SARS-CoV-2 and pediatric encephalopathy: Google analytics and predictive modeling

Abstract

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is rare in children and possesses distinctive clinical features. Aims and Objectives: The primary objective is to bring clinicians,’ researchers,’ and epidemiologists’ attention to pediatric encephalopathy as part of the clinical manifestations of SARS-CoV-2 in children. Materials and Methods: Google analytics aimed to explore the spatial-temporal (geographic-chronological) mapping of SARS-CoV-2 in conjunction with pediatric encephalopathy and encephalitis. We retrieved longitudinal data from Google Trends, for one year starting from December 2019, by deploying five search topics; “SARS-CoV-2,” “Coronavirus disease 2019, COVID-19” “Pediatric Encephalopathy,” “Pediatric Encephalitis,” and “Encephalitis in Children.” Results: Spatio-temporal mapping was most conclusive for “COVID-19” and “Severe acute respiratory syndrome coronavirus 2.” Internet users were least interested in topics related to pediatric encephalopathy and encephalitis in children; potentially, reflecting the rarity of these entities in SARS-CoV-2 infections in children. We are also reporting a case of atypical SARS-CoV-2 in an 8-year-old child, in which pediatric encephalopathy occurred in a PCR-confirmed COVID-19 case. Conclusion: Google analytics reconciled with the case report. SARS-CoV-2 in children may present with neurological rather than respiratory manifestations, which is atypical and rare. We are conveying two key messages; (1) pediatricians can collaborate with data scientists to realize evidence-based pediatric medicine and (2) digital data are worthy of exploration to guide subsequent rigor research, including randomized controlled trials and meta-analytics.