Effects of Reactive Social Distancing on the 1918 Influenza Pandemic

PLoS One. 2017 Jul 12;12(7):e0180545. doi: 10.1371/journal.pone.0180545. eCollection 2017.


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.

MeSH terms

  • Humans
  • Influenza Pandemic, 1918-1919 / prevention & control*
  • Influenza Pandemic, 1918-1919 / statistics & numerical data
  • Influenza, Human / epidemiology*
  • Influenza, Human / prevention & control
  • Influenza, Human / psychology
  • Models, Statistical
  • Social Distance*
  • United Kingdom

Grant support

DH was supported by General Research Fund (Early Career Scheme) from Hong Kong Research Grants Council (PolyU 251001/14M) and Start-up Fund for New Recruits from Hong Kong Polytechnic University.