scholarly journals Human mobility and poverty as key drivers of COVID-19 transmission and control

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Matan Yechezkel ◽  
Amit Weiss ◽  
Idan Rejwan ◽  
Edan Shahmoon ◽  
Shachaf Ben-Gal ◽  
...  
Keyword(s):  
Human Mobility ◽  
The Elderly ◽  
School Closure ◽  
Transmission Model ◽  
Mobility Patterns ◽  
Mortality And Morbidity ◽  
Effective Strategies ◽  
Mobility Data ◽  
Key Drivers ◽  
And Control

Abstract Background Applying heavy nationwide restrictions is a powerful method to curtail COVID-19 transmission but poses a significant humanitarian and economic crisis. Thus, it is essential to improve our understanding of COVID-19 transmission, and develop more focused and effective strategies. As human mobility drives transmission, data from cellphone devices can be utilized to achieve these goals. Methods We analyzed aggregated and anonymized mobility data from the cell phone devices of> 3 million users between February 1, 2020, to May 16, 2020 — in which several movement restrictions were applied and lifted in Israel. We integrated these mobility patterns into age-, risk- and region-structured transmission model. Calibrated to coronavirus incidence in 250 regions covering Israel, we evaluated the efficacy and effectiveness in decreasing morbidity and mortality of applying localized and temporal lockdowns (stay-at-home order). Results Poorer regions exhibited lower and slower compliance with the restrictions. Our transmission model further indicated that individuals from impoverished areas were associated with high transmission rates. Considering a horizon of 1–3 years, we found that to reduce COVID-19 mortality, school closure has an adverse effect, while interventions focusing on the elderly are the most efficient. We also found that applying localized and temporal lockdowns during regional outbreaks reduces the overall mortality and morbidity compared to nationwide lockdowns. These trends were consistent across vast ranges of epidemiological parameters, and potential seasonal forcing. Conclusions More resources should be devoted to helping impoverished regions. Utilizing cellphone data despite being anonymized and aggregated can help policymakers worldwide identify hotspots and apply designated strategies against future COVID-19 outbreaks.

scholarly journals Human mobility and poverty as key drivers of COVID-19 transmission and control

2020 ◽  
Author(s):  
Matan Yechezkel ◽  
Amit Weiss ◽  
Idan Rejwan ◽  
Edan Shahmoon ◽  
Shachaf Ben-Gal ◽  
...  
Keyword(s):  
Cell Phone ◽  
Human Mobility ◽  
The Elderly ◽  
School Closure ◽  
Transmission Model ◽  
Mobility Patterns ◽  
Effective Strategies ◽  
Mobility Data ◽  
Long Run ◽  
And Control

AbstractBackgroundApplying heavy nationwide restrictions is a powerful method to curtail COVID-19 transmission but poses a significant humanitarian and economic crisis. Thus, it is essential to improve our understanding of COVID-19 transmission and develop more focused and effective strategies. As human mobility drives transmission, data from cell phone devices can be utilized to achieve these goals.MethodsWe analyzed aggregated and anonymized mobility data from the cell-phone devices of>3 million users between February 1, 2020, to May 16, 2020 – in which several movement restrictions were applied and lifted in Israel. We integrated these mobility patterns into age-, risk- and region-structured transmission model. Calibrated to coronavirus incidence in 250 regions covering Israel, we evaluated the efficacy and effectiveness in decreasing mortality of applying localized and temporal lockdowns (stay-at-home order).ResultsPoorer regions exhibited lower and slower compliance with the restrictions. Our transmission model further indicated that individuals from poverty areas were associated with high transmission rates. Model projections suggested that, counterintuitively, school closure has an adverse effect and increases COVID-19 mortality in the long run, while interventions focusing on the elderly are the most efficient. We also found that applying localized and temporal lockdowns during regional outbreaks reduce mortality compared to nationwide lockdowns. These trends were consistent across vast ranges of epidemiological parameters, possible seasonal forcing, and even when we assumed that vaccination would be commercially available in 1-3 years.ConclusionsMore resources should be devoted to helping impoverished regions. Utilizing cellphone data despite being anonymized and aggregated can help policymakers worldwide identify hotspots and apply designated strategies against future COVID-19 outbreaks.

scholarly journals Mobility Patterns in Different Age Groups in Japan during the COVID-19 Pandemic: a Small Area Time Series Analysis through March 2021

2021 ◽  
Author(s):  
Shuhei Nomura ◽  
Yuta Tanoue ◽  
Daisuke Yoneoka ◽  
Stuart Gilmour ◽  
Takayuki Kawashima ◽  
...  
Keyword(s):  
Care Delivery ◽  
Human Mobility ◽  
Age Groups ◽  
Virus Transmission ◽  
Initial Response ◽  
Health Care Delivery System ◽  
Population Characteristics ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The Status

AbstractIn the COVID-19 era, movement restrictions are crucial to slow virus transmission and have been implemented in most parts of the world, including Japan. To find new insights on human mobility and movement restrictions encouraged (but not forced) by the emergency declaration in Japan, we analyzed mobility data at 35 major stations and downtown areas in Japan—each defined as an area overlaid by several 125-meter grids—from September 1, 2019 to March 19, 2021. Data on the total number of unique individuals per hour passing through each area were obtained from Yahoo Japan Corporation (i.e., more than 13,500 data points for each area). We examined the temporal trend in the ratio of the rolling seven-day daily average of the total population to a baseline on January 16, 2020, by ten-year age groups in five time frames. We demonstrated that the degree and trend of mobility decline after the declaration of a state of emergency varies across age groups and even at the subregional level. We demonstrated that monitoring dynamic geographic and temporal mobility information stratified by detailed population characteristics can help guide not only exit strategies from an ongoing emergency declaration, but also initial response strategies before the next possible resurgence. Combining such detailed data with data on vaccination coverage and COVID-19 incidence (including the status of the health care delivery system) can help governments and local authorities develop community-specific mobility restriction policies. This could include strengthening incentives to stay home and raising awareness of cognitive errors that weaken people's resolve to refrain from nonessential movement.

scholarly journals Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

2021 ◽  
Vol 4 ◽  
Author(s):  
A. Potgieter ◽  
I. N. Fabris-Rotelli ◽  
Z. Kimmie ◽  
N. Dudeni-Tlhone ◽  
J. P. Holloway ◽  
...  
Keyword(s):  
Human Mobility ◽  
Data Sources ◽  
Population Mobility ◽  
Mobility Patterns ◽  
Mobility Data ◽  
The World ◽  
Spatial Weight ◽  
Data Source ◽  
Reduced Mobility ◽  
Spatial Transmission

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.

scholarly journals Universal predictability of mobility patterns in cities

2014 ◽  
Vol 11 (100) ◽  
pp. 20140834 ◽  
Author(s):  
Xiao-Yong Yan ◽  
Chen Zhao ◽  
Ying Fan ◽  
Zengru Di ◽  
Wen-Xu Wang
Keyword(s):  
Spatial Scales ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Universal Prediction ◽  
Potential Applications ◽  
Mobility Behaviour ◽  
History Of ◽  
Different Characteristics ◽  
The City

Despite the long history of modelling human mobility, we continue to lack a highly accurate approach with low data requirements for predicting mobility patterns in cities. Here, we present a population-weighted opportunities model without any adjustable parameters to capture the underlying driving force accounting for human mobility patterns at the city scale. We use various mobility data collected from a number of cities with different characteristics to demonstrate the predictive power of our model. We find that insofar as the spatial distribution of population is available, our model offers universal prediction of mobility patterns in good agreement with real observations, including distance distribution, destination travel constraints and flux. By contrast, the models that succeed in modelling mobility patterns in countries are not applicable in cities, which suggests that there is a diversity of human mobility at different spatial scales. Our model has potential applications in many fields relevant to mobility behaviour in cities, without relying on previous mobility measurements.

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 Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Takahiro Yabe ◽  
Kota Tsubouchi ◽  
Naoya Fujiwara ◽  
Takayuki Wada ◽  
Yoshihide Sekimoto ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Social Contact ◽  
Mobility Patterns ◽  
State Of Emergency ◽  
Social Contacts ◽  
Policy Makers ◽  
Mobility Data ◽  
Contact Rates ◽  
Mobility Behavior

Abstract While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.

scholarly journals Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

2017 ◽  
Vol 4 (5) ◽  
pp. 160950 ◽  
Author(s):  
Cecilia Panigutti ◽  
Michele Tizzoni ◽  
Paolo Bajardi ◽  
Zbigniew Smoreda ◽  
Vittoria Colizza
Keyword(s):  
Infectious Disease ◽  
Mobile Phone ◽  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Disease Models ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Infectious Disease Models

The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

scholarly journals The effect of human mobility and control measures on the COVID-19 epidemic in China

2020 ◽  
Author(s):  
Moritz U.G. Kraemer ◽  
Chia-Hung Yang ◽  
Bernardo Gutierrez ◽  
Chieh-Hsi Wu ◽  
Brennan Klein ◽  
...  
Keyword(s):  
Human Mobility ◽  
Control Measures ◽  
Human Populations ◽  
Travel History ◽  
Mobility Data ◽  
Travel Restrictions ◽  
State Interventions ◽  
And Control ◽  
The Impact

AbstractThe ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19.

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

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.

scholarly journals Practical geospatial and sociodemographic predictors of human mobility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Corrine W. Ruktanonchai ◽  
Shengjie Lai ◽  
Chigozie E. Utazi ◽  
Alex D. Cunningham ◽  
Patrycja Koper ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Human Mobility ◽  
Human Movement ◽  
Spatiotemporal Analysis ◽  
International Travel ◽  
Sensitive Information ◽  
International Mobility ◽  
Mobility Patterns ◽  
Middle Income ◽  
And Control

AbstractUnderstanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.

Sign in / Sign up

Export Citation Format

Share Document