Investigating laypeople’s short- and long-term forecasts of COVID-19 infection cycles

Abstract

How do laypeople anticipate the severity of the COVID-19 pandemic in the short and long term? The evolution of COVID-19 infection cases is characterized by wave-shaped cycles, and we examine how individuals make forecasts for this type of time series. Over 42 weeks, we ran forecasting experiments and elicited weekly judgments from the general public to analyze their forecasting behavior (Study 1). We find that laypeople often tend to dampen trends when generating judgmental forecasts, but the degree to which this happens depends on the evolution of the cyclic time series data. The observed forecasting behavior reveals evidence of an optimism bias in that people do not expect the number of infection cases to grow at the observed rate while believing that infection rates would drop at an even faster rate than they are. Also, our results suggest that laypeople’s forecasting judgments are affected by the magnitude of the present wave relative to the previously observed ones. Further, we provide evidence that laypeople rely on a cognitive heuristic for generating long-term forecasts. People tend to rely on a linear discounting rule in that they lower their long-term forecasts proportionally to the interval of the forecast horizon, i.e., from tomorrow to 6 months and from 6 months to 1 year. We also find that this linear discounting rule can change to an exponential one in reaction to externally generated optimistic information signals such as vaccine approval. Furthermore, we replicated the major findings of Study 1 in a more controlled setting with a hypothetical pandemic scenario and artificially generated time series (Study 2). Overall, the current research contributes to the judgmental forecasting literature and provides practical implications for decision-makers in the pandemic.