Abstract
BackgroundMillions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed.MethodsWe present a spatial disease agent-based model (ABM) with agents’ behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior).ResultsWe run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of several macro metrics of interest: an epidemic curve, a risk perception curve, and a distribution of different types of coping strategies over time.ConclusionsOur results emphasize the importance of integrating behavioral aspects of decision making under risk into spatial disease ABMs using machine learning algorithms. This is especially relevant when studying cumulative impacts of behavioral changes and possible intervention strategies.
Highlights
Millions of people worldwide are exposed to deadly infectious diseases on a regular basis
We evaluated the epidemic evidence (EE) that agents record via their communication with neighbor households (CNH) and the media (M) agent
While a number of comprehensive disease agent-based model (ABM) have been developed, few explore the implications of these behavioral aspects and learning
Summary
We start by briefly describing the base ABM and focus closely on the describing the learning algorithms and their stepwise implementation to support agents’ intelligence. The following factors are included in the RP: the number of infected individuals in the household, visual pollution level at the water collection point, communication with other agents, media attention, and the memory of the household agent Together, these factors and the agents’ social interactions help agents to assess risk and select what decision they could make among several options. This point indicates the first heavy rainfall, when the population of agents depending on river water increases, and the disease diffusion via the dumpsites begins. Higher levels of cholera risk awareness trigger agents to make alternative decisions regarding water use (D2-D4 instead of D1), following the deterministic rule-based algorithm, and leads to a reduction in the number of infected individuals (Table 4). Intelligent BN2 individuals in Exp converge to using boiled water in the majority of the cases (right-hand side maps of Fig. 9), as it proved to be most rewarding alternative to D1
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
