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dc.contributor.authorFulk, Alexander
dc.contributor.authorRomero-Alvarez, Daniel
dc.contributor.authorAbu-Saymeh, Qays
dc.contributor.authorOnge, Jarron M. Saint
dc.contributor.authorPeterson, A. Townsend
dc.contributor.authorAgusto, Folashade B.
dc.identifier.citationFulk A, Romero-Alvarez D, Abu-Saymeh Q, Saint Onge JM, Peterson AT, Agusto FB (2022) Using Google Health Trends to investigate COVID-19 incidence in Africa. PLoS ONE 17(6): e0269573.
dc.description.abstractThe COVID-19 pandemic has caused over 500 million cases and over six million deaths globally. From these numbers, over 12 million cases and over 250 thousand deaths have occurred on the African continent as of May 2022. Prevention and surveillance remains the cornerstone of interventions to halt the further spread of COVID-19. Google Health Trends (GHT), a free Internet tool, may be valuable to help anticipate outbreaks, identify disease hotspots, or understand the patterns of disease surveillance. We collected COVID-19 case and death incidence for 54 African countries and obtained averages for four, five-month study periods in 2020–2021. Average case and death incidences were calculated during these four time periods to measure disease severity. We used GHT to characterize COVID-19 incidence across Africa, collecting numbers of searches from GHT related to COVID-19 using four terms: ‘coronavirus’, ‘coronavirus symptoms’, ‘COVID19’, and ‘pandemic’. The terms were related to weekly COVID-19 case incidences for the entire study period via multiple linear and weighted linear regression analyses. We also assembled 72 variables assessing Internet accessibility, demographics, economics, health, and others, for each country, to summarize potential mechanisms linking GHT searches and COVID-19 incidence. COVID-19 burden in Africa increased steadily during the study period. Important increases for COVID-19 death incidence were observed for Seychelles and Tunisia. Our study demonstrated a weak correlation between GHT and COVID-19 incidence for most African countries. Several variables seemed useful in explaining the pattern of GHT statistics and their relationship to COVID-19 including: log of average weekly cases, log of cumulative total deaths, and log of fixed total number of broadband subscriptions in a country. Apparently, GHT may best be used for surveillance of diseases that are diagnosed more consistently. Overall, GHT-based surveillance showed little applicability in the studied countries. GHT for an ongoing epidemic might be useful in specific situations, such as when countries have significant levels of infection with low variability. Future studies might assess the algorithm in different epidemic contexts.en_US
dc.publisherPublic Library of Scienceen_US
dc.rights© 2022 Fulk et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.en_US
dc.titleUsing Google Health Trends to investigate COVID-19 incidence in Africaen_US
kusw.kuauthorFulk, Alexander
kusw.kuauthorRomero-Alvarez, Daniel
kusw.kuauthorAbu-Saymeh, Qays
kusw.kuauthorOnge, Jarron M. Saint
kusw.kuauthorPeterson, A. Townsend
kusw.kuauthorAgusto, Folashade B.
kusw.kudepartmentEcology & Evolutionary Biologyen_US
kusw.kudepartmentBiodiversity Instituteen_US
dc.identifier.orcid 0000-0002-6762-6046en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US

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© 2022 Fulk et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
Except where otherwise noted, this item's license is described as: © 2022 Fulk et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.