COVID-19 predictability in the United States using Google Trends time series

Abstract

During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.

Exploring the use of web searches for risk communication during COVID-19 in Germany

“…Numerous studies have shown how analysing web search queries can assist in describing and predicting outbreaks especially in the context of sparse data 19 – 21 . This approach has also been used for COVID-19 already 22 , 23 . However, web searches have also been analysed to describe public attention in relation to a public health issue.…”

Revealing the spatial shifting pattern of COVID-19 pandemic in the United States

“…One of the most common approaches to tracking COVID-19 dynamics is through regular snapshots in the form of choropleth maps, or where the number of cases are mapped by administrative units such as states or counties 2 . One may gain a sense of dynamics - change over time - by manually toggling back and forth through the maps or by developing a change map, where rates or differences are calculated on a per-region basis between two snapshots 3 , 4 . While such mapping is integral to understanding and responding to the pandemic, there remains a subjective element when the viewer flips back and forth between maps or must interpret change between two fixed dates among many.…”

Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19 Pandemic and for Future Research

“…In this regard, scientists are increasingly adopting infoveillance tools to monitoring the infodemic on websites, social media, and newspapers [5]. In particular, Google Trends-an open online infoveillance tool developed by Google TM -has been widely used by the scientific community not only as for quantifying disinformation but also to make epidemiological predictions on the spread of infectious diseases, including COVID-19 [6][7][8][9]. This type of study is based on the search for statistical cross-correlations between users' web searches related to specific diseases, such as symptoms, drugs, therapies, vaccines, number of infected people, number of deaths, etc., and the number of disease contagions and deaths officially registered after a certain timespan.…”