Accepted for/Published in: JMIR Formative Research
Date Submitted: Jul 6, 2022
Date Accepted: Nov 8, 2022
Date Submitted to PubMed: Nov 29, 2022
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
COVID-19 Symptom-Related Google Searches and State-Level COVID-19 Case Incidence: Investigating Political Beliefs as a Predictor of Variation in the Temporal Symptom and Case Relationship
ABSTRACT
The ongoing COVID-19 pandemic provides an opportunity for researchers to investigate the relationship between search interest and case data, given that states publicly reported case data at a granular level. The purpose of this paper is to identify whether there are state-level differences in the lag time between spikes in symptom search incidence and spikes in reported COVID-19 case incidence and whether political climate is significantly associated with this lag time. Using publicly available data from Google Trends and the Centers for Disease Control and Prevention, 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. The strongest model fit was for a linear mixed model that included proportion of Trump votes in a state as the key predictor along with population, mean new cases, and mean new deaths as control variables. Although this was a significantly stronger model fit than the null intercept-only model, the political variable(s) were not associated with lag time in a statistically significant way.
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