Observer-dependent model for analyzing subjective parameters in epidemiology

Abstract

Although medical technologies for preventing the contagion and spread of infectious diseases have improved steadily throughout the last century, new infectious diseases are still emerging and spreading swiftly. The modeling of infectious disease spread is crucial in addressing the lack of predictive ability in epidemiology. Managing the spread of infectious diseases requires processing quantitative epidemiological data and the ability to capture the dynamics of the infectious disease in order to provide a measure of control.

In this thesis, I have introducing cognitive biases in diseases spread modeling. For the first time, to the author’s knowledge, the human subjective experience has been included in disease spread modeling, in the form of subjective and objective types of parameters. It is assumed that humans within a disease spread situation will be informed with at least limited information about the objective probability of disease contagion. From this information, humans form a subjective reaction, which includes a subjective assessment of the probability of contagion. Although the translation from the objective to a subjective probability of contagion is rooted in a biological basis, the translation has been adequately determined by previous research.