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Impact of government-imposed social distancing measures on COVID-19 morbidity and mortality around the world

Nicole K. Le, MD, MPH1; Alexander V. Le2; Joel P. Brooks, DO, MPH3; Sumun Khetpal, BS, BA4; Daniel Liauw, MD, MPH5; Ricardo Izurieta, MD, DrPH, MPH6; Miguel Reina Ortiz, MD, PhD6

1 Morsani College of Medicine, University of South , 12901 Bruce B Downs Blvd, Tampa, FL 33612, USA 2 Department of Biomedical Sciences, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA 3 Division of Allergy and Immunology, Children’s National Hospital, 111 Michigan Avenue NW, Washington, D.C., 20010, USA 4 Yale School of Medicine, Yale University, 333 Cedar St, New Haven, CT 06510, USA 5 University of North Carolina School of Medicine, 321 S Columbia St, Chapel Hill, NC 27516, USA 6 College of , University of South Florida, 13201 Bruce B Downs Blvd, Tampa, FL 33612, USA

Corresponding author: Nicole K. Le; 12901 Bruce B Downs Blvd, Tampa, FL 33612; [email protected]; (813) 974-8913

(Submitted: 28 April 2020 – Published online: 30 April 2020)

DISCLAIMER

This paper was submitted to the Bulletin of the World Health Organization and was posted to the COVID-19 open site, according to the protocol for public health emergencies for international concern as described in Vasee Moorthy et al. (http://dx.doi.org/10.2471/BLT.20.251561).

The information herein is available for unrestricted use, distribution and reproduction in any , provided that the original work is properly cited as indicated by the Creative Commons Attribution 3.0 Intergovernmental Organizations licence (CC BY IGO 3.0).

RECOMMENDED CITATION

Le NK, Le AV, Brooks JP, Khetpal S, Liauw D, Izurieta R, et al. Impact of government-imposed social distancing measures on COVID-19 morbidity and mortality around the world. [Preprint]. Bull World Health Organ. E-pub: 30 April 2020. doi: http://dx.doi.org/10.2471/BLT.20.262659

1 Abstract Objective: National governments have enacted various social distancing policies in response to the global caused by the severe acute respiratory syndrome virus 2 (SARS-CoV-2). This cohort study aims to assess the effect of government-imposed social distancing measures on COVID-19-related morbidity and mortality. Methods: Social distancing measures implemented by 181 countries or territories were classified into descriptive categories of stay-at-home orders, school closures, non-essential business closures, and severe travel restrictions. The impact of these social distancing measures on the change in slope of cumulative COVID-19 incidence (i.e. before and after the implementation) was estimated by a linear regression model, controlling for population-level sociodemographic and health-related characteristics. A similar approach was used for COVID- 19-related deaths. Findings: Implementations of social distancing measures were associated with the following slope differences in cumulative incidence: stay-at-home orders (β = 0.051, p < 0.001), school closures (β = 0.073, p < 0.001), closures of non-essential business (β = 0.058, p < 0.001), and severe travel restrictions (β = 0.050, p < 0.001). After implementation of stay-at-home orders, there was a decline in cumulative (β = 0.032, p < 0.001). Conclusions: Government-imposed social distancing measures were independently associated with a reduction in COVID-19 cumulative incidence. Further analysis of within country variation is needed to account for the dynamic enactment and implementation of social distancing measures.

Introduction

Since its first identification in early December 2019, the severe acute respiratory syndrome

coronavirus 2 (SARS-CoV-2), the virus causing Coronavirus 2019 (COVID-19), has

infected over 2.8 million people and claimed more than 200,000 lives worldwide, as of April 25,

2020.1,2 On January 30, 2020, the World Health Organization (WHO) first recommended social

distancing through the International Health Regulations (2005) Emergency Committee.3 On

March 11, 2020, the WHO characterized COVID-19 as a pandemic.4

Social distancing has reduced infectious disease burden in prior by minimizing physical contact between people.5 For example, quarantining, closing schools, prohibiting large

gatherings, and closing non-essential businesses were all implemented during the 1918–19

2 pandemic with some success.6-9 School closures have effectively reduced outbreaks

of chickenpox, , and measles viruses.10-12 Simulation studies have suggested that

implementing social distancing reduces the growth of COVID-19 cases.13,14 A global assessment of the impact of social distancing measures on COVID-19 morbidity and mortality has yet to be

reported. We hypothesize that social distancing measures are effective in combating COVID-19.

This study aims to assess the efficacy of government-imposed social distancing measures around the world on COVID-19 morbidity and mortality, as measured by changes in the slopes

of cumulative incidence and cumulative mortality rates.

Methods

Study Design

The implementation of social distancing measures by 181 countries and territories provided a

natural experiment to assess subsequent impact on COVID-19-related morbidity and mortality.

Social distancing measures were categorized as follows: stay-at-home/ orders

(recommended all day, not just a nighttime ); school closures (of all levels from pre-K to

universities); closures of non-essential businesses; and international travel restrictions

(cancellation of all international flights or closure of country borders). Only measures that were

executed nationwide qualified for study analysis. Official government websites were utilized to

obtain information regarding the time at which any of the four social distancing measures were

implemented nationally (see Supplemental Materials). If information was not available on

official government websites, news articles from leading global and national news outlets were

included (see Supplemental Materials).

3 Data collection

The daily cumulative number of COVID-19 cases and deaths for 181 countries and territories between January 22 and April 13, 2020 were collected from the Johns Hopkins Center for

Systems and System.1 This list of countries included in this study can be found in Table

1. January 22 is the earliest date for which data was available in the dataset. Data from

disputed territories (West Bank and Gaza and Western Sahara) and cruise ships were excluded

from this study; implementation of social distancing measures in those regions is difficult to ascertain from publicly available data.

Data management and Data analysis

For each day, the cumulative number of cases and deaths of each country were log10- transformed. The data was then divided into pre- and post-implementation periods for each

combination of measure and country/territory. Pre-implementation period was defined as the time between 14 days prior to the implementation date, or the date of the first reported case – whichever was earliest, and 5 days after the implementation date. These boundaries were set to account for the current length recommendation and the median incubation period time.15 The post-implementation period was defined as the time between 6 days after

the implementation date and April 13, 2020, when data collection was stopped).

The slopes for both the pre-implementation and post-implementation periods were estimated for each social distancing measure-country/territory combination using Microsoft Excel 2016.

The difference between the pre- and post-implementation period slopes (i.e. pre- implementation slope minus post-implementation slope) was also calculated for each measure-

4 country/territory combination. Bivariate analyses were done with t-tests for continuous

variables and chi-squared test for categorical variables.

A stepwise, backward linear regression model was used to evaluate the impact of social distancing measures on the change in slope, after controlling for proportions of populations by

gender16-19, age over 65 years20,21, smoking history22, and prevalence of diabetes23,

hypertension24, obesity25, HIV26, and malignancy27. Finally, we controlled for each country’s

disability-adjusted life years (DALYs) associated with chronic obstructive pulmonary disease

(COPD)28 and prior impact by severe acute respiratory syndrome (SARS)29, a similar, highly

infectious respiratory disease which emerged in 2003. The latter sought to account for

countries’ increased experience and existing health infrastructure in applying social distancing

measures, which may have differentially heightened the effect of government-imposed social

distancing. If a country did not implement the mitigation measure at all, the change in slopes was assigned as zero. Listwise deletion of missing data was done for covariates.

As a secondary outcome, doubling time for both COVID-19 cases and deaths was estimated.

This value was obtained for the pre- and post-implementation periods of each social distancing measure using the formula: (2) / , where m is the slope of the log10 of cumulative

10 cases and deaths.15 Data was𝑙𝑙𝑙𝑙𝑙𝑙 analyzed using𝑚𝑚 SAS 9.4.

Ethical considerations

This study utilized publicly available data without human subject contact, omitting the need for

IRB approval.

5

Results

Baseline epidemiologic and health-related characteristics

As of April 13, 2020, there were a total of 1,916,284 cases and 119,467 deaths from COVID-19

in the 181 affected countries and territories. A total of 91 countries/territories implemented a

stay-at-home order, 170 countries closed schools, 156 countries closed non-essential

businesses, and 136 countries severely restricted travel. Dates that each country implemented

the four categories of social distancing measures can be found in the Supplementary TableS1.

Table 2 shows the baseline epidemiological and comorbidity characteristics of the selected countries and territories.

Impact on COVID-19 morbidity

After controlling for other variables, stay-at-home orders were positively associated with a difference in slope (β = 0.051 (0.042-0.060), p < 0.001) (Table 3). School closures were positively associated with a difference in slope (β = 0. 073 (0.033-0.113), p < 0.001). Similarly, closures of non-essential businesses and severe travel limitations had a positive association with difference in slope (β = 0.058 (0.037-0.078) and β = 0.050 (0.029-0.071), respectively; p < 0.001 for both associations).

Collectively, countries that implemented a stay-at-home order had a 63.6% decrease in the slope of cumulative cases (p < 0.001). School closures resulted in a 61.1% decrease in the slope of cumulative cases (p < 0.001). A 61.7% decrease in slope of cumulative cases was seen with

6 the closure of non-essential businesses (p < 0.001), and a 56.9% decrease was seen with severe travel restrictions (p < 0.001) (Figure 1).

Impact on COVID-19 mortality

After adjusting for covariates, the only social distancing measure that was positively associated with a difference in slope was the stay-at-home order (β = 0.032 (0.019-0.046), p < 0.001)

(Table 3). School closures, closures of non-essential businesses, and severe travel restrictions were not associated with differences in slopes, with (β = 0.017 (-0.021-0.055), p = 0.390), (β =

0.037 (-0.002-0.077), p = 0.062), and (β = 0.019 (-0.001-0.038), p = 0.057), respectively.

When countries implemented stay-at-home orders, there was a 47.7% decrease in the slope of cumulative deaths (p = 0.256). School closures resulted in a 23.9% decrease in the slope of cumulative deaths (p < 0.001). A 43.9% decrease in slope of cumulative deaths was seen with the closure of non-essential businesses (p = 0.416), and a 37.1% decrease was seen with severe travel restrictions (p = 0.060) (Figure 2).

Doubling Time

During the pre-implementation period, the average doubling times (i.e. time for the number of cases to double) were 3.5 days (countries with stay-at-home orders), 2.8 days (countries with school closures), 3.2 days (countries with closures of non-essential businesses), and 3.4 days

(countries with severely restricted travel). After the implementation of the stay-at-home order, the average doubling time increased to 9.6 days. The closures of schools and non-essential businesses increased the doubling time to 7.2 and 8.3 days, respectively. Severe travel

7 restrictions increased the doubling time to 8.0 days. Paired t-tests showed increases in doubling

time between the pre- and post-implementation period (p < 0.001).

Discussion

Even as COVID-19 cases and deaths continue to increase, some countries have yet to enact social distancing measures while others have already lifted them.30,31 Our results suggest that

implementation of stay-at-home orders, closures of educational facilities and non-essential businesses, and travel bans have all independently contributed to “.” In fact, each of the 4 distancing measures decreased the slope of cumulative cases by at least 50%.

Given the natural course of SARS-CoV-2 , longer follow-up time is needed to better

evaluate the corresponding effect on mortality rate.

We hypothesize that the mortality rate reduction was due to the reduction in incidence of the

disease from the stay-at-home order.32 Furthermore, doubling time for cases increased by an

average of five days with the implementation of any of the four measures. These findings are

supported by other studies that have created conceptual models simulating the reduction of

cases due to physical distancing and travel control measures.13,14,33,34

Social distancing aims to protect those who are at high risk for COVID-19-related morbidity and mortality.35,36 It also decreases risk of overwhelming healthcare infrastructure, particularly

intensive care resources. This is particularly important in low and middle-income countries

(LMIC) that may not have the capacity or proper equipment to support high case numbers.

8 Systems in Ecuador and India have already experienced high strain with difficulty disposing of

bodies and hospitals having to close their doors to new patients.37,38

However, social distancing has its tradeoffs. It is difficult to sustain from a financial and

economic perspective. These restrictive measures have contributed to socioeconomic hardship

and resulted in catastrophic shocks to global financial markets.39 COVID-19 has caused

unprecedented unemployment benefits claims within the US, peaking at approximately 17

million in mid-April 2020.40 With a loss of jobs comes disruptions in supply chains, particularly in the food industry.41,42 Food shortages have plagued countries, especially those who rely on

imports from neighboring countries.43 For some countries in East Africa, including Somalia,

Ethiopia, and Kenya, food shortages are further exacerbated by locust swarms destroying food

crops.44-46

As a result, some countries (e.g. , , Japan, etc.) have begun lifting their social

distancing measures. A concern is that if COVID-19 is still actively spreading within the

community and people are no longer following the measures meant to limit its transmissibility,

the risk of a second of cases increases.47 This phenomenon has been seen with other

like SARS when social distancing measures were lifted prematurely.48

Our study depended on countries’ abilities to report their cases, raising the potential for

information bias. In addition, given the of the experiment (i.e. population-level data) our

results are subject to ecological bias. Further, it was not entirely possible to entirely disentangle

the impact of each social distancing measure as their implementations within each country

9 often overlapped in time. As we adopted a conservative approach and adhered to strict interpretations for when a country fully implemented a social distancing measure, we likely underestimated the effect of partial measures. To qualify as a fully implemented measure in our study, we required nationwide implementation. Most countries began enacting restrictions regionally before expanding them nationally and some restrictions may have been self-imposed despite no official ruling; these events are most likely to lead to effect underestimation. Lastly, our analysis did not account for the relaxing of mitigation measures, most of which occurred after April 13th.

Conclusions

Each government-imposed social distancing measure was associated with a reduced rate of

growth in COVID-19 cases. Further research is needed to examine the potential effects of

repealing these social distancing measures. The accompanying socioeconomic risks must be

weighed against the potential for unchecked and exponential growth in and deaths,

especially in the context of already-weakened health infrastructures.

Authors’ contributions

NKL contributed to the literature search, figures, study design, data collection, data analysis, data interpretation, writing. AVL contributed to the literature search, data collection, and writing. JPB contributed to the data collection and writing. SK contributed to the data collection and writing. DL contributed to the data collection and writing. RI contributed to data interpretation and writing. MRO contributed to study design, data analysis, data interpretation, and writing.

Funding: None

10 Disclosures: None

11 Figure 1. Median COVID-19 cumulative incidence during the pre- and post-implementation

periods (logarithmic scale). The box represents the interquartile range (IQR) with the median line in the middle. The “x” denotes the mean, and the points represent outliers. The whiskers represent datapoint within 1.5 times the IQR.

12 Figure 2. Median COVID-19 cumulative deaths during the pre- and post-implementation periods

(logarithmic scale). The box represents the interquartile range (IQR) with the median line in the middle. The “x” denotes the mean, and the points represent outliers. The whiskers represent datapoint within 1.5 times the IQR.

13 Table 1. List of countries/territories included in this study. Afghanistan Kyrgyzstan Saint Kitts and Nevis Albania Djibouti Laos Saint Lucia Dominica Latvia St Vincent & Grenadines Andorra Dominican Republic Lebanon San Marino Angola Ecuador Liberia Sao Tome and Principe Antigua and Barbuda Egypt Libya Saudi Arabia Argentina El Salvador Liechtenstein Senegal Armenia Equatorial Guinea Serbia Eritrea Luxembourg Seychelles Estonia Madagascar Sierra Leone Azerbaijan Eswatini Malawi Singapore Bahamas Ethiopia Malaysia Slovakia Bahrain Fiji Maldives Slovenia Bangladesh Finland Mali Somalia Barbados Malta South Africa Belarus Gabon Mauritania South Sudan Gambia Mauritius Belize Georgia Mexico Sri Lanka Benin Moldova Sudan Bhutan Ghana Monaco Suriname Bolivia Mongolia Sweden Bosnia and Herzegovina Grenada Montenegro Switzerland Botswana Guatemala Morocco Syria Brazil Guinea Mozambique Taiwan Brunei Guinea-Bissau Namibia Tanzania Bulgaria Guyana Nepal Thailand Burkina Faso Haiti Timor-Leste Burma Holy See New Zealand Togo Burundi Honduras Nicaragua Trinidad and Tobago Cabo Verde Hungary Niger Tunisia Cambodia Iceland Nigeria Turkey Cameroon India North Macedonia Uganda Indonesia Norway Ukraine Central African Republic Iran Oman United Arab Emirates Chad Iraq Pakistan Chile Ireland Panama Uruguay China Israel Papua New Guinea Colombia Paraguay Uzbekistan Congo (Brazzaville) Jamaica Peru Venezuela Congo (Kinshasa) Japan Philippines Vietnam Costa Rica Jordan Poland Yemen Cote d'Ivoire Kazakhstan Zambia

14 Croatia Kenya Qatar Zimbabwe Cuba Korea, South Romania Cyprus Kosovo Russia Czechia Kuwait Rwanda

15 Table 2. Epidemiological and comorbidity characteristics of countries affected by COVID-19. Closure of Non- Stay-at-Home Severe Travel School closures Essential Orders Restrictions Missing Characteristics Businesses Values Yes No Yes No Yes No Yes No n=91 n= 90 n=170 n= 11 n=156 n=25 n=136 n=45 Change in slope of cumulative 0.05 0.002 0.07 0.00 0.06 0.00 0.05 0.00 0/181 cases curve, (0.04) (0.02) * (0.07) (<0.01) * (0.05) (<0.01) * (0.07) (<0.01) * Mean (SD) Change in slope of cumulative 0.03 0.00 0.01 0.00 0.03 0.00 0.02 0.00 0/181 deaths curve, (0.06) (<0.01) * (0.002) (<0.01) * (0.07) (<0.01) * (0.07) (<0.01) * Mean (SD) Percentage of population age 10.16 8.29 9.05 11.94 8.84 11.63 7.99 12.93 1/181 over 65 years, (6.62) (6.51) (6.57) (7.03) (6.61) (6.22) (5.83) (7.46) * Mean (SD) Percentage of 49.68 50.56 50.10 50.33 50.22 49.49 50.02 50.41 Males, Mean 0/181 (2.79) (3.74) (3.39) (2.01) (3.51) (1.53) (2.80) (4.56) (SD) Smoking 21.38 21.22 21.48 16.70 20.98 23.83 20.10 24.88 Prevalence, 43/181 (9.55) (9.64) (9.67) (3.46) (9.72) (8.05) (9.75) (8.09) * Mean (SD) Diabetes 7.75 7.65 7.68 7.94 7.74 7.43 7.79 7.43 Prevalence, 3/181 (3.74) (4.17) (4.03) (2.56) (4.10) (2.77) (4.14) (3.35) Mean (SD) Hypertension 32.09 31.91 32.14 29.52 31.98 32.13 31.38 34.03 Prevalence, 11/181 (7.91) (8.17) (8.11) (6.02) (7.96) (8.58) (8.16) (7.25) Mean (SD) Obesity 19.94 16.77 18.26 20.09 18.37 18.23 17.48 20.97 Prevalence, 9/181 (8.64) (9.09) * (8.88) (11.14) (9.01) (9.02) (9.31) (7.43) * Mean (SD) HIV Prevalence, 1.92 1.55 1.75 1.53 1.78 1.37 1.89 1.08 46/181 Mean (SD) (4.96) (2.63) (4.02) (2.75) (4.06) (3.16) (4.21) (2.60) Malignancy 1.15 1.10 1.08 2.01 1.04 1.71 0.98 1.60 Prevalence, 7/181 (0.73) (0.97) (0.73) (1.97) (0.72) (1.36) * (0.74) (1.00) * Mean (SD) COPD DALY, 679.30 634.00 652.60 735.50 640.00 767.70 629.00 742.10 7/181 Mean (SD) (335.80) (386.80) (361.90) (366.40) (365.40) (321.00) (370.50) (322.10) Percentage of 18.68 14.44 14.71 45.45* 15.38 24.00 15.44 20.00 0/181 SARS Experience * p < 0.05

16 Table 3. Stepwise, backward linear regression for change in the slopes of cumulative incidence and death curves.

Social Change in Cumulative Incidence (Slope) Change in Cumulative Death (Slope) Distancing Measure Initial Model, Final Model, Initial Model, Final Model, p-value p-value p-value p-value β (95% CI) β (95% CI) β (95% CI) β (95% CI) Stay-at- 0.055 0.051 0.026 0.032 < 0.001 < 0.001 0.008 < 0.001 § home orders (0.044-0.067) (0.042-0.060) (0.007-0.044) (0.019-0.046) School 0.093 0.073 0.049 0.017 0.079 < 0.001 ⇞ 0.399 0.390 ⇞ closures (-0.011-0.196) (0.033-0.113) (-0.066-0.165) (-0.021-0.055) Closure of non- 0.069 0.058 0.035 0.037 < 0.001 < 0.001 † 0.232 0.062 ‡ essential (0.035-0.103) (0.037-0.078) (-0.023-0.092) (-0.002-0.077) businesses Severely 0.062 0.050 0.025 0.019 limited 0.002 < 0.001 § 0.203 0.057 (0.023-0.100) (0.029-0.071) (-0.014-0.065) (-0.001-0.038) travel Controlled for diabetes and hypertension ⇞ Controlled for age over 65 years †⇞ Controlled for gender § Controlled for diabetes ‡ Controlled for smoking CI = confidence interval

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