. We exploit the unique laboratory of Texas, a state in which the early adoption of local SIPOs by densely populated counties covered almost two-thirds of the state's population prior to adoption of a statewide SIPO on April 2, 2020. Using an event study framework, we document that countywide SIPO adoption is associated with an 8 percent increase in the percent of residents who remain at home full-time and between a 13 to 19 percent decrease in foot-traffic at venues that may contribute to the spread of COVID-19 such as restaurants, bars, hotels, and entertainment venues. These social distancing effects are largest in urbanized and densely populated counties. Then, we find that in early adopting urban counties, COVID-19 case growth fell by 21 to 26 percentage points two-and-a-half weeks following adoption of a SIPO, a result robust to controls for county-level heterogeneity in COVID-19 outbreak timing, coronavirus testing, the age distribution, and political preferences. We find that approximately 90 percent of the curbed growth in COVID-19 cases in Texas came from the early adoption of SIPOs by urbanized counties, suggesting that the later statewide shelter-in-place mandate yielded relatively few health benefits.

Predictors Associated with COVID-19 Deaths in Ethiopia.

Coronavirus disease is an infectious disease that causes a newly discovered coronavirus. Ethiopia is one of the most impacted countries relative to the proportion of COVID-19 case growth and infection. The purpose of this investigation was to identify the determinants of COVID-19 deaths in Ethiopia. The source of the data for this study was the 2020 Ethiopian Ministry of Health from March up to July using multiple linear regression models. Among 468,814 total tests, 9,027 were recovered, 20,900 were confirmed cases, and 365 were deaths from coronavirus diseases in Ethiopia. Critical cases (β= 0.570, p= 0.006) and average temperature (β= −35.061, p= 0.003) variables were statistically significant. Critical or serious cases significantly and positively affected the deaths from this pandemic disease, while the average temperature significantly and negatively affected the deaths from COVID-19 disease in Ethiopia.

Urban Flight Seeded the COVID-19 Pandemic Across the United States

We document large-scale urban flight in the United States in the wake of the COVID-19 pandemic. Populations that flee are disproportionately younger, whiter, and wealthier. Regions that saw migrant influx experience greater subsequent COVID-19 case growth, suggesting that urban flight was a vector of disease spread. Urban residents fled to socially connected areas, consistent with the notion that individuals were sheltering with friends and family or in second homes. The association of migration and subsequent case growth persists when instrumenting for migration with social networks, pointing to a causal association.

Predictors Associated with COVID-19 deaths in Ethiopia

Background: Coronavirus disease is an infectious disease that causes a newly discovered coronavirus. Ethiopia is one of the most impacted countries relative to the proportion of COVID-19 case growth and infection. The purpose of this investigation was to identify the determinants of COVID-19 disease in Ethiopia. Subjects & Methods: The source of the data for this study was the 2020 Ethiopia health ministry from March up to July using multiple linear regression models. Results: Among 468,814 total tests, 9,027 recovered, 20,900 confirmed cases, and 365 deaths from Coronavirus diseases in Ethiopia. Critical cases (β= 0.570, p= 0.006) and average temperature (β= -35.061, p= 0.003) variables were statistically significant. Conclusion: Critical or serious cases significantly and positively affect the deaths of this pandemic disease, while average temperature significantly and negatively affects the deaths of COVID-19 diseases in Ethiopia.

Correlations between COVID-19 Case Growth and Mental Health-Related Internet Search: An Unexpected Finding

The article's abstract is not available.

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.

Exploring the growth of COVID-19 cases using exponential modelling across 42 countries and predicting signs of early containment using machine learning.

The coronavirus disease 2019 (COVID‐19) pandemic spread by the single‐stranded RNA severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) belongs to the seventh generation of the coronavirus family. Following an unusual replication mechanism, its extreme ease of transmissivity has put many countries under lockdown. With the uncertainty of developing a cure/vaccine for the infection in the near future, the onus currently lies on healthcare infrastructure, policies, government activities, and behaviour of the people to contain the virus. This research uses exponential growth modelling studies to understand the spreading patterns of SARS‐CoV‐2 and identifies countries that showed early signs of containment until March 26, 2020. Predictive supervised machine learning models are built using infrastructure, environment, policies, and infection‐related independent variables to predict early containment. COVID‐19 infection data across 42 countries are used. Logistic regression results show a positive significant relationship between healthcare infrastructure and lockdown policies, and signs of early containment. Machine learning models based on logistic regression, decision tree, random forest, and support vector machines are developed and show accuracies between 76.2% and 92.9% to predict early signs of infection containment. Other policies and the decisions taken by countries to contain the infection are also discussed.

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

A stay-at-home social distancing mandate is a key nonpharmacological measure to reduce the transmission rate of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), but a high rate of adherence is needed. To examine the association between the rate of human mobility changes and the rate of confirmed cases of SARS-CoV-2 infection. This cross-sectional study used daily travel distance and home dwell time derived from millions of anonymous mobile phone location data from March 11 to April 10, 2020, provided by the Descartes Labs and SafeGraph to quantify the degree to which social distancing mandates were followed in the 50 US states and District of Columbia and the association of mobility changes with rates of coronavirus disease 2019 (COVID-19) cases. State-level stay-at-home orders during the COVID-19 pandemic. The main outcome was the association of state-specific rates of COVID-19 confirmed cases with the change rates of median travel distance and median home dwell time of anonymous mobile phone users. The increase rates are measured by the exponent in curve fitting of the COVID-19 cumulative confirmed cases, while the mobility change (increase or decrease) rates were measured by the slope coefficient in curve fitting of median travel distance and median home dwell time for each state. Data from more than 45 million anonymous mobile phone devices were analyzed. The correlation between the COVID-19 increase rate and travel distance decrease rate was -0.586 (95% CI, -0.742 to -0.370) and the correlation between COVID-19 increase rate and home dwell time increase rate was 0.526 (95% CI, 0.293 to 0.700). Increases in state-specific doubling time of total cases ranged from 1.0 to 6.9 days (median [interquartile range], 2.7 [2.3-3.3] days) before stay-at-home orders were enacted to 3.7 to 30.3 days (median [interquartile range], 6.0 [4.8-7.1] days) after stay-at-home social distancing orders were put in place, consistent with pandemic modeling results. These findings suggest that stay-at-home social distancing mandates, when they were followed by measurable mobility changes, were associated with reduction in COVID-19 spread. These results come at a particularly critical period when US states are beginning to relax social distancing policies and reopen their economies. These findings support the efficacy of social distancing and could help inform future implementation of social distancing policies should they need to be reinstated during later periods of COVID-19 reemergence.

Modeling and Forecasting of COVID-19 Growth Curve in India

In this article, we analyze the growth pattern of COVID-19 pandemic in India from March 4 to July 11 using regression analysis (exponential and polynomial), auto-regressive integrated moving averages (ARIMA) model as well as exponential smoothing and Holt–Winters models. We found that the growth of COVID-19 cases follows a power regime of ({t}^{2}, t,...) after the exponential growth. We found the optimal change points from where the COVID-19 cases shifted their course of growth from exponential to quadratic and then from quadratic to linear. After that, we saw a sudden spike in the course of the spread of COVID-19 and the growth moved from linear to quadratic and then to quartic, which is alarming. We have also found the best fitted regression models using the various criteria, such as significant p-values, coefficients of determination and ANOVA, etc. Further, we search the best-fitting ARIMA model for the data using the AIC (Akaike Information Criterion) and provide the forecast of COVID-19 cases for future days. We also use usual exponential smoothing and Holt–Winters models for forecasting purpose. We further found that the ARIMA (5, 2, 5) model is the best-fitting model for COVID-19 cases in India.

Patterns of the SARS-CoV-2 epidemic spread in a megacity

The comprehensive study incorporating epidemiological, molecular genetic and statistical research methods was conducted to analyze the spread of SARS-CoV-2 in Moscow during the COVID- 19pandemic. It was found that the exponential growth in COVID-19 cases was prevented due to themost stringent control and restrictive measures deployed in Moscow to break the chains of SARS-CoV-2 transmission and due to people who were very disciplined in complying with the self-isolation rules. The analysis ofthe dynamics in detection of new COVID-19 cases showed that in a megacity, the impact of social distancing andself-isolation would become apparent only after 3.5 incubation periods, where the maximum length of the periodis 14 days. It was discovered that the detection frequency of SARS-CoV-2 RNA in relatively healthy populationand its dynamics are important monitoring parameters, especially during the increase and stabilization in theCOVID-19 incidence, and are instrumental in predicting the development of the epidemic situation within a rangeof 1-2 incubation periods (14-28 days). In Moscow, the case fatality rate was 1.73% over the observation period(6/3/2020-23/6/2020). The epidemiological analysis of the COVID-19 situation in Moscow showed certain patterns of theSARS-CoV-2 spread and helped evaluate the effectiveness of the epidemic prevention measures aimed at breaking the routes of transmission of the pathogen.

Fangcang shelter hospitals are a One Health approach for responding to the COVID-19 outbreak in Wuhan, China

In February 2020, the exponential growth of COVID-19 cases in Wuhan city posed a huge economic burden to local medical systems. Consequently, Wuhan established Fangcang Shelter hospitals as a One Health approach for responding to and containing the COVID-19 outbreak by isolating and caring for mild-to-moderate cases. However, it is unclear to what degree the hospitals contained COVID-19. This study performed an interrupted time series analysis to compare the number of new confirmed cases of COVID-19 before and after the operation of Fangcang Shelter hospitals. The initial number of confirmed cases in Wuhan increased significantly by 68.54 cases per day prior to February 4, 2020. Compared with the number of cases noted 20 days before the use of Fangcang Shelter hospitals, a sustained reduction in the number of confirmed cases (trend change, −125.57; P < 0.0001) was noted 41 days after the use of the hospitals. Immediate-level changes were observed for confirmed cases (level change, 725.97; P = 0.025). These changes led to an estimated 5148 fewer confirmed cases (P < 0.0001). According to the mean confirmed cases of 395.71 per day before the intervention, we estimated that Wuhan had advanced the terminal phase of COVID-19 by 13 days. Furthermore, immediately after introduction of Fangcang Shelter Hospitals on February 5, the reproduction number dropped rapidly, from a pre-introduction rate of 4.0 to 2.0. The Fangcang Shelter hospitals most likely to reversed the epidemic trend of COVID-19 while a containment strategy was implemented in Wuhan. In a One Health perspective, Fangcang Shelter hospitals, with their functions of isolation and treatment of confirmed COVID-19 patients, engaging professionals from many disciplines, such as medicine, engineering, architecture, psychology, environmental health, and social sciences. The results of this study provide a valuable reference for health policy makers in other countries.

Early Impact of India's Nationwide Lockdown on Aggregate Population Mobility and COVID-19 Cases

Background: In March 2020, India imposed a strict nationwide lockdown, making social distancing the primary nonpharmacologic mitigation strategy for the COVID-19 pandemic. To date, there has not been a comprehensive evaluation of the policy’s impact on mobility changes and state-level trajectory of COVID case growth. Understanding the effectiveness of the initial lockdown will inform policy efforts, given rising case counts in India following relaxation of the lockdown. Methods: Using a dataset on aggregated mobility changes in India, we conducted a retrospective interrupted time series analysis between February 15th and April 26th, 2020 to assess the combined impact of India’s nationwide curfew and lockdown on aggregate community mobility. Our primary outcomes were change in mobility to retail and recreation sites and change in state-level COVID-19 case growth rate. Secondary outcomes included mobility changes to other sites and time spent at home. We next assessed state-level variation. Finally, we examined the relationship between changes in aggregate mobility and the case growth rate of COVID-19. Findings: We observed an immediate and pronounced decrease in mobility following the policy’s implementation for all location categories examined. For our primary outcome, the national lockdown was associated with an 86% decrease to retail and recreational sites. However, the effects were not homogeneously sustained in the post-policy period. Visits to grocery stores and pharmacies (slope = 0.62% increase in visits per day) and transit stations (slope = 0.24%) began to recover, whereas visits to retail and recreation (slope = -0.41%), and parks (slope -0.47%) continued to decline during the post period. Significant state-level variation in mobility response was observed (71% to 95% decrease in visits to retail and recreation sites). States with the largest decrease in visits to retail and recreational sites were associated with a 7.2% rate of increase of COVID-19 in contrast to those states with the lowest decrease in mobility, which exhibited a 53% rate increase in COVID-19 cases (p=0.03). Interpretation: Our findings provide early evidence that India’s nationwide policy has been effective in both achieving its initial goal of decreased mobility and slowing the spread of COVID-19 cases. As the policy was nationwide, further research is needed to understand the drivers of state-level variation in mobility and COVID-19 case growth. Funding: This study was not funded. Declaration of Interests: GAW is a paid visiting scientist at Google Research. Other authors declare no conflict of interest.

SARS-CoV-2 Detection for Diagnosis Purposes in the Setting of a Molecular Biology Research Lab.

The emergence of the SARS-CoV-2 virus and the exponential growth of COVID-19 cases have created a major crisis for public health systems. The critical identification of contagious asymptomatic carriers requires the isolation of viral nucleic acids, reverse transcription, and amplification by PCR. However, the shortage of specific proprietary reagents or the lack of automated platforms have seriously hampered diagnostic throughput in many countries. Here, we provide a procedure for SARS-CoV-2 detection for diagnostic purposes from clinical samples in the setting of a basic research molecular biology lab. The procedure details the necessary steps for daily analysis of up to 500 clinical samples with a team composed of 12 experienced researchers. The protocol has been designed to rely on widely available reagents and devices, to cope with heterogeneous clinical specimens, to guarantee nucleic acid extraction from very scarce biological material, and to minimize the rate of false-negative results.

Os primeiros 80 dias da pandemia da COVID-19 em Belo Horizonte: da contenção à flexibilização

Resumo Este artigo examina o contexto e as implicações da pandemia por Covid-19 na cidade de Belo Horizonte (BH) nos primeiros 80 dias da doença. Utilizamos um recorte analítico descritivo para mensurar a evolução dos casos, o excesso de óbitos, a taxa de transmissibilidade do vírus e a pressão da doença sobre o sistema de saúde de BH e região, através da taxa de ocupação hospitalar nos leitos públicos. Além disso, identificamos as principais políticas de contenção adotadas pelas autoridades locais, bem como as implicações da redução do distanciamento social. Nossos resultados demonstram que o Sistema Único de Saúde (SUS), bem gerido, é fundamental para o enfrentamento da pandemia e a mitigação de suas consequências para a população. O processo de flexibilização que se inicia tem imposto novos desafios que requererão monitoramento atento das autoridades e da sociedade.

Mandated Bacillus Calmette-Guérin (BCG) vaccination predicts flattened curves for the spread of COVID-19.

Bacillus Calmette-Guérin (BCG) vaccination may reduce the risk of a range of infectious diseases, and if so, it could protect against coronavirus disease 2019 (COVID-19). Here, we compared countries that mandated BCG vaccination until at least 2000 with countries that did not. To minimize any systematic effects of reporting biases, we analyzed the rate of the day-by-day increase in both confirmed cases (134 countries) and deaths (135 countries) in the first 30-day period of country-wise outbreaks. The 30-day window was adjusted to begin at the country-wise onset of the pandemic. Linear mixed models revealed a significant effect of mandated BCG policies on the growth rate of both cases and deaths after controlling for median age, gross domestic product per capita, population density, population size, net migration rate, and various cultural dimensions (e.g., individualism). Our analysis suggests that mandated BCG vaccination can be effective in the fight against COVID-19.

Open Science and the Role of Cardiology Journals in the COVID-19 Pandemic

In July 2020, Brazil has the world’s second highest Covid-19 death toll. The COVID-19 pandemic is spreading fast in America. Since the first case of COVID-19 was confirmed, it took 114 days in Brazil (February 26-June 19) and 98 days in the USA (January 21-April 29) for the number of cases to reach more than 1,000,000. Parallel to the rapid growth of COVID-19 cases, there has been a progression in the number of scientific publications. Until June 2020, more than [...]

Four health science librarians’ experiences: How they responded to the COVID-19 pandemic crisis

The COVID-19 pandemic has an unprecedented impact on the entire country. With exponential growth of COVID-19 cases in many areas, university administration and faculty faced mounting challenges on all fronts in meeting students’ needs in transition to remote learning environments. In the face of indefinite closure of libraries and university campuses, how could academic libraries respond to emerging needs in response to the rapidly evolving situation from one day to the next?

Real-time Forecasting of the COVID-19 Epidemic using the Richards Model in South Sulawesi, Indonesia

This paper discussed Real-time Forecasting of the COVID-19 Epidemic using daily cumulative cases of COVID-19 in South Sulawesi. Our aim is to make model for the growth of COVID-19 cases in South Sulawesi in the top 5 provinces with the largest COVID-19 cases in Indonesia and predict when this pandemic reaches the peak of spread and when it ends. This paper used the Richards model, which is an extension of a simple logistic growth model with additional scaling parameters. Data used in the paper as of June 24, 2020 were taken from the official website of the Indonesian government. Our results are that the maximum cumulative number of COVID-19 cases has reached 10,000 to 12,000 cases. The peak of the pandemic is estimated to occur from June to July 2020 while continuing to impose social restrictions. The condition in South Sulawesi shows a sloping curve around October 2020, which means that there are still additional cases but not significant. When entering November, the curve starts to flat which indicates the addition of very small cases until the pandemic ends. The results of the pandemic peak prediction are the same as the Indonesian data; what is different is the prediction of when the pandemic will end. In the best-case scenario, the current data will tend to slow down, with the COVID-19 pandemic in South Sulawesi expected to end in November 2020. Our modeling procedure can provide information about the ongoing COVID-19 pandemic in South Sulawesi that may facilitate real-time public health responses about future disease outbreaks.

Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study

BackgroundWithin 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. During this period, the US COVID-19 response was highly decentralised, with stay-at-home directives issued by state and local officials, subject to varying levels of enforcement. The absence of a centralised policy and timeline combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the USA a challenge.MethodsWe used daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zürich, Switzerland) from Jan 1 to April 20, 2020, to capture real-time trends in movement patterns for each US county, and used these data to generate a social distancing metric. We used epidemiological data to compute the COVID-19 growth rate ratio for a given county on a given day. Using these metrics, we evaluated how social distancing, measured by the relative change in mobility, affected the rate of new infections in the 25 counties in the USA with the highest number of confirmed cases on April 16, 2020, by fitting a statistical model for each county.FindingsOur analysis revealed that mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA, with Pearson correlation coefficients above 0·7 for 20 of the 25 counties evaluated. Additionally, the effect of changes in mobility patterns, which dropped by 35–63% relative to the normal conditions, on COVID-19 transmission are not likely to be perceptible for 9–12 days, and potentially up to 3 weeks, which is consistent with the incubation time of severe acute respiratory syndrome coronavirus 2 plus additional time for reporting. We also show evidence that behavioural changes were already underway in many US counties days to weeks before state-level or local-level stay-at-home policies were implemented, implying that individuals anticipated public health directives where social distancing was adopted, despite a mixed political message.InterpretationThis study strongly supports a role of social distancing as an effective way to mitigate COVID-19 transmission in the USA. Until a COVID-19 vaccine is widely available, social distancing will remain one of the primary measures to combat disease spread, and these findings should serve to support more timely policy making around social distancing in the USA in the future.FundingNone.

Statewide Stay-at-Home Directives on the Spread of COVID-19 in Metropolitan and Nonmetropolitan Counties in the United States.

Facing a dramatic spike of COVID-19 cases in the United States, President Trump issued the 15 Days to Slow the Spread guidelines on March 16 that included a nationwide “stay-at-home” advisory. By March 23, 9 states (CA, CT, IL, LA, NJ, NY, OH, OR, WA) issued stricter statewide shelter-in-place directives, and all 50 states and Washington, DC closed public schools and set restrictions on bars, restaurants, and public gatherings.2 By April 7, only 8 States (AR, IA, ND, NE, OK, SD, UT, WY) had not issued the statewide stay-at-home directives.1 Federal guidelines, state directives, and media coverage have increased the awareness of COVID-19 and aligned the public toward compliance.2 However, the implementations of the control measures may not be consistent across states,3 which provides a natural experiment that may help us understand their effects on COVID-19 progression. We report our initial assessment of the magnitude and timing of slowing spread in US counties by 3 types of state directives. We used county-level cumulative case count data from The New York Times.4 The dataset has state and county names, 5-digit county FIPS codes, and cumulative COVID-19 cases and deaths by date starting from January 21, 2020. We grouped the counties according to their state directive type (early, late, and none). Due to differences in population densities, commuting patterns, and infectious momentums,5 we also subdivided each directive group by metropolitan status (metro and nonmetro) based on the 2013 Rural-Urban Continuum Codes (RUCC).6 As communities were exposed to the novel coronavirus over time, we expected residents in the early directive states would be more likely to stay home, thus having less exposure than those in the no-directive states. We used March 16 as the start date to set the context for the federal advisory, and April 10 as the end date to have at least a 2-week window to evaluate the effect of early state directives. We model cumulative COVID-19 case counts in Poisson regression to calculate 3 measures for each group: the mean COVID-19 growth rate, when the growth rate changed, and how much it changed since March 23. The model has 2 explanatory variables: case report date and a binary indicator for before (0) and after (1) the estimated date of change. The interaction of the 2 captures the timing and magnitude of change in the growth rate. The estimated date is derived by searching the best model that has least Bayesian information criterion from 1 day after March 23 to 15 day after. For instance, if the estimated change date is March 30, then the parameter estimate for the interaction would indicate change in spread rate or growth rate from March 30 to April 10. The results for the early directive states are encouraging (Table 1). Although the mean daily increase in logarithmic cases is high (21.48%) in metropolitan counties, the growth has slowed down since March 29 with an average reduction of 12.8%, suggesting a flattened curve. In nonmetropolitan counties, both the pre-change growth rate (12.56%) and postchange reduction (5.79%) are relatively moderate. In addition, the estimated date of change is 6 days later than their metropolitan counterparts. In metropolitan counties, the results for late-directive states and no-directive states both indicate declined growth rates, with the late-directive and no-directive groups being 7.89% and 4.32%, respectively, since April 2nd. The results for their nonmetropolitan counterparts were significant. In this commentary, we assessed COVID-19 case growth rates, their changes, and timing. First, we found that counties in the 9 early directive states have flattened their curves regardless of metropolitan status; conversely, nonmetropolitan counties started to decline 6 days behind, suggesting a spillover effect from urban cores to rural areas within these states. For nonmetropolitan counties in late- or no-directive states, their slow growth rates were not flattening, but they did not accelerate either. We found that reductions in the growth rate in metropolitan counties were almost linearly decreasing from the early directive group to the no-directive group (early directive 12.8%, late-directive 7.89%, and no-directive 4.32%). Metropolitan counties without stay-at-home directives may benefit from the national advisory and effects from adjacent states. We conclude that timely issued statewide stay-at-home directives were effective in both metropolitan and nonmetropolitan counties. The effects could have spilled over to metropolitan counties without the directives.