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

流行病传播的速度令人惊讶，但社会中的一些人未能遵守旨在减少此类流行病传播的卫生和社交距离等指导方针。 人们可能无法遵守这些指南，因为他们无法准确预测他们将被感染的机会，或者他们准确地感知到感染的机会，但没有采取适当的预防措施。这些个体差异的来源对减少COVID-19等流行病传播的政策有效性具有重要影响。 该研究项目将使用实验方法来调查未能遵循指南是否是由于未能准确预测感染的可能性，如果是这样，哪些政策干预可能成功地改善人们对感染概率的预测。 主要的假设是，人们之所以会做出预测错误，是因为他们相信病毒会以线性速度增长，而感染则以指数速度增长，从而导致系统性预测错误。 因此，该研究项目的结果为设计政策以减少COVID-19和一般传染病的传播提供了重要投入。 该研究项目的结果还将有助于美国成为分析和设计减少传染病政策的全球领导者。 人们倾向于低估指数过程（例如涉及复利的过程）展开的速度。这在传染病爆发的早期阶段尤其重要，因为如果疾病具有充分的传播性，很少有阳性病例会爆发成广泛的大流行。 该提案使用激励的调查工具来研究COVID-19背景下的指数增长预测偏差（EGPB）。预测偏差定义为当与y周之前的实际数据一起呈现时，由于对COVID-19阳性检测x周的数量预测不足或过度而产生的系统误差。那些患有EGPB的人会大大低估疾病传播的速度，无法感知自己的风险，因此，对安全措施的依从性很低。该研究项目旨在测试这些假设，并看看简单的行为轻推是否有助于减少EGPB。这些假设触及了流行病学模型中忽略的病毒传播行为方面的核心，这些模型低估了疾病预防中的理性选择。该研究与公共卫生努力“使曲线变平”相关，这一努力主要依赖于遵守自我保护指南。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.