Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend

Wensen Huang; Bolin Cao; Guang Yang; Ningzheng Luo; Naipeng Chao

doi:10.1016/j.ipm.2020.102486

Turn to the Internet First? Using Online Medical Behavioral Data to Forecast COVID-19 Epidemic Trend

Inf Process Manag. 2021 May;58(3):102486. doi: 10.1016/j.ipm.2020.102486. Epub 2020 Dec 29.

Authors

Wensen Huang¹, Bolin Cao¹, Guang Yang¹, Ningzheng Luo², Naipeng Chao¹

Affiliations

¹ School of Media and Communication, Shenzhen University, No. 3688 Nanhai Avenue, Nanshan District, Shenzhen, China.
² Health 160, Shenzhen Ningyuan Technology Co., Ltd., Shenzhen, China.

Abstract

The surveillance and forecast of newly confirmed cases are important to mobilize medical resources and facilitate policymaking during a public health emergency. Digital surveillance using data available online has increasingly become a trend with the advancement of the Internet. In this study, we assessed the predictive value of multiple online medical behavioral data, including online medical consultation (OMC), online medical appointment (OMA), and online medical search (OMS) for the regional outbreak of coronavirus disease 2019 in Shenzhen, China during January 1, 2020 to March 5, 2020. Multivariate vector autoregression models were used for the prediction. The results identified a novel predictor, OMC, which can forecast the disease trend up to 2 days ahead of the official reports of confirmed cases from the local health department. OMS data had relatively weaker predictive power than OMC in our model, and OMA data failed to predict the confirmed cases. This study highlights the importance of OMC data and has implication in providing evidence-based guidelines for local authorities to evaluate risks and allocate resources during the pandemic.

Keywords: COVID-19; eHealth; online medical consultation; pandemic; telehealth.