Forecasting the spread of COVID-19 under different reopening strategies

AbstractWe combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID-19 cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19.

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Forecasting the Spread of COVID-19 under Different Reopening Strategies

10.1101/2020.05.26.20113993 ◽

2020 ◽

Cited By ~ 1

Author(s):

Meng Liu ◽

Raphael Thomadsen ◽

Song Yao

Keyword(s):

United States ◽

Growth Rate ◽

Population Density ◽

The United States ◽

Sir Model ◽

Social Distancing ◽

Mobility Data ◽

The Social ◽

The Us ◽

Case Data

ABSTRACTWe combine COVID-19 case data with demographic and mobility data to estimate a modified susceptible-infected-recovered (SIR) model for the spread of this disease in the United States. We find that the incidence of infectious COVID-19 individuals has a concave effect on contagion, as would be expected if people have inter-related social networks. We also demonstrate that social distancing and population density have large effects on the rate of contagion. The social distancing in late March and April substantially reduced the number of COVID-19 cases. However, the concave contagion pattern means that when social distancing measures are lifted, the growth rate is considerable but will not be exponential as predicted by standard SIR models. Furthermore, counties with the lowest population density could likely avoid high levels of contagion even with no social distancing. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19, about double what would occur if the US only restored to 50% of the way to normalcy.

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Impacts of social distancing policies on mobility and COVID-19 case growth in the US

Nature Communications ◽

10.1038/s41467-021-23404-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Gregory A. Wellenius ◽

Swapnil Vispute ◽

Valeria Espinosa ◽

Alex Fabrikant ◽

Thomas C. Tsai ◽

...

Keyword(s):

Public Health ◽

United States ◽

Infection Control ◽

State Level ◽

The United States ◽

Social Consequences ◽

Social Distancing ◽

Mobility Data ◽

The Us ◽

Public Health Officials

AbstractSocial distancing remains an important strategy to combat the COVID-19 pandemic in the United States. However, the impacts of specific state-level policies on mobility and subsequent COVID-19 case trajectories have not been completely quantified. Using anonymized and aggregated mobility data from opted-in Google users, we found that state-level emergency declarations resulted in a 9.9% reduction in time spent away from places of residence. Implementation of one or more social distancing policies resulted in an additional 24.5% reduction in mobility the following week, and subsequent shelter-in-place mandates yielded an additional 29.0% reduction. Decreases in mobility were associated with substantial reductions in case growth two to four weeks later. For example, a 10% reduction in mobility was associated with a 17.5% reduction in case growth two weeks later. Given the continued reliance on social distancing policies to limit the spread of COVID-19, these results may be helpful to public health officials trying to balance infection control with the economic and social consequences of these policies.

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Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing

Scientific Reports ◽

10.1038/s41598-021-83441-4 ◽

2021 ◽

Vol 11 (1) ◽

Cited By ~ 2

Author(s):

Corentin Cot ◽

Giacomo Cacciapaglia ◽

Francesco Sannino

Keyword(s):

Infection Rate ◽

The United States ◽

Strong Decrease ◽

Social Distancing ◽

Mobility Data ◽

Actual Measure ◽

The Us ◽

The Impact ◽

Political Decisions ◽

Mobility Reduction

AbstractWe employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Our analysis allows us to classify different shades of social distancing measures for the first wave of the pandemic. We observe a strong decrease in the infection rate occurring two to five weeks after the onset of mobility reduction. A universal time scale emerges, after which social distancing shows its impact. We further provide an actual measure of the impact of social distancing for each region, showing that the effect amounts to a reduction by 20–40% in the infection rate in Europe and 30–70% in the US.

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Correcting misperceptions of exponential coronavirus growth increases support for social distancing

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2006048117 ◽

2020 ◽

Vol 117 (28) ◽

pp. 16264-16266 ◽

Cited By ~ 12

Author(s):

Joris Lammers ◽

Jan Crusius ◽

Anne Gast

Keyword(s):

United States ◽

Exponential Growth ◽

The United States ◽

Statistical Literacy ◽

Social Distancing

The most effective way to stem the spread of a pandemic such as coronavirus disease 2019 (COVID-19) is social distancing, but the introduction of such measures is hampered by the fact that a sizeable part of the population fails to see their need. Three studies conducted during the mass spreading of the virus in the United States toward the end of March 2020 show that this results partially from people’s misperception of the virus’s exponential growth in linear terms and that overcoming this bias increases support for social distancing. Study 1 shows that American participants mistakenly perceive the virus’s exponential growth in linear terms (conservatives more so than liberals). Studies 2 and 3 show that instructing people to avoid the exponential growth bias significantly increases perceptions of the virus’s growth and thereby increases support for social distancing. Together, these results show the importance of statistical literacy to recruit support for fighting pandemics such as the coronavirus.

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Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study (Preprint)

10.2196/preprints.24591 ◽

2020 ◽

Author(s):

Romain Garnier ◽

Jan R Benetka ◽

John Kraemer ◽

Shweta Bansal

Keyword(s):

United States ◽

Black People ◽

The United States ◽

Control Measures ◽

Mobility Patterns ◽

Poverty Level ◽

Social Distancing ◽

Mobility Data ◽

Black Populations ◽

The Impact

BACKGROUND Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. OBJECTIVE We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. METHODS We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. RESULTS We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. CONCLUSIONS Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts.

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The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets (Preprint)

10.2196/preprints.21499 ◽

2020 ◽

Author(s):

Paiheng Xu ◽

Mark Dredze ◽

David A Broniatowski

Keyword(s):

United States ◽

South Carolina ◽

Social Mobility ◽

State Level ◽

The United States ◽

Mobility Index ◽

Social Distancing ◽

Mobility Data ◽

Response Planning ◽

Twitter Data

BACKGROUND Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and “flattens the curve” so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. OBJECTIVE The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. METHODS We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. RESULTS We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. CONCLUSIONS We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning.

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The US Public’s Perception of the Threat of COVID-19 During the Rapid Spread of the COVID-19 Outbreak: Cross-Sectional Survey Study

Journal of Medical Internet Research ◽

10.2196/23400 ◽

2021 ◽

Vol 23 (2) ◽

pp. e23400

Author(s):

Xiaolei Xiu ◽

Anran Wang ◽

Qing Qian ◽

Sizhu Wu

Keyword(s):

United States ◽

Close Contact ◽

The United States ◽

The Self ◽

Medical Conditions ◽

Effective Response ◽

Social Distancing ◽

Rapid Spread ◽

The Us ◽

And Behaviors

Background The rapid spread of the COVID-19 pandemic in the United States has made people uncertain about their perceptions of the threat of COVID-19 and COVID-19 response measures. To mount an effective response to this epidemic, it is necessary to understand the public's perceptions, behaviors, and attitudes. Objective We aimed to test the hypothesis that people’s perceptions of the threat of COVID-19 influence their attitudes and behaviors. Methods This study used an open dataset of web-based questionnaires about COVID-19. The questionnaires were provided by Nexoid United Kingdom. We selected the results of a questionnaire on COVID-19–related behaviors, attitudes, and perceptions among the US public. The questionnaire was conducted from March 29 to April 20, 2020. A total of 24,547 people who lived in the United States took part in the survey. Results In this study, the average self-assessed probability of contracting COVID-19 was 33.2%, and 49.9% (12,244/24,547) of the respondents thought that their chances of contracting COVID-19 were less than 30%. The self-assessed probability of contracting COVID-19 among women was 1.35 times that of males. A 5% increase in perceived infection risk was significantly associated with being 1.02 times (OR 1.02, 95% CI 1.02-1.02; P<.001) more likely to report having close contact with >10 people, and being 1.01 times (OR 1.01, 95% CI 1.01-1.01; P<.001) more likely to report that cohabitants disagreed with taking steps to reduce the risk of contracting COVID-19. However, there was no significant association between participants who lived with more than 5 cohabitants or less than 5 cohabitants (P=.85). Generally, participants who lived in states with 1001-10,000 COVID-19 cases, were aged 20-40 years, were obese, smoked, drank alcohol, never used drugs, and had no underlying medical conditions were more likely to be in close contact with >10 people. Most participants (21,017/24,547, 85.6%) agreed with washing their hands and maintaining social distancing, but only 20.2% (4958/24,547) of participants often wore masks. Additionally, male participants and participants aged <20 years typically disagreed with washing their hands, maintaining social distancing, and wearing masks. Conclusions This survey is the first attempt to describe the determinants of the US public’s perception of the threat of COVID-19 on a large scale. The self-assessed probability of contracting COVID-19 differed significantly based on the respondents’ genders, states of residence, ages, body mass indices, smoking habits, alcohol consumption habits, drug use habits, underlying medical conditions, environments, and behaviors. These findings can be used as references by public health policy makers and health care workers who want to identify populations that need to be educated on COVID-19 prevention and health.

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Socio-economic disparities in social distancing during the COVID-19 pandemic in the United States

10.1101/2020.11.07.20201335 ◽

2020 ◽

Author(s):

Romain Garnier ◽

Jan R. Benetka ◽

John Kraemer ◽

Shweta Bansal

Keyword(s):

United States ◽

Black People ◽

The United States ◽

Control Measures ◽

Mobility Patterns ◽

Poverty Level ◽

Social Distancing ◽

Mobility Data ◽

Black Populations ◽

The Impact

AbstractImportanceEliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic.ObjectiveTo assess how mobility patterns have varied across the United States during the COVID-19 pandemic, and identify associations with socio-economic factors of populations.Design, Setting, and ParticipantsWe used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level between February and May 2020. Using linear mixed models, we assessed the associations between social distancing and socio-economic variables, including the proportion of people below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density.Main outcomes and ResultsWe find that the speed, depth, and duration of social distancing in the United States is heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; and in contrast, that social distancing is intense in counties with higher population densities and larger Black populations.Conclusions and relevanceSocio-economic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of COVID-19 in communities across the United States. This is likely to amplify existing health disparities, and needs to be addressed to ensure the success of ongoing pandemic mitigation efforts.

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Social Distancing Has Merely Stabilized COVID-19 in the US

10.1101/2020.04.27.20081836 ◽

2020 ◽

Cited By ~ 3

Author(s):

Aaron B. Wagner ◽

Elaine L. Hill ◽

Sean E. Ryan ◽

Ziteng Sun ◽

Grace Deng ◽

...

Keyword(s):

United States ◽

Reproduction Number ◽

The United States ◽

Policy Implications ◽

Significant Evidence ◽

Social Distancing ◽

Point Estimates ◽

The Us ◽

The World

AbstractSocial distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19. In this work we analyze the effect of current social distancing measures in the United States. We quantify the reduction in doubling rate, by state, that is associated with social distancing. We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states. At the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases. Instead, social distancing has merely stabilized the spread of the disease. We provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing. We also discuss the policy implications of our findings.

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A Decision Analytic Approach for Social Distancing Policies During COVID-19 Pandemic

10.1101/2020.06.24.20139329 ◽

2020 ◽

Author(s):

Zeynep Ertem ◽

Ozgur M. Araz ◽

Mayteé Cruz-Aponte

Keyword(s):

Public Health ◽

United States ◽

Population Density ◽

The United States ◽

Disease Spread ◽

Public Health Issue ◽

Mobility Patterns ◽

Empirical Estimation ◽

Social Distancing ◽

The Right

AbstractThe COVID-19 pandemic has become a crucial public health issue in many countries including the United States. In the absence of the right vaccine strain and sufficient antiviral stockpiles on hand, non-pharmaceutical interventions have become valuable public health tools at the early stages of the pandemic and they are employed by many countries across the globe. These interventions are designed to increase social distancing between individuals to reduce the transmission of the virus and eventually dampen the burden on the healthcare system. The virus transmissibility is a function of the average number of contacts individuals have in their communities and it is highly dependent on population density and daily mobility patterns, along with other social factors. These show significant variation across the United States. In this article, we study the effectiveness of social distancing measures in communities with different population density. Specifically, first we show how the empirical estimation of reproduction number differs for two completely different states, thus the experience of the COVID-19 outbreak is drastically different, suggesting different outbreak growth rates in practice. Second, we develop an age-structured compartmental model for simulating the disease spread in order to demonstrate the variation in the observed outbreak characteristics. We find that early trigger and late trigger options present a trade-off between the peak magnitude and the overall death toll of the outbreak which may also vary across different populations.

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