Game Theory of Social Distancing in Response to an Epidemic

Social distancing practices are changes in behavior that prevent disease transmission by reducing contact rates between susceptible individuals and infected individuals who may transmit the disease. Social distancing practices can reduce the severity of an epidemic, but the benefits of social distancing depend on the extent to which it is used by individuals. Individuals are sometimes reluctant to pay the costs inherent in social distancing, and this can limit its effectiveness as a control measure. This paper formulates a differential-game to identify how individuals would best use social distancing and related self-protective behaviors during an epidemic. The epidemic is described by a simple, well-mixed ordinary differential equation model. We use the differential game to study potential value of social distancing as a mitigation measure by calculating the equilibrium behaviors under a variety of cost-functions. Numerical methods are used to calculate the total costs of an epidemic under equilibrium behaviors as a function of the time to mass vaccination, following epidemic identification. The key parameters in the analysis are the basic reproduction number and the baseline efficiency of social distancing. The results show that social distancing is most beneficial to individuals for basic reproduction numbers around 2. In the absence of vaccination or other intervention measures, optimal social distancing never recovers more than 30% of the cost of infection. We also show how the window of opportunity for vaccine development lengthens as the efficiency of social distancing and detection improve.

[1]  Frederick Chen,et al.  Rational behavioral response and the transmission of STDs. , 2004, Theoretical population biology.

[2]  M E J Newman,et al.  Predicting epidemics on directed contact networks. , 2006, Journal of theoretical biology.

[3]  Jan Medlock,et al.  Optimal Timing of Disease Transmission in an Age-Structured Population , 2007, Bulletin of mathematical biology.

[4]  Frederick Chen Modeling the effect of information quality on risk behavior change and the transmission of infectious diseases. , 2009, Mathematical biosciences.

[5]  D. Earn,et al.  Group interest versus self-interest in smallpox vaccination policy , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[6]  W. O. Kermack,et al.  Contributions to the mathematical theory of epidemics—I , 1991, Bulletin of mathematical biology.

[7]  J. Hyman,et al.  Effects of behavioral changes in a smallpox attack model. , 2005, Mathematical biosciences.

[8]  Franz J. Weissing,et al.  Self-Interest versus Group-Interest in Antiviral Control , 2008, PloS one.

[9]  James M Hyman,et al.  Infection-age structured epidemic models with behavior change or treatment , 2007, Journal of biological dynamics.

[10]  Alberto d'Onofrio,et al.  Information-related changes in contact patterns may trigger oscillations in the endemic prevalence of infectious diseases. , 2009, Journal of theoretical biology.

[11]  P. Francis,et al.  Optimal tax/subsidy combinations for the flu season , 2004 .

[12]  Timothy C. Reluga,et al.  Evolving public perceptions and stability in vaccine uptake. , 2006, Mathematical biosciences.

[13]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[14]  P. Fine,et al.  Individual versus public priorities in the determination of optimal vaccination policies. , 1986, American journal of epidemiology.

[15]  G. Milne,et al.  Simulation suggests that rapid activation of social distancing can arrest epidemic development due to a novel strain of influenza , 2009, BMC public health.

[16]  Frederick Chen A Susceptible-infected Epidemic Model with Voluntary Vaccinations , 2006, Journal of mathematical biology.

[17]  L. Bobisud Optimal control of a deterministic epidemic , 1977 .

[18]  Timothy C. Reluga,et al.  An SIS epidemiology game with two subpopulations , 2009, Journal of biological dynamics.

[19]  Jan Medlock,et al.  The discounted reproductive number for epidemiology. , 2009, Mathematical biosciences and engineering : MBE.

[20]  John T. Workman,et al.  Optimal Control Applied to Biological Models , 2007 .

[21]  Timothy C. Reluga,et al.  A general approach for population games with application to vaccination. , 2011, Mathematical biosciences.

[22]  Deborah Lacitignola,et al.  Global stability of an SIR epidemic model with information dependent vaccination. , 2008, Mathematical biosciences.

[23]  R. G. Hawkins,et al.  Public Investment, the Rate of Return, and Optimal Fiscal Policy , 1970 .

[24]  Joshua M. Epstein,et al.  Coupled Contagion Dynamics of Fear and Disease: Mathematical and Computational Explorations , 2007, PloS one.

[25]  Jan Medlock,et al.  Resistance mechanisms matter in SIR models. , 2007, Mathematical biosciences and engineering : MBE.

[26]  A. Nizam,et al.  Containing Pandemic Influenza at the Source , 2005, Science.

[27]  D. Cummings,et al.  Strategies for containing an emerging influenza pandemic in Southeast Asia , 2005, Nature.

[28]  C. Watkins,et al.  The spread of awareness and its impact on epidemic outbreaks , 2009, Proceedings of the National Academy of Sciences.

[29]  Ana Perisic,et al.  Social Contact Networks and Disease Eradicability under Voluntary Vaccination , 2009, PLoS Comput. Biol..

[30]  L. Meyers Contact network epidemiology: Bond percolation applied to infectious disease prediction and control , 2006 .

[31]  L. Finelli,et al.  Emergence of a novel swine-origin influenza A (H1N1) virus in humans. , 2009, The New England journal of medicine.

[32]  Phillip D. Stroud,et al.  EpiSimS simulation of a multi-component strategy for pandemic influenza , 2008, SpringSim '08.