Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015)

Abstract

BackgroundIndividual-based models (IBMs) are useful to simulate events subject to stochasticity and/or heterogeneity, and have become well established to model the potential (re)emergence of pathogens (e.g., pandemic influenza, bioterrorism). Individual heterogeneity at the host and pathogen level is increasingly documented to influence transmission of endemic diseases and it is well understood that the final stages of elimination strategies for vaccine-preventable childhood diseases (e.g., polio, measles) are subject to stochasticity. Even so it appears IBMs for both these phenomena are not well established. We review a decade of IBM publications aiming to obtain insights in their advantages, pitfalls and rationale for use and to make recommendations facilitating knowledge transfer within and across disciplines.MethodsWe systematically identified publications in Web of Science and PubMed from 2006-2015 based on title/abstract/keywords screening (and full-text if necessary) to retrieve topics, modeling purposes and general specifications. We extracted detailed modeling features from papers on established vaccine-preventable childhood diseases based on full-text screening.ResultsWe identified 698 papers, which applied an IBM for infectious disease transmission, and listed these in a reference database, describing their general characteristics. The diversity of disease-topics and overall publication frequency have increased over time (38 to 115 annual publications from 2006 to 2015). The inclusion of intervention strategies (8 to 52) and economic consequences (1 to 20) are increasing, to the detriment of purely theoretical explorations. Unfortunately, terminology used to describe IBMs is inconsistent and ambiguous. We retrieved 24 studies on a vaccine-preventable childhood disease (covering 7 different diseases), with publication frequency increasing from the first such study published in 2008. IBMs have been useful to explore heterogeneous between- and within-host interactions, but combined applications are still sparse. The amount of missing information on model characteristics and study design is remarkable.ConclusionsIBMs are suited to combine heterogeneous within- and between-host interactions, which offers many opportunities, especially to analyze targeted interventions for endemic infections. We advocate the exchange of (open-source) platforms and stress the need for consistent “branding”. Using (existing) conventions and reporting protocols would stimulate cross-fertilization between research groups and fields, and ultimately policy making in decades to come.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-017-2699-8) contains supplementary material, which is available to authorized users.

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“…We employed an individual agent-based model based on work by Ferguson et al 2 30 31 Individual-based models are increasingly used to model epidemic spread with explicit representation of demographic and spatial factors such as population distribution, workplace data, school data, and mobility. 20 Individual properties. Individuals are randomly assigned an age based on Swedish demographic data, 21 and they are also assigned a household.…”

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“…However, confronting outbreaks of emerging infections requires swift responses and thus the ability to evaluate quickly and early potential outcomes [1]. As such, computer simulations of epidemic models undoubtedly hold the potential as the first-aid toolbox for decision making amid the crisis [1,3,4].…”

“…However, confronting outbreaks of emerging infections requires swift responses and thus the ability to evaluate quickly and early potential outcomes [1]. As such, computer simulations of epidemic models undoubtedly hold the potential as the first-aid toolbox for decision making amid the crisis [1,3,4].A majority of epidemic modelling studies has exclusively relied on the availability of outbreak data [5,6,7,8]. This approach requires that sufficient incidence data are available; for example, data at the end of an epidemic or at least until its peak [9].…”

“…It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/133421 doi: bioRxiv preprint first posted online Nguyen et al The interplays between within-host infection and between hosts transmission led to arising attempts connecting the two levels [14,21,22,12,23,24,25,4], but the approach is still at its infancy [13]. On one hand, most of these models were conceptual and theoretical [13] or rely on assumptive and previously obtained parameter estimates [26,15,27].…”

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