Faculty Opinions recommendation of Comparing metapopulation dynamics of infectious diseases under different models of human movement.

2021 ◽  
Author(s):  
Chris Bauch
Keyword(s):  
Infectious Diseases ◽  
Human Movement ◽  
Metapopulation Dynamics

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

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.

scholarly journals Effect of daily human movement on some characteristics of dengue dynamics

2020 ◽  
Author(s):  
Mayra R. Tocto-Erazo ◽  
Daniel Olmos-Liceaga ◽  
José A. Montoya-Laos
Keyword(s):  
Mathematical Modeling ◽  
Infectious Diseases ◽  
Numerical Simulations ◽  
High Activity ◽  
Human Movement ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
Key Factor ◽  
Low Activity ◽  
Outbreak Size

Human movement is a key factor in infectious diseases spread such as dengue. Here, we explore a mathematical modeling approach based on a system of ordinary differential equations to study the effect of human movement on characteristics of dengue dynamics such as the existence of endemic equilibria, and the start, duration, and amplitude of the outbreak. The model considers that every day is divided into two periods: high-activity and low-activity. Periodic human movement between patches occurs in discrete times. Based on numerical simulations, we show unexpected scenarios such as the disease extinction in regions where the local basic reproductive number is greater than 1. In the same way, we obtain scenarios where outbreaks appear despite the fact that the local basic reproductive numbers in these regions are less than 1 and the outbreak size depends on the length of high-activity and low-activity periods.

scholarly journals Comparing metapopulation dynamics of infectious diseases under different models of human movement

2021 ◽  
Vol 118 (18) ◽  
pp. e2007488118
Author(s):  
Daniel T. Citron ◽  
Carlos A. Guerra ◽  
Andrew J. Dolgert ◽  
Sean L. Wu ◽  
John M. Henry ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Human Movement ◽  
Global Scale ◽  
Disease Dynamics ◽  
Movement Model ◽  
Bioko Island ◽  
Geographical Distances ◽  
Infectious Disease Dynamics ◽  
Human Population Movement

Newly available datasets present exciting opportunities to investigate how human population movement contributes to the spread of infectious diseases across large geographical distances. It is now possible to construct realistic models of infectious disease dynamics for the purposes of understanding global-scale epidemics. Nevertheless, a remaining unanswered question is how best to leverage the new data to parameterize models of movement, and whether one’s choice of movement model impacts modeled disease outcomes. We adapt three well-studied models of infectious disease dynamics, the susceptible–infected–recovered model, the susceptible–infected–susceptible model, and the Ross–Macdonald model, to incorporate either of two candidate movement models. We describe the effect that the choice of movement model has on each disease model’s results, finding that in all cases, there are parameter regimes where choosing one movement model instead of another has a profound impact on epidemiological outcomes. We further demonstrate the importance of choosing an appropriate movement model using the applied case of malaria transmission and importation on Bioko Island, Equatorial Guinea, finding that one model produces intelligible predictions of R0, whereas the other produces nonsensical results.

scholarly journals Comparing Metapopulation Dynamics of Infectious Diseases under Different Models of Human Movement

2020 ◽  
Author(s):  
Daniel T. Citron ◽  
Carlos A. Guerra ◽  
Andrew J. Dolgert ◽  
Sean L. Wu ◽  
John M. Henry ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Human Movement ◽  
Global Scale ◽  
Disease Dynamics ◽  
Movement Model ◽  
Sis Model ◽  
Bioko Island ◽  
Geographical Distances ◽  
Infectious Disease Dynamics

Newly available data sets present an exciting opportunity to investigate how human population movement contributes to the spread of infectious diseases across large geographical distances. It is now possible to construct realistic models of infectious disease dynamics for the purposes of understanding global-scale epidemics. Nevertheless, a remaining unanswered question is how best to leverage the new data to parameterize models of movement, and whether one’s choice of movement model impacts modeled disease outcomes. We adapt three well-studied models of infectious disease dynamics, the SIR model; the SIS model; and the Ross-Macdonald model, to incorporate either of two candidate movement models. We describe the effect that the choice of movement model has on each disease model’s results, finding that in all cases there are parameter regimes where choosing one movement model instead of another has a profound impact on epidemiological outcomes. We further demonstrate the importance of choosing an appropriate movement model using the applied case of malaria transmission and importation on Bioko Island, Equatorial Guinea, finding that one model produces intelligible predictions of R0 while the other produces nonsensical results.

Metapopulation Dynamics of Infectious Diseases

2004 ◽  
pp. 415-445 ◽  
Author(s):  
Matt J. Keeling ◽  
Ottar N. Bjørnstad ◽  
Bryan T. Grenfell
Keyword(s):  
Infectious Diseases ◽  
Metapopulation Dynamics

scholarly journals A Bayesian Machine Learning Approach for Spatio-Temporal Prediction of COVID-19 Cases

2021 ◽  
Author(s):  
Poshan Niraula ◽  
Jorge Mateu ◽  
Somnath Chaudhuri
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Infectious Diseases ◽  
Human Movement ◽  
Random Factor ◽  
Neural Network Method ◽  
Temporal Prediction ◽  
Space And Time ◽  
Network Method ◽  
The Impact

Abstract Modeling the behavior and spread of infectious diseases on space and time is key in devising public policies for preventive measures. This behavior is so complex that there are lots of uncertainties in both the data and in the process itself. We argue here that these uncertainties should be taken into account in the modeling strategy. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We thus present here a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units. A key feature is that our combined model considers the impact of human movement on the spread of the infectious disease, as an additional random factor to the also considered spatial neighborhood and temporal correlation components. Our model is evaluated over a COVID-19 dataset for 245 health zones of Castilla-Leon (Spain). The results show that a Bayesian model informed by a neural network method is generally able to predict the number of cases of COVID-19 in both space and time, with the human mobility factor having a strong influence on the model.

scholarly journals 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

10.2196/24432 ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. e24432
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 Identifier (IRRID) DERR1-10.2196/24432

scholarly journals 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)

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

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