Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches

Abstract

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. Extending our methods to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 4.9 to 10.1 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.

Fig 1. COVID-19 (ILI-)symptomatic case count estimates compared with reported case counts at the national and state levels from March 1, 2020 to (A) April 4, 2020 and (B) May 16, 2020.

Cases are presented on a log scale. Adjusted methods take into account increased visit propensity (div-Hist, div-Vir, COVID Scaling) and excess influenza and pneumonia deaths along with a lower estimated case fatality rate (mMAP). In places where the ILI-based methods show no divergence in observed and predicted ILI visits, the estimates of COVID-19 cannot be calculated and are not shown. Note that Florida does not provide ILI data, so only mMAP could be estimated there.

Fig 2. Distribution of the state-level ratios of estimated to reported case counts from March 1, 2020 to April 4, 2020.

The right-hand plot shows the results of using all methods together: taking the min, median, and max of the state-level estimates across methods. Adjusted methods take into account increased visit propensity (div-Hist, div-Vir, COVID Scaling) and excess influenza and pneumonia deaths along with a lower estimated case fatality rate (mMAP).

Fig 3. Cumulative weekly case counts from March 1 to May 16, 2020 for the United States, New York, Washington, and Louisiana, as estimated by each method and the reported cases.

The estimate for each week indicates total cases up to the denoted date. Solid lines indicate the adjusted estimates with shading for the unadjusted estimate ranges. Adjusted methods take into account increased visit propensity (div-Hist, div-Vir, COVID Scaling) and excess influenza and pneumonia deaths along with a lower estimated case fatality rate (mMAP). Refer to S2 Fig for results over all locations.

Fig 4. The underlying influenza surveillance data for the last five seasons.

The top subplot shows the ILINet total number of patients and participating providers. The bottom subplot shows the total reported numbers of influenza tests conducted and positive influenza tests.

Fig 5. L2 errors by location for March, April, and May 2019, comparing SVD Historical Projection with a baseline historical average for ILI prediction.

Fig 6. COVID-19 is treated as an intervention, and we measure COVID-19 impact on observed CDC ILI, using historical projected ILI, virology predicted ILI, and historical projection predicted ILI as counterfactuals.

The difference between the higher observed CDC ILI and the lower predicted ILI is the measured impact of COVID-19. The impact directly maps to an estimate of COVID-19 ILI-symptomatic case counts. Virology predicted ILI is omitted when virology data is not available. We note that this approach is meaningful only at the beginning of the outbreak (March 2020), while ILI surveillance systems are still fully operational and before they are impacted by COVID-19. The disappearance of the divergence does not mean that the outbreak is over, but rather that the ILI signal is no longer reliable. In this figure, as a counterfactual we also include Incidence Decay and Exponential Adjustment (IDEA), a model-based method we explored with details in S1 Text.

Fig 7

(A) Model estimates of the cumulative number of infections using the GLEAM model by May 16, 2020 for each state. (B) Correlation between the number of reported cases of COVID-19 for each state and the model estimates of the total number of infections by May 16, 2020.