In this paper, we demonstrate how Bayesian Neural Networks (BNNs) can be used to both provide a forecast and a corresponding uncertainty without significant loss in forecasting accuracy compared to traditional NNs.
Our analysis shows that searches for "fever" and "cough" were the most correlated with future case counts, with searches preceding case counts by 16-17 days.
Furthermore, we analyse the time series of online search queries in relation to confirmed COVID-19 cases data jointly across multiple countries, uncovering interesting patterns.
Social and Information Networks
We provide a brief technical description of an online platform for disease monitoring, titled as the Flu Detector (fludetector. cs. ucl. ac. uk).