Using Google Health Trends to investigate COVID-19 incidence in Africa

  1. Alexander Fulk
  2. Daniel Romero-Alvarez
  3. Qays Abu-Saymeh
  4. Jarron M. Saint Onge
  5. A. Townsend Peterson
  6. Folashade B. Agusto

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Abstract

The 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.

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  1. Version published to 10.1371/journal.pone.0269573
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    We enumerate some limitations of the present modeling exercise. Because of the timeframe of the study and the availability of GHT data as weekly counts, we had to convert daily cases to weekly cases, limiting the data for the analysis to only 51 observations, decreasing the statistical power of our approach. Moreover, the four terms related with COVID-19 that were selected might not be as popular in the region as expected. English is not the primary language used in many African countries, although language has shown to be a permeable barrier (Althouse et al., 2011; Romero-Alvarez et al., 2020). Finally, we lacked complete data for some of the predictors (e.g., prevalence of severe food insecurity in the population; Supplementary Table 3) which impedes the interpretability of several of the indicators used; however, even the ones available for all the countries and that have proved useful in other research studies (e.g., total population, signal volatility, disease incidence, etc; Gluskin et al., 2014; Romero-Alvarez et al., 2020), were inconclusive here.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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  3. Version published to 10.1101/2021.03.26.21254369 on medRxiv