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Google Search Interests and New Cases of COVID-19 in Bangladesh: A Vector Autoregression Analysis for Disease Surveillance

Background: The use of Google search engine has been widely used in
public health-related concerns. Previous studies found that Google
search trends (GST) can predict influenza, mortality, Zika epidemics,
Ebola, etc. This study examines the relationship between the timing of
Coronavirus-related Google search trends, lockdown, and new cases of
COVID-19 in Bangladesh. Methods: We use national-level Google search
trend data to examine whether the timing of Google search terms, i.e.,
their lag effects are associated with actual COVID-19 new cases from
March 2, 2020, to December 7, 2020. We examine the effects of search
terms (facemasks, handwash, n95) on the actual COVID-19 new cases using
the vector autoregression (VAR) model. Results: Our general recursive
vector autoregression model shows that search on facemasks and hand-wash
can potentially decrease the risk communication of COVID-19 new cases.
We find that the search on facemasks can substantially reduce that risk
in the sense that search can increase the use of facemasks. We also
examine the lag effect of lockdown and find that the effects are not
sizeable on the risk communication because their lag-effects are
different. The results of the impulse-response functions show that among
the protective measures, lag effects of facemasks can substantially
decrease the future risk communication of COVID-19 new cases.
Conclusion: Because wearing facemasks can substantially reduce the risk
of COVID-19 new cases, the government can utilize the Google search
trends related to COVID-19 to disseminate the preventive information on
COVID-19 and thus minimize the new cases and deaths.