Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom’s National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest—as opposed to infections—using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2–23.2) and 22.1 (17.4–26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of the disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.

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Background: Accurate SARS-CoV-2 serological assays are critical for COVID-19 serosurveillance. However, previous studies have indicated possible cross-reactivity of these assays, including in malaria-endemic areas. Methods: We tested 213 well-characterized pre-pandemic samples from Nigeria using two SARS-CoV-2 serological assays: Abbott Architect IgG and Euroimmun NCP IgG assay, both targeting SARS-CoV-2 nucleocapsid protein. To assess antibody binding strength, an avidity assay was performed on these samples and on plasma from SARS-CoV-2 PCR-positive persons. Results: Thirteen (6.1%) of 212 samples run on the Abbott assay and 38 (17.8%) of 213 run on the Euroimmun assay were positive. Anti-Plasmodium IgG levels were significantly higher among false-positives for both Abbott and Euroimmun; no association was found with active P. falciparum infection. An avidity assay using various concentratIons of urea wash in the Euroimmun assay reduced loosely-bound IgG: of 37 positive/borderline pre-pandemic samples, 46%, 86%, 89%, and 97% became negative using 2M, 4M, 5M, and 8M urea washes, respectively. The wash slightly reduced avidity of antibodies from SARS-CoV-2 patients within 28 days of PCR confirmation; thereafter avidity increased for all urea concentrations except 8M. Conclusions: This validation found moderate to substantial cross-reactivity on two SARS-CoV-2 serological assays using samples from a malaria-endemic setting. A simple urea wash appeared to alleviate issues of cross-reactivity.

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