COVID-19: Ontario government looking to extend provincewide stay-at-home order into June The Ontario government is looking to extend the. Licensed child care centres and licensed home-based child care services are open with Corporations that are not following the orders can be fined up to. Answer: The order specifically allows “auto-supply, auto-repair, and related facilities.” Automobile sales should be allowed but must be done in.

### : Covid 19 stay at home order

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By late-February, it became increasingly clear that sustained community transmission of coronavirus had taken hold in parts of the United States, particularly on the West Coast and, soon after, the New York City region. With little testing available and no significant federal response beyond instituting international travel restrictions at the time (the President downplayed the threat of COVID-19 well into March), some jurisdictions took matters into their own hands and began implementing social distancing measures.

On March 4, King County officials in Washington State first recommended that certain vulnerable groups (including people over 60 years old and those with underlying health conditions) stay home, businesses allow more telecommuting options, and the cancellation or postponement of large public events; school closures began in parts of the state within a couple of days. On March 6, San Francisco similarly warned vulnerable residents to avoid outings and larger groups, businesses to suspend non-essential travel and consider telecommuting, and cancelation of all non-essential large events, and the city began restricting event size a few days later. By March 16, six Bay Area counties became the first in the nation to announce shelter-in-place orders and on March 19, the State of California became the first to mandate a state-wide order.

Since then, additional states and communities across the country began implementing mandatory stay-at-home orders (and other social distancing measures, including school closures, many of which began state-wide on March 16, and closing non-essential businesses).  But, with conflicting messages from the federal government and a lack of clear guidelines, states have not been on the same page, and implementation has been scattershot at best. In some cases, contiguous states have had different policies (such as South Carolina, which has no mandate and North Carolina which does). Moreover, in some states (including those with rapidly rising caseloads), Governors resisted issuing state-wide orders, leaving decisions up to local jurisdictions, creating confusion and concern and prompting calls from local officials and public health experts for state-wide action.  This was the case, for example, in Florida, Georgia, and Texas until recently and remains so in a handful of other states.  It’s hard to see how a highly infectious virus is going to pay very close attention to different policies across states, let alone within them. Indeed, new data shows that there was more travel in counties without such orders.

There have been some exceptions to this. In recognition of regional commuter and other patterns, Washington DC, Virginia and Maryland have operated much more in lockstep (although there have been some differences). All three implemented similar voluntary state-at-home orders during the third week of March and a coordinated mandatory one announced on March 30. In the Mayor of DC’s voicemail message to residents, she specifically said, “pandemics don’t care about borders. That’s why we are all doing the same thing and we are telling everyone in the Capital Region – DC, Maryland, and Virginia – please stay home.”

Still, by March 25, when covid 19 stay at home order were more than 12,000 cases reported in the U.S., only 19 states had mandatory stay-at-home orders in effect. An additional 11 instituted orders taking effect by March 30. The recent announcement by the White House that federal social distancing guidelines would be extended through at least April 30, an acknowledgment that the outbreak is significantly worse than it had previously suggested, is likely to lead to more uniformity across states. Already, since the announcement, several additional states have announced their intention to implement stay-at-home orders. Many states have gone well beyond the White House guidance, which is a recommendation, not a requirement. At the time of this writing, 9 states had not yet issued state-wide orders.

It is still too early to know whether those states that implemented such measures earlier will see better outcomes (although data from both Seattle and San Francisco suggest that they are working) or whether differential implementation across the country and within states will affect the success of all communities, as the virus may continue to spread across geographic borders it doesn’t recognize. At the very least, this scattershot approach can result in ongoing transmission in one state or community, even as transmission is interrupted in a neighboring area. This could extend the period of spread for the U.S. overall and prolong the need for social distancing in much of the country. We are engaged in a “natural experiment” of differing approaches to the epidemic on a massive scale, and we are likely to see over the coming weeks what the consequences of that will be.

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State Mandated Stay-At-Home Orders by Date of Implementation

 Statewide Stay-at-Home Orders State Date Announced Effective Date Alabama April 3 April 4 Alaska March 27 March 28 Arizona March 30 March 31 Arkansas – – California March 19 March 19 Colorado March 26 March 26 Connecticut March 20 March 23 Delaware March 22 March 24 District of Columbia March 30 April 1 Florida April 1 April 3 Georgia April 2 April 3 Hawaii March 23 March 25 Idaho March 25 March 25 Illinois March 20 March 21 Indiana March 23 March 24 Iowa – – Kansas March 28 March 30 Kentucky March 22 March 26 Louisiana March 22 March 23 Maine March 31 April 2 Maryland March 30 March 30 Massachusetts March 23 March 24 Michigan March 23 March 24 Minnesota March 25 March 27 Mississippi March 31 April 3 Missouri April 3 April 6 Montana March 26 March 28 Nebraska – – Nevada April 1 April 1 New Hampshire March 26 March 27 New Jersey March 20 March 21 New Mexico March 23 March 24 New York March 20 March 22 North Carolina March 27 March 30 North Dakota – – Ohio March 22 March 23 Oklahoma – – Oregon March 23 March 23 Pennsylvania March 23 April 1 Rhode Island March 28 March 28 South Carolina – – South Dakota – – Tennessee March 30 March 31 Texas March 31 April 2 Utah – – Vermont March 24 March 24 Virginia March 30 March 30 Washington March 23 March 23 West Virginia March 23 March 24 Wisconsin March 24 March 25 Wyoming – – NOTE: The Governor of Pennsylvania began ordering stay-at-home orders for some counties on March 23, before implementing a state-wide order effective April 1.SOURCE: KFF analysis of state government websites.

Источник: https://www.kff.org/policy-watch/stay-at-home-orders-to-fight-covid19/

## Stay-at-home policy is a case of exception fallacy: an internet-based ecological study

### Abstract

A recent mathematical model has suggested that staying at home did not play a dominant role in reducing COVID-19 transmission. The second wave of cases in Europe, in regions that were considered as COVID-19 controlled, may raise some concerns. Our objective was to assess the association between staying at home (%) and the reduction/increase in the number of deaths due to COVID-19 in several regions in the world. In this ecological study, data from www.google.com/covid19/mobility/, ourworldindata.org and covid.saude.gov.br were combined. Countries with > 100 deaths and with a Healthcare Access and Quality Index of ≥ 67 were included. Data were preprocessed and analyzed using the difference between number of deaths/million between 2 regions and the covid 19 stay at home order between the percentage of staying at home. The analysis was performed using linear regression with special attention to residual analysis. After preprocessing the data, 87 regions around the world were included, yielding 3741 pairwise comparisons for linear regression analysis. Only 63 (1.6%) comparisons were significant. With our results, we were not able to explain if COVID-19 mortality is reduced by staying at home in ~ 98% of the comparisons after epidemiological weeks 9 to 34.

### Introduction

By late January, 2021, approximately 2.1 million people worldwide had died from the new coronavirus (COVID-19)1. Wearing masks, taking personal precautions, testing for COVID-19 and social distancing have been advocated for controlling the pandemic2,3,4. To achieve source control and stop transmission, social distancing has been interpreted by many as staying at home. Such policies across multiple jurisdictions were suggested by some experts5. These measures were supported by the World Health Organization6,7, local authorities8,9,10, and encouraged on social media platforms11,12,13.

Some mathematical models and meta-analyses have shown a marked reduction in COVID-19 cases14,15,16,17,18,19 and deaths20,21 associated with lockdown policies. Brazilian researchers have published mathematical models of spreading patterns22 and suggested implementing social distancing measures and protection policies to control virus transmission23. By May 5th, 2020, an early report, using the number of curfew days in 49 countries, found evidence that lockdown could be used to suppress the spread of COVID-1924. Measures to address the COVID-19 pandemic with Average american savings 2020 Interventions (NPIs) were adopted after Brazil enacted Law No. 1397925, and this was followed by many states such as Rio de Janeiro26, the Federal District of Brasília (Decree No. 40520, dated March 14th, 2020)27, the city of São Paulo (Decree No. 59.283, dated March 16th, 2020)28, and the State of Rio Grande do Sul (Decree No. 55240/2020, dated May 10th, 2020)29. It was expected that, with these actions, the number of deaths by COVID-19 would be reduced. Of note, the country’s most populous state, São Paulo, adopted rigorous quarantine measures and put them into effect on March 24th, 202028. Internationally, Peru adopted the world's strictest lockdown30.

Recently, Google LLC published datasets indicating changes in mobility (compared to an average baseline before the COVID-19 pandemic). These reports were created with aggregated, anonymized sets of daily and dynamic data at country and sub-regional levels drawn from users who had enabled the Location History setting on their cell phones. These data reflect real-world changes in social behavior and provide information on mobility trends for places like grocery stores, pharmacies, parks, public transit stations, retail and recreation locations, residences, and workplaces, when compared to the baseline period prior to the pandemic31. Mobility in places of residence provides information about the “time spent in residences”, which we will hereafter call “staying at home” and use as a surrogate for measuring adherence to stay-at-home policies.

Studies using Google COVID-19 Community Mobility Reports and the daily number of new COVID-19 cases have shown that over 7 weeks a covid 19 stay at home order correlation between staying at home and the reduction of COVID-19 cases in 20 counties in the United States32; COVID-19 cases decreased by 49% after 2 weeks of staying at home33; the incidence of new cases/100,000 people was also reduced34; social distancing policies were associated with reduction in COVID-19 spread in the US35; as well as in 49 countries around the world24. A recent report using Brazilian and European data has shown a correlation between NPI stringency and the spread of COVID-1936,37; these analyses are debatable, however, due to their short time span and the type of time series behavior38, or for their use of Pearson’s correlation in the context of non-stationary time series35. The same statistical tools cannot be applied to stationary and non-stationary time series alike39, and the latter is the case with this COVID-19 data. A 2020 Cochrane systematic review of this topic reported that they were not completely certain about this evidence for several reasons. The COVID-19 studies based their models on limited data and made different assumptions about the virus17; the stay-at-home variable was analyzed as a binary indicator40; and the number of new cases could have been substantially undocumented41; all which may have biased the results. A sophisticated mathematical model based on a high-dimensional system of partial differential equations to represent disease spread has been proposed42. According to this model, staying at home did not play a dominant role in disease transmission, but the combination of these, together with the use of face masks, hand washing, early-case detection (PCR test), and the use of hand sanitizers for at least 50 days could have reduced the number of new cases. Finally, after 2 months, the simulations that drove the world to lockdown have been questioned43. These studies applied relatively complex epidemiological models with unrealistic assumptions or parameters that were either user-chosen or not deemed to work properly. Furthermore, the effects in the death rates were directly inferred from the aftermath of a given intervention without a control group. Finally, the temporal delay between the introduction of a certain intervention and the actual measurable variation in death rates was not properly taken into account44,45.

The rationale we are looking for is the association between two variables: deaths/million and the percentage of people who remained in their residences. Comparison, however, is difficult due to the non-stationary nature of the data. To overcome these problems, we proposed a novel approach to assess the association between staying at home values and the reduction/increase in the number of deaths due to COVID-19 in several regions around the world. If the variation in the difference between the number of deaths/million in two countries, say A and B, and the variation in the difference of the staying at home values between A and B present similar patterns, this is due to an association between the two variables. In contrast, if these patterns are very different, this is evidence that staying at home values and the number of deaths/million are not related (unless, of course, other unaccounted for factors are at play). In view of this, the proposed approach avoids altogether the problems enumerated above, allowing a new approach to the problem.

After more than 25 epidemiological weeks of this pandemic, verifying if staying at home had an impact on mortality rates is of particular interest. A PUBMED search with the terms “COVID-19” AND (Mobility) (search made on September 8th, 2020) yielded 246 articles; of these, 35 were relevant to mobility measures and COVID-19, but none compared mobility reduction to mortality rates.

### Results

A flowchart of the data manipulation is depicted in Fig. 1. Briefly, Google COVID-19 Community Mobility Report data between February 16th and August 21st, 2020, yielded 138 separate countries and their regions. The website Our World in Data provided data on 212 countries (between December 31st, 2019, and August 26th, 2020), and the Brazilian Health Ministry website provided data on all states (n = 27) and cities (n = 5,570) in Brazil (February 25th to August 26th, 2020).

After data compilation, a total of 87 regions and countries were selected: 51 countries, 27 States in Brazil, six major Brazilian State capitals [Manaus, Amazonas (AM), Fortaleza, Ceará (CE), Belo Horizonte, Minas Gerais (MG), Rio de Janeiro, Rio de Janeiro (RJ), São Paulo, São Paulo (SP) and Porto Alegre, Rio Grande do Sul (RS)], and three major cities throughout the world (Tokyo, Berlin and New York) (Fig. 1).

Characteristics of these 87 regions are presented in Table 1 (further details are in Supplemental Material—Characteristics of Regions).

### Comparisons

The restrictive analysis between controlled and not controlled areas yielded 33 appropriate comparisons, as shown in Table 2. Only one comparison out of 33 (3%)—state of Roraima (Brazil) versus state of Rondonia (Brazil)—was significant (p-value = 0.04). After correction for residual analysis, it did not pass the autocorrelation test (Lagrange Multiplier test = 0.04). (Further details are in Supplemental Material—Restrictive Analysis).

The global comparison yielded 3,741 combinations; from these, 184 (4.9%) had a p-value < 0.05, after correcting for False Discovery Rate (Table S1). After performing the residual analysis, by testing for cointegration between response and covariate, normality of the residuals, presence of residual autocorrelation, homoscedasticity, and functional specification, only 63 (1.6%) of models passed all tests (Table S2). Closer inspection of several cases where the model did not pass all the tests revealed a common factor: the presence of outliers, mostly due to differences in the epidemiological week in which deaths started to be reported. A heat map showing the comparison between the 87 regions is presented in Fig. 2.

Characteristics of these 87 regions are presented in Table 1 (further details are in Auxiliary Supplementary Material—Characteristics of Regions) .

#### Comparisons

The restrictive analysis between controlled and not controlled areas yielded 33 appropriate comparisons, as shown in Table 2. Only one comparison out of 33 (3%)—State of Roraima (Brazil) versus State of Rondonia (Brazil)—was significant (p-value = 0.04). After correction for residual analysis, it did not pass the autocorrelation test (p-value of the Lagrange Multiplier test = 0.04). (Further details are in Auxiliary Supplementary Material—Restrictive Analysis).

The global comparison yielded 3,741 combinations; from these, 184 (4.9%) had a p-value < 0.05, after correcting for False Discovery Rate (Table S1 suppl). After performing the residual analysis, by testing for cointegration between response and covariate, normality of the residuals, presence of residual autocorrelation, homoscedasticity, and functional specification, only 63 (1.6%) of models passed all tests (Table S2—suppl). Closer inspection of several cases where the model did not pass all the tests revealed a common factor: the presence of outliers, mostly due to differences in the epidemiological week in which deaths started to be reported. A heat map showing the comparison between the 87 regions is presented in Fig. 2.

### Discussion

We were not able to explain the variation of deaths/million in different regions in the world by social isolation, herein analyzed as differences in staying at home, compared to baseline. In the restrictive and global comparisons, only 3% and 1.6% of the comparisons were significantly different, respectively. These findings are in accordance with those found by Klein et al.46 These authors explain why lockdown was the least probable cause for Sweden's high death rate from COVID-1946. Likewise, Chaudry et al. made a country-level exploratory analysis, using a variety of socioeconomic and health-related characteristics, similar to what we have done here, and reported that full lockdowns and wide-spread testing were not associated with COVID-19 mortality per million people47. Different from Chaudry et al., in our dataset, after 25 epidemiological weeks, (counting from the 9th epidemiological week onwards in 2020) we included regions and countries with a "plateau" and a downslope phase in their epidemiological curves. Our findings are in accordance with the dataset of daily confirmed COVID-19 deaths/million in the UK. Pubs, restaurants, and barbershops were open in Ireland on June 29th and masks were not mandatory48; after more than 2 months, no spike was observed; indeed, death rates kept falling49. Peru has been considered to be the most strict lockdown country in the world30, nevertheless, by September 20th, it had the highest number of deaths/million50. Of note, differences were also observed between regions that were considered to be COVID-19 controlled, e.g., Sweden versus Macedonia. Possible explanations for these significant differences may be related to the magnitude of deaths in these countries. After October 2020, when our study was published in a preprint server for Health Sciences, new articles were published with similar results51,52,53,54.

Our results are different from those published by Flaxman et al. The authors applied a very complex calculation that NPIs would prevent 3.1 million deaths across 11 European countries44. The discrepant results can be explained by different approaches to the data. While Flaxman et al. assumed a constant reproduction number (Rt) to calculate the total number of deaths, which eventually did not occur, we calculated the difference between the actual number of deaths between 2 countries/regions. The projections published by Flaxman et al.44 have been disputed by other authors. Kuhbandner and Homburg described the circular logic that this study involved. Flaxman et al. estimated the Rt from daily deaths associated with SARS-CoV-2 using an a priori restriction that Rt may only change on those dates when interventions become effective. However, in the case of a finite population, the effective reproduction number falls automatically and necessarily over time since the number of infections would otherwise diverge55. A recent preprint report from Chin et al.56 explored the two models proposed by the Imperial College44 by expanding the scope to 14 European countries from the 11 countries studied in the original paper. They added a third model that considered banning public events as the only covariate. The authors concluded that the claimed benefits of lockdown appear grossly exaggerated since inferences drawn from effects of NPIs are non-robust and highly sensitive to model specification56.

The same explanation for the discrepancy can be applied to other publications where mathematical models were created to predict outcomes14,15,16,17,18. Most of these studies dealt with COVID-19 cases 33,34 and not observed deaths. Despite its limitations, reported deaths are likely to be more reliable than new case data. Further explanations for different results in the literature, besides methodological aspects, could be justified by the complexity of the virus dynamic, by its interaction with the environment, or they may be related to a seasonal pattern that was, by coincidence, established at the same time when infection rates started to decrease due to seasonal dynamics57. It is unwise to try to explain a complex and multifactorial condition, with the inherent constant changes, using a single variable. An initial approach would employ a linear regression to verify the influence of one factor covid 19 stay at home order an outcome. Herein we were not able to identify this association. Our study was not designed to explain why the stay-at-home measures do not contain the spread of the virus SARS-CoV-2. However, possible explanations that need further analysis may involve genetic factors58, the increment of viral load, and transmission in households and in close quarters where ventilation is reduced.

This study has a few limitations. Different from the established paradigm of randomized clinical trial, this is an ecological study. An ecological study observes findings at the population level and generates hypotheses59. Population-level studies play an essential part in defining the most important public health problems to be tackled59, which is the case here. Another limitation was the use of Google Community Mobility Reports as a surrogate marker for staying at home. This may underestimate the real value: for instance, if a user´s cell phone is switched off while at home, the observation will be absent from the database. Furthermore, the sample does not represent 100% of the population. This tool, nevertheless, has been used by other authors to demonstrate the efficacy in reducing the number of new cases after NPI60,61. Using different methodologies for measuring mobility may introduce bias and would prevent comparisons between different countries. The number of deaths may be another issue. Death figures may be underestimated, however, reported deaths may be more relevant than new case data. The arbitrary criteria used for including countries and regions, the restrictive comparisons, and our definition of an area as COVID-19 controlled are open for criticism. Nonetheless, these arbitrary criteria were created a priori to the selection of the countries. With these criteria, we expected to obtain representative regions of the world, compare similar regions, and obtain accurate data. By using a HAQI of ≥ 67, we assumed that data from these countries would be accurate, reliable, and health conditions were generally good. Nevertheless, the global analysis of the regions ($$n = 3741$$ comparisons) overcame any issue of the restrictive comparison. Indeed, the global comparison confirmed the results found in the restrictive one; only 1.6% of the death rates could be explained by staying at home. Also, our effective sample size in all studies is only 25 epidemiological weeks, which is a very small sample size for a time series regression. The small sample size and the non-stationary nature of COVID-19 data are challenges for statistical models, but our analysis, with 25 epidemiological weeks, is relatively larger than previous publications which used only 7 weeks62. A short interval of observation between the introduction of an NPI and the observed effect on death rates yields no sound conclusion, and is a case where the follow-up period is not long enough to capture the outcome, as seen in previous publications44,45. The effects of small samples in this case are related to possible large type II errors and also affect the consistency of the ordinary least square estimates. Nevertheless, given the importance of social isolation promoted by world authorities63, we expected a higher incidence of significant comparisons, even though it could be an ecological fallacy. The low number of significant associations between regions for mortality rate and the percentage of staying at home may be a case of exception fallacy, which is a generalization of individual characteristics applied at the group-level characteristics64.

There are strengths to highlight. Inclusion criteria and the Healthcare Access and Quality Index were incorporated. We obtained representative regions throughout the world, including major cities from 4 different continents. Special attention was given to compiling and analyzing the dataset. We also devised a tailored approach to deal with challenges presented by the data. To our knowledge, our modeling approach is unique in pooling information from multiple countries all at once using up-to-date data. Some criteria, such as population density, percentage of urban population, HDI, and HAQI, were established to compare similar regions. Finally, we gave special attention to the residual analysis in the linear regression, an absolutely essential aspect of studies using small samples.

In conclusion, using this methodology and current data, in ~ 98% of the comparisons using 87 different regions of the world we found no evidence that the number of deaths/million is reduced by staying at home. Regional differences in treatment methods and the natural course of the virus may also be major factors in this pandemic, and further studies are necessary to better understand it.

### Rationale and approach for analyzing the time series data

The proposed approach was tailored to present a way to evaluate the influence of time spent at home and the number of deaths between two countries/regions while avoiding common problems of other models presented in the literature. We focused on detecting the variation of the differences between the number of deaths and how much people followed stay-at-home orders in two regions in each epidemiological week.

For instance, let us consider two similar regions we shall call ‘Stay In county’ and ‘Go Out county’. Both regions started with the same number of cases. After the first 1000 cases were recorded, White blood cell from cells at work In county declared that all people should stay at home, while Go Out county allowed people to circulate freely. After a few epidemiological weeks, we examine the data collected on the number of deaths in both counties and how much time people stayed at home by using geolocation software. If the difference between the number of deaths in Stay In county and Go Out county (variable A) is affected by the difference of the percentage of time people stayed at home in these two areas (variable B), then we can consider that the difference in the number of deaths by COVID-19 is influenced by the difference in the percentage of time people stayed at home. Both effects can be detected using linear regression and careful examination of the problem.

Time series on COVID-19 mortality (deaths/millions) display a non-stationary pattern. The daily data present a very distinct seasonal behavior on the weekends, with valleys on Saturdays and Sundays followed by peaks on Mondays (Figure S1). To account for seasonality, one may introduce dummy variables for Saturdays, Sundays, and Mondays, regress the number of deaths in these dummy variables, and then analyze the residuals. However, in most cases, the residuals are still non-stationary, and special treatment would be required in each case. Although this approach may be feasible for a few series, we are interested in analyzing hundreds of time series from different countries and regions. Hence, we need a more efficient way to deal with this amount of data. The covariates present another issue in regressing the daily time series of deaths/staying at home. The covariates are typically correlated with error terms due to public policies adopted by regions/countries. Mechanisms controlling social isolation are intrinsically related to the number of deaths/cases in each location. An increase in the death rate may cause more stringent policies to be adopted, which increases the percentage of people staying at home. This change causes an imbalance between the observed number of deaths and staying at home levels. In a regression model, this discrepancy is accounted for in the error term. Hence, the error term will change in accordance with staying at home levels.

Data aggregation by epidemiological week is a plausible alternative (Figure S2). In this way, artificial seasonality, imposed by work scheduled during weekends and the effect of governmental control over social interaction, in a regression framework, are mitigated. The drawback is that the sample size is significantly reduced from 187 days (Figure S1) to 26 epidemiological weeks (Figure S2).

Aggregation by epidemiological week, however, still yields non-stationary time series in most cases. To overcome this problem, we differentiated each time series. Recall that if $$Z_{t}$$denotes the number of deaths in the t-th epidemiological week, we define the first difference of $$Z_{t}$$ as

$${ }\Delta Z_{t} = Z_{t} - Z_{t - 1}$$

Intuitively, $$\Delta Z_{t}$$ denotes the variation of deaths between weeks $$t$$ and t-1, also known as the flux of deaths. The same is valid for the staying at home time series. This simple operation yielded, in most cases, stationary time series, verified with the so-called Phillips-Perron stationarity test65. In the few cases where the resulting time series did not reject the null hypothesis of non-stationarity (technically, the existence of a unitary root, in the time series characteristic polynomial), this was due to the presence of one or two outliers combined with the small sample size. These outliers were usually related to the very low incidence of COVID-19 deaths by the 9th epidemiological week when paired with countries with a significant number of deaths in that same week, thus resulting in an outlier which cannot be accounted for by linear regression.

To investigate pairwise behavior, we propose a method to assess the relationship between deaths and staying at home data between various countries and regions. For two countries/regions, say A and B, let $$Y_{t}^{A}$$ and $$Y_{t}^{B}$$denote the number of deaths per million at epidemiological week $$t$$ for country A and B respectively, while $$X_{t}^{A}$$ and $$X_{t}^{B}$$ denote the staying at home at epidemiological week $$t$$ for A and B, respectively. The idea is to regress the difference $$\Delta Y_{t}^{A} - \Delta Y_{t}^{B} = \Delta \left( {Y_{t}^{A} - Y_{t}^{B} } \right)$$ on $$\Delta X_{t}^{A} - \Delta X_{t}^{B} = \Delta \left( {X_{t}^{A} - X_{t}^{B} } \right)$$. Formally, we perform the regression

$$\Delta \left( {Y_{t}^{A} - Y_{t}^{B} } \right){ } = { }\beta_{0} + \beta_{1} \Delta \left( {X_{t}^{A} - X_{t}^{B} } \right) + \varepsilon_{t} ,$$

where $$\beta_{0}$$ and $$\beta_{1}$$ are unknown coefficients and $$\varepsilon_{t}$$ denotes an error term. Estimation of $$\beta_{0}$$ and $$\beta_{1}$$ is carried out through ordinary least squares. The interpretation of the model is important. We are regressing the difference in the variation of deaths between locations A and B into the difference in the variation of staying at home values between the same location.

If the number of deaths in locations A and B have a similar functional behavior over time, then $$Y_{t}^{A} - Y_{t}^{B}$$ tends to be near-constant, and $$\Delta \left( {Y_{t}^{A} - Y_{t}^{B} } \right)$$ tends to oscillate around zero. If the same applies to $$\Delta \left( {X_{t}^{A} - X_{t}^{B} } \right)$$, then we expect $$\beta_{1} \ne 0$$; consequently, we conclude that the behavior, between A and B, is similar and the number of deaths and the percentage of staying at home are associated in these regions. The other non-spurious situation implying $$\beta_{1} \ne 0$$ occurs when the variation in the number of deaths in locations A and B increases/decreases over time following a certain pattern, while the variation in the percentage of “staying at home” values also increases/decreases following the same pattern (apart from the direction). In this situation, we found different epidemiological patterns as in the variation in the number of deaths, and in the staying at home values, in locations A and B were on opposite trends. However, if these patterns were similar (proportional), this would be captured in the difference and, as a consequence, in the regression. This means that the different trends were near proportional and, hence, the variation in staying at home is associated with the variation in deaths.

In the section below “Definition of areas with and without controlled cases of COVID-19”, each country/region was classified into a binary class: either controlled or not controlled areas for COVID-19. The proposed method allows for insights regarding the association of the number of deaths and staying at home levels between countries/regions with similar/different degrees of COVID-19 control. Assumptions related to consistency, efficiency, and asymptotic normality of the ordinary least squares, in the context of time series regression, can be found in66. Since we are comparing many time series, to avoid any problem with spurious regression, we performed a cointegration test between the response and covariates. In this context, this is equivalent to testing the stationarity of $$\varepsilon_{t}$$, which was done by performing the Phillips-Perron test. Residual analysis is of utmost importance in linear regression, especially in the context of small samples. The steps and tests performed in the covid 19 stay at home order analysis are described in the statistical analysis section.

### Study design

This is an ecological study using data available on the Internet.

### Setting—data collection on mobility

Google COVID-19 Community Mobility Reports31 provided data on mobility from 138 countries67,68 and regions between February 15th and August 21st, 2020. Data regarding the average times spent at home was generated in comparison to the baseline. Baseline was considered to be the median value from between January 3rd and February 6th, 2020. Data obtained between February 15th and August 21th 2020 was divided into epidemiological weeks (epi-weeks) and the mean percentage of time spent staying at home per week was obtained.

### Data collection on mortality

Numbers of daily deaths from selected regions were obtained from open pnc bank near me open late on August 27st, 2020.

### Inclusion criteria for analysis

Only regions with mobility data and with more than 100 deaths, by August 26th, 2020, were included in this study. This criteria has been chosen since the majority of epidemiological studies start when 100 cases are reached69,70. For data quality, only countries with Healthcare Access and Quality Index (HAQI) of ≥ 6771 were included. The HAQI has been divided into 10 subgroups. The median class is 63.4–69.7. The average in this median class is 66.55 (rounding up to 67). By choosing a HAQI of ≥ 67, we assumed that data from these countries were reliable and healthcare was of high quality. For Brazilian regions, a HAQI was substituted for the Human Development Index (HDI), and those with < 0.549 (low) were excluded.

Three major cities with > 100 deaths and well-established results (Tokyo, Japan; Berlin, Germany, and New York, USA) were selected as controlled areas.

### Dataset of COVID-19 cases and associated data to reduce bias

After inclusion of the countries/regions, further data were obtained to reduce comparison bias, including population density (people/km2), percentage of the urban population, HDI, and the total area of the region in square kilometers. All data were obtained from open databases72,73,74.

### Definition of areas with and without controlled cases of COVID-19

Regions were classified as controlled for cases of COVID-19 if they present at least 2 out of the 3 following conditions: a) type of transmission classified as “clusters of cases”, b) a downward curve of newly reported deaths in the last 7 days, and c) a flat curve in the cumulative total number of deaths in the last 7 days (variation of 5%) according to the World Health Organization75. An example is shown in Figure S3.

Data from the cities (Tokyo, Berlin, New York, Fortaleza, Belo Horizonte, Manaus, Rio de Janeiro, São Paulo, and Porto Alegre) were obtained from official government sites76,77,78,79. Tokyo, Berlin and New York were chosen for having controlled the COVID-19 dissemination, for representing 3 different continents, and for similarity to major Brazilian cities (Fortaleza, Belo Horizonte, Manaus, Rio de Janeiro, São Paulo, and Porto Alegre).

### Merged database

Different databases from the sites mentioned above were merged using Microsoft Excel Power Query (Microsoft Office 2010 for Windows Version 14.0.7232.5000) and manually inspected for consistency.

### Processing the data—cleaning

Data collected from multiple regions were processed using Python 3.7.3 in the Jupyter Notebook80 environment through the use of the Python Data Analysis Library in Google Colab Research81. Details of preprocessing are described in Python script (Supplement). Briefly, after taking the sum of deaths/million per epi-week, and the average of the variable “staying at home” per epi-week, non-stationary patterns were mitigated by subtracting weekt by weekt-1.

### Time series data setup and variables

Details regarding the pre-processing and methodological details were presented on the Approach for analyzing the time series data section. Our variables were the difference in the variation of deaths between locations A and B (dependent variable—outcome), and the difference in the variation of staying at home values between the same location (independent variable).

### Comparison between areas

Direct comparison, between regions with and without controlled COVID-19 cases, was considered in two scenarios: 1) Restrictive if, at least 3 out of 4 of the following conditions were similar: a) population density, b) percentage of the urban population, c) HDI and d) total area of the region. Similarity was considered adequate when a variation in conditions a), b), and c) was within 30%, while, for condition d), a variation of 50% was considered adequate. 2) Global: all regions and countries were compared to each other.

The restrictive comparison used parameters related to how close people may have made physical contact. The major route of transmission for COVID-19 is from person-to-person via respiratory droplets and direct personal and physical contact within a community setting82,83.

### Statistical analysis

After data preprocessing, the association between the number of deaths and staying at home was verified using a linear regression approach. Data were analyzed using the Python model statsmodels.api v0.12.0 (statsmodels.regression.linear_model.OLS; statsmodels.org), and double-checked using R version 3.6.184. False Discovery Rate proposed by Benjamini-Hochberg (FDR-BH) was used for multiple testing85.

We checked the residuals for heteroskedasticity using White’s test86; for the presence of autocorrelation using the Lagrange Multiplier test87; for normality using the Shapiro–Wilk’s normality test88; and for functional specification using the Ramsey’s RESET test89. All tests were performed with a 5% significance level and the analysis was performed with R version 3.6.184.

Data from 30 restrictive comparisons were manually inspected and checked a third time using Microsoft Excel (Microsoft). A heat map was designed using GraphPad Prism version 8.4.3 for Mac (GraphPad Software, San Diego, California, USA). Graphs plotting the number of deaths/million and staying at home over epidemiological weeks were obtained from Google Sheets90.

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## The Impact of COVID-19 Stay-At-Home Orders on Health Behaviors in Adults

Objective: Stay-at-home orders in response to the coronavirus disease 2019 (COVID-19) pandemic have forced abrupt changes to daily routines. This study assessed lifestyle changes across different BMI classifications in response to the global pandemic.

Methods: The online survey targeting adults was distributed in April 2020 and collected information on dietary behaviors, physical activity, and mental health. All questions were presented as "before" and "since" the COVID-19 pandemic.

Results: In total, 7,753 participants were included; 32.2% of the sample were individuals with normal weight, 32.1% had overweight, and 34.0% had obesity. During the pandemic, overall scores for healthy eating increased (P < 0.001), owing to less eating out and increased cooking (P < 0.001). Sedentary leisure behaviors increased, while time spent in physical activity (absolute time and intensity adjusted) declined (P < 0.001). Anxiety scores increased 8.78 ± 0.21 during the pandemic, and the magnitude of increase was significantly greater in people with obesity (P ≤ 0.01). Weight gain was reported in 27.5% of the total sample compared with 33.4% in participants with obesity.

Conclusions: The COVID-19 pandemic has produced significant health effects, well beyond the virus itself. Government mandates together with fear of contracting the virus have significantly impacted lifestyle behaviors alongside declines in mental health. These deleterious impacts have disproportionally affected individuals with obesity.

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### Acknowledgements

We are grateful to Dr. Jair Ferreira, from the Epidemiology Department of the Universidade Federal do Rio Grande do Sul, for his critical feedback.

### Affiliations

1. School of Medicine, Department of Obstetrics and Gynecology, Universidade Federal do Rio Grande do Sul, Rua Ramiro Barcelos 2400, Porto Alegre, RS, CEP 90035-003, Brazil

R. F. Savaris

2. Mathematics and Statistics Institute and Programa de Pós-Graduação em Estatística, Universidade Federal do Rio Grande do Sul, 9500, Bento Gonçalves Avenue, Porto Alegre, RS, 91509-900, Brazil

G. Pumi

3. Applied Computing Graduate Program, University of Vale do Rio dos Sinos (UNISINOS), Av. Unisinos, 950, São Leopoldo, RS, 93022-750, Brazil

J. Dalzochio & R. Kunst

4. Serv. Ginecologia e Obstetrícia, Hospital de Clínicas de Porto Alegre, Rua Ramiro Barcelos 2350, Porto Alegre, RS, CEP 90035-903, Brazil

R. F. Savaris

5. Postgraduate of BigData, Data Science and Machine Learning Course, Unisinos, Porto Alegre, RS, Brazil

R. F. Savaris

### Contributions

R.F.S. was responsible for the conception of the study, designed the methodology, tested code components in Python and R, verified reproducibility, made formal analysis, data collection, provided other analysis tools, was responsible for data curation, wrote the initial draft, interpreted the data, reviewed the manuscript, created the data presentation, oversight execution, coordinate execution of the project. G.P. conceived the project, designed the methodology, implemented the computer code and algorithm in R, verified the outputs, conceived and formalized the statistical model applied, wrote the original draft and interpreted the data. R.K. participated in the conception of the study, implemented computer code in Python, validated results, provided computer resources, maintained research data for initial use, critically reviewed the initial draft, reviewed final draft, was the external mentor to the core team and coordinated the planning of the project. J.D., programmed the algorithms in Python, provided techniques to reduce data dimensionality, maintained software code in Python, reviewed and approved the final draft.

### Corresponding author

Correspondence to R. F. Savaris.

### Competing interests

The authors declare no competing interests.

### Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

### Rights and permissions

Источник: https://www.nature.com/articles/s41598-021-84092-1

## Stay-at-home orders tied to drop in Covid-19 spread

"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 cases," the researchers from the University of Wisconsin-Madison wrote in the study published in the journal JAMA Network Open.
They used location data from more than 45 million cellphones between March 11 and April 10 to work out daily travel distance and time spent at home across all 50 states. This helped them judge how well people obeyed social distancing mandates.
It looks like they did, to some degree.
"Implementation of stay-at-home social distancing policies were associated with human movement changes," the researchers wrote. "That is, people generally reduced their daily travel distances and increased their home dwell time."
At the same time, the rate of increase in cases in the five states with the highest level of infection at the time -- New York, New Jersey, Michigan, California and Massachusetts -- slowed down after the stay-at-home orders were implemented.
Their results "suggest that stay-at-home orders were associated with reduction of the Covid-19 pandemic spread and with flattening the curve."
It's possible other control measures -- such as mask wearing -- could have played a part in the reduction of cases.

### Once orders lifted, movement increased

"Based on location data from mobile devices, in 97.6% of counties with mandatory stay-at-home orders issued by states or territories, these orders were associated with decreased median population movement after the order start date," researchers from the CDC and the Georgia Tech Research Institute wrote.
The researchers also found that in areas where orders were lifted or expired, movement "significantly increased" immediately afterward.
The studies can help governments decide how to control the pandemic in the future, the Wisconsin researchers said.
"The findings come at a particularly critical period, when US states are beginning to reopen their economies but COVID-19 cases are surging," they wrote.
"At such a time, our study suggests the efficacy of stay-at-home social distancing measures and could inform future public health policy making."
Источник: https://edition.cnn.com/2020/09/08/health/stay-at-home-orders-coronavirus-study-wellness/index.html

## COVID-19 restrictionsMap of COVID-19 case trends, restrictions and mobility

Updated Nov. 13, td bank mortgage service center is no mask requirement in Alaska, however the state recommends mask wearing in public spaces where physical distancing isn't possible. This summer the state also opened its borders to all travelers and is offering visitors a free COVID vaccine. The state's emergency declaration expired in February. Gov. Mike Dunleavy announced life could go back to the way it was "prior to the virus" on May 22, 2020, but local governments could still offer health guidance.

Stay-at-home order: Started March 11, 2020; ended on April 21, 2020

Affected sectors: Health, Cosmetology

Caseload: The number of confirmed new cases is shrinking, with 2,740 for the seven days ending November 17 compared to 3,699 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 6.11% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Maine

Updated Nov. 13, 2021

Public health authorities recommend everyone resume mask use in all 16 counties in Maine, where transmission of coronavirus is elevated. Maine on May 24 lifted all capacity limits and requirements to physically distance in outdoor settings, as well as the state's outdoor mask requirements. That same day, the state had removed all capacity limits in public indoor venues, and eliminated the indoor mask mandate for vaccinated individuals. Maine also eliminated physical distancing requirements indoors, other than in settings in which people are eating or drinking such as restaurants and bars.

Stay-at-home order: Started April 2, amazon prime promotional credit ended on May 31, 2020

Affected sectors: Outdoor recreation, Cosmetology

Caseload: The number of confirmed new cases is shrinking, with 3,540 for the seven days ending November 17 compared to 3,811 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 3.94% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Vermont

Updated Oct. 30, 2021

Vermont lifted all of its remaining COVID-19 restrictions June 14 after becoming the first state in the U.S. to have 80% of its eligible population get one dose of the vaccine. All remaining restrictions about wearing masks, physical distancing, or crowd size limits had been rescinded by the state.

Stay-at-home order: Started March 24, 2020; ended on May 15, 2020

Affected sectors: Outdoor recreation, Retail

Caseload: The number of confirmed new cases is growing, with 2,546 for the seven days ending November 17 compared to 2,183 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 4.05% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in New Hampshire

Updated Oct. 30, 2021

Effective May 8, all of the COVID restrictions limiting New Hampshire businesses became recommendations instead. Gov. Chris Sununu ordered all New Hampshire schools to fully reopen, five days a week, by April 19. Sununu allowed the state's mask mandate to expire April 16.

Stay-at-home order: Started March 27, 2020; ended on June 15, 2020

Affected sectors: Health, Retail

Caseload: The number of confirmed new cases is growing, with 6,249 for the seven days ending November 17 compared to 4,691 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 2.95% less than the seven days prior, data from SafeGraph show.

### Restrictions tighten slightly in Washington

Updated Nov. 6, 2021

Starting Sept. 13, Washington's indoor mask mandate was expanded to include outdoor events with 500 or more attendees, regardless of vaccination status. Most government-imposed pandemic restrictions had been lifted June 30, meaning restaurants, bars, gyms, retail stores and religious worship spaces are now allowed to resume operations at full indoor capacity – up from the most recent limit of 50%.

Stay-at-home order: Started March 23, 2020; ended on May 4, 2020

Affected sectors: Outdoor recreation, Retail

Caseload: The number of confirmed new cases is shrinking, with 12,129 for the seven days ending November 17 compared to 12,819 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 3.75% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Idaho

Updated Nov. 13, 2021

Idaho returned to Stage 4 on May 11, which effectively lifted restrictions on gathering sizes and business operations.

Stay-at-home order: Started March 25, 2020; ended on April 30, 2020

Affected sectors: Retail, Restaurants

Caseload: The number of confirmed new cases is shrinking, with 3,207 for the seven days ending November 17 compared to 4,149 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 2.19% less than the seven days prior, data from SafeGraph show.

### Restrictions have been lifted in Montana

Updated Oct. 30, 2021

Montana's state of emergency expired at the end of June. Gov. Greg Gianforte on May 10 signed legislation effectively invalidating local mask mandates and other virus-related public health measures that counties and cities have adopted in the wake of the COVID-19 pandemic. Gianforte on Feb. 12 lifted the statewide mask mandate put in place by his predecessor Steve Bullock. He removed health mandates issued by Bullock on Jan. 15, saying the restrictions are harmful to the state's businesses.

Stay-at-home order: Started March 26, 2020; ended on April 24, 2020

Affected sectors: Retail, Restaurants

Caseload: The number of confirmed new cases is shrinking, with 3,589 for the seven days ending November 17 compared to 4,233 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 4.06% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in North Dakota

Updated Nov. 6, 2021

Gov. Doug Borgum on April 30 rescinded North Dakota's state of emergency. North Dakota's mask mandate expired Jan. 18. The state also moved to low/green risk level, which increased the recommended occupancy limit for bars, restaurants and other food service establishments.

Stay-at-home order: Never issued

Affected sectors: Retail, Cosmetology

Caseload: The number of confirmed new cases is shrinking, with 3,315 for the seven days ending November 17 compared to 3,569 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 3.75% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Minnesota

Updated Nov. 13, 2021

Capacity and distancing limits for busineses, indoor events and gatherings were removed May 28. Beginning May 7, limits were lifted for most outdoor dining, and on early closing times for bars and restaurants.

Stay-at-home order: Started March 27, 2020; ended on May 4, 2020

Affected sectors: Outdoor recreation, Retail

Caseload: The number of confirmed new cases is growing, with 29,566 for the seven days ending November 17 compared to 23,713 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 4.54% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted Wisconsin

Updated Nov. 6, 2021

At the end of March, the Wisconsin Supreme Court declared the statewide mask mandate invalid and blocked Gov. Tony Evers from issuing any new public health emergency orders mandating face masks without the legislature's approval. Several cities have since lifted their local mask mandates. Wisconsin eased restrictions to allow more indoor visitation in nursing homes March 10.

Stay-at-home order: Started March 25, 2020; ended on May 26, 2020

Affected sectors: Parks, Retail

Caseload: The number of confirmed new cases is growing, with 23,975 for the seven days ending November 17 compared to 20,209 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 3.5% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Michigan

Updated Nov. 13, 2021

The Michigan Senate approved a series of bills aimed at curtailing or banning COVID-19 vaccine and mask mandates for students and others participating in activities pertaining to K-12 schools. Gov. Gretchen Whitmer vetoed two bills Sept. 10, including another GOP-backed effort to curtail powers used by the administration to combat COVID-19. That legislation attempted to prevent the Whitmer administration from using the public threat alert system to send out notifications regarding new mask rules, gathering restrictions or similar health and safety orders. Michigan ended all restrictions to masking and gathering requirements June 22.

Stay-at-home order: Started March 24, 2020; ended on June 5, 2020

Affected sectors: Outdoor recreation, Retail

Caseload: The number of confirmed new cases is growing, with 58,849 for the seven days ending November 17 compared to 31,593 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 3.12% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in New York

Updated Nov. 6, 2021

The New York State Department of Health instituted a universal mask requirement in all schools. New York City restaurants and other some indoor spaces now require proof of vaccination for entry. New York hit its goal June 15 of reaching a 70% first-shot vaccination rate for COVID-19, and the state lifted many of its remaining safety restrictions. Retail stories, restaurants, offices, gyms, amusement centers, hair salons can now make it optional to have capacity and social distancing restrictions, as well as ease COVID disinfection protocols. The state of emergency expired June 24. New York, New Jersey and Connecticut previously ended many COVID-19 capacity limits May 19.

Stay-at-home order: Started March 22, 2020; ended on May 15, 2020

Affected sectors: Outdoor recreation, Health

Caseload: The number of confirmed new cases is growing, with 40,344 for the seven days ending November 17 compared to 32,072 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 3.63% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Connecticut

Updated Nov. 13, 2021

Connecticut's Department of Public Health said Aug. 1 that all incividuals should wear masks in public indoor settings, regardless of vaccine status. Effective Aug. 5, individual municipalities could mandate masks to be worn by everyone in indoor public places. Gov. Ned Lamont had previously lifted all COVID-19 restrictions, including Connecticut's mask mandate for vaccinated individuals, May 19.

Stay-at-home order: Started March 23, 2020; ended on May 20, 2020

Affected sectors: Retail, Restaurants

Caseload: The number of confirmed new cases is growing, with 4,559 for the seven days ending November 17 compared to 2,534 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 5.92% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Rhode Island

Updated Nov. 6, 2021

All health-care workers in all state-licensed health facilities were required to be vaccinated by Oct. 1. On May 21, Rhode Island lifted almost all of its remaining coronavirus limitations, a week earlier than originally planned. Restrictions on "higher risk" activities, including indoor performances and night clubs, were lifted June 18. Since May 18, fully vaccinated Rhode Islanders — those two weeks past their final dose — are no longer required to cover their faces or observe social distance, indoors or outdoors, in most situations.

Stay-at-home order: Started March 28, 2020; ended on May 8, 2020

Affected sectors: Restaurants, Retail

Caseload: The number of confirmed new cases is growing, with 2,768 for the seven days ending November 17 compared to 1,998 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 5.81% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Massachusetts

Updated Nov. 13, 2021

Gov. Charlie Baker on July 30 recommended fully vaccinated Massachusetts residents wear masks in certain public indoor settings, but did not institute a new mask mandate. The state had dropped its mask mandate and all remaining coronavirus restrictions May 29. Baker on May 25 filed legislation to extend certain emergency measures currently in place via executive orders, which expired June 15.

Stay-at-home order: Started April 24, 2020; ended on May 18, 2020

Affected sectors: Outdoor recreation, Health

Caseload: The number of confirmed new cases is growing, with 15,006 for the seven days ending November 17 compared to 11,334 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 5.51% less than the seven days prior, data from SafeGraph show.

### Restrictions tighten in Oregon

Updated Nov. 6, 2021

Masks are required in most public outdoor settings as of Aug. 27, regardless of a person's vaccination status. Weeks ago, Gov. Kate Brown had institued a mask mandate for indoor spaces, which started Aug. 13. Oregon had previously lifted mask mandates, social distancing and other restrictions June 30.

Stay-at-home order: Started March 23, 2020

Affected sectors: Schools, Health

Caseload: The number of confirmed new cases is shrinking, with 5,470 for the seven days ending November 17 compared to 6,557 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 2.87% less than the seven days prior, data from SafeGraph show.

### Restrictions tighten slightly in Nevada

Updated Oct. 30, 2021

Most Nevada counties are under an indoor mask mandate due to substantial transmission of COVID-19, starting Sept. 10. A previous directive from Gov. Steve Bank of the west locations in colorado in May allowed individual counties to assume full control of COVID-19 restrictions, and the state was completely reopened June 1.

Stay-at-home order: Started March 31, 2020; ended on May 15, 2020

Affected sectors: Restaurants, Retail

Caseload: The number of confirmed new cases is shrinking, with 4,385 for the seven days ending November 17 compared to 4,545 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 4.66% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Wyoming

Updated Nov. 6, 2021

Statewide public health orders ended in late May. Moving forward, specific protocols for K-12 schools during the pandemic are determined at the district level. Public health restrictions on indoor gatherings of more than 500 people were lifted. Gov. Mark Gordon lifted Wyoming's mask mandate March 16, and resumed "normal operations" for bars, restaurants, theaters and gyms.

Stay-at-home order: Never issued

Affected sectors: Cosmetology, Fitness

Caseload: The number of confirmed new cases is shrinking, with 1,960 for the seven days ending November 17 compared to 2,295 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 9.71% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in South Dakota

Updated Nov. 6, 2021

Gov. Kristi Noem has repeatedly said she won't issue a statewide mask requirement or lockdown and has voiced doubts about health experts who say face coverings prevent infections from spreading. Noem's "Back to Normal Plan" laid out actions for residents, employers, schools and health care providers once four criteria categories are met, including a downward trajectory of documented coronavirus cases for 14 days in an area with sustained community spread.

Stay-at-home order: Never issued

Affected sectors: Retail, Health

Caseload: The number of confirmed new cases is shrinking, with 2,652 for the seven days ending November 17 compared to 2,699 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 5.88% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Iowa

Updated Nov. 13, 2021

A federal judge ordered the state of Iowa to immediately halt enforcement of a law that prevents school boards from ordering masks to be worn to help prevent the spread of COVID-19. Gov. Kim Reynolds lifted the state's limited mask requirement Feb. 7, along with the social distancing requirements and other COVID-19 mitigation measures she had in place for businesses and social gatherings.

Stay-at-home order: Never issued

Affected sectors: Health, Restaurants

Caseload: The number of confirmed new cases is growing, with 9,234 for the seven days ending November 17 compared to 8,227 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 4.91% less than the seven days prior, data from SafeGraph show.

### Restrictions tighten slightly in Illinois

Updated Nov. 13, 2021

A new statewide indoor mask mandate went into effect Aug. 29, and beginning Sept. 5, Pritzker said he would use his executive authority to require all workers in K-12 schools, all workers in private and public hospitals, nursing homes and other health care settings, and all workers and students in colleges and universities, to get vaccinated against COVID-19 or submit to weekly testing. Illinois had entered Phase 5 of reopening June 11, which removed all remaining capacity limits and restrictions on all sectors of the economy.

Stay-at-home order: Started March 21, 2020; ended on May 30, 2020

Affected sectors: Health, Outdoor recreation

Caseload: The number of confirmed new cases is growing, with 24,868 for the seven days ending November 17 compared to 21,857 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 4.57% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Indiana

Updated Nov. 13, 2021

Gov. Eric Holcomb once again renewed the state's public health emergency order at the end of October, extending it until December. Holcomb removed the mask mandate June 1 in most state facilities. Face masks were required in public schools through June 30, but beginning July 1, local school boards have the power to enact measures for their school districts. Indiana's mask mandate ended April 6.

Stay-at-home order: Started March 25, 2020; ended on May 1, 2020

Affected sectors: Health, Gatherings

Caseload: The number of confirmed new cases is growing, with 18,193 for the seven days ending November 17 compared to 14,259 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 3.99% less than the seven days prior, data from SafeGraph show.

### Most restrictions lifted in Ohio

Updated Nov. 6, 2021

Unmasked and unvaccinated K-12 students and staff members can remain in class after in-school exposure to the virus if they wear a mask for 14 days under new state guidance. Ohio had lifted the state mask mandate and all remaining coronavirus health orders except those for nursing homes and assisted living facilities on June 2. Gov. Mike DeWine lifted Ohio's state of emergency June 18.

Stay-at-home order: Started March 23, 2020; ended on May 30, 2020

Affected sectors: Retail, Health

Caseload: The number of confirmed new cases is growing, with 34,638 for the seven days ending November 17 compared to 29,590 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 3.48% less than the seven days prior, data from SafeGraph show.

### Restrictions tighten slightly in Pennsylvania

Updated Nov. 6, 2021

Masks are required in all Pennsylvania K-12 schools, Gov. Tom Wolf announced Aug. 31. The Department of Health order took effect Tuesday, Sept. 7. Pennsylvania had previously ended its mask mandate June 28. The state eliminated all capacity limits on businesses on Memorial Day and relaxed its COVID-19 restrictions on indoor and outdoor gatherings May 17, allowing for greater numbers of people to attend events like proms, graduations and fairs, festivals or concerts.

Stay-at-home order: Started April 1, 2020; ended on May 8, 2020

Affected sectors: Outdoor recreation, Retail

Caseload: The number of confirmed new cases is growing, with 37,243 for the seven days ending November 17 compared to 31,578 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 2.81% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in New Jersey

Updated Oct. 30, 2021

In late July, Gov. Phil Murphy and Health Commissioner Judy Persichilli issued a statement that said they "strongly recommend" masks for everyone in indoor situations of "increased risk." Murphy on June 4 had signed a bill to end the public health emergency. On May 17, he lifted New Jersey's travel restrictions and said schools would be back in person next school year. Most capacity limits at restaurants, stores, offices and a host of other sites were lifted May 19 in New Jersey, New York and Connecticut in a coordinated effort by the hardest-hit region in the U.S. to emerge from the pandemic.

Stay-at-home order: Started March 21, 2020

Affected sectors: Outdoor recreation, Parks

Caseload: The number of confirmed new cases is growing, with 12,032 for the seven days ending November 17 compared to 9,172 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 4.05% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in California

Updated Nov. 13, 2021

As of July 28, California recommends all individuals wear masks indoors regardless of vaccination status; some individual counties have mask mandates. On June 15, California retired the color-coded tier system that since 2020 has set occupancy limits at businesses and imposed other rules aimed at slowing the spread of coronavirus. Most businesses are allowed to resume normal operations and the state's mask mandate was lifted for vaccinated individuals.

Stay-at-home order: Started March 19, 2020

Affected sectors: Health, Schools

Caseload: The number of confirmed new cases is shrinking, with 33,117 for the seven days ending November 17 compared to 42,084 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 8.4% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Utah

Updated Nov. 6, 2021

Utah on May 4 ended mandated limits on gatherings and social distancing related to the coronavirus after the state reached several metrics laid out in a so-called “COVID-19 endgame” bill passed earlier this year. The statewide mask mandate ended April 10.

Stay-at-home order: Never issued

Affected sectors: Gatherings, Restaurants

Caseload: The number of confirmed new cases is growing, with 11,711 for the seven days ending November 17 compared to 11,202 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 5.45% less than the seven days prior, data from SafeGraph show.

Updated Nov. 13, 2021

The Colorado health department is preparing for the possibility of a statewide mask or vaccine mandate. The state lifted all capacity limits for large indoor gatherings June 1. Colorado's color-coded COVID-19 dial expired April 16, meaning that control over COVID-19 restrictions were back in the hands of counties' respective public health agencies.

Stay-at-home order: Started March 26, 2020; ended on May 8, 2020

Affected sectors: Health, Cosmetology

Caseload: The number of confirmed new cases is shrinking, with 21,415 for the seven days ending November 17 compared to 21,999 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 12.77% less than the seven days prior, data from SafeGraph show.

### Restrictions tighten in Nebraska hospitals

Updated Oct. 30, 2021

Gov. Pete Ricketts announced during his last COVID-19 press conference in May that he signed an executive order suspending previous executive orders made during the pandemic. The state's emergency order ended June 30. Nebraska transitioned its state-wide mask mandate to an advisory in April.

Stay-at-home order: Never issued

Affected sectors: Restaurants, Cosmetology

Caseload: The number of confirmed new cases is growing, with 6,159 for the seven days ending November 17 compared to 5,645 the seven days prior.

Mobility: For the seven days ending M pokora mieux que nous feat soprano 15, 2021, the share of residents leaving their homes was about 5.6% less than the seven days prior, data from SafeGraph show.

### Restrictions have been lifted in Missouri

Updated Nov. 13, 2021

Missouri lifted its mask mandate for everyone in outdoor areas in April and for fully vaccinated people in indoor areas in May. Gov. Mike Parson enacted a law June 15 limiting the duration of local public health restrictions and barring governments from requiring proof of COVID-19 vaccination to use public facilities and transportation. Parson let his statewide social distancing order lapse June 9, 2020, leaving it to local governments to impose limits on public life amid the coronavirus outbreak.

Stay-at-home order: Started April 6, 2020; ended on May 3, 2020

Affected sectors: Restaurants, Retail

Caseload: The number of confirmed new cases is growing, with 9,929 for the seven days ending November 17 compared to 8,389 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 4.29% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Kentucky

Updated Nov. 13, 2021

The Kentucky General Assembly passed a controversial proposal Sept. 9 that gets rid of the state's mask mandate for public schools. Masks are back in Kentucky state offices following an advisory July 27 by the U.S. Centers for Disease Control and Prevention that people should wear them indoors in areas of "substantial and high transmission." Gov. Andy Breshear on June 11 signed an executive order rescinding all of his previous restrictions.

Stay-at-home order: Started March 26, 2020

Affected sectors: Health, Restaurants

Caseload: The number of confirmed new cases is growing, with 9,814 for the seven days ending November 17 compared to 8,814 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 3.36% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in West Virginia

Updated Nov. 6, 2021

West Virginia dropped its mask mandate June 20. Limits on public gatherings were lifted April 20, under a new executive order. Gov. Jim Justice loosened other pandemic restrictions on March 5 at restaurants, bars and most businesses to allow full capacity at those establishments where social distancing is possible.

Stay-at-home order: Started March 24, 2020; ended on May 4, 2020

Affected sectors: Cosmetology, Restaurants

Caseload: The number of confirmed new cases is growing, with 5,441 for the seven days ending November 17 compared to 5,186 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 1.15% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Maryland

Updated Nov. 13, 2021

Maryland ended most of its coronavirus emergency restrictions July 1, including the state's mask order. All businesses in the state, including restaurants and entertainment and sporting venues, could resume normal operations May 15.

Stay-at-home order: Started March 30, 2020; ended on May 15, 2020

Affected sectors: Outdoor recreation, Beaches

Caseload: The number of confirmed new cases is growing, with 5,906 for the seven days ending November 17 compared to 5,033 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 1.97% less than the seven days prior, data from SafeGraph show.

### Restrictions tighten slightly in Virginia

Updated Oct. 30, 2021

Gov. Ralph Northam issued a universal mask mandate for K-12 schools Aug. 12. Northam previously recommended individuals wear masks in public places, regardless of vaccine status, in line with recent CDC guidelines. Capacity and social distancing restrictions ended May 28 in Virginia.

Stay-at-home order: Started March 30, 2020; ended on June 10, 2020

Affected sectors: Health, Restaurants

Caseload: The number of confirmed new cases is growing, with 10,020 for the seven days ending November 17 compared to 9,104 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 2.4% less than the seven days prior, data from SafeGraph show.

### Restrictions tighten slightly in Delaware

Updated Nov. 13, 2021

In response to rising coronavirus case and hospitalization levels throughout the state, hospital systems in Delaware are implementing stricter visitation policies. Gov. John Carney issued a statewide school mask mandate in August, which covers students and staff in public and private schools. Effective May 21, capacity restrictions at Delaware restaurants, stores, places of worship and other businesses were lifted, enabling them to host as many people as they can fit under the state's social distancing requirement.

Stay-at-home order: Started March 24, 2020; ended on May 15, 2020

Affected sectors: Retail, Cosmetology

Caseload: The 5th third bank customer service phone number of confirmed new cases is growing, with 2,190 for the seven days ending November 17 compared to 1,729 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 3.87% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Arizona

Updated Nov. 13, 2021

School districts in Arizona can enact their own mask and vaccine standards as a result of a sweeping court ruling. Gov. Doug Ducey on March 25 had lifted COVID-19 restrictions covid 19 stay at home order Arizona businesses and events and prohibited, in most cases, the enforcement of local mask mandates. Events drawing more than 50 people, such as youth sports tournaments and concerts, no longer require governmental approval. Businesses including bars, restaurants, gyms, theaters and water parks can operate at full capacity.

Stay-at-home order: Started March 30, 2020; ended on May 15, 2020

Affected sectors: Health, Retail

Caseload: The number of confirmed new cases is growing, with 25,615 for the seven days ending November 17 compared to 22,757 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 3.4% less than the seven days prior, data from SafeGraph show.

### Restrictions tighten slightly in New Mexico

Updated Nov. 6, 2021

New Mexico reinstated an indoor mask mandate Aug. 17, which was later extended through Nov. 12. New Mexico essentially reopened July 1, retiring restrictions on mass gatherings and business activity that have been in place since the COVID-19 pandemic reached the state last year.

Stay-at-home order: Started March 24, 2020; ended on May 15, 2020

Affected sectors: Retail, Outdoor recreation

Caseload: The number of confirmed new cases is growing, with 9,756 for the seven days ending November 17 compared to 8,887 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 3.63% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Kansas

Updated Nov. 13, 2021

Several school districts in Kansas have mask mandates. Gov. Laura Kelly on April 6 said school districts must allow five-day-a-week of in-person classes. More permanent restrictions on the Democratic governor's emergency powers were passed by the majority-GOP Legislature in late March. Kelly won't be able to issue any emergency orders shutting down businesses or limiting gatherings.

Stay-at-home order: Started March 30, 2020; ended on May 3, 2020

Affected sectors: Retail, Restaurants

Caseload: The number of confirmed new cases is growing, with 7,843 for the seven days ending November 17 compared to 6,801 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 5% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Arkansas

Updated Nov. 13, 2021

Arkansas' latest state of emergency expired Sept. 26. Gov. Asa Hutchinson had previously lifted Arkansas' mask mandate March 30, and on Feb. 26 had changed other public health directives to guidelines, meaning they would no longer be mandatory.

Stay-at-home order: Never issued

Affected sectors: Health, Fitness

Caseload: The number of confirmed new cases is growing, with 3,878 for the seven days ending November 17 compared to 3,136 the seven days prior.

Mobility: For the seven days ending March 15, 2021, the share of residents leaving their homes was about 2.51% less than the seven days prior, data from SafeGraph show.

### Restrictions lifted in Tennessee

Updated Nov. 6, 2021

Источник: https://www.usatoday.com/storytelling/coronavirus-reopening-america-map/

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