About: ABSTRACT BACKGROUND Since January 2020, Coronavirus (COVID-19) cases have risen exponentially in the United States. Accurate data on COVID-19 cases has been difficult to report due to lack of testing as well as the overload of the U.S. healthcare system. This study aims to evaluate whether a digital surveillance model using Google Trends is feasible, and whether accurate predictions can be made regarding new cases. METHODS Data on total and daily new cases in each U.S. state was collected and used in this study from late January to early April. Information regarding ten keywords was collected and correlation analyses were performed for individual states as well as for the United States overall. RESULTS Ten keywords were analyzed from Google Trends. “Face mask”, “Lysol”, and “COVID stimulus check” had the strongest correlations when looking at the United States as a whole, with R values of 0.88, 0.82 and 0.79 respectively. Lag and lead Pearson correlations were assessed for every state and all ten keywords from 16 days before the first case in each state to 16 days after the first case. Strong correlations were seen up to 16 days prior to the first reported cases in some states. CONCLUSION This study demonstrates the feasibility of syndromic surveillance of internet search terms to monitor new infectious diseases such as COVID-19. This information could enable better preparation and planning of healthcare systems.   Goto Sponge  NotDistinct  Permalink

An Entity of Type : fabio:Abstract, within Data Space : covidontheweb.inria.fr associated with source document(s)

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  • ABSTRACT BACKGROUND Since January 2020, Coronavirus (COVID-19) cases have risen exponentially in the United States. Accurate data on COVID-19 cases has been difficult to report due to lack of testing as well as the overload of the U.S. healthcare system. This study aims to evaluate whether a digital surveillance model using Google Trends is feasible, and whether accurate predictions can be made regarding new cases. METHODS Data on total and daily new cases in each U.S. state was collected and used in this study from late January to early April. Information regarding ten keywords was collected and correlation analyses were performed for individual states as well as for the United States overall. RESULTS Ten keywords were analyzed from Google Trends. “Face mask”, “Lysol”, and “COVID stimulus check” had the strongest correlations when looking at the United States as a whole, with R values of 0.88, 0.82 and 0.79 respectively. Lag and lead Pearson correlations were assessed for every state and all ten keywords from 16 days before the first case in each state to 16 days after the first case. Strong correlations were seen up to 16 days prior to the first reported cases in some states. CONCLUSION This study demonstrates the feasibility of syndromic surveillance of internet search terms to monitor new infectious diseases such as COVID-19. This information could enable better preparation and planning of healthcare systems.
Subject
  • United States
  • G7 nations
  • Computer surveillance
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