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.

scholarly journals Modelling the influence of human behaviour on the spread of infectious diseases: a review

2010 ◽  
Vol 7 (50) ◽  
pp. 1247-1256 ◽  
Author(s):  
Sebastian Funk ◽  
Marcel Salathé ◽  
Vincent A. A. Jansen
Keyword(s):  
Infectious Disease ◽  
Experimental Study ◽  
Infectious Diseases ◽  
Systematic Investigation ◽  
Human Behaviour ◽  
Disease Models ◽  
Disease Dynamics ◽  
Behavioural Responses ◽  
Infectious Disease Dynamics ◽  
Source Of Information

Human behaviour plays an important role in the spread of infectious diseases, and understanding the influence of behaviour on the spread of diseases can be key to improving control efforts. While behavioural responses to the spread of a disease have often been reported anecdotally, there has been relatively little systematic investigation into how behavioural changes can affect disease dynamics. Mathematical models for the spread of infectious diseases are an important tool for investigating and quantifying such effects, not least because the spread of a disease among humans is not amenable to direct experimental study. Here, we review recent efforts to incorporate human behaviour into disease models, and propose that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, and according to the assumed effects of such behaviour. We highlight recent advances as well as gaps in our understanding of the interplay between infectious disease dynamics and human behaviour, and suggest what kind of data taking efforts would be helpful in filling these gaps.

scholarly journals Human mobility patterns predict divergent epidemic dynamics among cities

2013 ◽  
Vol 280 (1766) ◽  
pp. 20130763 ◽  
Author(s):  
Benjamin D. Dalziel ◽  
Babak Pourbohloul ◽  
Stephen P. Ellner
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Population Size ◽  
Human Mobility ◽  
Pathogen Transmission ◽  
Full Time ◽  
Disease Dynamics ◽  
Mobility Patterns ◽  
Epidemic Dynamics ◽  
Infectious Disease Dynamics

The epidemic dynamics of infectious diseases vary among cities, but it is unclear how this is caused by patterns of infectious contact among individuals. Here, we ask whether systematic differences in human mobility patterns are sufficient to cause inter-city variation in epidemic dynamics for infectious diseases spread by casual contact between hosts. We analyse census data on the mobility patterns of every full-time worker in 48 Canadian cities, finding a power-law relationship between population size and the level of organization in mobility patterns, where in larger cities, a greater fraction of workers travel to work in a few focal locations. Similarly sized cities also vary in the level of organization in their mobility patterns, equivalent on average to the variation expected from a 2.64-fold change in population size. Systematic variation in mobility patterns is sufficient to cause significant differences among cities in infectious disease dynamics—even among cities of the same size—according to an individual-based model of airborne pathogen transmission parametrized with the mobility data. This suggests that differences among cities in host contact patterns are sufficient to drive differences in infectious disease dynamics and provides a framework for testing the effects of host mobility patterns in city-level disease data.

scholarly journals Interdisciplinary Collaborations Required: Teaching Health Educators Infectious Disease Dynamics

2020 ◽  
Vol 6 (3) ◽  
pp. 159-161
Author(s):  
Tyler G. James ◽  
Catherine A. Lippi
Keyword(s):  
Infectious Disease ◽  
Health Education ◽  
Infectious Diseases ◽  
Public Health Education ◽  
Health Educators ◽  
Disease Dynamics ◽  
Effective Science ◽  
Global Burden Of Diseases ◽  
Infectious Disease Dynamics ◽  
Burden Of Diseases

Infectious diseases, including zoonotic infectious diseases, are some of the leading causes of the global burden of diseases. Public health education/promotion specialists are specifically trained in methods and theory to deliver risk communication that can help decrease the transmissibility, morbidity, and mortality of infectious diseases. However, the limited training of health educators in infectious disease dynamics represents a critical barrier for health educators wishing to engage in this work. In this commentary, we describe the importance of health education/promotion specialists being trained on infectious disease dynamics to engage in effective science and health communication locally and globally.

scholarly journals A model for the co-evolution of dynamic social networks and infectious disease dynamics

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hendrik Nunner ◽  
Vincent Buskens ◽  
Mirjam Kretzschmar
Keyword(s):  
Infectious Disease ◽  
Social Networks ◽  
Social Network ◽  
Health Behavior ◽  
Infectious Diseases ◽  
Network Formation ◽  
Disease Spread ◽  
Disease Dynamics ◽  
Dynamic Social Networks ◽  
Infectious Disease Dynamics

AbstractRecent research shows an increasing interest in the interplay of social networks and infectious diseases. Many studies either neglect explicit changes in health behavior or consider networks to be static, despite empirical evidence that people seek to distance themselves from diseases in social networks. We propose an adaptable steppingstone model that integrates theories of social network formation from sociology, risk perception from health psychology, and infectious diseases from epidemiology. We argue that networking behavior in the context of infectious diseases can be described as a trade-off between the benefits, efforts, and potential harm a connection creates. Agent-based simulations of a specific model case show that: (i) high (perceived) health risks create strong social distancing, thus resulting in low epidemic sizes; (ii) small changes in health behavior can be decisive for whether the outbreak of a disease turns into an epidemic or not; (iii) high benefits for social connections create more ties per agent, providing large numbers of potential transmission routes and opportunities for the disease to travel faster, and (iv) higher costs of maintaining ties with infected others reduce final size of epidemics only when benefits of indirect ties are relatively low. These findings suggest a complex interplay between social network, health behavior, and infectious disease dynamics. Furthermore, they contribute to solving the issue that neglect of explicit health behavior in models of disease spread may create mismatches between observed transmissibility and epidemic sizes of model predictions.

scholarly journals Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Amy Wesolowski ◽  
Elisabeth zu Erbach-Schoenberg ◽  
Andrew J. Tatem ◽  
Christopher Lourenço ◽  
Cecile Viboud ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Human Movement ◽  
Disease Dynamics ◽  
Infectious Disease Dynamics

scholarly journals A model for the co-evolution of dynamic social networks and infectious disease dynamics

2021 ◽  
Author(s):  
Hendrik Nunner ◽  
Vincent Buskens ◽  
Mirjam Kretzschmar
Keyword(s):  
Infectious Disease ◽  
Social Networks ◽  
Social Network ◽  
Health Behavior ◽  
Infectious Diseases ◽  
Network Formation ◽  
Disease Spread ◽  
Generic Model ◽  
Disease Dynamics ◽  
Infectious Disease Dynamics

Abstract Recent research shows an increasing interest in the interplay of social networks and infectious diseases. Many studies either neglect explicit changes in health behavior or consider networks to be static, despite empirical evidence that people seek to distance themselves from diseases in social networks. We propose an adaptable steppingstone model that integrates theories of social network formation from sociology, risk perception from health psychology, and infectious diseases from epidemiology. We argue that networking behavior in the context of infectious diseases can be described as a trade-off between the benefits, efforts, and potential harm a connection creates. Agent-based simulations using a generic model implementation show that: (i) high (perceived) health risks create strong social distancing, thus resulting in low epidemic sizes, (ii) small changes in health behavior can be decisive for whether the outbreak of a disease turns into an epidemic or not, (iii) high benefits for social connections create more ties per agent, providing large numbers of potential transmission routes and opportunities for the disease to travel faster, and (iv) higher costs of maintaining ties with infected others reduce final size of epidemics only when benefits of indirect ties are relatively low. These findings suggest a complex interplay between social network, health behavior, and infectious disease dynamics. Furthermore, they contribute to solving the issue that neglect of explicit health behavior in models of disease spread may create mismatches between observed transmissibility and epidemic sizes of model predictions.

scholarly journals Contact structure, mobility, environmental impact and behaviour: the importance of social forces to infectious disease dynamics and disease ecology

2017 ◽  
Vol 372 (1719) ◽  
pp. 20160454 ◽  
Author(s):  
Ronan F. Arthur ◽  
Emily S. Gurley ◽  
Henrik Salje ◽  
Laura S. P. Bloomfield ◽  
James H. Jones
Keyword(s):  
Infectious Disease ◽  
Infectious Diseases ◽  
Social Factors ◽  
Disease Ecology ◽  
Contact Structure ◽  
Human Populations ◽  
Disease Dynamics ◽  
Social Ecological ◽  
The Social ◽  
Infectious Disease Dynamics

Human factors, including contact structure, movement, impact on the environment and patterns of behaviour, can have significant influence on the emergence of novel infectious diseases and the transmission and amplification of established ones. As anthropogenic climate change alters natural systems and global economic forces drive land-use and land-cover change, it becomes increasingly important to understand both the ecological and social factors that impact infectious disease outcomes for human populations. While the field of disease ecology explicitly studies the ecological aspects of infectious disease transmission, the effects of the social context on zoonotic pathogen spillover and subsequent human-to-human transmission are comparatively neglected in the literature. The social sciences encompass a variety of disciplines and frameworks for understanding infectious diseases; however, here we focus on four primary areas of social systems that quantitatively and qualitatively contribute to infectious diseases as social–ecological systems. These areas are social mixing and structure, space and mobility, geography and environmental impact, and behaviour and behaviour change. Incorporation of these social factors requires empirical studies for parametrization, phenomena characterization and integrated theoretical modelling of social–ecological interactions. The social–ecological system that dictates infectious disease dynamics is a complex system rich in interacting variables with dynamically significant heterogeneous properties. Future discussions about infectious disease spillover and transmission in human populations need to address the social context that affects particular disease systems by identifying and measuring qualitatively important drivers. This article is part of the themed issue ‘Opening the black box: re-examining the ecology and evolution of parasite transmission’.

scholarly journals Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model

Epidemics ◽  
2018 ◽  
Vol 22 ◽  
pp. 56-61 ◽  
Author(s):  
Sebastian Funk ◽  
Anton Camacho ◽  
Adam J. Kucharski ◽  
Rosalind M. Eggo ◽  
W. John Edmunds
Keyword(s):  
Infectious Disease ◽  
Real Time ◽  
Mechanistic Model ◽  
Disease Dynamics ◽  
Infectious Disease Dynamics

scholarly journals Risk Causal Analysis of Traffic-Intensive Waters Based on Infectious Disease Dynamics

2019 ◽  
Vol 7 (8) ◽  
pp. 277
Author(s):  
Yong-jun Chen ◽  
Qing Liu ◽  
Cheng-peng Wan
Keyword(s):  
Infectious Disease ◽  
Risk Factors ◽  
Causal Model ◽  
Cloud Model ◽  
Rapid Development ◽  
River Estuary ◽  
Yangtze River Estuary ◽  
Disease Dynamics ◽  
Transmission Process ◽  
Infectious Disease Dynamics

Accidents occur frequently in traffic-intensive waters, which restrict the safe and rapid development of the shipping industry. Due to the suddenness, randomness, and uncertainty of accidents in traffic-intensive waters, the probability of the risk factors causing traffic accidents is usually high. Thus, properly analyzing those key risk factors is of great significance to improve the safety of shipping. Based on the analysis of influencing factors of ship navigational risks in traffic-intensive waters, this paper proposes a cloud model to excavate the factors affecting navigational risk, which could accurately screen out the key risk factors. Furthermore, the risk causal model of ship navigation in traffic-intensive waters is constructed by using the infectious disease dynamics method in order to model the key risk causal transmission process. Moreover, an empirical study of the Yangtze River estuary is conducted to illustrate the feasibility of the proposed models. The research results show that the cloud model is useful in screening the key risk factors, and the constructed causal model of ship navigational risks in traffic-intensive waters is able to provide accurate analysis of the transmission process of key risk factors, which can be used to reduce the navigational risk of ships in traffic-intensive waters. This research provides both theoretical basis and practical reference for regulators in the risk management and control of ships in traffic-intensive waters.

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