We consider a model due to Piero Poletti and collaborators that adds spontaneous human behavioral change to the standard SIR epidemic model. In its simplest form, the Poletti model adds one differential equation, motivated by evolutionary game theory, to the SIR model. The new equation describes the evolution of a variable $x$ that represents the fraction of the population using normal behavior. The remaining fraction $1-x$ uses altered behavior such as staying home, social isolation, mask wearing, etc. Normal behavior offers a higher payoff when the number of infectives is low; altered behavior offers a higher payoff when the number is high. We show that the entry-exit function of geometric singular perturbation theory can be used to analyze the model in the limit in which behavior changes on a much faster time scale than that of the epidemic. In particular, behavior does not change as soon as a different behavior has a higher payoff; current behavior is sticky. The delay until behavior changes in predicted by the entry-exit function.
The impact of behavioral homophily and conformity on epidemic spreading in networks with large groups
Université Laval（拉瓦尔大学）；Universitat Rovira i Virgili（罗维拉-依希尔维特里大学）；Northeastern University（东北大学）；University of Vermont（佛蒙特大学）；Santa Fe Institute（圣塔菲研究所）；Complexity Science Hub Vienna（维也纳复杂科学中心）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
Investigating the effect of adaptive optimal control function in epidemic dynamics: predictions and strategy evolution based on SIR/V game theoretic framework
The spreading dynamics of an epidemic and the collective behavioral pattern of the population over which it spreads are deeply intertwined and the latter can critically shape the outcome of the former. Motivated by this, we design a parsimonious game-theoretic behavioral--epidemic model, in which an interplay of realistic factors shapes the co-evolution of individual decision-making and epidemics on a network. Although such a co-evolution is deeply intertwined in the real-world, existing models schematize population behavior as instantaneously reactive, thus being unable to capture human behav…
Reward Engineering for Spatial Epidemic Simulations: A Reinforcement Learning Platform for Individual Behavioral Learning
Department of Computer Science University of British Columbia（计算机科学系，不列颠哥伦比亚大学）；Department of Mathematics and Institute of Applied Mathematics University of British Columbia（数学系和应用数学研究所，不列颠哥伦比亚大学）
The last decade saw the advent of increasingly realistic epidemic models that leverage on the availability of highly detailed census and human mobility data. Data-driven models aim at a granularity down to the level of households or single individuals. However, relatively little systematic work has been done to provide coupled behavior-disease models able to close the feedback loop between behavioral changes triggered in the population by an individual's perception of the disease spread and the actual disease spread itself. While models lacking this coupling can be extremely successful in mild…
Human movement decisions during Coronavirus Disease 2019
International Institute for Zoonosis Control, Hokkaido University（国际动物传染病控制研究所，北海道大学）；Faculty of Environmental Earth Science, Hokkaido University（环境地球科学学院，北海道大学）；Recruit Co., Ltd（recruit株式会社）；Department of Biology, Faculty of Science, Kyushu University（理学部生物学系，九州大学）
Studies on how to model the interplay between diseases and behavioral responses (so-called coupled disease-behavior interaction) have attracted increasing attention. Owing to the lack of obvious clinical evidence of diseases, or the incomplete information related to the disease, the risks of infection cannot be perceived and may lead to inappropriate behavioral responses. Therefore, how to quantitatively analyze the impacts of asymptomatic infection on the interplay between diseases and behavioral responses is of particular importance. In this Letter, under the complex network framework, we st…
Voluntary nonpharmaceutical intervention as a feedback-evolving game
CEFE, Univ Montpellier, CNRS, EPHE, IRD（法国国家科学研究中心、高等教育与研究学院、热带研究所）；Institute of Tropical Medicine, Nagasaki University（长崎大学热带医学研究所）；Multidisciplinary Pandemic Research Center, Nagasaki University（长崎大学多学科流行病研究中心）；National Institute of Genetics（国立遗传学研究所）；SOKENDAI（综合研究大学院大学）；The University of Queensland（昆士兰大学）；Yokohama National University（横滨国立大学）；The University of Tokyo（东京大学）
A multi-scale immuno-epidemiological behavioral model for influenza-like illness: connecting scales through symptom scores
Mississippi State University（密西西比州立大学）；The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；Florida Atlantic University（佛罗里达大西洋大学）；Texas Christian University（德克萨斯基督教大学）
It is well known that behavioral changes in contact patterns may significantly affect the spread of an epidemic outbreak. Here we focus on simple endemic models for recurrent epidemics, by modelling the social contact rate as a function of the available information on the present and past disease prevalence. We show that social behaviour change alone may trigger sustained oscillations. This indicates that human behavior might be a critical explaining factor of oscillations in time-series of endemic diseases. Finally, we briefly show how the inclusion of seasonal variations in contacts may impl…
Changes in human behavior are increasingly recognized as a major determinant of epidemic dynamics. Although collective activity can be modified through imposed measures to control epidemic progression, spontaneous changes can also arise as a result of uncoordinated individual responses to the perceived risk of contagion. Here we introduce a stochastic epidemic model that implements population responses driven by individual- and time-dependent risk-taking propensity. The model reveals an emergent mechanism for the generation of multiple infection waves of decreasing amplitude without the need t…
The Epi-LLM Framework: probing LLM behavioral priors through epidemiological agent-based models
Big Data Institute, Li Ka Shing Center for Health Information and Discovery, University of Oxford, Oxford, United Kingdom（大数据研究所、李嘉诚健康信息与发现中心、牛津大学、牛津、英国）；Leverhulme Centre for Demographic Science, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom（勒弗赫姆人口科学中心、努尔菲尔德人口健康系、牛津大学、牛津、英国）；Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom（流行病学科学研究所、努尔菲尔德医学系、牛津大学、牛津、英国）；Department of Genomics and Computational Biology, UMass Chan Medical School, United States（基因组与计算生物学系、UMass Chan医学学校、美国）；Broad Institute of Harvard and MIT, United States（哈佛大学和麻省理工学院Broad研究所、美国）；The Queen’s College, University of Oxford, Oxford, United Kingdom（女王学院、牛津大学、牛津、英国）
Background: The ongoing COVID-19 epidemic dilated rapidly throughout India. To end the global COVID-19 pandemic major behavioral, social distancing, contact tracing, and state interventions has been undertaken to reduce the outbreak and avert the persistence of the coronavirus in humans in India and worldwide. In absence of any vaccine or therapeutics, forecasting is of utmost priority for health care planning and control the transmission of COVID-19. Methods: We have proposed a mathematical model that explain the transmission dynamics of COVID-19 in India. Based on the estimated data our mode…
