Collective cognitive epidemiology: Introducing subjective parameters into disease spread models

Modern instances of disease emergence have shown that human subjective reactions to a novel disease can be as important as the objective reality of the disease spread. Therefore, this work introduces human cognitive heuristics and biases into epidemiological modeling. Human subjective perception and reaction to the presence of disease is represented in the difference between the objective and subjective probability of contagion. It is assumed that humans within a disease spread situation will have either limited or full information about the objective probability of contagion. From this information, humans subjectively react, forming a subjective assessment of the probability of contagion. Although the translation from the objective to the subjective probability of contagion is rooted in a biological basis, the translation has been adequately determined by previous research in Prospect Theory as developed by Daniel Kahneman and Amos Tversky. The formulation of Lotka-Volterra epidemiology models with parameters for perceived probability of contagion was followed by numerical experimentation and sensitivity analyses that determined values of the parameters that create cyclic population behavior, whether growing or dampened, as well as acyclic behavior. The results show that the model is capable of capturing stable as well as unstable behavior, and is able to model key epidemiological disease behaviors and states, such as epidemic and endemic conditions.