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1.
28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 ; : 4279-4289, 2022.
Article in English | Scopus | ID: covidwho-2020397

ABSTRACT

Recurring outbreaks of COVID-19 have posed enduring effects on global society, which calls for a predictor of pandemic waves using various data with early availability. Existing prediction models that forecast the first outbreak wave using mobility data may not be applicable to the multiwave prediction, because the evidence in the USA and Japan has shown that mobility patterns across different waves exhibit varying relationships with fluctuations in infection cases. Therefore, to predict the multiwave pandemic, we propose a Social Awareness-Based Graph Neural Network (SAB-GNN) that considers the decay of symptom-related web search frequency to capture the changes in public awareness across multiple waves. Our model combines GNN and LSTM to model the complex relationships among urban districts, inter-district mobility patterns, web search history, and future COVID-19 infections. We train our model to predict future pandemic outbreaks in the Tokyo area using its mobility and web search data from April 2020 to May 2021 across four pandemic waves collected by Yahoo Japan Corporation under strict privacy protection rules. Results demonstrate our model outperforms state-of-the-art baselines such as ST-GNN, MPNN, and GraphLSTM. Though our model is not computationally expensive (only 3 layers and 10 hidden neurons), the proposed model enables public agencies to anticipate and prepare for future pandemic outbreaks. © 2022 Owner/Author.

2.
IISE Annual Conference and Expo 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2011316

ABSTRACT

Outbreaks of the COVID-19 pandemic, caused by the SARS-CoV-2 virus, have led to the creation of social distancing and lockdown policies to reduce the spread of the virus. Consequently, public/private transportation services, schools, workplaces, and retail stores' operations were disrupted. We gather user mobility reports worldwide to learn impacts of early COVID-19 outbreaks on human mobility patterns and trends. Building time series of six types of activities tracked in the Google Community Mobility Reports (CMR), we develop visualization tools and interactive dashboards for linking mobility and COVID-19 infection data at different levels (from county- and state-level in the US, to country level for the rest of the world). We show that the relationship between mobility and COVID-19 infection changes over time, and therefore the stage of the pandemic is essentially important for understanding how containment policies can affect infections and deaths caused by the COVID-19 pandemic. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

3.
Health Place ; 77: 102891, 2022 Aug 11.
Article in English | MEDLINE | ID: covidwho-1983101

ABSTRACT

Biweekly county COVID-19 data were linked with Longitudinal Employer-Household Dynamics data to analyze population risk exposures enabled by pre-pandemic, country-wide commuter networks. Results from fixed-effects, spatial, and computational statistical approaches showed that commuting network exposure to COVID-19 predicted an area's COVID-19 cases and deaths, indicating spillovers. Commuting spillovers between counties were independent from geographic contiguity, pandemic-time mobility, or social media ties. Results suggest that commuting connections form enduring social linkages with effects on health that can withstand mobility disruptions. Findings contribute to a growing relational view of health and place, with implications for neighborhood effects research and place-based policies.

4.
SSRN; 2022.
Preprint in English | SSRN | ID: ppcovidwho-341536

ABSTRACT

The COVID-19 pandemic has been causing tremendous impacts on travel and transportation systems. A growing number of studies employed big mobility datasets (BMD) such as these from Apple Inc. and Google LLC for evaluating the impacts. Findings from these studies have generated broad influence beyond the academic communities, as they appear in public media and governmental reports supporting policy making. However, there is limited understanding of the quality of BMD for transportation applications. This study quantitatively compares BMD with transit data from governmental agencies in four regions of the US to build up a systematic understanding of the quality of BMD, using data from Google and Apple as examples. We observe that BMD could vastly deviate from the agency data in values, leading to either over- or under-estimation of the impacts. Some similarities in the two types of data can be observed in their trend patterns, as indicated by the high correlations. The potential influence of errors in BMD on transportation applications is explored, concluding that ignoring the errors would mislead policy-making. Further investigations reveal that the deviation and correlation relationship vary over time and across regions, suggesting no general mitigation rule applicable to correct BMD of all regions and time periods. Findings in this study raise caution for future works using BMD for assessing the performance of transit systems.

5.
International Journal of Health Governance ; : 15, 2022.
Article in English | Web of Science | ID: covidwho-1927487

ABSTRACT

Purpose This paper aims to explore empirically the interactions between the coronavirus disease 2019 (COVID-19) pandemic, economic mobility and containment policy to test the effectiveness of mobility restrictions in controlling the spread of the disease. Design/methodology/approach This study used weekly regional data for the 17 Philippine regions and estimated the effect of shocks using a panel vector autoregression (VAR) model. Findings The authors conclude that COVID-19 deaths and incidence primarily respond to shocks that affect the lethality and transmissibility of the disease, and mobility restrictions and strict quarantine levels do not seem to have any impact on these outcomes. The movement of people during this pandemic period, on the other hand, seems to respond more to economic factors and government restrictions and less to the presence of and the characteristics of the disease. Originality/value Since the pandemic is a public bad, community cooperation is a must to address it. Clear government messaging that dispels doubts on the safety of the newly developed vaccines and that encourages public acceptance and trust might be a better nudge compared to a heavy-handed and threatening approach.

6.
Acm Transactions on Spatial Algorithms and Systems ; 8(2):30, 2022.
Article in English | English Web of Science | ID: covidwho-1883315

ABSTRACT

As countries look toward re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging. While contact tracing only aims to track past activities of infected users, one path to safe reopening is to develop reliable spatiotemporal risk scores to indicate the propensity of the disease. Existing works which aim at developing risk scores either rely on compartmental model-based reproduction numbers (which assume uniform population mixing) or develop coarse-grain spatial scores based on reproduction number (R0) and macro-level density-based mobility statistics. Instead, in this article, we develop a Hawkes process-based technique to assign relatively fine-grain spatial and temporal risk scores by leveraging high-resolution mobility data based on cell-phone originated location signals. While COVID-19 risk scores also depend on a number of factors specific to an individual, including demography and existing medical conditions, the primary mode of disease transmission is via physical proximity and contact. Therefore, we focus on developing risk scores based on location density and mobility behaviour. We demonstrate the efficacy of the developed risk scores via simulation based on real-world mobility data. Our results show that fine-grain spatiotemporal risk scores based on high-resolution mobility data can provide useful insights and facilitate safe re-opening.

7.
Cities ; 126: 103697, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1783241

ABSTRACT

The outbreak of the COVID-19 pandemic disrupted all walks of life, including the transportation sector. Fear of the contagion coupled with government regulations to restrict mobility altered the travel behavior of the public. This study proposes integrating freely accessible aggregate mobility datasets published by tech giants Apple and Google, which opens a broader avenue for mobility research in the light of difficult data collection circumstances. A comparative analysis of the changes in usage of different mobility modes during the national lockdown and unlock policy periods across 6 Indian cities (Bangalore, Chennai, Delhi, Hyderabad, Mumbai, and Pune) explain the spatio-temporal differences in mode usages. The study shows a preference for individual travel modes (walking and driving) over public transit. Comparisons with pre-pandemic mode shares present evidence of inertia in the choice of travel modes. Association investigations through generalized linear mixed-effects models identify income, vehicle registrations, and employment rates at the city level to significantly impact the community mobility trends. The methods and interpretations from this study benefit government, planners, and researchers to boost informed policymaking and implementation during a future emergency demanding mobility regulations in the high-density urban conglomerations.

8.
Int J Med Inform ; 162: 104755, 2022 Apr 01.
Article in English | MEDLINE | ID: covidwho-1768182

ABSTRACT

INTRODUCTION: SARS-CoV-2 was declared a pandemic by the WHO on March 11th, 2020. Public protective measures were enforced in every country to limit the diffusion of SARS-CoV-2. Its transmission, mainly by droplets, has been measured by the effective reproduction number (Rt) that counts the number of secondary cases caused in a population by an average infectious individual at time t. Current strategies to calculate Rt reflect the number of secondary cases after several days, due to a delay from symptoms onset to reporting. We propose a complementary Rt estimation using supervised machine learning techniques to predict short term variations with more timely results. MATERIAL AND METHODS: Our primary goal was to predict Rt of the current day in the twelve provinces of Lombardy with the highest possible accuracy, and with no influence of the local testing strategies. We gathered data about mobility, weather, and pollution from different public sources as a proxy of human behavior and public health measures. We built four supervised machine learning algorithms with different strategies: the outcome variable was the daily median Rt values per province obtained from officially adopted algorithms. RESULTS: Data from 243 days for every province were presented to our four models (from February 15th, 2020, to October 14th, 2020). Two models using differential calculation of Rt instead of the raw values showed the highest mean coefficient of determination (0.93 for both) and residuals reported the lowest mean error (-0.03 and 0.01) and standard deviation (0.13 for both) as well. The one with access to the value of Rt of the day before heavily relied on that feature for prediction, while the other one had more distributed weights. DISCUSSION: The model that had not access to the Rt value of the previous day and used Rt differential value as outcome (FDRt) was considered the most robust according to the metrics. Its forecasts were able to predict the trend that Rt values would have developed over different weeks, but it was not particularly accurate in predicting the precise value of Rt. A correlation among mobility, atmospheric, features, pollution and Rt values is plausible, but further testing should be performed.

9.
Annals of GIS ; : 1-14, 2022.
Article in English | Academic Search Complete | ID: covidwho-1730537

ABSTRACT

Without a widely distributed vaccine, controlling human mobility has been identified and promoted as the primary strategy to mitigate the transmission of COVID-19. Many studies have reported the relationship between human mobility and COVID-19 transmission by utilizing the spatial-temporal information of mobility data from various sources. To better understand the role of human mobility in the pandemic, we conducted a systematic review of articles that measure the relationship between human mobility and COVID-19 in terms of their data sources, mathematical models, and key findings. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we selected 47 articles from the Web of Science Core Collection up to September 2020. Restricting human mobility reduced the transmission of COVID-19, although the effectiveness and stringency of policy implementation vary temporally and spatially across different stages of the pandemic. We call for prompt and sustainable measures to control the pandemic. We also recommend researchers 1) to enhance multi-disciplinary collaboration;2) to adjust the implementation and stringency of mobility-control policies in corresponding to the rapid change of the pandemic;3) to improve mathematical models used in analysing, simulating, and predicting the transmission of the disease;and 4) to enrich the source of mobility data to ensure data accuracy and suability. [ FROM AUTHOR] Copyright of Annals of GIS is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

10.
SSRN; 2021.
Preprint in English | SSRN | ID: ppcovidwho-325634

ABSTRACT

As the COVID-19 pandemic continues to upend the way people move, work, and gather, governments, businesses, and public health researchers have looked increasingly at mobility data to support pandemic response. This data, assets that describe human location and movement, generally has been collected for purposes directly related to a company’s business model, including optimizing the delivery of consumer services, supply chain management or targeting advertisements. However, these call detail records, smartphone-mobility data, vehicle-derived GPS, and other mobility data assets can also be used to study patterns of movement. These patterns of movement have, in turn, been used by organizations to forecast disease spread and inform decisions on how to best manage activity in certain locations. Researchers at The GovLab and Cuebiq, supported by the Open Data Institute, identified 51 notable projects from around the globe launched by public sector and research organizations with companies that use mobility data for these purposes. It curated five projects among this listing that highlight the specific opportunities (and risks) presented by using this asset. Though few of these highlighted projects have provided public outputs that make assessing project success difficult, organizations interviewed considered mobility data to be a useful asset that enabled better public health surveillance, supported existing decision-making processes, or otherwise allowed groups to achieve their research goals. The report summarizes some of the major points identified in those case studies. While acknowledging that location data can be a highly sensitive data type that can facilitate surveillance or expose data subjects if used carelessly, it finds mobility data can support research and inform decisions when applied toward narrowly defined research questions through frameworks that acknowledge and proactively mitigate risk. These frameworks can vary based on the individual circumstances facing data users, suppliers, and subjects. However, there are a few conditions that can enable users and suppliers to promote publicly beneficial and responsible data use and overcome the serious obstacles facing them.

11.
Comput Urban Sci ; 1(1): 22, 2021.
Article in English | MEDLINE | ID: covidwho-1514102

ABSTRACT

Effectively monitoring the dynamics of human mobility is of great importance in urban management, especially during the COVID-19 pandemic. Traditionally, the human mobility data is collected by roadside sensors, which have limited spatial coverage and are insufficient in large-scale studies. With the maturing of mobile sensing and Internet of Things (IoT) technologies, various crowdsourced data sources are emerging, paving the way for monitoring and characterizing human mobility during the pandemic. This paper presents the authors' opinions on three types of emerging mobility data sources, including mobile device data, social media data, and connected vehicle data. We first introduce each data source's main features and summarize their current applications within the context of tracking mobility dynamics during the COVID-19 pandemic. Then, we discuss the challenges associated with using these data sources. Based on the authors' research experience, we argue that data uncertainty, big data processing problems, data privacy, and theory-guided data analytics are the most common challenges in using these emerging mobility data sources. Last, we share experiences and opinions on potential solutions to address these challenges and possible research directions associated with acquiring, discovering, managing, and analyzing big mobility data.

12.
Epidemics ; 37: 100505, 2021 12.
Article in English | MEDLINE | ID: covidwho-1446617

ABSTRACT

We present a compartmental extended SEIQRD metapopulation model for SARS-CoV-2 spread in Belgium. We demonstrate the robustness of the calibration procedure by calibrating the model using incrementally larger datasets and dissect the model results by computing the effective reproduction number at home, in workplaces, in schools, and during leisure activities. We find that schools and home contacts are important transmission pathways for SARS-CoV-2 under lockdown measures. School reopening has the potential to increase the effective reproduction number from Re=0.66±0.04 (95 % CI) to Re=1.09±0.05 (95 % CI) under lockdown measures. The model accounts for the main characteristics of SARS-CoV-2 transmission and COVID-19 disease and features a detailed representation of hospitals with parameters derived from a dataset consisting of 22 136 hospitalized patients. Social contact during the pandemic is modeled by scaling pre-pandemic contact matrices with Google Community Mobility data and with effectivity-of-contact parameters inferred from hospitalization data. The calibrated social contact model with its publically available mobility data, although coarse-grained, is a cheap and readily available alternative to social-epidemiological contact studies under lockdown measures, which were not available at the start of the pandemic.


Subject(s)
COVID-19 , SARS-CoV-2 , Belgium/epidemiology , Communicable Disease Control , Humans , Pandemics/prevention & control
13.
Spat Stat ; 49: 100540, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1440369

ABSTRACT

Spatial dependence is usually introduced into spatial models using some measure of physical proximity. When analysing COVID-19 case counts, this makes sense as regions that are close together are more likely to have more people moving between them, spreading the disease. However, using the actual number of trips between each region may explain COVID-19 case counts better than physical proximity. In this paper, we investigate the efficacy of using telecommunications-derived mobility data to induce spatial dependence in spatial models applied to two Spanish communities' COVID-19 case counts. We do this by extending Besag York Mollié (BYM) models to include both a physical adjacency effect, alongside a mobility effect. The mobility effect is given a Gaussian Markov random field prior, with the number of trips between regions as edge weights. We leverage modern parametrizations of BYM models to conclude that the number of people moving between regions better explains variation in COVID-19 case counts than physical proximity data. We suggest that this data should be used in conjunction with physical proximity data when developing spatial models for COVID-19 case counts.

14.
Expert Syst Appl ; 182: 115190, 2021 Nov 15.
Article in English | MEDLINE | ID: covidwho-1233423

ABSTRACT

In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on the dynamics of COVID-19 transmission. There is a dearth of literature that considers these two variables when predicting the spread of COVID-19 cases. In this paper, we analyzed the correlation between the number of COVID-19 cases and human mobility, and meteorological data in Brazilian capitals. We found that the correlation between such variables depends on the regions where the cities are located. We employed the variables with a significant correlation with COVID-19 cases to predict the number of COVID-19 infections in all Brazilian capitals and proposed a prediction method combining the Ensemble Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX. After analyzing the results poor predictions were further investigated using a signal processing-based anomaly detection method. Computational tests showed that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an improvement of 30.69% in the average root mean squared error (RMSE) was noticed when applying the EEMD-ARIMAX method to the data normalized after the anomaly detection.

15.
JMIR Mhealth Uhealth ; 9(5): e27342, 2021 05 11.
Article in English | MEDLINE | ID: covidwho-1223830

ABSTRACT

BACKGROUND: During the second wave of COVID-19 in August 2020, the Tokyo Metropolitan Government implemented public health and social measures to reduce on-site dining. Assessing the associations between human behavior, infection, and social measures is essential to understand achievable reductions in cases and identify the factors driving changes in social dynamics. OBJECTIVE: The aim of this study was to investigate the association between nighttime population volumes, the COVID-19 epidemic, and the implementation of public health and social measures in Tokyo. METHODS: We used mobile phone location data to estimate populations between 10 PM and midnight in seven Tokyo metropolitan areas. Mobile phone trajectories were used to distinguish and extract on-site dining from stay-at-work and stay-at-home behaviors. Numbers of new cases and symptom onsets were obtained. Weekly mobility and infection data from March 1 to November 14, 2020, were analyzed using a vector autoregression model. RESULTS: An increase in the number of symptom onsets was observed 1 week after the nighttime population volume increased (coefficient=0.60, 95% CI 0.28 to 0.92). The effective reproduction number significantly increased 3 weeks after the nighttime population volume increased (coefficient=1.30, 95% CI 0.72 to 1.89). The nighttime population volume increased significantly following reports of decreasing numbers of confirmed cases (coefficient=-0.44, 95% CI -0.73 to -0.15). Implementation of social measures to restaurants and bars was not significantly associated with nighttime population volume (coefficient=0.004, 95% CI -0.07 to 0.08). CONCLUSIONS: The nighttime population started to increase after decreasing incidence of COVID-19 was announced. Considering time lags between infection and behavior changes, social measures should be planned in advance of the surge of an epidemic, sufficiently informed by mobility data.


Subject(s)
COVID-19 , Cell Phone , Humans , Pandemics , SARS-CoV-2 , Tokyo/epidemiology
16.
BMC Public Health ; 21(1): 226, 2021 01 27.
Article in English | MEDLINE | ID: covidwho-1099882

ABSTRACT

BACKGROUND: As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. METHODS: In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. RESULTS: We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. CONCLUSIONS: To prepare for the potential spread within Taiwan, we utilized Facebook's aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.


Subject(s)
COVID-19/epidemiology , Communicable Diseases, Imported/epidemiology , Disease Outbreaks , Travel/statistics & numerical data , Forecasting , Humans , Models, Biological , Risk , Social Media , Taiwan/epidemiology , Travel/legislation & jurisprudence
17.
Ethics Inf Technol ; : 1-6, 2021 Feb 02.
Article in English | MEDLINE | ID: covidwho-1098962

ABSTRACT

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.

18.
Saf Sci ; 132: 104925, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-1065600

ABSTRACT

This work presents a mobility indicator derived from fully anonymised and aggregated mobile positioning data. Even though the indicator does not provide information about the behaviour of individuals, it captures valuable insights into the mobility patterns of the population in the EU and it is expected to inform responses against the COVID-19 pandemic. Spatio-temporal harmonisation is carried out so that the indicator can provide mobility estimates comparable across European countries. The indicators are provided at a high spatial granularity (up to NUTS3). As an application, the indicator is used to study the impact of COVID-19 confinement measure on mobility in Europe. It is found that a large proportion of the change in mobility patterns can be explained by these measures. The paper also presents a comparative analysis between mobility and the infection reproduction number R t over time. These findings will support policymakers in formulating the best data-driven approaches for coming out of confinement, mapping the socio-economic effects of the lockdown measures and building future scenarios in case of new outbreaks.

19.
J Med Internet Res ; 23(1): e24591, 2021 01 22.
Article in English | MEDLINE | ID: covidwho-1042067

ABSTRACT

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.


Subject(s)
COVID-19/economics , COVID-19/prevention & control , Physical Distancing , Socioeconomic Factors , COVID-19/epidemiology , Female , Humans , Male , Pandemics , Population Density , Poverty/statistics & numerical data , Salaries and Fringe Benefits/statistics & numerical data , United States/epidemiology
20.
EMBO Mol Med ; 12(11): e13171, 2020 11 06.
Article in English | MEDLINE | ID: covidwho-874991

ABSTRACT

The rapid spread of SARS-CoV-2 and its threat to health systems worldwide have led governments to take acute actions to enforce social distancing. Previous studies used complex epidemiological models to quantify the effect of lockdown policies on infection rates. However, these rely on prior assumptions or on official regulations. Here, we use country-specific reports of daily mobility from people cellular usage to model social distancing. Our data-driven model enabled the extraction of lockdown characteristics which were crossed with observed mortality rates to show that: (i) the time at which social distancing was initiated is highly correlated with the number of deaths, r2  = 0.64, while the lockdown strictness or its duration is not as informative; (ii) a delay of 7.49 days in initiating social distancing would double the number of deaths; and (iii) the immediate response has a prolonged effect on COVID-19 death toll.


Subject(s)
COVID-19/pathology , Quarantine , COVID-19/epidemiology , COVID-19/mortality , COVID-19/virology , Humans , Pandemics , Physical Distancing , SARS-CoV-2/isolation & purification , Survival Rate , Time Factors
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