Incorporating individual health-protective decisions into disease transmission models: a mathematical framework

Abstract

It is anticipated that the next generation of computational epidemic models will simulate both infectious disease transmission and dynamic human behaviour change. Individual agents within a simulation will not only infect one another, but will also have situational awareness and a decision algorithm that enables them to modify their behaviour. This paper develops such a model of behavioural response, presenting a mathematical interpretation of a well-known psychological model of individual decision making, the health belief model, suitable for incorporation within an agent-based disease-transmission model. We formalize the health belief model and demonstrate its application in modelling the prevalence of facemask use observed over the course of the 2003 Hong Kong SARS epidemic, a well-documented example of behaviour change in response to a disease outbreak.

Figure 1.

Contextual information external to the agent influences the agent's scores on the health belief model constructs. These constructs determine an individual's behaviour, which in turn affects overall disease dynamics in the simulation.

Figure 2.

Media model of the daily number of news stories (solid line) fit to media coverage of the Hong Kong SARS epidemic (diamonds). Data retrieved from Google News.

Figure 3.

The fraction of the population wearing a facemask in our simulation (solid line) and as observed during the Hong Kong SARS outbreak (diamonds). The three lines show the mean, the lowest and the highest of the Monte Carlo simulations.

Figure 4.

The model fit (solid line) to the observed prevalence (diamonds) under a 10% decrease in the parameter δ (time discounting of SARS cases) relative to the optimal parameter set.