SIS and SIR Epidemic Models Under Virtual Dispersal

We review behavioural change models (BCMs) for infectious disease transmission in humans. Following the Cochrane collaboration guidelines and the PRISMA statement, our systematic search and selection yielded 178 papers covering the period 2010–2015. We observe an increasing trend in published BCMs, frequently coupled to (re)emergence events, and propose a categorization by distinguishing how information translates into preventive actions. Behaviour is usually captured by introducing information as a dynamic parameter (76/178) or by introducing an economic objective function, either with (26/178) or without (37/178) imitation. Approaches using information thresholds (29/178) and exogenous behaviour formation (16/178) are also popular. We further classify according to disease, prevention measure, transmission model (with 81/178 population, 6/178 metapopulation and 91/178 individual-level models) and the way prevention impacts transmission. We highlight the minority (15%) of studies that use any real-life data for parametrization or validation and note that BCMs increasingly use social media data and generally incorporate multiple sources of information (16/178), multiple types of information (17/178) or both (9/178). We conclude that individual-level models are increasingly used and useful to model behaviour changes. Despite recent advancements, we remain concerned that most models are purely theoretical and lack representative data and a validation process.

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“…[129], Bichara et al . [130], Liu & Zheng [131]Van Segbroeck et al . [12], Shaw & Schwartz [132], Jolad et al .…”

Section: Resultsmentioning

confidence: 99%

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

Verelst

Willem

Beutels

2016

J. R. Soc. Interface.

328

311

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“…Therefore, if the residence time matrix P is irreducible, patches are strongly connected, then System 2 supports a sharp threshold property. That is, the disease dies out or persists (in all patches) whenever the basic reproduction number R0 is less than or greater than unity (24). R0 is given by…”

Section: A Lagrangian Approach Of Modeling Mobility and Infectious DImentioning

confidence: 99%

“…Patch-specific disease persistence can be established using the average Lyapunov theorem (68) (see ref. 24 for more details).…”

Section: A Lagrangian Approach Of Modeling Mobility and Infectious DImentioning

confidence: 99%

“…Patches are connected by their ability to transfer relevant information between one another, which, in the context of disease dynamics, is modeled by the ability of individuals to move between patches. Patches may be constructed (defined) by species (human and mosquito) with movement explicitly modeled via patch-specific residence times and under a framework that sees disease dynamics as the result of location-dependent interactions (23,24).…”

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confidence: 99%

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Perspectives on the role of mobility, behavior, and time scales in the spread of diseases

Castillo‐Chavez

Bichara

Morin

2016

Proc. Natl. Acad. Sci. U.S.A.

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The dynamics, control, and evolution of communicable and vectorborne diseases are intimately connected to the joint dynamics of epidemiological, behavioral, and mobility processes that operate across multiple spatial, temporal, and organizational scales. The identification of a theoretical explanatory framework that accounts for the pattern regularity exhibited by a large number of host-parasite systems, including those sustained by host-vector epidemiological dynamics, is but one of the challenges facing the coevolving fields of computational, evolutionary, and theoretical epidemiology. Host-parasite epidemiological patterns, including epidemic outbreaks and endemic recurrent dynamics, are characteristic to well-identified regions of the world; the result of processes and constraints such as strain competition, host and vector mobility, and population structure operating over multiple scales in response to recurrent disturbances (like El Niño) and climatological and environmental perturbations over thousands of years. It is therefore important to identify and quantify the processes responsible for observed epidemiological macroscopic patterns: the result of individual interactions in changing social and ecological landscapes. In this perspective, we touch on some of the issues calling for the identification of an encompassing theoretical explanatory framework by identifying some of the limitations of existing theory, in the context of particular epidemiological systems. Fostering the reenergizing of research that aims at disentangling the role of epidemiological and socioeconomic forces on disease dynamics, better understood as complex adaptive systems, is a key aim of this perspective.infectious disease | risk | complex adaptive systems | mobility | behavior

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“…In short, we address how group heterogeneity, or groupness, patch heterogeneity, or patchiness, mobility patterns and behavior each alter or mitigate disease dynamics. In this sense, our paper is a direct extension of [7,8,9,12] but also other studies that capture dispersal through Lagrangian approaches -in which it is possible to track host movement after the interpatch mixing - [15,26,38,39] and a recent paper [19] that investigates the effects of daily movements in the context of Dengue.…”

Section: Introductionmentioning

confidence: 99%

Multi-patch and multi-group epidemic models: a new framework

Bichara

Iggidr

2017

J. Math. Biol.

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We develop a multi-patch and multi-group model that captures the dynamics of an infectious disease when the host is structured into an arbitrary number of groups and interacts into an arbitrary number of patches where the infection takes place. In this framework, we model host mobility that depends on its epidemiological status, by a Lagrangian approach. This framework is applied to a general SEIRS model and the basic reproduction number [Formula: see text] is derived. The effects of heterogeneity in groups, patches and mobility patterns on [Formula: see text] and disease prevalence are explored. Our results show that for a fixed number of groups, the basic reproduction number increases with respect to the number of patches and the host mobility patterns. Moreover, when the mobility matrix of susceptible individuals is of rank one, the basic reproduction number is explicitly determined and was found to be independent of the latter if the matrix is also stochastic. The cases where mobility matrices are of rank one capture important modeling scenarios. Additionally, we study the global analysis of equilibria for some special cases. Numerical simulations are carried out to showcase the ramifications of mobility pattern matrices on disease prevalence and basic reproduction number.

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SIS and SIR Epidemic Models Under Virtual Dispersal

Cited by 93 publications

References 67 publications

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

Perspectives on the role of mobility, behavior, and time scales in the spread of diseases

Multi-patch and multi-group epidemic models: a new framework

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