scholarly journals Fine-grained data reveal segregated mobility networks and opportunities for local containment of COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chao Fan ◽  
Ronald Lee ◽  
Yang Yang ◽  
Ali Mostafavi
Keyword(s):  
United States ◽  
Human Mobility ◽  
Virus Transmission ◽  
The United States ◽  
Mobility Control ◽  
Control Measures ◽  
Fine Grained ◽  
Effective Mobility ◽  
Travel Restrictions ◽  
Mobility Reduction

AbstractDeriving effective mobility control measures is critical for the control of COVID-19 spreading. In response to the COVID-19 pandemic, many countries and regions implemented travel restrictions and quarantines to reduce human mobility and thus reduce virus transmission. But since human mobility decreased heterogeneously, we lack empirical evidence of the extent to which the reductions in mobility alter the way people from different regions of cities are connected, and what containment policies could complement mobility reductions to conquer the pandemic. Here, we examined individual movements in 21 of the most affected counties in the United States, showing that mobility reduction leads to a segregated place network and alters its relationship with pandemic spread. Our findings suggest localized area-specific policies, such as geo-fencing, as viable alternatives to city-wide lockdown for conquering the pandemic after mobility was reduced.

scholarly journals Differential effects of intervention timing on COVID-19 spread in the United States

2020 ◽  
Vol 6 (49) ◽  
pp. eabd6370 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
United States ◽  
Disease Transmission ◽  
Human Mobility ◽  
The United States ◽  
Control Measures ◽  
Basic Reproductive Number ◽  
Transmission Model ◽  
Reproductive Number ◽  
Mobility Data ◽  
Nonpharmaceutical Interventions

Assessing the effects of early nonpharmaceutical interventions on coronavirus disease 2019 (COVID-19) spread is crucial for understanding and planning future control measures to combat the pandemic. We use observations of reported infections and deaths, human mobility data, and a metapopulation transmission model to quantify changes in disease transmission rates in U.S. counties from 15 March to 3 May 2020. We find that marked, asynchronous reductions of the basic reproductive number occurred throughout the United States in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same measures been implemented 1 to 2 weeks earlier, substantial cases and deaths could have been averted and that delayed responses to future increased incidence will facilitate a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive control in combatting the COVID-19 pandemic.

scholarly journals Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yixuan Pan ◽  
Aref Darzi ◽  
Aliakbar Kabiri ◽  
Guangchen Zhao ◽  
Weiyu Luo ◽  
...  
Keyword(s):  
United States ◽  
Real Time ◽  
Human Mobility ◽  
The United States ◽  
Control Measures ◽  
Social Distancing ◽  
First Case ◽  
Mobility Behaviour ◽  
Novel Coronavirus ◽  
Behaviour Changes

AbstractSince the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including in the United States, as a major community mitigation strategy. However, our understanding remains limited in how people would react to such control measures, as well as how people would resume their normal behaviours when those orders were relaxed. We utilize an integrated dataset of real-time mobile device location data involving 100 million devices in the contiguous United States (plus Alaska and Hawaii) from February 2, 2020 to May 30, 2020. Built upon the common human mobility metrics, we construct a Social Distancing Index (SDI) to evaluate people’s mobility pattern changes along with the spread of COVID-19 at different geographic levels. We find that both government orders and local outbreak severity significantly contribute to the strength of social distancing. As people tend to practice less social distancing immediately after they observe a sign of local mitigation, we identify several states and counties with higher risks of continuous community transmission and a second outbreak. Our proposed index could help policymakers and researchers monitor people’s real-time mobility behaviours, understand the influence of government orders, and evaluate the risk of local outbreaks.

scholarly journals Differential Effects of Intervention Timing on COVID-19 Spread in the United States

2020 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
United States ◽  
Disease Transmission ◽  
Metropolitan Areas ◽  
Human Mobility ◽  
The United States ◽  
Aggressive Response ◽  
Control Measures ◽  
County Level ◽  
Social Distancing ◽  
Us Counties

Assessing the effects of early non-pharmaceutical interventions1-5 on COVID-19 spread in the United States is crucial for understanding and planning future control measures to combat the ongoing pandemic6-10. Here we use county-level observations of reported infections and deaths11, in conjunction with human mobility data12 and a metapopulation transmission model13,14, to quantify changes of disease transmission rates in US counties from March 15, 2020 to May 3, 2020. We find significant reductions of the basic reproductive numbers in major metropolitan areas in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same control measures been implemented just 1-2 weeks earlier, a substantial number of cases and deaths could have been averted. Specifically, nationwide, 61.6% [95% CI: 54.6%-67.7%] of reported infections and 55.0% [95% CI: 46.1%-62.2%] of reported deaths as of May 3, 2020 could have been avoided if the same control measures had been implemented just one week earlier. We also examine the effects of delays in re-implementing social distancing following a relaxation of control measures. A longer response time results in a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive response in controlling the COVID-19 pandemic.

scholarly journals Economic Impact Payment, Human Mobility and the COVID-19 Mitigation in the United States

2021 ◽  
Author(s):  
Ruohao Zhang
Keyword(s):  
United States ◽  
Economic Impact ◽  
Human Mobility ◽  
Virus Transmission ◽  
The United States ◽  
Economic Assistance ◽  
Compensation Policy ◽  
Mobility Data ◽  
The Government ◽  
Mitigation Effort

This paper studies the relationship between the individual's income and COVID-19 mitigation effort contribution. The paper suggests that in addition to the government mandatory policies, the income compensation policy is an alternative government instrument that helps increase the individual and social aggregate COVID-19 mitigation effort. I empirically test the effect of the income compensation policy by utilizing the United States economic impact payment (EIP) in April 2020 as a quasi-natural experiment, and use the cellphone GPS measured human mobility data as the outcome indicator of the COVID-19 mitigation effort. I find that by receiving EIP, individuals on average significantly increase median home dwell time by 3%-5% (about 26-45 minutes). This paper highlights an unintended effect of EIP: in addition to providing economic assistance, EIP also helps mitigate the COVID-19 virus transmission.

scholarly journals Analysis and Prediction of Human Mobility in the United States during the Early Stages of the COVID-19 Pandemic using Regularized Linear Models

2022 ◽  
pp. 036119812110677
Author(s):  
Meghna Chakraborty ◽  
Md Shakir Mahmud ◽  
Timothy J. Gates ◽  
Subhrajit Sinha
Keyword(s):  
United States ◽  
Test Data ◽  
Linear Models ◽  
Human Mobility ◽  
The United States ◽  
Elastic Net ◽  
Training Data ◽  
Superior Performance ◽  
Data Set ◽  
Travel Restrictions

Since the United States started grappling with the COVID-19 pandemic, with the highest number of confirmed cases and deaths in the world as of August 2020, most states have enforced travel restrictions resulting in drastic reductions in mobility and travel. However, the long-term implications of this crisis to mobility still remain uncertain. To this end, this study proposes an analytical framework that determines the most significant factors affecting human mobility in the United States during the early days of the pandemic. Particularly, the study uses least absolute shrinkage and selection operator (LASSO) regularization to identify the most significant variables influencing human mobility and uses linear regularization algorithms, including ridge, LASSO, and elastic net modeling techniques, to predict human mobility. State-level data were obtained from various sources from January 1, 2020 to June 13, 2020. The entire data set was divided into a training and a test data set, and the variables selected by LASSO were used to train models by the linear regularization algorithms, using the training data set. Finally, the prediction accuracy of the developed models was examined on the test data. The results indicate that several factors, including the number of new cases, social distancing, stay-at-home orders, domestic travel restrictions, mask-wearing policy, socioeconomic status, unemployment rate, transit mode share, percent of population working from home, and percent of older (60+ years) and African and Hispanic American populations, among others, significantly influence daily trips. Moreover, among all models, ridge regression provides the most superior performance with the least error, whereas both LASSO and elastic net performed better than the ordinary linear model.

scholarly journals Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England

Science ◽  
2021 ◽  
Vol 372 (6538) ◽  
pp. eabg3055 ◽  
Author(s):  
Nicholas G. Davies ◽  
Sam Abbott ◽  
Rosanna C. Barnard ◽  
Christopher I. Jarvis ◽  
Adam J. Kucharski ◽  
...  
Keyword(s):  
United States ◽  
Severe Acute Respiratory Syndrome ◽  
Reproduction Number ◽  
The United States ◽  
Control Measures ◽  
Transmission Model ◽  
Educational Institutions ◽  
Stringent Control ◽  
Dynamic Transmission Model ◽  
Credible Intervals

A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States.

Preliminary Estimates of Costs of Foodborne Disease in the United States

1989 ◽  
Vol 52 (8) ◽  
pp. 595-601 ◽  
Author(s):  
EWEN C. D. TODD
Keyword(s):  
United States ◽  
Food Industry ◽  
The United States ◽  
Control Measures ◽  
Effective Control ◽  
Foodborne Diseases ◽  
Hemorrhagic Colitis ◽  
E Coli ◽  
The Cost ◽  
Total Impact

Although the full economic impact of foodborne diseases has yet to be measured, preliminary studies show that the cost of illness, death, and business lost is high indeed. This impact is probably greatest in developing countries, but few facts are known. For the United States, preliminary estimates are 12.6 million cases costing $8.4 billion. These may seem excessive but other authors have postulated even higher case and dollar figures. Microbiological diseases (bacterial and viral) represent 84% of the United States' costs, with salmonellosis and staphylococcal intoxication being the most economically important diseases (annually $4.0 billion and $1.5 billion, respectively). Other costly types of illnesses are toxoplasmosis ($445 million), listeriosis ($313 million), campylobacteriosis ($156 million), trichinosis ($144 million), Clostridium perfringens enteritis ($123 million), and E. coli infections including hemorrhagic colitis ($223 million). Botulism has a high cost per case ($322,200), but its total impact is only $87 million because relatively few cases occur (270). This is because the food industry has been able to introduce effective control measures. Salmonellosis, however, is much more widespread (2.9 million cases) and affects all sectors of the food industry.

scholarly journals Fine-grained, spatio-temporal datasets measuring 200 years of land development in the United States

2020 ◽  
Author(s):  
Johannes H. Uhl ◽  
Stefan Leyk ◽  
Caitlin M. McShane ◽  
Anna E. Braswell ◽  
Dylan S. Connor ◽  
...  
Keyword(s):  
United States ◽  
Temporal Resolution ◽  
Data Extraction ◽  
Historical Analysis ◽  
Land Development ◽  
Remote Sensing Data ◽  
The United States ◽  
Building Stock ◽  
Fine Grained ◽  
Spatio Temporal

Abstract. The collection, processing and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth’s surface. While satellite-based earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially-explicit settlement data for the United States that extend back to the early nineteenth century, and is consistently enumerated at fine spatial and temporal granularity (i.e., 250 m spatial, and 5 a temporal resolution). We create these time series using a large, novel building stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at unprecedented spatial and temporal resolution. The datasets are available at https://dataverse.harvard.edu/dataverse/hisdacus (Uhl and Leyk, 2020a, b, c, d).

scholarly journals Similarities and Differences in COVID-19 Awareness, Concern, and Symptoms by Race and Ethnicity in the United States: Cross-Sectional Survey (Preprint)

2020 ◽  
Author(s):  
Jeb Jones ◽  
Patrick S Sullivan ◽  
Travis H Sanchez ◽  
Jodie L Guest ◽  
Eric W Hall ◽  
...  
Keyword(s):  
United States ◽  
Race And Ethnicity ◽  
Online Survey ◽  
Statistical Tests ◽  
The United States ◽  
Control Measures ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Similarities And Differences ◽  
Hispanic White

BACKGROUND Existing health disparities based on race and ethnicity in the United States are contributing to disparities in morbidity and mortality during the coronavirus disease (COVID-19) pandemic. We conducted an online survey of American adults to assess similarities and differences by race and ethnicity with respect to COVID-19 symptoms, estimates of the extent of the pandemic, knowledge of control measures, and stigma. OBJECTIVE The aim of this study was to describe similarities and differences in COVID-19 symptoms, knowledge, and beliefs by race and ethnicity among adults in the United States. METHODS We conducted a cross-sectional survey from March 27, 2020 through April 1, 2020. Participants were recruited on social media platforms and completed the survey on a secure web-based survey platform. We used chi-square tests to compare characteristics related to COVID-19 by race and ethnicity. Statistical tests were corrected using the Holm Bonferroni correction to account for multiple comparisons. RESULTS A total of 1435 participants completed the survey; 52 (3.6%) were Asian, 158 (11.0%) were non-Hispanic Black, 548 (38.2%) were Hispanic, 587 (40.9%) were non-Hispanic White, and 90 (6.3%) identified as other or multiple races. Only one symptom (sore throat) was found to be different based on race and ethnicity (<i>P</i>=.003); this symptom was less frequently reported by Asian (3/52, 5.8%), non-Hispanic Black (9/158, 5.7%), and other/multiple race (8/90, 8.9%) participants compared to those who were Hispanic (99/548, 18.1%) or non-Hispanic White (95/587, 16.2%). Non-Hispanic White and Asian participants were more likely to estimate that the number of current cases was at least 100,000 (<i>P</i>=.004) and were more likely to answer all 14 COVID-19 knowledge scale questions correctly (Asian participants, 13/52, 25.0%; non-Hispanic White participants, 180/587, 30.7%) compared to Hispanic (108/548, 19.7%) and non-Hispanic Black (25/158, 15.8%) participants. CONCLUSIONS We observed differences with respect to knowledge of appropriate methods to prevent infection by the novel coronavirus that causes COVID-19. Deficits in knowledge of proper control methods may further exacerbate existing race/ethnicity disparities. Additional research is needed to identify trusted sources of information in Hispanic and non-Hispanic Black communities and create effective messaging to disseminate correct COVID-19 prevention and treatment information.

Sign in / Sign up

Export Citation Format

Share Document