Utilizing “Google Trends” data to support early detection of epidemic outbreaks: a preliminary study

Abstract

Purpose: This study examined the potential application of Google Trends in supporting early epidemic detection and health campaigns, using the COVID-19 pandemic in Indonesia as a case study.

Method:  COVID-19 case data from 2020 to 2022 were collected. Search patterns were analyzed using Indonesian keywords for symptoms: “demam”, “sakit kepala”, “pilek”, “bersin”, “sakit tenggorokan”, “perut”, “batuk”, “nafsu makan”, “muntah”, “lesu”, “mual”, and “diare.” The search patterns were then compared to the COVID-19 case data.

Results:  We observed a pattern alignment between Google Trends and COVID-19 case peaks. Additionally, differences in lag time were identified between search trends and case peaks across SARS-CoV-2 variants. For instance, the peaks of “sakit tenggorokan” and “batuk” searches lagged about one week for Omicron, around two weeks for Delta, and more than two weeks for Alpha.

Conclusion: Internet search activity can support early detection of epidemics and inform timely health campaigns. Moreover, search trends might offer a novel approach to estimate disease incubation periods.