Effects of reactive social distancing on the 1918 influenza pandemic

The 1918 influenza pandemic was characterized by multiple epidemic waves. We investigated reactive social distancing, a form of behavioral response where individuals avoid potentially infectious contacts in response to available information on an ongoing epidemic or pandemic. We modelled its effects on the three influenza waves in the United Kingdom. In previous studies, human behavioral response was modelled by a Power function of the proportion of recent influenza mortality in a population, and by a Hill function, which is a function of the number of recent influenza mortality. Using a simple epidemic model with a Power function and one common set of parameters, we provided a good model fit for the observed multiple epidemic waves in London boroughs, Birmingham and Liverpool. We further applied the model parameters from these three cities to all 334 administrative units in England and Wales and including the population sizes of individual administrative units. We computed the Pearson’s correlation between the observed and simulated for each administrative unit. We found a median correlation of 0.636, indicating that our model predictions are performing reasonably well. Our modelling approach is an improvement from previous studies where separate models are fitted to each city. With the reduced number of model parameters used, we achieved computational efficiency gain without over-fitting the model. We also showed the importance of reactive behavioral distancing as a potential non-pharmaceutical intervention during an influenza pandemic. Our work has both scientific and public health significance.

[1]  D. Cummings,et al.  Deciphering the impacts of vaccination and immunity on pertussis epidemiology in Thailand , 2013, Proceedings of the National Academy of Sciences.

[2]  Piero Poletti,et al.  Spontaneous behavioural changes in response to epidemics. , 2009, Journal of theoretical biology.

[3]  David J. Philp,et al.  Quantifying social distancing arising from pandemic influenza , 2007, Journal of The Royal Society Interface.

[4]  Piero Poletti,et al.  Coinfection can trigger multiple pandemic waves , 2008, Journal of Theoretical Biology.

[5]  Edward L. Ionides,et al.  Plug-and-play inference for disease dynamics: measles in large and small populations as a case study , 2009, Journal of The Royal Society Interface.

[6]  Aaron A. King,et al.  Time series analysis via mechanistic models , 2008, 0802.0021.

[7]  J. Watmough,et al.  Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. , 2002, Mathematical biosciences.

[8]  S. Altizer,et al.  Resolving the roles of immunity, pathogenesis, and immigration for rabies persistence in vampire bats , 2013, Proceedings of the National Academy of Sciences.

[9]  Niall Johnson,et al.  Updating the Accounts: Global Mortality of the 1918-1920 "Spanish" Influenza Pandemic , 2002, Bulletin of the history of medicine.

[10]  Troy Day,et al.  Mechanistic modelling of the three waves of the 1918 influenza pandemic , 2011, Theoretical Ecology.

[11]  Sanyang Liu,et al.  Nonlinear mixed-effects state space models with applications to HIV dynamics , 2013 .

[12]  J. McVernon,et al.  The influence of changing host immunity on 1918-19 pandemic dynamics. , 2014, Epidemics.

[13]  J. Barry The site of origin of the 1918 influenza pandemic and its public health implications , 2004, Journal of Translational Medicine.

[14]  Frank Diederich,et al.  Mathematical Epidemiology Of Infectious Diseases Model Building Analysis And Interpretation , 2016 .

[15]  C. Bauch,et al.  Behavioral Epidemiology of Infectious Diseases: An Overview , 2012, Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases.

[16]  R. Rosenfeld Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[17]  Steve Leach,et al.  Potential Impact of Antiviral Drug Use during Influenza Pandemic , 2005, Emerging infectious diseases.

[18]  D. Earn,et al.  Generality of the Final Size Formula for an Epidemic of a Newly Invading Infectious Disease , 2006, Bulletin of mathematical biology.

[19]  C. Bretó On idiosyncratic stochasticity of financial leverage effects , 2013, 1312.5496.

[20]  C. Steiner,et al.  Identifying the Interaction Between Influenza and Pneumococcal Pneumonia Using Incidence Data , 2013, Science Translational Medicine.

[21]  Christopher T. McCaw,et al.  A Biological Model for Influenza Transmission: Pandemic Planning Implications of Asymptomatic Infection and Immunity , 2007, PloS one.

[22]  Fabrice Carrat,et al.  Explaining rapid reinfections in multiple-wave influenza outbreaks: Tristan da Cunha 1971 epidemic as a case study , 2011, Proceedings of the Royal Society B: Biological Sciences.

[23]  Bruce E. Hansen Discussion of "Feature Matching in Time Series Modeling" by Y. Xia and H. Tong , 2011 .

[24]  P. Manfredi,et al.  Vaccinating behaviour, information, and the dynamics of SIR vaccine preventable diseases. , 2007, Theoretical population biology.

[25]  R. Eggo,et al.  Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United States , 2010, Journal of The Royal Society Interface.

[26]  S. J. Koopman Discussion of `Particle Markov chain Monte Carlo methods – C. Andrieu, A. Doucet and R. Holenstein’ [Review of: Particle Markov chain Monte Carlo methods] , 2010 .

[27]  M. Pascual,et al.  Inapparent infections and cholera dynamics , 2008, Nature.

[28]  P. Rohani,et al.  Resolving pertussis immunity and vaccine effectiveness using incidence time series , 2012, Expert review of vaccines.

[29]  J. Hyman,et al.  The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda. , 2004, Journal of theoretical biology.

[30]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]/Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .

[31]  E L Ionides,et al.  Inference for nonlinear dynamical systems , 2006, Proceedings of the National Academy of Sciences.

[32]  M. Pascual,et al.  The Potential Elimination of Plasmodium vivax Malaria by Relapse Treatment: Insights from a Transmission Model and Surveillance Data from NW India , 2013, PLoS neglected tropical diseases.

[33]  B. Súdre,et al.  Early transmission dynamics of Ebola virus disease (EVD), West Africa, March to August 2014 - Euro surveillance 17 September 2014. , 2014, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[34]  J. Robins,et al.  Transmissibility of 1918 pandemic influenza , 2004, Nature.

[35]  Yves F. Atchad'e,et al.  Iterated filtering , 2009, 0902.0347.

[36]  J. Dushoff,et al.  Inferring the causes of the three waves of the 1918 influenza pandemic in England and Wales , 2013, Proceedings of the Royal Society B: Biological Sciences.

[37]  Neil M. Ferguson,et al.  The effect of public health measures on the 1918 influenza pandemic in U.S. cities , 2007, Proceedings of the National Academy of Sciences.

[38]  M. Keeling,et al.  Modeling Infectious Diseases in Humans and Animals , 2007 .

[39]  J. Dushoff,et al.  Effects of School Closure on Incidence of Pandemic Influenza in Alberta, Canada , 2012 .

[40]  Pejman Rohani,et al.  Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola , 2014, Proceedings of the Royal Society B: Biological Sciences.

[41]  Yingcun Xia,et al.  Feature Matching in Time Series Modeling , 2011, 1104.3073.

[42]  Beatrice Downie Effects of School Closure on Incidence of Pandemic Influenza in Alberta, Canada , 2012, Annals of Internal Medicine.

[43]  Anindya Bhadra,et al.  Malaria in Northwest India: Data Analysis via Partially Observed Stochastic Differential Equation Models Driven by Lévy Noise , 2011 .

[44]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[45]  Edward L. Ionides Discussion of “Feature Matching in Time Series Modeling” by Y. Xia and H. Tong , 2011 .

[46]  Gerardo Chowell,et al.  The 1918–1919 influenza pandemic in England and Wales: spatial patterns in transmissibility and mortality impact , 2008, Proceedings of the Royal Society B: Biological Sciences.

[47]  Mercedes Pascual,et al.  Forcing Versus Feedback: Epidemic Malaria and Monsoon Rains in Northwest India , 2010, PLoS Comput. Biol..

[48]  Darren J. Wilkinson,et al.  Discussion of Particle Markov chain Monte Carlo , 2008 .

[49]  Huaiping Zhu,et al.  Media/psychological impact on multiple outbreaks of emerging infectious diseases , 2007 .

[50]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[51]  N. Grassly,et al.  The role of older children and adults in wild poliovirus transmission , 2014, Proceedings of the National Academy of Sciences.