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

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.

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Modeling the impact of human mobility and travel restrictions on the potential spread of SARS-CoV-2 in Taiwan

10.1101/2020.04.07.20053439 ◽

2020 ◽

Cited By ~ 3

Author(s):

Meng-Chun Chang ◽

Rebecca Kahn ◽

Yu-An Li ◽

Cheng-Sheng Lee ◽

Caroline O. Buckee ◽

...

Keyword(s):

Human Mobility ◽

Human Movement ◽

Disease Spread ◽

Medical Sciences ◽

Movement Data ◽

Intercity Travel ◽

Travel Restrictions ◽

Imported Cases ◽

Risk Areas ◽

The Impact

ABSTRACTBackgroundAs 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.MethodsHere, in collaboration with Facebook Data for Good, we built metapopulation models that incorporate human movement data with the goals of identifying the high risk areas of disease spread and assessing the potential effects of local travel restrictions in Taiwan. We compared the impact of intracity vs. intercity travel restrictions on both the total number of infections and the speed of outbreak spread and developed an interactive application that allows users to vary inputs and assumptions.FindingsWe found that intracity travel reductions have a higher impact on overall infection numbers than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. We also identified the most highly connected areas that may serve as sources of importation during an outbreak. 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.InterpretationIn Taiwan, most cases to date were imported or linked to imported cases. 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. Both intracity and intercity movement affect outbreak dynamics, with the former having more of an impact on the total numbers of cases and the latter impacting geographic scope. These findings have important implications for guiding future policies for travel restrictions during outbreaks in Taiwan.FundingMinistry of Science and Technology in Taiwan and National Institute of General Medical Sciences in USA

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

10.1101/2020.10.22.20217752 ◽

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.

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Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans

10.1101/2019.12.29.886408 ◽

2019 ◽

Cited By ~ 1

Author(s):

Gustavo Machado ◽

Jason Ardila Galvis ◽

Francisco Paulo Nunes Lopes ◽

Joana Voges ◽

Antônio Augusto Rosa Medeiros ◽

...

Keyword(s):

Disease Control ◽

Disease Surveillance ◽

Control Strategies ◽

Disease Spread ◽

Growth Stages ◽

Case Scenario ◽

Modeling Framework ◽

Worst Case ◽

Movement Data ◽

The Impact

SummaryTracking animal movements over time can fundamentally determine the success of disease control interventions throughout targeting farms that are tightly connected. In commercial pig production, animals are transported between farms based on growth stages, thus it generates time-varying contact networks that will influence the dynamics of disease spread. Still, risk-based surveillance strategies are mostly based on a static network. In this study, we reconstructed the static and temporal pig networks of one Brazilian state from 2017 to 2018, comprising 351,519 movements and 48 million transported pigs. The static networks failed to capture time-respecting movement pathways. Therefore, we propose a time-dependent network susceptible-infected (SI) model to simulate the temporal spread of an epidemic over the pig network globally through the temporal movement of animals among farms, and locally with a stochastic compartmental model in each farm, configured to calculate the minimum number of target farms needed to achieve effective disease control. In addition, we propagated disease on the pig temporal network to calculate the cumulative contacts as a proxy of epidemic sizes and evaluated the impact of network-based disease control strategies. The results show that targeting the first 1,000 farms ranked by degree would be sufficient and feasible to diminish disease spread considerably. Our finding also suggested that assuming a worst-case scenario in which every movement transmit disease, pursuing farms by degree would limit the transmission to up to 29 farms over the two years period, which is lower than the number of infected farms for random surveillance, with epidemic sizes of 2,593 farms. The top 1,000 farms could benefit from enhanced biosecurity plans and improved surveillance, which constitute important next steps in strategizing targeted disease control interventions. Overall, the proposed modeling framework provides a parsimonious solution for targeted disease surveillance when temporal movement data is available.

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Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics

10.1101/2020.08.12.20173252 ◽

2020 ◽

Author(s):

Viktor Jirsa ◽

Spase Petkoski ◽

Huifang Wang ◽

Marmaduke Woodman ◽

Jan Fousek ◽

...

Keyword(s):

Human Mobility ◽

Disease Spread ◽

Vulnerable Groups ◽

Psychosocial Variables ◽

Cross Sectional ◽

Societal Factors ◽

Spread Model ◽

Careful Handling ◽

Diverse Groups ◽

The Impact

During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.

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Estimating infection-related human mobility networks based on time series data of COVID-19 infection in Japan

10.1101/2021.08.02.21261486 ◽

2021 ◽

Author(s):

Tetsuya Yamada ◽

Shoi Shi

Keyword(s):

Time Series ◽

Infectious Diseases ◽

Time Series Data ◽

Human Mobility ◽

Emerging Infectious Diseases ◽

Human Movement ◽

Data Driven ◽

Disease Spread ◽

Series Data ◽

Data Infrastructure

Comprehensive and evidence-based countermeasures against emerging infectious diseases have become increasingly important in recent years. COVID-19 and many other infectious diseases are spread by human movement and contact, but complex transportation networks in 21 century make it difficult to predict disease spread in rapidly changing situations. It is especially challenging to estimate the network of infection transmission in the countries that the traffic and human movement data infrastructure is not yet developed. In this study, we devised a method to estimate the network of transmission of COVID-19 from the time series data of its infection and applied it to determine its spread across areas in Japan. We incorporated the effects of soft lockdowns, such as the declaration of a state of emergency, and changes in the infection network due to government-sponsored travel promotion, and predicted the spread of infection using the Tokyo Olympics as a model. The models used in this study are available online, and our data-driven infection network models are scalable, whether it be at the level of a city, town, country, or continent, and applicable anywhere in the world, as long as the time-series data of infections per region is available. These estimations of effective distance and the depiction of infectious disease networks based on actual infection data are expected to be useful in devising data-driven countermeasures against emerging infectious diseases worldwide.

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Travel distance and human movement predict paths of emergence and spatial spread of chikungunya in Thailand

Epidemiology and Infection ◽

10.1017/s0950268818001917 ◽

2018 ◽

Vol 146 (13) ◽

pp. 1654-1662 ◽

Cited By ~ 1

Author(s):

S. Chadsuthi ◽

B. M. Althouse ◽

S. Iamsirithaworn ◽

W. Triampo ◽

K. H. Grantz ◽

...

Keyword(s):

Human Movement ◽

Spatial Distance ◽

Spatiotemporal Pattern ◽

Rubber Plantation ◽

Population Mobility ◽

Long Distance ◽

Spatial Spread ◽

Incidence Data ◽

Southern Thailand ◽

The Impact

AbstractHuman movement contributes to the probability that pathogens will be introduced to new geographic locations. Here we investigate the impact of human movement on the spatial spread of Chikungunya virus (CHIKV) in Southern Thailand during a recent re-emergence. We hypothesised that human movement, population density, the presence of habitat conducive to vectors, rainfall and temperature affect the transmission of CHIKV and the spatiotemporal pattern of cases seen during the emergence. We fit metapopulation transmission models to CHIKV incidence data. The dates at which incidence in each of 151 districts in Southern Thailand exceeded specified thresholds were the target of model fits. We confronted multiple alternative models to determine which factors were most influential in the spatial spread. We considered multiple measures of spatial distance between districts and adjacency networks and also looked for evidence of long-distance translocation (LDT) events. The best fit model included driving-distance between districts, human movement, rubber plantation area and three LDT events. This work has important implications for predicting the spatial spread and targeting resources for control in future CHIKV emergences. Our modelling framework could also be adapted to other disease systems where population mobility may drive the spatial advance of outbreaks.

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Changing travel patterns in China during the early stages of the COVID-19 pandemic

Nature Communications ◽

10.1038/s41467-020-18783-0 ◽

2020 ◽

Vol 11 (1) ◽

Cited By ~ 3

Author(s):

Hamish Gibbs ◽

◽

Yang Liu ◽

Carl A. B. Pearson ◽

Christopher I. Jarvis ◽

...

Keyword(s):

Public Health ◽

Human Mobility ◽

Transportation Network ◽

Mainland China ◽

Disease Spread ◽

Travel Patterns ◽

Network Analyses ◽

Early Stages ◽

Travel Restrictions ◽

Spatio Temporal

Abstract Understanding changes in human mobility in the early stages of the COVID-19 pandemic is crucial for assessing the impacts of travel restrictions designed to reduce disease spread. Here, relying on data from mainland China, we investigate the spatio-temporal characteristics of human mobility between 1st January and 1st March 2020, and discuss their public health implications. An outbound travel surge from Wuhan before travel restrictions were implemented was also observed across China due to the Lunar New Year, indicating that holiday travel may have played a larger role in mobility changes compared to impending travel restrictions. Holiday travel also shifted healthcare pressure related to COVID-19 towards locations with lower healthcare capacity. Network analyses showed no sign of major changes in the transportation network after Lunar New Year. Changes observed were temporary and did not lead to structural reorganisation of the transportation network during the study period.

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Incubation periods impact the spatial predictability of cholera and Ebola outbreaks in Sierra Leone

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1913052117 ◽

2020 ◽

Vol 117 (9) ◽

pp. 5067-5073 ◽

Cited By ~ 3

Author(s):

Rebecca Kahn ◽

Corey M. Peak ◽

Juan Fernández-Gracia ◽

Alexandra Hill ◽

Amara Jambai ◽

...

Keyword(s):

Sierra Leone ◽

Incubation Period ◽

Population Connectivity ◽

Disease Spread ◽

Long Distance ◽

Spatial Spread ◽

Transmission Parameters ◽

Incubation Periods ◽

Using Data ◽

The Impact

Forecasting the spatiotemporal spread of infectious diseases during an outbreak is an important component of epidemic response. However, it remains challenging both methodologically and with respect to data requirements, as disease spread is influenced by numerous factors, including the pathogen’s underlying transmission parameters and epidemiological dynamics, social networks and population connectivity, and environmental conditions. Here, using data from Sierra Leone, we analyze the spatiotemporal dynamics of recent cholera and Ebola outbreaks and compare and contrast the spread of these two pathogens in the same population. We develop a simulation model of the spatial spread of an epidemic in order to examine the impact of a pathogen’s incubation period on the dynamics of spread and the predictability of outbreaks. We find that differences in the incubation period alone can determine the limits of predictability for diseases with different natural history, both empirically and in our simulations. Our results show that diseases with longer incubation periods, such as Ebola, where infected individuals can travel farther before becoming infectious, result in more long-distance sparking events and less predictable disease trajectories, as compared to the more predictable wave-like spread of diseases with shorter incubation periods, such as cholera.

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

10.1101/2020.03.02.20026708 ◽

2020 ◽

Cited By ~ 7

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.

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To die so far from Dixie : modeling epidemic dysentery in a Civil War prison camp

10.32469/10355/48696 ◽

2015 ◽

Author(s):

◽

Rebecca Shattuck Lander

Keyword(s):

Civil War ◽

Disease Transmission ◽

National Level ◽

Human Movement ◽

Pathogen Transmission ◽

Disease Spread ◽

Computer Simulation Model ◽

Prison Camp ◽

The Impact ◽

The Way

Epidemics have played a role in shaping human experiences of conflict among both soldiers and civilians. Prisoners of war, displaced populations, and confined refugees have experienced, and continue to experience, outbreaks of infectious disease, which are exacerbated by physical, environmental, and psychological stressors. Observations of epidemics at the global, regional, or national level are not always able to provide a complete picture of the unique health challenges of these wartime populations. This research develops and applies a computer simulation model to examine the way human behaviors, and the impact of those behaviors on the environment, can impact the way diarrheal diseases develop and spread in confined high-density living situations. This simulation was tested against the recorded death and sickness patterns for a dysentery outbreak at Camp Douglas, Illinois, a 19th Century Civil War prison camp. The agent-based simulation used in this research is a unique approach, and is based on the feedback relationship between human movement and behavior and the resulting contamination of physical spaces with infectious material, rather than direct person-to-person pathogen transmission. The results of this simulation suggests that modeling disease transmission based on environmental result in distinct epidemic dynamics. The results of this research emphasize the importance of examining the relationship between humans, their environment, and patterns of health and disease. Additionally, it highlights the way that model design can help to increase knowledge of how even limited movement and interaction options available to confined individuals can lead to significant differences in patterns of disease spread and epidemic development, which can help to better design public health interventions targeted at confined populations.

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