Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

Background Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). Objective Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). Methods We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. Results This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Conclusions Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. International Registered Report Identifier (IRRID) DERR1-10.2196/24432

Download Full-text

Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics (Preprint)

10.2196/preprints.24432 ◽

2020 ◽

Author(s):

Zhenlong Li ◽

Xiaoming Li ◽

Dwayne Porter ◽

Jiajia Zhang ◽

Yuqin Jiang ◽

...

Keyword(s):

Public Health ◽

Big Data ◽

Infectious Diseases ◽

Spatial Scales ◽

Human Mobility ◽

Human Movement ◽

Data Sources ◽

Mobile Phone Data ◽

Spatial Spread ◽

Mobility Data

BACKGROUND Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). OBJECTIVE Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). METHODS We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. RESULTS This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. CONCLUSIONS Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/24432

Download Full-text

Comparative Analysis of Geolocation Information through Mobile-Devices under Different COVID-19 Mobility Restriction Patterns in Spain

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020073 ◽

2021 ◽

Vol 10 (2) ◽

pp. 73

Author(s):

Raquel Pérez-Arnal ◽

David Conesa ◽

Sergio Alvarez-Napagao ◽

Toyotaro Suzumura ◽

Martí Català ◽

...

Keyword(s):

Mobile Phones ◽

Human Mobility ◽

The Other ◽

Data Sources ◽

Experimental Setting ◽

Mobile Data ◽

Restriction Patterns ◽

Mobility Data ◽

The World ◽

Proper Interpretation

The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility, being the greatest facilitator for the spread of the virus, is at the epicenter of this change. In order to study mobility under COVID-19, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to future crisis, we need to understand all possible mobility data sources at our disposal. Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. Furthermore, the sample of society they cover is large and representative. On the other hand, these data are not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting due to both its large and fast pandemic peak and its implementation of a sustained, generalized lockdown. Our work illustrates how a direct and naive comparison between sources can be misleading, as certain days (e.g., Sundays) exhibit a directly adverse behavior. After understanding their particularities, we find them to be partially correlated and, what is more important, complementary under a proper interpretation. Finally, we confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain.

Download Full-text

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

Frontiers in Big Data ◽

10.3389/fdata.2021.718351 ◽

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.

Download Full-text

Emerging geo-data sources to reveal human mobility dynamics during COVID-19 pandemic: opportunities and challenges

Computational Urban Science ◽

10.1007/s43762-021-00022-x ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

Xiao Li ◽

Haowen Xu ◽

Xiao Huang ◽

Chenxiao Guo ◽

Yuhao Kang ◽

...

Keyword(s):

Data Privacy ◽

Large Scale ◽

Human Mobility ◽

Data Sources ◽

Data Uncertainty ◽

Connected Vehicle ◽

Research Experience ◽

Mobility Data ◽

Vehicle Data ◽

Spatial Coverage

AbstractEffectively 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.

Download Full-text

Mapping imported malaria in Bangladesh using parasite genetic and human mobility data

eLife ◽

10.7554/elife.43481 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 22

Author(s):

Hsiao-Han Chang ◽

Amy Wesolowski ◽

Ipsita Sinha ◽

Christopher G Jacob ◽

Ayesha Mahmud ◽

...

Keyword(s):

Mobile Phone ◽

Human Mobility ◽

Genetic Data ◽

Epidemiological Surveillance ◽

Mobile Phone Data ◽

Spatial Spread ◽

Control Programs ◽

Mobility Data ◽

Imported Infections ◽

National Control

For countries aiming for malaria elimination, travel of infected individuals between endemic areas undermines local interventions. Quantifying parasite importation has therefore become a priority for national control programs. We analyzed epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data to measure the spatial spread of malaria parasites in southeast Bangladesh. We developed a genetic mixing index to estimate the likelihood of samples being local or imported from parasite genetic data and inferred the direction and intensity of parasite flow between locations using an epidemiological model integrating the travel survey and mobile phone calling data. Our approach indicates that, contrary to dogma, frequent mixing occurs in low transmission regions in the southwest, and elimination will require interventions in addition to reducing imported infections from forested regions. Unlike risk maps generated from clinical case counts alone, therefore, our approach distinguishes areas of frequent importation as well as high transmission.

Download Full-text

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

10.20944/preprints202106.0211.v1 ◽

2021 ◽

Author(s):

Arminn Potgieter ◽

Inger Fabris-Rotelli ◽

Zaid Kimmie ◽

Nontembeko Dudeni-Tlhone ◽

Jenny Holloway ◽

...

Keyword(s):

South Africa ◽

Human Mobility ◽

Data Sources ◽

Population Mobility ◽

Mobility Patterns ◽

Mobility Data ◽

The World ◽

Spatial Weight ◽

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 convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful.

Download Full-text

The interplay of spatial spread of COVID-19 and human mobility in the urban system of China during the Chinese New Year

Environment and Planning B Urban Analytics and City Science ◽

10.1177/2399808320954211 ◽

2020 ◽

pp. 239980832095421 ◽

Cited By ~ 1

Author(s):

Xiaoyan Mu ◽

Anthony Gar-On Yeh ◽

Xiaohu Zhang

Keyword(s):

Human Mobility ◽

Disease Incidence ◽

Urban System ◽

Disease Spread ◽

New Wave ◽

Spatial Spread ◽

Mobility Data ◽

Rapid Spread ◽

Disease Spreading ◽

Scientific Attention

The rapid spread of infectious diseases is devastating to the healthcare systems of all countries. The dynamics of the spatial spread of epidemic have received considerable scientific attention. However, the understanding of the spatial variation of epidemic severity in the urban system is lagging. Using synchronized epidemic data and human mobility data, integrated with other multiple-sourced data, this study examines the interplay between disease spread of coronavirus disease (COVID-19) and inter-city and intra-city mobility among 319 Chinese cities. The results show a disease spreading process consisting of a major transfer (inter-city) diffusion before the Chinese New Year and a subsequent local (intra-city) diffusion after the Chinese New Year in the urban system of China. The variations in disease incidence between cities are mainly driven by inter-city mobility from Wuhan, the epidemic center of COVID-19. Cities that are closer to the epidemic center and with more population in the urban area will face higher risks of disease incidence. Warm and humid weather could help mitigate the spread of COVID-19. The extensive inter-city and intra-city travel interventions in China have reduced approximately 70% and 40% inter-city and intra-city mobility, respectively, and effectively slowed down the spread of the disease by minimizing human to human transmission together with other disease monitoring, control, and preventive measures. These findings could provide valuable insights into understanding the dynamics of disease spread in the urban system and help to respond to another new wave of pandemic in China and other parts of the world.

Download Full-text

Forecasting the spatial transmission of influenza in the United States

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1708856115 ◽

2018 ◽

Vol 115 (11) ◽

pp. 2752-2757 ◽

Cited By ~ 38

Author(s):

Sen Pei ◽

Sasikiran Kandula ◽

Wan Yang ◽

Jeffrey Shaman

Keyword(s):

Human Mobility ◽

State Level ◽

Human Movement ◽

The United States ◽

Spatial Spread ◽

Influenza Outbreak ◽

Mobility Data ◽

Real Time Information ◽

Time Information ◽

Spatial Transmission

Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,—i.e., spatial spread, defined as the week that local incidence increases above a baseline threshold—up to 6 wk in advance of this event. In addition, the metapopulation prediction system forecasts influenza outbreak onset, peak timing, and peak intensity more accurately than isolated location-specific forecasts. The proposed framework could be applied to emergent respiratory viruses and, with appropriate modifications, other infectious diseases.

Download Full-text

Estimation of Human Mobility Patterns for Forecasting the Early Spread of Disease

Healthcare ◽

10.3390/healthcare9091224 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1224

Author(s):

Zhengyan Li ◽

Huichun Li ◽

Xue Zhang ◽

Chengli Zhao

Keyword(s):

Large Scale ◽

Human Mobility ◽

Mainland China ◽

Flow Data ◽

Mobility Patterns ◽

Spatial Spread ◽

Structure Information ◽

Mobility Data ◽

Spread Model ◽

Scale Population

Human mobility data are indispensable in modeling large-scale epidemics, especially in predicting the spatial spread of diseases and in evaluating spatial heterogeneity intervention strategies. However, statistical data that can accurately describe large-scale population migration are often difficult to obtain. We propose an algorithm model based on the network science approach, which estimates the travel flow data in mainland China by transforming location big data and airline operation data into network structure information. In addition, we established a simplified deterministic SEIR (Susceptible-Exposed-Infectious-Recovered)-metapopulation model to verify the effectiveness of the estimated travel flow data in the study of predicting epidemic spread. The results show that individual travel distance in mainland China is mainly within 100 km. There is far more travel between prefectures within the same province than across provinces. The epidemic spatial spread model incorporating estimated travel data accurately predicts the spread of COVID-19 in mainland China. The results suggest that there are far more travelers than usual during the Spring Festival in mainland China, and the number of travelers from Wuhan mainly determines the number of confirmed cases of COVID-19 in each prefecture.

Download Full-text