No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic

Background In the absence of a cure in the time of a pandemic, social distancing measures seem to be the most effective intervention to slow the spread of disease. Various simulation-based studies have been conducted to investigate the effectiveness of these measures. While those studies unanimously confirm the mitigating effect of social distancing on disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. However, real transactional data can reduce uncertainty and provide a less noisy picture of the effectiveness of social distancing. Objective The aim of this paper was to integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics from the European Centre for Disease Prevention and Control) to study the role of social distancing policies in 26 countries and analyze the transmission rate of the coronavirus disease (COVID-19) pandemic over the course of 5 weeks. Methods Relying on the susceptible-infected-recovered (SIR) model and official COVID-19 reports, we first calculated the weekly transmission rate (β) of COVID-19 in 26 countries for 5 consecutive weeks. Then, we integrated these data with the Google and Apple mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between the mobility factors and β values. Results Gradient boosted trees regression analysis showed that changes in mobility patterns resulting from social distancing policies explain approximately 47% of the variation in the disease transmission rates. Conclusions Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing the spread of COVID-19. In addition to providing less noisy and more generalizable support for the idea of social distancing, we provide specific insights for public health policy makers regarding locations that should be given higher priority for enforcing social distancing measures.

Download Full-text

No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic (Preprint)

10.2196/preprints.19862 ◽

2020 ◽

Author(s):

Dursun Delen ◽

Enes Eryarsoy ◽

Behrooz Davazdahemami

Keyword(s):

Disease Transmission ◽

Transmission Rate ◽

Disease Spread ◽

Data Sets ◽

Mobility Patterns ◽

Social Distancing ◽

Mobility Data ◽

Simulation Based ◽

Transactional Data ◽

Cross National

BACKGROUND In the absence of a cure in the time of a pandemic, social distancing measures seem to be the most effective intervention to slow the spread of disease. Various simulation-based studies have been conducted to investigate the effectiveness of these measures. While those studies unanimously confirm the mitigating effect of social distancing on disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. However, real transactional data can reduce uncertainty and provide a less noisy picture of the effectiveness of social distancing. OBJECTIVE The aim of this paper was to integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics from the European Centre for Disease Prevention and Control) to study the role of social distancing policies in 26 countries and analyze the transmission rate of the coronavirus disease (COVID-19) pandemic over the course of 5 weeks. METHODS Relying on the susceptible-infected-recovered (SIR) model and official COVID-19 reports, we first calculated the weekly transmission rate (β) of COVID-19 in 26 countries for 5 consecutive weeks. Then, we integrated these data with the Google and Apple mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between the mobility factors and β values. RESULTS Gradient boosted trees regression analysis showed that changes in mobility patterns resulting from social distancing policies explain approximately 47% of the variation in the disease transmission rates. CONCLUSIONS Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing the spread of COVID-19. In addition to providing less noisy and more generalizable support for the idea of social distancing, we provide specific insights for public health policy makers regarding locations that should be given higher priority for enforcing social distancing measures.

Download Full-text

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.

Download Full-text

Lockdown related travel behavior undermines the containment of SARS-CoV-2

10.1101/2020.10.22.20217752 ◽

2020 ◽

Author(s):

Nishant Kishore ◽

Rebecca Kahn ◽

Pamela P. Martinez ◽

Pablo M. De Salazar ◽

Ayesha S. Mahmud ◽

...

Keyword(s):

Rural Areas ◽

Travel Behavior ◽

Human Mobility ◽

Unintended Consequences ◽

Disease Spread ◽

Mobility Patterns ◽

Long Distance ◽

Mobility Data ◽

Travel Restrictions ◽

The Impact

ABSTRACTIn response to the SARS-CoV-2 pandemic, unprecedented policies of travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns - defined here as restrictions on both local movement or long distance travel - will determine how effective these kinds of interventions are. Here, we measure the impact of the announcement and implementation of lockdowns on human mobility patterns by analyzing aggregated mobility data from mobile phones. We find that following the announcement of lockdowns, both local and long distance movement increased. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. We find that travel surges following announcements of lockdowns can increase seeding of the epidemic in rural areas, undermining the goal of the lockdown of preventing disease spread. Appropriate messaging surrounding the announcement of lockdowns and measures to decrease unnecessary travel are important for preventing these unintended consequences of lockdowns.

Download Full-text

Control Strategies for the Third wave of COVID-19 infection in India: A Mathematical Model Incorporating Vaccine Effectiveness

10.36227/techrxiv.17705957 ◽

2022 ◽

Author(s):

Ashutosh Mahajan ◽

Namitha Sivadas ◽

Pooja Panda

Keyword(s):

Transmission Rate ◽

Control Strategies ◽

Control Measures ◽

Disease Spread ◽

Effective Control ◽

Third Wave ◽

Social Distancing ◽

Vaccination Rates ◽

New Variant ◽

The Future

The waning effectiveness of the COVID-19 vaccines and the emergence of a new variant Omicron has given rise to the possibility of another outbreak of the infection in India. COVID-19 has caused more than 34 million reported cases and 475 thousand deaths in India so far, and it has affected the country at the root level, socially as well as economically. After going through different control measures, mass vaccination has been achieved to a large extent for the highly populous country, and currently under progress. India has already been hit by a massive second wave of infection in April-June, 2021 mainly due to the delta variant, and might see a third wave in the near future that needs to be controlled with effective control strategies. In this paper, we present a compartmental epidemiological model with vaccinations incorporating the dose-dependent effectiveness. We study a possible sudden outbreak of SARS-CoV2 variants in the future, and bring out the associated predictions for various vaccination rates and point out optimum control measures. Our results show that for transmission rate 30% higher than the current rate due to emergence of new variant or relaxation of social distancing conditions, daily new cases can peak to 250k in March 2022, taking the second dose effectiveness dropping to 50% in the future. A combination of vaccination and controlled lockdown or social distancing is the key to tackling the current situation and for the coming few months. Our simulation results show that social distancing measures show better control over the disease spread than the higher vaccination rates.

Download Full-text

Changes in prescribing of oral capecitabine versus intravenous (IV) 5-fluorouracil (5-FU) in gastrointestinal (GI) cancers during the COVID-19 pandemic.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e18596 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e18596-e18596

Author(s):

Yasin Civelek ◽

Daniel Cullen ◽

David Joseph Debono ◽

Michael Jordan Fisch ◽

John Barron ◽

...

Keyword(s):

Prescribing Patterns ◽

Cancer Type ◽

Population Mobility ◽

Mobility Patterns ◽

Oral Capecitabine ◽

Social Distancing ◽

Start Date ◽

Mobility Data ◽

Before And After ◽

Gi Cancers

e18596 Background: Several oncology guidelines recommend using oral drugs vs. IV to minimize COVID-19 risk for patients with cancer. We examined the association between prescribing patterns of oral capecitabine vs. IV 5FU for GI cancers and social distancing, measured by the change in population mobility patterns in response to shelter-in place policies, during the pandemic. Methods: Using claims data for commercially insured members, we included patients 18 years of age or older with colorectal, gastroesophageal, or pancreatic cancer, who had continuous health plan coverage for at least 2 months before and 1 month after initiating chemotherapy with capecitabine or 5-FU from January 2017 to August 2020. We analyzed unadjusted trends in proportion of chemotherapy that was oral during pandemic (March 1st to August 31st, 2020) compared to previous years. Then, we conducted difference-in-differences analysis using COVID-19 Community Mobility Reports, by Google, and utilizing different levels of changes in mobility trends across states over time. In our main model, we used a 20% decrease in retail and recreation visits as our threshold and compared the prescribing rates in states below and above the threshold as well as before and after the pandemic began. We also used different thresholds and categories of places to check the sensitivity of our findings. Models are adjusted for age, gender, month of year, urban status, comorbidities, and state of residence at chemotherapy start date. Results: A total of 17,414 nationally distributed patients (69% colorectal, 13% gastroesophageal, 18% pancreatic) were included (mean age, 58.8 years; 41% female). During the pandemic, 1,875 patients (65% colorectal, 15% gastroesophageal, 20% pancreatic) were identified. The proportion of oral regimens did not change significantly for colorectal and gastroesophageal patients and decreased by 7.4 percentage points (pp) (p < 0.01) for pancreatic patients. In regression modelling with mobility data, oral prescribing rates for colorectal patients increased by 3.1 pp (p < 0.01), largely driven by increases for female patients (9.2 pp, p = 0.02). We observed a decrease in oral prescribing rates among pancreatic patients (-1.20 pp, p = 0.04) and did not observe a significant change for gastroesophageal patients. Our results are not sensitive to different social distancing specifications. Conclusions: We observed differential impact of the pandemic on oral prescribing rates by GI cancer type and gender. Oral prescribing increased among colorectal cancer patients driven mostly by higher oral prescribing in females. For pancreatic and gastroesophageal patients, oral prescriptions either remained unchanged or decreased. This observation may reflect a variable impact of the pandemic on women as compared to men and might involve heightened caregiving responsibilities for women.

Download Full-text

Chopping the tail: how preventing superspreading can help to maintain COVID-19 control

10.1101/2020.06.30.20143115 ◽

2020 ◽

Cited By ~ 6

Author(s):

Morgan P. Kain ◽

Marissa L. Childs ◽

Alexander D. Becker ◽

Erin A. Mordecai

Keyword(s):

Los Angeles ◽

Disease Transmission ◽

Transmission Rate ◽

Skewed Distribution ◽

Model Framework ◽

Rate Distribution ◽

Social Distancing ◽

Individual Level ◽

Epidemic Dynamics ◽

The Impact

AbstractDisease transmission is notoriously heterogeneous, and SARS-CoV-2 is no exception. A skewed distribution where few individuals or events are responsible for the majority of transmission can result in explosive, superspreading events, which produce rapid and volatile epidemic dynamics, especially early or late in epidemics. Anticipating and preventing superspreading events can produce large reductions in overall transmission rates. Here, we present a compartmental (SEIR) epidemiological model framework for estimating transmission parameters from multiple imperfectly observed data streams, including reported cases, deaths, and mobile phone-based mobility that incorporates individual-level heterogeneity in transmission using previous estimates for SARS-CoV-1 and SARS-CoV-2. We parameterize the model for COVID-19 epidemic dynamics by estimating a time-varying transmission rate that incorporates the impact of non-pharmaceutical intervention strategies that change over time, in five epidemiologically distinct settings—Los Angeles and Santa Clara Counties, California; Seattle (King County), Washington; Atlanta (Dekalb and Fulton Counties), Georgia; and Miami (Miami-Dade County), Florida. We find the effective reproduction number ℛE dropped below 1 rapidly following social distancing orders in mid-March, 2020 and remained there into June in Santa Clara County and Seattle, but climbed above 1 in late May in Los Angeles, Miami, and Atlanta, and has trended upward in all locations since April. With the fitted model, we ask: how does truncating the tail of the individual-level transmission rate distribution affect epidemic dynamics and control? We find interventions that truncate the transmission rate distribution while partially relaxing social distancing are broadly effective, with impacts on epidemic growth on par with the strongest population-wide social distancing observed in April, 2020. Given that social distancing interventions will be needed to maintain epidemic control until a vaccine becomes widely available, “chopping off the tail” to reduce the probability of superspreading events presents a promising option to alleviate the need for extreme general social distancing.

Download Full-text

Control Strategies for the Third wave of COVID-19 infection in India: A Mathematical Model Incorporating Vaccine Effectiveness

10.1101/2021.12.27.21268464 ◽

2021 ◽

Author(s):

Namitha A Sivadas ◽

Ashutosh Mahajan ◽

Pooja Panda

Keyword(s):

Transmission Rate ◽

Control Strategies ◽

Control Measures ◽

Disease Spread ◽

Effective Control ◽

Third Wave ◽

Social Distancing ◽

Vaccination Rates ◽

New Variant ◽

The Future

The waning effectiveness of the COVID-19 vaccines and the emergence of a new variant Omicron has given rise to the possibility of another outbreak of the infection in India. COVID-19 has caused more than 34 million reported cases and 475 thousand deaths in India so far, and it has affected the country at the root level, socially as well as economically. After going through different control measures, mass vaccination has been achieved to a large extent for the highly populous country, and currently under progress. India has already been hit by a massive second wave of infection in April-June, 2021 mainly due to the delta variant, and might see a third wave in the near future that needs to be controlled with effective control strategies. In this paper, we present a compartmental epidemiological model with vaccinations incorporating the dose-dependent effectiveness. We study a possible sudden outbreak of SARS-CoV2 variants in the future, and bring out the associated predictions for various vaccination rates and point out optimum control measures. Our results show that for transmission rate 30% higher than the current rate due to emergence of new variant or relaxation of social distancing conditions, daily new cases can peak to 250k in March 2022, taking the second dose effectiveness dropping to 50% in the future. Combination of vaccination and controlled lockdown or social distancing is the key to tackling the current situation and for the coming few months. Our simulation results show that social distancing measures show better control over the disease spread than the higher vaccination rates.

Download Full-text

Disruption of Metapopulation Structure Reduces Tasmanian Devil Facial Tumour Disease Spread at the Expense of Abundance and Genetic Diversity

Pathogens ◽

10.3390/pathogens10121592 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1592

Author(s):

Rowan Durrant ◽

Rodrigo Hamede ◽

Konstans Wells ◽

Miguel Lurgi

Keyword(s):

Genetic Diversity ◽

Disease Transmission ◽

Transmission Rate ◽

Extinction Risk ◽

Disease Prevalence ◽

Disease Spread ◽

Tasmanian Devil ◽

Local Populations ◽

Metapopulation Structure ◽

Devil Facial Tumour Disease

Metapopulation structure plays a fundamental role in the persistence of wildlife populations. It can also drive the spread of infectious diseases and transmissible cancers such as the Tasmanian devil facial tumour disease (DFTD). While disrupting this structure can reduce disease spread, it can also impair host resilience by disrupting gene flow and colonisation dynamics. Using an individual-based metapopulation model we investigated the synergistic effects of host dispersal, disease transmission rate and inter-individual contact distance for transmission, on the spread and persistence of DFTD from local to regional scales. Disease spread, and the ensuing population declines, are synergistically determined by individuals’ dispersal, disease transmission rate and within-population mixing. Transmission rates can be magnified by high dispersal and inter-individual transmission distance. The isolation of local populations effectively reduced metapopulation-level disease prevalence but caused severe declines in metapopulation size and genetic diversity. The relative position of managed (i.e., isolated) local populations had a significant effect on disease prevalence, highlighting the importance of considering metapopulation structure when implementing metapopulation-scale disease control measures. Our findings suggest that population isolation is not an ideal management method for preventing disease spread in species inhabiting already fragmented landscapes, where genetic diversity and extinction risk are already a concern.

Download Full-text

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.

Download Full-text

Relationships between Local Green Space and Human Mobility Patterns during COVID-19 for Maryland and California, USA

Sustainability ◽

10.3390/su12229401 ◽

2020 ◽

Vol 12 (22) ◽

pp. 9401

Author(s):

Seulkee Heo ◽

Chris C. Lim ◽

Michelle L. Bell

Keyword(s):

Disease Transmission ◽

Vegetation Index ◽

Green Space ◽

Early Stage ◽

Human Mobility ◽

P Value ◽

State Parks ◽

Mobility Patterns ◽

Mobility Data ◽

State And Local

Human mobility is a significant factor for disease transmission. Little is known about how the environment influences mobility during a pandemic. The aim of this study was to investigate an effect of green space on mobility reductions during the early stage of the COVID-19 pandemic in Maryland and California, USA. For 230 minor civil divisions (MCD) in Maryland and 341 census county divisions (CCD) in California, we obtained mobility data from Facebook Data for Good aggregating information of people using the Facebook app on their mobile phones with location history active. The users’ movement between two locations was used to calculate the number of users that traveled into an MCD (or CCD) for each day in the daytime hours between 11 March and 26 April 2020. Each MCD’s (CCD’s) vegetation level was estimated as the average Enhanced Vegetation Index (EVI) level for 1 January through 31 March 2020. We calculated the number of state and local parks, food retail establishments, and hospitals for each MCD (CCD). Results showed that the daily percent changes in the number of travels declined during the study period. This mobility reduction was significantly lower in Maryland MCDs with state parks (p-value = 0.045), in California CCDs with local-scale parks (p-value = 0.048). EVI showed no association with mobility in both states. This finding has implications for the potential impacts of green space on mobility under an outbreak. Future studies are needed to explore these findings and to investigate changes in health effects of green space during a pandemic.

Download Full-text