Clustered Breeding Sites: Shelters for Vector-Borne Diseases

Dias, Juliane Cristine; Monteiro, Luiz Henrique Alves

doi:10.1155/2018/2575017

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Other approaches for detecting and predicting depression employ, for instance, artificial intelligence techniques [13][14][15] and signal processing tools [16][17][18]. Here, the model is formulated in terms of a game with spatial structure, a state transition rule (from healthy to sick), and a parameter adaptation algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Game Theory-Based Model for Predicting Depression due to Frustration in Competitive Environments

Loula

Monteiro

2020

Computational and Mathematical Methods in Medicine

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A computational model based on game theory is here proposed to forecast the prevalence of depression caused by frustration in a competitive environment. This model comprises a spatially structured game, in which the individuals are socially connected. This game, which is equivalent to the well-known prisoner’s dilemma, represents the payoffs that can be received by the individuals in the labor market. These individuals may or may not have invested in a formal academic education. It is assumed that an individual becomes depressed when the difference between the average payoff earned by the neighbors in this game and the personal payoff surpasses a critical number, which can be distinct for men and women. Thus, the transition to depression depends on two thresholds, whose values are tuned for the model accurately predicting the percentage of individuals that become depressed due to a frustrating payoff. Here, this tuning is performed by using data of young adults living in the United Kingdom in 2014-2016.

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