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Abstract
Effective public health response to novel pandemics relies on accurate and timely surveillance of pandemic spread, as well as characterization of the clinical course of the disease in affected individuals. We sought to determine whether Internet search patterns can be useful for tracking COVID-19 spread, and whether these data could also be useful in understanding the clinical progression of the disease in 32 countries across six continents. Temporal correlation analyses were conducted to characterize the relationships between a range of COVID-19 symptom-specific search terms and reported COVID-19 cases and deaths for each country from January 1 through April 20, 2020. Increases in COVID-19 symptom-related searches preceded increases in reported COVID-19 cases and deaths by an average of 18.53 days (95% CI 15.98–21.08) and 22.16 days (20.33–23.99), respectively. Cross-country ensemble averaging was used to derive average temporal profiles for each search term, which were combined to create a search-data-based view of the clinical course of disease progression. Internet search patterns revealed a clear temporal pattern of disease progression for COVID-19: Initial symptoms of fever, dry cough, sore throat and chills were followed by shortness of breath an average of 5.22 days (3.30–7.14) after initial symptom onset, matching the clinical course reported in the medical literature. This study shows that Internet search data can be useful for characterizing the detailed clinical course of a disease. These data are available in real-time at population scale, providing important benefits as a complementary resource for tracking pandemics, especially before widespread laboratory testing is available.
Highlights
Accurate real-time surveillance of disease spread is essential for effective pandemic response and for the allocation of scarce healthcare resources[1,2]
A detailed review of these studies is provided in the Discussion section below. Unlike these recent studies which have focused on using Internet search data for tracking pandemic spread, in this study we examine whether Internet search data can be used for another important purpose: characterizing the clinical course of symptoms in affected individuals, especially during the early stages of an emergent pandemic
Internet search volume and COVID-19 cases and deaths deriving from other symptom onset definitions, are all around 5 days, matching the clinical course of the disease reported in the literature[30,31,32,33,34]
Summary
Accurate real-time surveillance of disease spread is essential for effective pandemic response and for the allocation of scarce healthcare resources[1,2]. While laboratory testing remains the primary method for diagnosing individuals, using laboratory testing for population-level surveillance has its limitations—it is difficult to achieve population-level coverage due to delays in development and scaling of de novo laboratory testing methodologies during the crucial early stages of an emergent pandemic[4,5,6]. Aggregated data on Internet search volumes are freely available in near-real-time and at population-scale in areas with sufficient Internet penetration[8] These Internet search patterns have been used to track a wide range of health phenomena, including influenza[9], MERS10, measles[11], abortion[12] and immunization compliance[13], and are a potential complementary source of information for population-level surveillance of pandemic spread
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