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

2020 ◽  
pp. 239980832095421 ◽  
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.

scholarly journals Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Meng-Chun Chang ◽  
Rebecca Kahn ◽  
Yu-An Li ◽  
Cheng-Sheng Lee ◽  
Caroline O. Buckee ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Initial Conditions ◽  
Human Mobility ◽  
Human Movement ◽  
Disease Spread ◽  
Spatial Spread ◽  
Movement Data ◽  
Intercity Travel ◽  
Travel Restrictions ◽  
The Impact

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.

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 Predicting COVID-19 incidence in French hospitals using human contact network analytics

2021 ◽  
Author(s):  
Christian Selinger ◽  
Marc Choisy ◽  
Samuel Alizon
Keyword(s):  
Hospital Admissions ◽  
Human Mobility ◽  
Disease Incidence ◽  
Contact Network ◽  
Mobility Data ◽  
Human Contact ◽  
Network Analytics ◽  
Administrative Units ◽  
Individual Tracking ◽  
National Analysis

Coronavirus disease (COVID-19) was detected in Wuhan, China in 2019 and spread worldwide within few weeks. The COVID-19 epidemic started to gain traction in France in March 2020. Sub-national hospital admissions and deaths were then recorded daily and served as the main policy indicators. Concurrently, mobile phone positioning data have been curated to determine the frequency of users being colocalized within a given distance. Contrarily to individual tracking data, these can provide a proxy of human contact networks between subnational administrative units. Motivated by numerous studies correlating human mobility data and disease incidence, we developed predictive time series models of hospital incidence between July 2020 and April 2021. Adding human contact network analytics such as clustering coefficients, contact network strength, null links or curvature as regressors, we found that predictions can be improved substantially (more than 50%) both at the national and sub-national for up to two weeks. Our sub-national analysis also revealed the importance of spatial structure, as incidence in colocalized administrative units improved predictions. This original application of network analytics from co-localisation data to epidemic spread opens new perspectives for epidemics forecasting and public health.

scholarly journals HUMAN MOBILITY DURING THE COVID-19 PANDEMIC IN WESTERN BALKAN COUNTRIES

2021 ◽  
Author(s):  
Srđan Damjanović ◽  
◽  
Predrag Katanić ◽  
Vesna Petrović ◽  
◽  
...  
Keyword(s):  
Human Mobility ◽  
Daily Basis ◽  
Disease Spread ◽  
Handheld Devices ◽  
The Balkans ◽  
Freedom Of Movement ◽  
Mobility Data ◽  
The World ◽  
History Of ◽  
Chinese Province

At the end of 2019, a new coronavirus appeared in the Chinese province of Wuhan, causing the appearance of the disease COVID-19. The disease spread very quickly to other countries in the world, including the Balkans. The governments of many countries have decided to combat the spread of the COVID-19 virus in the community through social distancing measures. Decisions to ban the movement of people were easy to make, but they were very difficult to implement and enforce in practice. Some of the countries monitored their citizens through various applications installed on smartphones. This led to criticism by many NGOs, as they felt that this violated basic human rights of freedom of movement and privacy. Some lawsuits were even filed in the courts because the citizens felt that they were denied rights guaranteed by the respective constitution. Google uses the ability to monitor all those citizens around the world on a daily basis who use smartphones or handheld devices, which provide the option to record the "location history" of the users. This is possible for them, since most people have voluntarily agreed to this option on their devices. In early 2020, Google began publishing global mobility data on a daily basis through a report called “Community Mobility Reports”. The report shows the percentage change in human activity at six grouped locations. Data obtained in the reference days before the outbreak of the COVID-19 pandemic are used as a basis for comparison. In this paper, we studied the dynamics of human mobility during the COVID-19 pandemic in 7 countries of the Balkans: Bosnia and Herzegovina, Serbia, Croatia, North Macedonia, Bulgaria, Greece, and Romania. For Montenegro and Albania Google did not provide data on human mobility. We present the processed data graphically. For all examined countries, we statistically analyzed the obtained data and presented them in a table.

scholarly journals Lockdowns result in changes in human mobility which may impact the epidemiologic dynamics of SARS-CoV-2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nishant Kishore ◽  
Rebecca Kahn ◽  
Pamela P. Martinez ◽  
Pablo M. De Salazar ◽  
Ayesha S. Mahmud ◽  
...  
Keyword(s):  
Urban Areas ◽  
Human Mobility ◽  
Unintended Consequences ◽  
Disease Spread ◽  
Epidemic Spread ◽  
Long Distance ◽  
Mobility Data ◽  
The World ◽  
Travel Restrictions ◽  
Long Distance Movement

AbstractIn response to the SARS-CoV-2 pandemic, unprecedented travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns—defined as restrictions on both local movement or long distance travel—will determine how effective these kinds of interventions are. Here, we evaluate the effects of lockdowns on human mobility and simulate how these changes may affect epidemic spread by analyzing aggregated mobility data from mobile phones. We show that in 2020 following lockdown announcements but prior to their implementation, both local and long distance movement increased in multiple locations, and urban-to-rural migration was observed around the world. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. Our model shows that this increased movement has the potential to increase seeding of the epidemic in less urban areas, which could undermine the goal of the lockdown in preventing disease spread. Lockdowns play a key role in reducing contacts and controlling outbreaks, but appropriate messaging surrounding their announcement and careful evaluation of changes in mobility are needed to mitigate the possible unintended consequences.

scholarly journals A Survey on Deep Learning for Human Mobility

2023 ◽  
Vol 55 (1) ◽  
pp. 1-44
Author(s):  
Massimiliano Luca ◽  
Gianni Barlacchi ◽  
Bruno Lepri ◽  
Luca Pappalardo
Keyword(s):  
Deep Learning ◽  
Predictive Power ◽  
Human Mobility ◽  
Well Being ◽  
Learning Approaches ◽  
Comprehensive Description ◽  
Mobility Data ◽  
Flow Generation ◽  
Disease Spreading ◽  
Gps Traces

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above, and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.

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

eLife ◽  
2019 ◽  
Vol 8 ◽  
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.

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

Epidemics ◽  
2021 ◽  
pp. 100534
Author(s):  
Tanjona Ramiadantsoa ◽  
C. Jessica E. Metcalf ◽  
Antso Hasina Raherinandrasana ◽  
Santatra Randrianarisoa ◽  
Benjamin L. Rice ◽  
...  
Keyword(s):  
Human Mobility ◽  
Data Sources ◽  
Spatial Spread ◽  
Mobility Data

scholarly journals Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh

2020 ◽  
Vol 17 (167) ◽  
pp. 20190809
Author(s):  
Solveig Engebretsen ◽  
Kenth Engø-Monsen ◽  
Mohammad Abdul Aleem ◽  
Emily Suzanne Gurley ◽  
Arnoldo Frigessi ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Mobile Phone ◽  
Human Mobility ◽  
Mobility Model ◽  
Disease Spread ◽  
Spatial Synchrony ◽  
Mobility Data ◽  
Daily Mobility ◽  
Spatio Temporal ◽  
Good Proxy

Human mobility plays a major role in the spatial dissemination of infectious diseases. We develop a spatio-temporal stochastic model for influenza-like disease spread based on estimates of human mobility. The model is informed by mobile phone mobility data collected in Bangladesh. We compare predictions of models informed by daily mobility data (reference) with that of models informed by time-averaged mobility data, and mobility model approximations. We find that the gravity model overestimates the spatial synchrony, while the radiation model underestimates the spatial synchrony. Using time-averaged mobility resulted in spatial spreading patterns comparable to the daily mobility model. We fit the model to 2014–2017 influenza data from sentinel hospitals in Bangladesh, using a sequential version of approximate Bayesian computation. We find a good agreement between our estimated model and the case data. We estimate transmissibility and regional spread of influenza in Bangladesh, which are useful for policy planning. Time-averaged mobility appears to be a good proxy for human mobility when modelling infectious diseases. This motivates a more general use of the time-averaged mobility, with important implications for future studies and outbreak control. Moreover, time-averaged mobility is subject to less privacy concerns than daily mobility, containing less temporal information on individual movements.

Dark leaf spot (Alternaria brassicicola) on Chinese cabbage: spatial patterns

2005 ◽  
Vol 56 (7) ◽  
pp. 699 ◽  
Author(s):  
L. Y. Chen ◽  
T. V. Price ◽  
M. J. Silvapulle
Keyword(s):  
Chinese Cabbage ◽  
Leaf Spot ◽  
Disease Incidence ◽  
Field Trials ◽  
Alternaria Brassicicola ◽  
Disease Spread ◽  
Distance Class ◽  
Maximum Information ◽  
Spatial Spread ◽  
Core Cluster

The spatial spread of dark leaf spot caused by Alternaria brassicicola on Chinese cabbage was characterised over 2 years. The study was conducted in 2 field trials using ordinary runs, mapping, spatial autocorrelation, and 2-dimensional distance class analyses. Diseased plants were generally clustered and cluster orientation coincided with the line of inoculation. Disease spread was greater within than across rows. The maximum number of spatial lags with significantly positive autocorrelations occurred when disease incidence levels reached 20–80% in summer 1993–94. Core cluster size generally increased with disease incidence. Two-dimensional distance class analysis was the best analytical method among those used in describing spatial spread of the disease as it did not only provide maximum information but also considered missing data.

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