背景:在每个州,在美国,COVID-19大流行的出现以通常与执政党相对应的政策和言论为标志。这些不同的反应引发了广泛的持续讨论,即一个州的政治领导不仅可能影响给定州的COVID-19病例数,还可能影响大流行的主观个人经历。
目的:本研究利用来自Google搜索趋势和疾病控制与预防中心(CDC)每日病例数据的州级数据来调查COVID-19症状相对搜索量增加与病例数据相应增加之间的时间关系。我的目的是确定在数据的4个峰值(RQ1)中的每个峰值中是否存在状态级别的滞后时间模式差异,以及给定状态下的政治气候是否与这些差异有关(RQ2)。
方法:使用来自Google趋势和CDC的公开数据,线性混合模型用于解释随机状态级截距。滞后时间作为症状搜索数据的峰值(持续下降之前的持续增加)与病例数据的相应峰值之间的天数进行操作,并针对各个状态的4个峰值中的每一个手动计算。谷歌提供了一个数据集,可以跟踪400多种潜在COVID-19症状的相对搜索发生率,在0-100标度上进行归一化。我使用了CDC对11种最常见的COVID-19症状的定义,并创建了一个可操作症状搜索的单一结构变量。为了衡量政治气候,我考虑了2020年特朗普在一个州的普选比例,以及控制州长的政党的虚拟变量,以及衡量联邦国会代表比例控制的连续变量。
结果:总体拟合最强的是线性混合模型,该模型包括2020年特朗普投票的比例作为感兴趣的预测变量,并包括每日平均病例和死亡人数以及人口的控制。由于缺乏模型拟合,其他政治气候变量被丢弃。研究结果表明,有证据表明,各州的滞后时间存在统计学上的显着差异,但没有任何衡量政治气候的单个变量可以预测这些差异。
结论:鉴于在这种政治气候下可能会有未来的流行病,重要的是要了解政治领导如何影响人们对公共卫生危机的看法和相应的应对措施.虽然这项研究没有完全模拟这种关系,我相信,未来的研究可以建立在我通过使用不同的理论模型进行分析所确定的州一级差异的基础上,计算滞后时间的方法,或地理建模的水平。
BACKGROUND: Across each state, the emergence of the COVID-19 pandemic in the United States was marked by policies and rhetoric that often corresponded to the political party in power. These diverging responses have sparked broad ongoing discussion about how the political leadership of a state may affect not only the COVID-19 case numbers in a given state but also the subjective individual experience of the pandemic.
OBJECTIVE: This study leverages state-level data from
Google Search Trends and Centers for Disease Control and Prevention (CDC) daily
case data to investigate the temporal relationship between increases in relative search volume for COVID-19 symptoms and corresponding increases in
case data. I aimed to identify whether there are state-level differences in patterns of lag time across each of the 4 spikes in the data (RQ1) and whether the political climate in a given state is associated with these differences (RQ2).
METHODS: Using publicly available data from
Google Trends and the CDC, linear mixed modeling was utilized to account for random state-level intercepts. Lag time was operationalized as number of days between a peak (a sustained increase before a sustained decline) in symptom search data and a corresponding spike in case data and was calculated manually for each of the 4 spikes in individual states.
Google offers a data set that tracks the relative search incidence of more than 400 potential COVID-19 symptoms, which is normalized on a 0-100 scale. I used the CDC\'s definition of the 11 most common COVID-19 symptoms and created a single construct variable that operationalizes symptom searches. To measure political climate, I considered the proportion of 2020 Trump popular votes in a state as well as a dummy variable for the political party that controls the governorship and a continuous variable measuring proportional party control of federal Congressional representatives.
RESULTS: The strongest overall fit was for a linear mixed model that included proportion of 2020 Trump votes as the predictive variable of interest and included controls for mean daily cases and deaths as well as population. Additional political climate variables were discarded for lack of model fit. Findings indicated evidence that there are statistically significant differences in lag time by state but that no individual variable measuring political climate was a statistically significant predictor of these differences.
CONCLUSIONS: Given that there will likely be future pandemics within this political climate, it is important to understand how political leadership affects perceptions of and corresponding responses to public health crises. Although this study did not fully model this relationship, I believe that future research can build on the state-level differences that I identified by approaching the analysis with a different theoretical model, method for calculating lag time, or level of geographic modeling.