Towards a Characterization of Behavior-Disease Models

Abstract

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 epidemics, they obviously will be of limited use in situations where social disruption or behavioral alterations are induced in the population by knowledge of the disease. Here we propose a characterization of a set of prototypical mechanisms for self-initiated social distancing induced by local and non-local prevalence-based information available to individuals in the population. We characterize the effects of these mechanisms in the framework of a compartmental scheme that enlarges the basic SIR model by considering separate behavioral classes within the population. The transition of individuals in/out of behavioral classes is coupled with the spreading of the disease and provides a rich phase space with multiple epidemic peaks and tipping points. The class of models presented here can be used in the case of data-driven computational approaches to analyze scenarios of social adaptation and behavioral change.

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References 46

1. 1.

   Ferguson N. Capturing human behaviour. Nature. 2007;466:733.

   Not in our library10.1038/446733a

2. 2.

   Funk S, Salathé M, Jansen VAA. Modelling the inuence of human behaviour on the spread of infectious diseases: a review. J R Soc Interface. 2010;7:1247-1256.

   Not in our library10.1098/rsif.2010.0142

3. 3.

   Wallinga J, Teunis P, Kretzschmar M. Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. Am J Epi. 2006;164:936-944.

   Not in our library10.1093/aje/kwj317

4. 4.

   Merler S, Ajelli M. The role of population heterogeneity and human mobility in the spread of pandemic inuenza. Proc Roy Soc B: Biol Sci. 2010;277:557-565.

   Not in our library10.1098/rspb.2009.1605

5. 5.

   Balcan D, Colizza V, Gonçalves B, Hu H, Ramasco J, et al. Multiscale mobility networks and the large scale spreading of infectious diseases. Proc Natl Acad Sci. 2009;106:21484-2189.

   Not in our library10.1073/pnas.0906910106

6. 6.
   Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions · 2007

   No notice
7. 7.

   Colizza V, Barrat A, Barthélemy, Vespignani A. The role of the airline transportation network in the prediction and predictability of global epidemics. Proc Natl Acad Sci USA. 2006;103:2015-2020.

   Not in our library10.1073/pnas.0510525103

8. 8.

   Hollingsworth T, Ferguson N, Anderson R. Will travel restrictions control the international spread of pandemic inuenza? Nat Med. 2006;12:497-499.

   Not in our library10.1038/nm0506-497

9. 9.
   Delaying the International Spread of Pandemic Influenza · 2006

   No notice
10. 10.

    Ruan S, Wang W, Levin S. The effect of global travel on the spread of sars. Math Biosci Eng. 2006;3:205-218.

    Not in our library10.3934/mbe.2006.3.205

11. 11.

    Ginelli F, Hinrichsen H, Livi R, Mukamel D, Torcini A. Contact processes with long-range interactions. J Stat Mech. 2006;P08008.

    Not in our library

12. 12.

    Hufnagel L, Brockmann D, Geisel T. Forecast and control of epidemics in a globalized world. Proc Natl Acad Sci USA. 2004;101:15124-15129.

    Not in our library10.1073/pnas.0308344101

13. 13.

    Grais RF, Ellis HJ, Glass G. Modeling the spread of annual inuenza epidemics in the us: the potential role of air travel. Health Care Manage Sci. 2004;7:127-134.

    Not in our library10.1023/b:hcms.0000020652.38181.da

14. 14.

    Grais RF, Ellis HJ, Glass G. Assessing the impact of airline travel on the geographic spread of pandemic inuenza. Eur J Epidemiol. 2003;18:1065-1072.

    Not in our library10.1023/a:1026140019146

15. 15.

    Flahault A, Valleron AJ. A method for assessing the global spread of hiv-1 infection based on air-travel. Math Popul Stud. 1991;3:1-11.

    Not in our library10.1080/08898489209525336

16. 16.

    Rvachev LA, Longini IM. A mathematical model for the global spread of inuenza. Math Biosci. 1985;75:3-32.

    Not in our library

17. 17.

    Longini IM, Nizam A, S X, Ungchusak K, Hanshaoworakul W, et al. Containing pandemic inuenza at the source. Science. 2005;309:1083-1087.

    Not in our library10.1126/science.1115717

18. 18.

    Eubank S, Guclu H, Kumar VSA, Marathe MV, Srinivasan A, et al. Modelling disease outbreaks in realistic urban social networks. Nature. 2004;429:180-184.

    Not in our library10.1038/nature02541

19. 19.

    Ferguson NM, Cummings DAT, Cauchemez S, Fraser C, Riley S, et al. Strategies for containing an emerging inuenza pandemic in southeast asia. Nature. 2005;437:209.

    Not in our library10.1038/nature04017

20. 20.
    FluTE, a Publicly Available Stochastic Influenza Epidemic Simulation Model · 2010

    No notice
21. 21.
    Seasonal transmission potential and activity peaks of the new influenza A(H1N1): a Monte Carlo likelihood analysis based on human mobility · 2009

    No notice
22. 22.

    Bajardi P, Poletto C, Balcan D, Hu H, Gonçalves B, et al. Modeling vaccination campaigns and the fall/winter 2009 activity of the new a(h1n1) inuenza in the northern hemisphere. Emerging Health Threats. 2009;2:e11.

    Not in our library10.3134/ehtj.09.011

23. 23.

    Gardner D. The Science of Fear: How the Culture of Fear Manipulates Your Brain. 2009.

    Not in our library

24. 24.

    Vespignani A. Predicting the behavior of techno-social systems. Science. 2009;325:425-428.

    Not in our library10.1126/science.1171990

25. 25.

    Cruz-Pacheco G, Duran L, Esteva L, Minzoni A, López-Cervantes M, et al. Modelling of the inuenza a(h1n1) outbreak in mexico city, april-may 2009, with control sanitary. Euro Surveillance. 2009;14:19254.

    Not in our library

26. 26.

    Hatchett R, Mecher C, M L. Public health interventions and epidemic intensity during the 1918 inuenza pandemic. Proc Natl Acad Sci. 2007;104:7582-7587.

    Not in our library10.1073/pnas.0610941104

27. 27.

    Bootsma M, Ferguson N. Public health interventions and epidemic intensity during the 1918 inuenza pandemic. Proc Natl Acad Sci. 2007;104:7588-7593.

    Not in our library10.1073/pnas.0611071104

28. 28.

    Markel H, Lipman H, Navarro J, Sloan A, Michalsen J, et al. Nonpharmaceutical interventions implemented by us cities during the 1918–1919 inuenza pandemic. JAMA. 2007;298:6.

    Not in our library10.1001/jama.298.6.644

29. 29.

    Fenichel E, Castillo-Chavez C, Ceddia M, Chowell G, Gonzalez Parrae P, et al. Adaptive human behavior in epidemiological models. Proc Natl Acad Sci USA. 2011;108:6306-6311.

    Not in our library10.1073/pnas.1011250108

30. 30.

    Chen F, Jiang M, Rabidoux S, Robinson S. Public avoidance and epidemics: Insights from an economic model. J Theor Biol. 2011;278:107-119.

    Not in our library10.1016/j.jtbi.2011.03.007

31. 31.

    Poletti P, Ajelli M, Merler S. The effect of risk perception on the 2009 h1n1 pandemic inuenza dynamics. PLoS ONE. 2011;6:e16460.

    Not in our library10.1371/journal.pone.0016460

32. 32.

    Funk S, Gilad E, Watkins C, Jansen V. The spread of awareness and its impact on epidemic outbreaks. Proc Natl Acad Sci. 2009;106:6872-6877.

    Not in our library10.1073/pnas.0810762106

33. 33.

    Poletti P, Caprile B, Ajelli M, Pugliese A, S M. Spontaneous behavioural changes in response to epidemics. J Theor Biol. 2009;260:31-40.

    Not in our library10.1016/j.jtbi.2009.04.029

34. 34.

    Coelho F, Codeço C. Dynamic modeling of vaccinating behavior as a function of individual beliefs. PLoS Comput Biol. 2009;5:e1000425.

    Not in our library10.1371/journal.pcbi.1000425

35. 35.
    Coupled Contagion Dynamics of Fear and Disease: Mathematical and Computational Explorations · 2008

    No notice
36. 36.

    Bagnoli F, Lio P, Sguanci L. Risk perception in epidemic modeling. Phys Rev E. 2007;76:061904.

    Not in our library10.1103/physreve.76.061904

37. 37.

    Crosby AW. America's forgotten pandemic: the inuenza of 1918. 1990.

    Not in our library

38. 38.

    Campbell GL, Hughes JM. Plague in india: a new warning from an old nemesis. Ann Intern Med. 1995;122:151-153.

    Not in our library10.7326/0003-4819-122-2-199501150-00014

39. 39.
    SARS-related Perceptions in Hong Kong · 2005

    No notice
40. 40.

    Goffman W, Newill V. Generalization of epidemic theory an application to the transmission of ideas. Nature. 1964;204:225-228.

    Not in our library10.1038/204225a0

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• Modelling anti-vaccine sentiment as a cultural pathogen · 2020
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• Manipulating concept spread using concept relationships. · 2018
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• Behavioural change models for infectious disease transmission: a systematic review (2010–2015) · 2016
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