Anosmia and other SARS-CoV-2 positive test-associated symptoms, across three national, digital surveillance platforms as the COVID-19 pandemic and response unfolded: an observation study

Background Multiple participatory surveillance platforms were developed across the world in response to the COVID-19 pandemic, providing a real-time understanding of community-wide COVID-19 epidemiology. During this time, testing criteria broadened and healthcare policies matured. We sought to test whether there were consistent associations of symptoms with SARS-CoV-2 test status across three national surveillance platforms, during periods of testing and policy changes, and whether inconsistencies could better inform our understanding and future studies as the COVID-19 pandemic progresses.

Methods Four months (1st April 2020 to 31st July 2020) of observation through three volunteer COVID-19 digital surveillance platforms targeting communities in three countries (Israel, United Kingdom, and United States). Logistic regression of self-reported symptom on self-reported SARS-CoV-2 test status (or test access), adjusted for age and sex, in each of the study cohorts. Odds ratios over time were compared to known changes in testing policies and fluctuations in COVID-19 incidence.

Findings Anosmia/ageusia was the strongest, most consistent symptom associated with a positive COVID-19 test, based on 658,325 tests (5% positive) from over 10 million respondents in three digital surveillance platforms using longitudinal and cross-sectional survey methodologies. During higher-incidence periods with broader testing criteria, core COVID-19 symptoms were more strongly associated with test status. Lower incidence periods had, overall, larger confidence intervals.

Interpretation The strong association of anosmia/ageusia with self-reported SARS-CoV-2 test positivity is omnipresent, supporting its validity as a reliable COVID-19 signal, regardless of the participatory surveillance platform or testing policy. This analysis highlights that precise effect estimates, as well as an understanding of test access patterns to interpret differences, are best done only when incidence is high. These findings strongly support the need for testing access to be as open as possible both for real-time epidemiologic investigation and public health utility.

Funding NIH, NIHR, Alzheimer’s Society, Wellcome Trust

Evidence before this study As the COVID-19 pandemic has evolved, testing capacity expanded and governmental guidelines adapted, generally encouraging testing with a broader set of symptoms, not just fever with respiratory symptoms. In parallel, multiple large-scale citizen science digital surveillance platforms launched to complement knowledge from laboratory and somewhat smaller clinical studies. Symptoms such as loss of sense of smell have been identified as strongly predictive of COVID-19 infection in both clinical and syndromic surveillance analyses, and have therefore been used to inform these testing policy changes and access expansion.

Added value of this study This study identifies symptoms that are or are not consistently associated with SARS-CoV-2 test positivity over time and across three country-based COVID-19 surveillance platforms in the United States, United Kingdom and Israel. These platforms are website and smartphone based, as well as cross-sectional and longitudinal. The study period of 4 months covers fluctuating COVID-19 prevalence during the fall of the first wave and, in some areas, rise of the second wave. In addition, the study period overlaps expansion of test access and test seeking. Importantly, these analyses track and highlight the value of individual symptoms to predict SARS-CoV-2 test positivity under a range of conditions.

Implications of all the available evidence Despite differences in surveillance methodology, access to SARS-CoV-2 testing and disease prevalence, loss of sense of smell or taste was consistently the strongest predictor of COVID-19 infection across all platforms over time. As access to testing broadened, the relevance of COVID-like symptoms and consistency of their predictive ability became apparent. However, confidence bounds generally widened with a fall in COVID-19 incidence. Therefore, for the most robust symptom-based COVID-19 prediction models should consider surveillance data during periods of higher incidence and improved test access, and effect estimates that replicate across different epidemiologic conditions and platforms.