论文标题
使用调查监测大流行期间的公共行为：通过流行病建模的概念证明
Monitoring Public Behavior During a Pandemic Using Surveys: Proof-of-Concept Via Epidemic Modelling
论文作者
论文摘要
为缓解疾病的封锁是一项平衡行为：非药物干预措施可以大大减少疾病的传播，但干预措施也具有相当大的社会成本。因此，决策者需要几乎实时信息来校准限制水平。我们在COVID-19-19大流行中的第二波浪潮中对丹麦进行了每日调查，以监视公众对宣布的锁定的反应。一个关键问题要求受访者在过去24小时内陈述他们的密切联系数量。在这里，我们通过流行病建模之间建立了调查数据，移动性数据和住院治疗之间的联系。然后，我们将贝叶斯分析评估调查响应的有用性，作为监测锁定效果的工具，然后将预测性能与移动性数据的效果进行比较。我们发现，与流动性不同，在全国范围内实施非药物干预措施之前，所有地区的自我报告接触都显着下降，并且与移动性数据相比，预测未来的住院治疗。对联系类型的详细分析表明，与朋友和陌生人的联系优于同事和家庭成员（家庭之外）在相同的预测任务上的联系。因此，代表性调查符合可靠的非私人入侵监测工具，以跟踪非药物干预措施的实施和研究潜在的传输路径。
Implementing a lockdown for disease mitigation is a balancing act: Non-pharmaceutical interventions can reduce disease transmission significantly, but interventions also have considerable societal costs. Therefore, decision-makers need near real-time information to calibrate the level of restrictions. We fielded daily surveys in Denmark during the second wave of the COVID-19 pandemic to monitor public response to the announced lockdown. A key question asked respondents to state their number of close contacts within the past 24 hours. Here, we establish a link between survey data, mobility data, and hospitalizations via epidemic modelling. Using Bayesian analysis, we then evaluate the usefulness of survey responses as a tool to monitor the effects of lockdown and then compare the predictive performance to that of mobility data. We find that, unlike mobility, self-reported contacts decreased significantly in all regions before the nation-wide implementation of non-pharmaceutical interventions and improved predicting future hospitalizations compared to mobility data. A detailed analysis of contact types indicates that contact with friends and strangers outperforms contact with colleagues and family members (outside the household) on the same prediction task. Representative surveys thus qualify as a reliable, non-privacy invasive monitoring tool to track the implementation of non-pharmaceutical interventions and study potential transmission paths.
