Population Mobility Data Research Articles

Exploring infectious disease spread as a function of seasonal and pandemic-induced changes in human mobility.

Community-level changes in population mobility can dramatically change the trajectory of any directly-transmitted infectious disease, by modifying where and between whom contact occurs. This was highlighted throughout the COVID-19 pandemic, where community response and nonpharmaceutical interventions changed the trajectory of SARS-CoV-2 spread, sometimes in unpredictable ways. Population-level changes in mobility also occur seasonally and during other significant events, such as hurricanes or earthquakes. To effectively predict the spread of future emerging directly-transmitted diseases, we should better understand how the spatial spread of infectious disease changes seasonally, and when communities are actively responding to local disease outbreaks and travel restrictions. Here, we use population mobility data from Virginia spanning Aug 2019-March 2023 to simulate the spread of a hypothetical directly-transmitted disease under the population mobility patterns from various months. By comparing the spread of disease based on where the outbreak begins and the mobility patterns used, we determine the highest-risk areas and periods, and elucidate how seasonal and pandemic-era mobility patterns could change the trajectory of disease transmission. Through this analysis, we determine that while urban areas were at highest risk pre-pandemic, the heterogeneous nature of community response induced by SARS-CoV-2 cases meant that when outbreaks were occurring across Virginia, rural areas became relatively higher risk. Further, the months of September and January led to counties with large student populations to become particularly at risk, as population flows in and out of these counties were greatly increased with students returning to school.

MPSTAN: Metapopulation-Based Spatio-Temporal Attention Network for Epidemic Forecasting.

Accurate epidemic forecasting plays a vital role for governments to develop effective prevention measures for suppressing epidemics. Most of the present spatio-temporal models cannot provide a general framework for stable and accurate forecasting of epidemics with diverse evolutionary trends. Incorporating epidemiological domain knowledge ranging from single-patch to multi-patch into neural networks is expected to improve forecasting accuracy. However, relying solely on single-patch knowledge neglects inter-patch interactions, while constructing multi-patch knowledge is challenging without population mobility data. To address the aforementioned problems, we propose a novel hybrid model called metapopulation-based spatio-temporal attention network (MPSTAN). This model aims to improve the accuracy of epidemic forecasting by incorporating multi-patch epidemiological knowledge into a spatio-temporal model and adaptively defining inter-patch interactions. Moreover, we incorporate inter-patch epidemiological knowledge into both model construction and the loss function to help the model learn epidemic transmission dynamics. Extensive experiments conducted on two representative datasets with different epidemiological evolution trends demonstrate that our proposed model outperforms the baselines and provides more accurate and stable short- and long-term forecasting. We confirm the effectiveness of domain knowledge in the learning model and investigate the impact of different ways of integrating domain knowledge on forecasting. We observe that using domain knowledge in both model construction and the loss function leads to more efficient forecasting, and selecting appropriate domain knowledge can improve accuracy further.

New insights into grocery store visits among east Los Angeles residents using mobility data

In this study, we employed spatially aggregated population mobility data, generated from mobile phone locations in 2021, to investigate patterns of grocery store visits among residents east and northeast of Downtown Los Angeles, in which 60% of the census tracts had previously been designated as “food deserts”. Further, we examined whether the store visits varied with neighborhood sociodemographics and grocery store accessibility. We found that residents averaged 0.4 trips to grocery stores per week, with only 13% of these visits within home census tracts, and 40% within home and neighboring census tracts. The mean distance from home to grocery stores was 2.2 miles. We found that people visited grocery stores more frequently when they lived in neighborhoods with higher percentages of Hispanics/Latinos, renters and foreign-born residents, and a greater number of grocery stores. This research highlights the utility of mobility data in elucidating grocery store use, and factors that may facilitate or be a barrier to store access. The results point to limitations of using geographically constrained metrics of food access like food deserts.

Analyzing regional connectivity through population mobility data from cellular operators

Analyzing regional connectivity through population mobility data from cellular operators

Population mobility data provides meaningful indicators of fast food intake and diet-related diseases in diverse populations

The characteristics of food environments people are exposed to, such as the density of fast food (FF) outlets, can impact their diet and risk for diet-related chronic disease. Previous studies examining the relationship between food environments and nutritional health have produced mixed findings, potentially due to the predominant focus on static food environments around people’s homes. As smartphone ownership increases, large-scale data on human mobility (i.e., smartphone geolocations) represents a promising resource for studying dynamic food environments that people have access to and visit as they move throughout their day. This study investigates whether mobility data provides meaningful indicators of diet, measured as FF intake, and diet-related disease, evaluating its usefulness for food environment research. Using a mobility dataset consisting of 14.5 million visits to geolocated food outlets in Los Angeles County (LAC) across a representative sample of 243,644 anonymous and opted-in adult smartphone users in LAC, we construct measures of visits to FF outlets aggregated over users living in neighborhood. We find that the aggregated measures strongly and significantly correspond to self-reported FF intake, obesity, and diabetes in a diverse, representative sample of 8,036 LAC adults included in a population health survey carried out by the LAC Department of Public Health. Visits to FF outlets were a better predictor of individuals’ obesity and diabetes than their self-reported FF intake, controlling for other known risks. These findings suggest mobility data represents a valid tool to study people’s use of dynamic food environments and links to diet and health.

A nationwide lockdown and deaths due to COVID-19 in the Indian subcontinent

During the COVID-19 pandemic, the effects of nationwide lockdowns on health outcomes have been widely studied in Western, developed countries. However, the effects of lockdowns in emerging and developing countries are largely unknown. We used data from India and Bangladesh to study the effect of nationwide restrictions on public movement in Bangladesh in April 2021 on health outcomes, specifically COVID-19 incidence and mortality. India and Bangladesh had nearly identical development of the COVID-19 Delta wave the weeks before the lockdown. We leveraged longitudinal data from the pre- and post-intervention period in both countries in a structural causal model, suggesting that the reported deaths in Bangladesh due to COVID-19 would have been ∼117% higher (95% PI: 72%–170%) in April 2021 had there been fewer restrictions. Further, we used population mobility data from Google to study behavioural changes in the two countries, supporting the hypothesis that the intervention had substantial effects on the mobility trends of the Bangladeshi population, which in turn reduced the number of COVID-19 deaths.

Geolocated Twitter-based population mobility in Victoria, Australia, during the staged COVID-19 restrictions

Using geotagged Twitter data in Victoria, we created a mobility index and studied the changes during the staged restrictions during the coronavirus disease 2019 (COVID-19) pandemic. We describe preliminary evidence that geotagged Twitter data may be used to provide real-time population mobility data and information on the impact of restrictions on such mobility.

Greenspace use during the COVID-19 pandemic: A longitudinal population mobility study in the United Kingdom

BackgroundThe coronavirus pandemic greatly disrupted the lives of people. Restrictions introduced worldwide to limit the spread of infection included stay-at-home orders, closure of venues, restrictions to travel and limits to social contacts. During this time, parks and outdoor greenspaces gained prominent attention as alternative location for respite. Population mobility data offers a unique opportunity to understand the impact of the pandemic on outdoor behaviour. We examine the role of the restrictions on park use throughout the full span of the pandemic while controlling for weather and region. MethodsThis study provides a longitudinal population analysis of park visitation using Google COVID-19 Community Mobility Reports data in the UK. Daily park visitation was plotted and ANOVA analyses tested season and year effects in visitation. Then, regressions examined park visitation beyond weather (temperature and rain), according to COVID-19 restrictions, while controlling for region specificities through unit fixed effect models. ResultsTime series and ANOVA analyses documented the significant decrease in park visitation in the spring of 2020, the seasonal pattern in visitation, and an overall sustained and elevated use over nearly three years. Regressions confirmed park visitation increased significantly when temperature was greater and when it rained less. More visitation was also seen when there were fewer COVID-19 cases and when the stringency level of restrictions was lower. Of special interest, a significant interaction effect was found between temperature and stringency, with stringency significantly supressing the effect of higher temperature on visitation. ConclusionsCOVID-19 restrictions negatively impacted park visitation on warm days. Given the general health, social, and wellbeing benefits of greenspace use, one should consider the collateral negative impact of restrictions on park visitation. When social distancing of contacts is required, the few remaining locations where it can safely occur should instead be promoted.

Exploring and visualizing temporal relations in multivariate time series

This paper introduces an approach to analyzing multivariate time series (MVTS) data through progressive temporal abstraction of the data into patterns characterizing the behavior of the studied dynamic phenomenon. The paper focuses on two core challenges: identifying basic behavior patterns of individual attributes and examining the temporal relations between these patterns across the range of attributes to derive higher-level abstractions of multi-attribute behavior. The proposed approach combines existing methods for univariate pattern extraction, computation of temporal relations according to the Allen’s time interval algebra, visual displays of the temporal relations, and interactive query operations into a cohesive visual analytics workflow. The paper describes the application of the approach to real-world examples of population mobility data during the COVID-19 pandemic and characteristics of episodes in a football match, illustrating its versatility and effectiveness in understanding composite patterns of interrelated attribute behaviors in MVTS data.

International Importation Risk Estimation of SARS-CoV-2 Omicron Variant with Incomplete Mobility Data

A novel Omicron subvariant named BQ.1 emerged in Nigeria in July 2022 and has since become a dominant strain, causing a significant number of repeated infections even in countries with high-vaccination rates. Due to the high flow of people between Western Africa and other non-African countries, there is a high risk of Omicron BQ.1 being introduced to other countries from Western Africa. In this context, we developed a model based on deep neural networks to estimate the probability that the Omicron BQ.1 introduced to other countries from Western Africa based on the incomplete population mobility data from Western Africa to other non-African countries. Our study found that the highest risk was in France and Spain during the study period, while the importation risk of other 13 non-African countries including Canada and the United States is also high. Our approach sheds light on how deep learning techniques can assist in the development of public health policies, and it has the potential to be extended to other types of viruses.

A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports

The rapidly spreading COVID-19 disease had already infected more than 190 countries. As a result of this scenario, nations everywhere monitored confirmed cases of infection, cures, and fatalities and made predictions about what the future would hold. In the event of a pandemic, governments had set limit rules for the spread of the virus and save lives. Multiple computer methods existed for forecasting epidemic time series. Deep learning was one of the most promising methods for time-series prediction. In this research, we propose a model for predicting the spread of COVID-19 in Egypt based on deep learning sequence-to-sequence regression, which makes use of data on the population mobility reports. The presented model utilized a new combined dataset from two different sources. The first source is Google population mobility reports, and the second source is the number of infected cases reported daily “world in data” website. The suggested model could predict new cases of COVID-19 infection within 3–7 days with the least amount of prediction error. The proposed model achieved 96.69% accuracy for 3 days of prediction. This study is noteworthy since it is one of the first trials to estimate the daily influx of new COVID-19 infections using population mobility data instead of daily infection rates.

Analysis of COVID-19 outbreak in Hubei province based on Tencent's location big data.

Rapid urbanization has gradually strengthened the spatial links between cities, which greatly aggravates the possibility of the spread of an epidemic. Traditional methods lack the early and accurate detection of epidemics. This study took the Hubei province as the study area and used Tencent's location big data to study the spread of COVID-19. Using ArcGIS as a platform, the urban relation intensity, urban centrality, overlay analysis, and correlation analysis were used to measure and analyze the population mobility data of 17 cities in Hubei province. The results showed that there was high similarity in the spatial distribution of urban relation intensity, urban centrality, and the number of infected people, all indicating the spatial distribution characteristics of "one large and two small" distributions with Wuhan as the core and Huanggang and Xiaogan as the two wings. The urban centrality of Wuhan was four times higher than that of Huanggang and Xiaogan, and the urban relation intensity of Wuhan with Huanggang and Xiaogan was also the second highest in the Hubei province. Meanwhile, in the analysis of the number of infected persons, it was found that the number of infected persons in Wuhan was approximately two times that of these two cities. Through correlation analysis of the urban relation intensity, urban centrality, and the number of infected people, it was found that there was an extremely significant positive correlation among the urban relation intensity, urban centrality, and the number of infected people, with an R2 of 0.976 and 0.938, respectively. Based on Tencent's location big data, this study conducted the epidemic spread research for "epidemic spatial risk classification and prevention and control level selection" to make up for the shortcomings in epidemic risk analysis and judgment. This could provide a reference for city managers to effectively coordinate existing resources, formulate policy, and control the epidemic.

Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks

The pandemic of COVID-19 is undoubtedly one of the biggest challenges for modern healthcare. In order to analyze the spatio-temporal aspects of the spread of COVID-19, technology has helped us to track, identify and store information regarding positivity and hospitalization, across different levels of municipal entities. In this work, we present a method for predicting the number of positive and hospitalized cases via a novel multi-scale graph neural network, integrating information from fine-scale geographical zones of a few thousand inhabitants. By leveraging population mobility data and other features, the model utilizes message passing to model interaction between areas. Our proposed model manages to outperform baselines and deep learning models, presenting low errors in both prediction tasks. We specifically point out the importance of our contribution in predicting hospitalization since hospitals became critical infrastructure during the pandemic. To the best of our knowledge, this is the first work to exploit high-resolution spatio-temporal data in a multi-scale manner, incorporating additional knowledge, such as vaccination rates and population mobility data. We believe that our method may improve future estimations of positivity and hospitalization, which is crucial for healthcare planning.

Assessing pandemic era stadium events and infections using mobile phone based population mobility data: An exploratory study from Ireland, 2021

Mass gathering events (MGEs) attracting local, national, or international crowds presented particular challenges in the context of the coronavirus disease 2019 (COVID-19) pandemic. Sporting, religious, music and other cultural events held during the early months of the pandemic, without social distancing or other safeguards, have been regarded as so-called ‘super spreader’ events. By the summer of 2020, MGEs were generally banned or subject to severe restrictions. Regular European sporting fixtures such as England’s Football Association and Germany’s Bundesliga matches began to return in the autumn with protective measures in place, such as matches initially held behind closed doors, and later with sub-capacity crowd limits and mandatory social distancing [1, 2, 3, 4, 5]. With protective measures in place, and proof of COVID-19 vaccination or recovery required for entry, a series of six sporting MGEs, ‘the All-Ireland Finals’ were held in the Republic of Ireland’s largest stadium, Croke Park in Dublin, during August-September 2021. This study draws on a high-resolution human population mobility dataset to quantify journeys to/from the stadium area on MGE days by destination. The anonymised, aggregated, data used is based on mobile phone usage, and consists of a series of fine-grained geographical origin-destination matrices presenting daily estimates of area to area journey numbers. With mobility from the stadium area serving as a proxy for MGE attendance, this study explores associations between MGE attendance numbers and local COVID-19 infections over subsequent five week periods. No evidence was found of association between attendance at any of the six 2021 All-Ireland MGEs and COVID-19 infections over subsequent five week periods. This finding contrasts with studies of comparable MGEs in 2020, such as English Association Football matches held during spring 2020, and German Bundesliga football matches held during autumn 2020. These differing outcomes may point to the effectiveness of transmission mitigation policies and behaviours.

The Impact Model of Social Restriction Policies and New Habit Adaptation to Community Mobility Patterns in Responding and Control Covid-19 Pandemic in Indonesia

The Indonesian government has carried out various policies in suppressing the spread of Covid-19 in Indonesia. The restriction policies that have been implemented encourage the community to adapt their behavior following the health protocols set by the government. The propagated spread transmission of Covid-19, followed by changes in the community mobility pattern, and influences increasing cases.
 
 This study assessed the impact of the policies implemented by the government on the changing patterns of community mobility and controlling Covid-19 in Indonesia. Aggregate data on Covid-19 epidemiological parameters and population mobility data from Google Community Report were analyzed by trend and modeling using linear regression analysis from the beginning of the March 2020-June 2021 pandemic Covid-19 in Indonesia.
 
 The results showed an increase in Covid-19 cases following the community mobility in Indonesia. The pattern of community mobility was different in each restriction policy and in various places as shown Table 1 and Figure 1. Social restriction policies were not fully effective in controlling Covid-19 in Indonesia. The effectiveness of restrictive policies and patterns of community mobility in controlling Covid-19 requires consistent and continuous supervision from the government.
 Policies based on comprehensive epidemiological research were essential considerations in efforts to control and prevent the transmission of Covid-19.

Geographic disparities and determinants of COVID-19 incidence risk in the greater St. Louis Area, Missouri (United States).

BackgroundEvidence seems to suggest that the risk of Coronavirus Disease 2019 (COVID-19) might vary across communities due to differences in population characteristics and movement patterns. However, little is known about these differences in the greater St Louis Area of Missouri and yet this information is useful for targeting control efforts. Therefore, the objectives of this study were to investigate (a) geographic disparities of COVID-19 risk and (b) associations between COVID-19 risk and socioeconomic, demographic, movement and chronic disease factors in the Greater St. Louis Area of Missouri, USA.MethodsData on COVID-19 incidence and chronic disease hospitalizations were obtained from the Department of Health and Missouri Hospital Association, respectively. Socioeconomic and demographic data were obtained from the 2018 American Community Survey while population mobility data were obtained from the SafeGraph website. Choropleth maps were used to identify geographic disparities of COVID-19 risk and several sociodemographic and chronic disease factors at the ZIP Code Tabulation Area (ZCTA) spatial scale. Global negative binomial and local geographically weighted negative binomial models were used to investigate associations between ZCTA-level COVID-19 risk and socioeconomic, demographic and chronic disease factors.ResultsThere were geographic disparities found in COVID-19 risk. Risks tended to be higher in ZCTAs with high percentages of the population with a bachelor’s degree (p<0.0001) and obesity hospitalizations (p<0.0001). Conversely, risks tended to be lower in ZCTAs with high percentages of the population working in agriculture (p<0.0001). However, the association between agricultural occupation and COVID-19 risk was modified by per capita between ZCTA visits. Areas that had both high per capita between ZCTA visits and high percentages of the population employed in agriculture had high COVID-19 risks. The strength of association between agricultural occupation and COVID-19 risk varied by geographic location.ConclusionsGeographic disparities of COVID-19 risk exist in the St. Louis area and are associated with sociodemographic factors, population movements, and obesity hospitalization risks. The latter is particularly concerning due to the growing prevalence of obesity and the known immunological impairments among obese individuals. Therefore, future studies need to focus on improving our understanding of the relationships between COVID-19 vaccination efficacy, obesity and waning of immunity among obese individuals so as to better guide vaccination regimens and reduce disparities.

What factors affect the structural resilience of urban networks during COVID-19 epidemic? A comparative analysis in China

ABSTRACT Under the COVID-19 epidemic, the structural resilience of urban networks has become an important guarantee for social and economic stability and recovery. In this study, population mobility data and freight demand information are selected to construct urban networks. The complex network is used to describe the development level from five aspects of diversity, equilibrium, reciprocity, assortativity and transmission of network structure. And the entropy weight TOPSIS method is used to calculate the structural resilience of urban network based on the description of development level. We find that as the epidemic increases, the structure of population mobility network and freight demand network shows an evolutionary trend from ‘single-center’ to ‘dual-center’, and then to ‘polycentric’. Meanwhile, a flat network structure is conducive to higher structural resilience. Because the impact of diversity on the structural resilience of population mobility network and freight demand network is respectively 0.541 and 0.142, while the impact of equilibrium is respectively 0.440 and 0.769. The network with high diversity and equilibrium tends to form flat structure and show stronger resilience. Besides, the ‘long-range link’ in the population mobility network and the phenomenon of ‘imbalance transfer’ in the freight demand network can also help to improve the structural resilience of the network. Therefore, cities can establish a flat network through polycentric construction and cross-regional cooperation, while improving the heterogeneity of the urban network, they should also promote all-round and multi-faceted development of the network.

Fine-Grained Population Mobility Data-Based Community-Level COVID-19 Prediction Model

Predicting the number of infections in the anti-epidemic process is extremely beneficial to the government in developing anti-epidemic strategies, especially in fine-grained geographic units. Previous works focus on low spatial resolution prediction, e.g., county-level, and preprocess data to the same geographic level, which loses some useful information. In this paper, we propose a fine-grained population mobility data-based model (FGC-COVID) utilizing data of two geographic levels for community-level COVID-19 prediction. We use the population mobility data between Census Block Groups (CBGs), which is a finer-grained geographic level than community, to build the graph and capture the dependencies between CBGs using graph neural networks (GNNs). To mine as finer-grained patterns as possible for prediction, a spatial weighted aggregation module is introduced to aggregate the embeddings of CBGs to community level based on their geographic affiliation and spatial autocorrelation. Extensive experiments on 300 days LA city COVID-19 data indicate our model outperforms existing forecasting models on community-level COVID-19 prediction.

Tourism demand forecasting from the perspective of mobility: a brand-new predictive variable generated from intercity population mobility big data

ABSTRACT Few studies have investigated tourism demand forecasting from a mobility perspective. We explore whether population mobility data improve the ability to forecast tourist volumes in scenic spots. We selected Jiuzhaigou and Mount Siguniang in China as study cases and ensured the robustness of the results using single- and multistep rolling evaluation techniques. The findings reveal a significant correlation, long-term cointegration, and two-way Granger causality between intercity population flow and tourist flow. The comparison model with population flow as a variable outperforms the benchmark model. We prove the feasibility of leveraging population flow in tourism demand forecasting and suggest further research.

Identifying the Socioeconomic, Demographic, and Political Determinants of Social Mobility and Their Effects on COVID-19 Cases and Deaths: Evidence From US Counties.

BackgroundThe spread of COVID-19 at the local level is significantly impacted by population mobility. The U.S. has had extremely high per capita COVID-19 case and death rates. Efficient nonpharmaceutical interventions to control the spread of COVID-19 depend on our understanding of the determinants of public mobility.ObjectiveThis study used publicly available Google data and machine learning to investigate population mobility across a sample of US counties. Statistical analysis was used to examine the socioeconomic, demographic, and political determinants of mobility and the corresponding patterns of per capita COVID-19 case and death rates.MethodsDaily Google population mobility data for 1085 US counties from March 1 to December 31, 2020, were clustered based on differences in mobility patterns using K-means clustering methods. Social mobility indicators (retail, grocery and pharmacy, workplace, and residence) were compared across clusters. Statistical differences in socioeconomic, demographic, and political variables between clusters were explored to identify determinants of mobility. Clusters were matched with daily per capita COVID-19 cases and deaths.ResultsOur results grouped US counties into 4 Google mobility clusters. Clusters with more population mobility had a higher percentage of the population aged 65 years and over, a greater population share of Whites with less than high school and college education, a larger percentage of the population with less than a college education, a lower percentage of the population using public transit to work, and a smaller share of voters who voted for Clinton during the 2016 presidential election. Furthermore, clusters with greater population mobility experienced a sharp increase in per capita COVID-19 case and death rates from November to December 2020.ConclusionsRepublican-leaning counties that are characterized by certain demographic characteristics had higher increases in social mobility and ultimately experienced a more significant incidence of COVID-19 during the latter part of 2020.