Systematic biases in disease forecasting – The role of behavior change

• Published in: Epidemics Vol. 27; pp. 96 - 105
• Main Authors: Eksin, Ceyhun, Paarporn, Keith, Weitz, Joshua S.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-06-2019; Elsevier
• Subjects: Disease forecasting; Epidemiology; Nonlinear dynamics; Social distancing; Nonlinear dynamics; Disease forecasting; Social distancing; Epidemiology
• ISSN: 1755-4365; 1878-0067
• DOI: 10.1016/j.epidem.2019.02.004

•Speed–final size relationship in the SIR model becomes tenuous with behavior change.•Final size predictions should not be predicated on estimates of initial speed of spread.•Temporality of awareness to disease prevalence affects disease progress.•Forecasting models that do not account individual behavior change can overshoot.•Sequential forecasting based on noisy observations of cases can learn behavior change.
In a simple susceptible-infected-recovered (SIR) model, the initial speed at which infected cases increase is indicative of the long-term trajectory of the outbreak. Yet during real-world outbreaks, individuals may modify their behavior and take preventative steps to reduce infection risk. As a consequence, the relationship between the initial rate of spread and the final case count may become tenuous. Here, we evaluate this hypothesis by comparing the dynamics arising from a simple SIR epidemic model with those from a modified SIR model in which individuals reduce contacts as a function of the current or cumulative number of cases. Dynamics with behavior change exhibit significantly reduced final case counts even though the initial speed of disease spread is nearly identical for both of the models. We show that this difference in final size projections depends critically in the behavior change of individuals. These results also provide a rationale for integrating behavior change into iterative forecast models. Hence, we propose to use a Kalman filter to update models with and without behavior change as part of iterative forecasts. When the ground truth outbreak includes behavior change, sequential predictions using a simple SIR model perform poorly despite repeated observations while predictions using the modified SIR model are able to correct for initial forecast errors. These findings highlight the value of incorporating behavior change into baseline epidemic and dynamic forecast models.