Retrospective analysis of the possibility of predicting the COVID-19 outbreak from Internet searches and social media data, China, 2020

Abstract

Since the outbreak of COVID-19 in December 2019 in Wuhan, Zhejiang has become the province with the largest number of cases. The aim of this article is to present Zhejiang province's experience of establishing an accurate and smart control mechanism for epidemic prevention and control and resumption of work and production using a 'five-colour epidemic chart'. The number of confirmed cases, proportion of local cases, and occurrence of clustered outbreaks were used as evaluation indicators to calculate the county-level epidemic risk and were assigned different weight coefficients; the absence of cases for 3 and 7 consecutive days was used as the adjustment index. When the first chart was published on February 9, there were 1 very-high-risk, 12 high-risk, and 12 low-risk counties. Under the five-colour chart, Zhejiang began to adopt precise measures to prevent and control the epidemic and resume work and production. By February 24, the low-risk counties had expanded to 82, with no high-risk and very-high-risk counties. The epidemic situation in Zhejiang province has been effectively controlled. The experience of epidemic prevention and control in Zhejiang is worthy to be emulated and learned by other countries and regions

Trends and Prediction in Daily New Cases and Deaths of COVID-19 in the United States: An Internet Search-Interest Based Model

“…Second, we extensively examined nine COVID-19-related search terms, which are more than the two used in a previous study. 7 Our data also suggest that pneumonia and heart problems were highly relevant to the daily new cases and deaths in the USA. This finding may be explained by the frequent pneumonia and cardiac injuries seen in COVID-19 patients.…”

“…20 Finally, to our knowledge, we are first to examine the correlations of search interest with the COVID-19 daily new cases and deaths in the USA and show greater correlations (Pearson's r > 0.97) than reported in the Chinese data. 7 This study is limited by the retrospective nature of the modeling part and may have some related biases. Moreover, due to the different testing strategies and criteria used in the USA and other countries, 15-18 the comparison of our findings to those of other countries should be interpreted with caution.…”

Trends and prediction in daily incidence and deaths of COVID-19 in the United States: a search-interest based model

“…Finally, we rigorously examined the correlation of search interest with the COVID-19 daily incidence and deaths, and show greater correlations (Pearson's r>0.97) than reported in Chinese data. 7 This study is limited by the retrospective nature of the modeling part, and may have some related biases. Moreover, due to the relatively low testing rate in the U.S., the daily incidence and deaths may be underestimated.…”

“…(which was not certified by peer review) search interest relative to the highest search interest for the given time and region. 7,12 A value of 100 is the peak popularity for the term, while a score of 0 means there were not enough data for this term.…”

“…Recently, internet search interest was found correlated with daily incidence of COVID-19 in China, with the lagging time of 8 to 10 days. 7 Google search interest was also used to track or model COVID-19 trends in Europe, Iran and Taiwan. [8][9][10] Indeed, internet search interest has been used for modelling and detecting influenza epidemics in the U.S. and Australia.…”