In the field of infodemiology, internet sources and data are employed to inform public health and policy 13, 14. Infodemiology, i.e., information epidemiology, is a concept that was introduced by Gunther Eysenbach 11, 12. To find new methods and approaches for disease surveillance, it is crucial to take advantage of real-time internet data. The worldwide geographical distribution of COVID-19 cases and deaths by country is depicted in Fig. The most affected countries with more than 100 k cases (in absolute numbers, not divided by population) were the US, with 715,105 confirmed cases and 37,889 deaths Spain, with 191,726 confirmed cases and 20,043 deaths Italy, with 175,925 confirmed cases and 23,227 deaths France, with 147,969 confirmed cases and 18,681 deaths Germany, with 142,614 confirmed cases and 4405 deaths and the UK, with 114,217 confirmed cases and 15,464 deaths. As of 16:48 GMT on April 18th, 2020 9, there were 2,287,369 confirmed cases worldwide, with 157,468 confirmed deaths and 585,838 recovered patients. The novel coronavirus was transmitted to all parts of Europe within the next few weeks, and as a result, the WHO declared COVID-19 to be a pandemic on March 11th, 2020. The first serious COVID-19 outbreak in Europe was identified in northern Italy during February, with the country recording its first death on February 21st 8. On January 30th, the WHO declared the epidemic to be a public health emergency 1, and the disease caused by the virus received its official name, that is, COVID-19, on February 11th 7. However, the virus quickly spread to other Chinese regions and neighboring countries, while Wuhan, identified as the epicenter of the outbreak, was cut off by authorities on January 23rd, 2020 6.

On January 14th, the World Health Organization (WHO) tweeted that Chinese preliminary investigations reported that no human-to-human transmission had been identified 5. China reported its first COVID-19-related death on January 11th, while on January 13th, the first case outside China was identified 4. The outbreak first came to international attention after the World Health Organization (WHO) reports said that there was a cluster of pneumonia cases on Twitter on January 4th 2, followed by the release of an official report on January 5th 3. In December 2019, a novel coronavirus of unknown source was identified in a cluster of patients in the city of Wuhan, Hubei, China 1. 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. 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. 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. 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. 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. 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.

Google trends usa 2021