Effects of reactive social distancing on the 1918 influenza pandemic

Abstract

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.

Impact of Wearing Masks, Hand Hygiene, and Social Distancing on Influenza, Enterovirus, and All-Cause Pneumonia During the Coronavirus Pandemic: Retrospective National Epidemiological Surveillance Study

“…The observed decrease began in week 6, and success may be mainly attributed to wearing a mask and hand hygiene. Moreover, previous studies found social distancing had not significantly reduced transmission of influenza and other viruses [ 14 , 29 , 30 ]. SARS-CoV-2 is highly contagious; the effects of these practices may vary in areas with different viruses, societies, cultures, health care resources, population densities, disease prevalence, and proportion of subclinical carriers.…”

Systematic biases in disease forecasting - the role of behavior change

“…For k > 1, the social distancing model is nonlinear. As the nonlinearity (k) increases, individuals become more sensitive to disease prevalence 20,21 .…”

Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model

“…Due to the absence of vaccine and treatment, the prevention and control of COVID-19 have mainly relied on behavioral prevention measures, which include keeping social distance, wearing masks, stay-at-home order, and closure of nonessential business ( Yu et al, 2017 ). In the United States, most of the states had declared a state of emergency and issued a stay-at-home order.…”

“…As for disease transmission, different mathematical models and statistical methods have been applied to predict the future trend, which include multivariate linear regression ( Thomson et al, 2006 ), grey forecasting method ( Wang, 2018 ), time series techniques and neural networks ( Liu et al, 2016 ) and the susceptible/exposed/infective/recovered (SEIR) model ( Sanyi et al, 2020 ; Xia et al, 2020 ; Yu et al, 2017 ) among others. In particular, the SEIR model is the most popular mathematical model which have been employed to study the COVID-19 pandemic since its outbreak in China ( Afonso et al, 2020 ; Das et al, 2020 ; He et al, 2020 ; López & Rodo, 2020 ; Wu et al, 2020 ; Yang et al, 2020 ; Zhan et al, 2020 ; Zhao et al, 2020 ).…”