Modelling the influence of human behaviour on the spread of infectious diseases: a review

By Sebastian Funk, Marcel Salathé, Vincent A. A. Jansen. Journal of the Royal Society Interface, 2010

1,288 citations recorded by the source corpus.

Subfields: Modeling and Simulation, Public Health, Environmental and Occupational Health, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/e472957e0af8118f5dd29b006a4a17987ff4b625

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Citations in the corpus

Social Influence and the Collective Dynamics of Opinion Formation (2013, background): “Similar mechanisms of opinion dynamics can take place in a variety of social contexts, such as within a group of friends exchanging opinions about their willingness to get vaccinated against influenza [13,14].”
Epidemic of COVID-19 in China and associated Psychological Problems (2020, neutral): “During the occasion of an epidemic, those with mental illness often experience discrimination and feel alienated when they seek care for their physical health needs (Funk et al., 2010) .”
Non-pharmaceutical interventions during the COVID-19 pandemic: A review (2020, background): “[3] Sebastian Funk, Marcel Salathé, Vincent A.”
Anxiety, depression, traumatic stress and COVID-19-related anxiety in the UK general population during the COVID-19 pandemic (2020, background): “Our secondary aim was to identify groups that are psychologically vulnerable during the pandemic, by assessing the relationship between levels of anxiety, depression and traumatic stress and (1) age; (2) household income; (3) economic threat due to COVID-19; (4) health related risk factors (being ma”
Multilayer networks (2013, background): “[126] for a review article on modelling the influence of human behavior on the spread of diseases.”
Modeling infectious disease dynamics in the complex landscape of global health (2015, background): “epidemics can have profound effects on the course of an outbreak (9, 10).”
Modelling dynamical processes in complex socio-technical systems (2011, background): “…done to provide coupled behaviour–disease models able to close the feedback loop between NATUREPHYSICS | VOL 8 | JANUARY 2012 | www.nature.com/naturephysics behavioural changes triggered in the population by an individual’s perception of the disease spread and the actual disease spread 109,110 .”
Prevalence of Health Misinformation on Social Media: Systematic Review (2021, background): “Health professionals and organizations are also using this medium to disseminate health-related knowledge on healthy habits and medical information for disease prevention, as it represents an unprecedented opportunity to increase health literacy, self-efficacy, and treatment adherence among populati”
Effect of gyrodactylid ectoparasites on host behaviour and social network structure in guppies Poecilia reticulata (2011, background): “Previous work in humans, for example, suggests that individuals may modify their social interactions in response to a threat of infection and that this behaviour may be adaptive in reducing the risk of infection (Funk et al. 2010).”
Statistical physics of vaccination (2016, background): “Up to now, we have acquired a thorough understanding for the interrelationship between vaccination decision and disease dynamics: via discrete, memoryless strategies, human decisionmaking can profoundly influence epidemic incidence as well as vaccination coverage [398, 405, 416, 570].”
On the Use of Human Mobility Proxies for Modeling Epidemics (2013, result): “During this timeframe the value of the basic reproductive number is kept constant, and no change in behavior that could be self-initiated in response to the epidemic [47,48], or imposed by public health interventions is considered, for the sake of clarity in the comparison of the results.”
Exploring the impacts of COVID-19 on travel behavior and mode preferences (2020, neutral): “Past studies have highlighted that human mobility and interaction patterns directly contribute to the spread of infectious diseases, particularly during pandemics (Funk et al., 2010; Belik et al., 2011; Rizzo et al., 2014; Yan et al., 2018; Peixoto et al., 2020).”
Mathematical models to characterize early epidemic growth: A Review (2016, result): “While previous efforts have reviewed transmission models that have incorporated the influence of human behavior on epidemic spread [99] and the challenges in incorporating the dynamics of behavior in infectious disease models [100], recent findings [5,7] support the need to develop modeling framewor”
Coupled disease–behavior dynamics on complex networks: A review (2015, methodology): “Along this line of research, the most commonly used approach is adaptive networks [119] [120] [121] [122] , where nodes frequently adjust their connections according to the environment or states of neighboring nodes.”
Modeling human mobility responses to the large-scale spreading of infectious diseases (2011, background): “For this reason there has been an increasing focus in the development of formal models aimed at closing the epidemic-spreading→behavioral-changes→epidemic-spreading feed-back loop for the past decade (se”