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

Abstract

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.

The impact of unplanned school closure on children’s social contact: rapid evidence review

“…Basing policy on historical patterns may give false confidence in results and not capture uncertainty adequately. Recent reviews of the incorporation of human behaviour into infectious disease models have advocated the use of appropriate, detailed, real-world behavioural data within infectious disease modelling [44,45]. We hope that our identification of real-world data concerning social contact and mixing behaviour during unexpected school closures will help improve existing models and promote rigorous quantitative research in this area.…”

The impact of regular school closure on seasonal influenza epidemics: a data-driven spatial transmission model for Belgium

“…While a large body of literature has recently focused on behavioral changes during an epidemic [91][92][93][94][95], still little is known to quantify them [30][31][32][33][34][96][97][98][99]. Our work focused on Belgium, as a rather detailed survey was conducted in the country to estimate contact rates in the population of different age classes at different periods of the calendar year [32].…”

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

“…These generally employ sophisticated modelling and simulation techniques to identify the rumour propagation dynamics. However, this is still in its infancy and one recent systematic review of behavioural change models found that most papers investigating spread of health-related information and behavioural changes are theoretical, failing to use real-life social media data (Verelst et al, 2016). The literature on misinformation spread is growing, but spans disparate disciplines, including communication studies, epidemiology, psychology, and computational science.…”