RAPID: Failure to Predict Infection Risk and its Impact on the Spread of COVID-19

The speed with which pandemics spread is surprising, yet some in society fail to abide by guidelines such as hygiene and social distancing that are designed to decrease the spread of such epidemics. People may fail to adhere to these guidelines either because they fail to accurately predict the chance that they will be infected, or they accurately perceive the chance of infection but do not take appropriate precaution. These sources of individual variation have important implications for policy effectiveness in reducing the spread of epidemics, such as COVID-19. This research project will use experimental methods to investigate whether failure to follow guidelines is due to failure to accurately predict the chance of an infection and if so, what policy interventions are likely to succeed in improving people?s prediction of the probability of an infection. The major hypothesis is that people make prediction errors because they believe that the virus will grow at a linear rate while the infection grows at exponential rate, thus leading to systematic prediction errors. The results of this research project therefore provide important inputs into designing policies to reduce the spread of COVID-19 in particular and infectious diseases generally. The results of this research project will also help to establish the US as a global leader in the analysis of, and the design of policies to reduce infectious diseases.

People tend to underestimate the speed at which exponential processes (such as, those involving compound interest) unfolds. This is especially relevant in the early stages of an infectious disease outbreak when few positive cases can explode into a widespread pandemic if the disease is sufficiently transmittable. This proposal uses an incentivized, survey instrument to study an exponential-growth prediction bias (EGPB) in the context of COVID-19. Prediction bias is defined as the systematic error arising from under or over -prediction of the number of COVID-19 positive detections x-weeks hence when presented with y-weeks of prior, actual data on the same. Those who suffer from EGPB will greatly underestimate how quickly a disease spreads, fail to perceive their own onrushing risk, and hence, show low compliance with safety measures. This research project aims to test these hypotheses and to see if simple, behavioral nudges can help reduce EGPB. The hypotheses get to the heart of the behavioral aspects of virus transmission missed in epidemiological models that underplay rational choice in disease prevention. The research is relevant for public health efforts to ?flatten the curve? which critically rely on compliance with self-protection guidelines.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.