Clinical Predictors of SARS-CoV-2 Testing Pressure on Clinical Laboratories: A Multinational Study Analyzing Google Trends and Over 100 Million Diagnostic Tests

Abstract

Objective Evidence has shown that Google searches for clinical symptom keywords correlates with the number of new weekly patients with COVID-19. This multinational study assessed whether demand for SARS-CoV-2 tests could also be predicted by Google searches for key COVID-19 symptoms. Methods The weekly number of SARS-CoV-2 tests performed in Italy and the United States was retrieved from official sources. A concomitant electronic search was performed in Google Trends, using terms for key COVID-19 symptoms. Results The model that provided the highest coefficient of determination for the United States (R2 = 82.8%) included a combination of searching for cough (with a time lag of 2 weeks), fever (with a time lag of 2 weeks), and headache (with a time lag of 3 weeks; the time lag refers to the amount of time between when a search was conducted and when a test was administered). In Italy, headache provided the model with the highest adjusted R2 (86.8%), with time lags of both 1 and 2 weeks. Conclusion Weekly monitoring of Google Trends scores for nonspecific COVID-19 symptoms is a reliable approach for anticipating SARS-CoV-2 testing demands ~2 weeks in the future.

Understanding COVID‐19 Risk in Patients With Immune‐Mediated Inflammatory Diseases: A Population‐Based Analysis of SARS–CoV‐2 Testing

“…Last, access to testing varies in different countries depending on local recommendations, coverage, and testing capacity, which may affect the generalizability of the results. While our SARS–CoV‐2 testing estimates reflect the situation in Ontario, we believe that the general pattern can be extrapolated to other countries, as a gradual increased testing capacity resulting in wider testing indications has been a global characteristic of the pandemic (42). Furthermore, it is likely that our results pertaining to the relative risk of SARS–CoV‐2 infection in IMIDs are generalizable, as they consider a wide range of patients and carefully matched comparators adjusted for multiple confounding factors.…”

Symptomatology associated with the diffusion of the SARS-CoV-2 Lambda variant in Peru: An infodemiologic analysis

“…Multiple previous studies, either by our group or by other teams which have investigated the trends in online search patterns within the context of regional COVID-19 outbreaks, have consistently shown that the frequency of Google searches of pathognomonic and non-pathognomonic (i.e., general) symptom keywords and SARS-CoV-2 surveillance data have a strong relationship, through which their temporal association enables predicting future trajectories of epidemic based on internet search volumes in a period of time, as well as the demand for SARS-CoV-2 testing and test positivity. [7][8][9] However, this depends on consistency of symptoms over time and may also be influenced by emergence of novel SARS-CoV-2 variants with distinctive biological and clinical characteristics (i.e., higher virulence and/or pathogenicity, immune evasion, greater stability, and so forth).…”