Study population and setting
Leveraging existing influenza-like illness (ILI) surveillance systems in the United States (ILINet) and data from the past 10 years, this study modeled the excess number of non-influenza ILI to estimate the true prevalence of SARS-CoV-2 infection. Using excess ILI estimates as a proxy for COVID-19 burden, authors then identified surges in non-influenza ILI, estimated ILI admission rates and prevalence of SARS-CoV-2 from the surge, estimated the rate at which patients who are positive for SARS-CoV-2 and have ILI symptoms are positively identified as having COVID-19, and estimated epidemic growth rates (e.g., doubling time, or the length of time it takes for an epidemic to double in size) using an SEIR model.
Summary of Main Findings
Authors identified surges in non-influenza ILI that exceeded expected levels beginning in early March 2020. Some states reported additional non-influenza ILI in excess of up to 50% higher than any previously reported level. Authors were able to assess care-seeking for ILI in New York City, and found that both care-seeking and admission to hospital emergency departments for ILI increased during the month of March, suggesting that care-seeking for mild ILI actually decreased. The rate at which COVID-19 patients were identified using ILI surveillance varied largely by state and over time, but increased over the month of March (from approximately 1% to 12.5% across the United States). Authors estimated that COVID-19-associated deaths doubled every 3.01 days across the United States during the month of March. Assuming the ILI surge is due entirely to COVID-19, authors estimated the slowest doubling time for cases across the United States, starting January 15, is 4 days.
ILINet is a robust syndromic surveillance platform, and authors were able to estimate background non-influenza ILI levels using ten years of historic data.
Authors assumed the patient population reported by providers to ILINet is representative of their entire state. However, ILINet is limited to approximately 2,600 voluntarily enrolled outpatient providers across the United States and may not be representative of statewide populations, and likely underestimates the number of influenza and/or COVID-19 cases. To account for this latter limitation, authors scaled the data in order to compare ILINet data to confirmed COVID-19 case counts per state. Authors also assumed that SARS-CoV-2 is entirely responsible for the excess non-influenza ILI (i.e., the identified surge). Although this is likely, not accounting for increases in other circulating viruses, such as other coronaviruses or RSV, contributes to uncertainty in the model results. However, this assumption does become more robust as the prevalence of SARS-CoV-2 continues to increase. Asymptomatic infections would not be captured in ILINet. The SEIR models were US-wide, and as such were unable to capture regional variations in transmission or the effects of interventions.
This study is the first to leverage ILINet, a pre-existing and robust influenza-like illness surveillance system, to quantify the prevalence, detection rate, epidemic growth rates, and clinical rates of SARS-CoV-2 in the United States.
This review was posted on: 11 August 2020