ReportCORONAVIRUS

Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States

See allHide authors and affiliations

Science Translational Medicine  29 Jul 2020:
Vol. 12, Issue 554, eabc1126
DOI: 10.1126/scitranslmed.abc1126
  • Fig. 1 An early surge of ILI visits across the United States.

    The proportion of patients presenting with ILI that could not be explained by influenza or typical seasonal variation (that is, excess ILI) is shown for four states (blue line and ribbons represent the posterior median as well as 95 and 50% credible sets; results from all analyzed states are shown in fig. S1). ILI that could not be attributed to influenza was calculated on the basis of influenza laboratory surveillance data (2019–2020 flu season is shown in red, and prior seasons are shown in black). A time-series model was used to infer seasonal variation of non-influenza ILI. Excess ILI was then calculated as the difference between non-influenza ILI from 2019 to 2020 and the seasonal baseline of non-influenza ILI. Excess ILI after 7 March is highlighted in darker blue as these data correlated strongly with observed COVID-19 case counts (fig. S2).

  • Fig. 2 The ILI surge imposes a dependence between growth rate and clinical rate in epidemiological models.

    (A and B) SARS-CoV-2 prevalence estimates based on the ILI surge are consistent with an epidemiological model parameterized based on a 15 January epidemic start date and a doubling time equal to that observed for new deaths within the United States (A) or Italy (B). Epidemiological models were either stochastic (simulated via tau leaping) or deterministic (solved by numerical integration). In addition to our raw estimates of the ILI surge size (unadjusted), we provide adjusted prevalence estimates accounting for subclinical cases by assuming an 18% asymptomatic rate and a 40% rate of health care seeking of symptomatic ILI patients (adjusted). Epidemic trajectories were simulated using an SEIR model (black lines). The increasing gap between ILI prevalence estimates and SEIR trajectories (orange) suggests the presence of additional factors such as social distancing, changes in care-seeking behavior, or heterogeneity in susceptibility or transmission. (C) More generally, the size of the clinical population estimated from ILI data imposes a dependence between epidemic doubling time, the clinical rate, and the lag between onset of infectiousness and ILI reporting. Combinations of these three variables that are consistent (black) or inconsistent (gray) are shown, as well as a smoothed estimate of clinical rate as a function of doubling time.

Supplementary Materials

  • stm.sciencemag.org/cgi/content/full/12/554/eabc1126/DC1

    Fig. S1. Excess ILI for each U.S. state.

    Fig. S2. Excess ILI correlates strongly with patterns of newly confirmed COVID-19 cases.

    Fig. S3. Surveillance data from New York City emergency departments.

    Fig. S4. Prevalence of SARS-CoV-2 infections between 8 and 28 March 2020.

    Fig. S5. Syndromic case detection rates by state.

    Fig. S6. Estimating the infection fatality rate (IFR) of COVID-19 based on the unadjusted ILI surge.

    Fig. S7. Investigating model sensitivity when ILI is modeled without first removing signal from influenza.

    Fig. S8. Investigating model sensitivity when seasonal trends in non-influenza ILI are identified using an alternative statistical model.

  • The PDF file includes:

    • Figure S1. Excess ILI for each US state.
    • Figure S2. Excess ILI correlates strongly with patterns of newly confirmed COVID-19 cases.
    • Figure S3. Surveillance data from New York City emergency departments.
    • Figure S4: Prevalence of SARS-CoV-2 infections between March 8 and March 28, 2020.
    • Figure S5. Syndromic case detection rates by state.
    • Figure S6: Estimating the infection fatality rate (IFR) of COVID-19 based on the unadjusted ILI surge.
    • Figure S7: Investigating model sensitivity when ILI is modeled without first removing signal from influenza.
    • Figure S8. Investigating model sensitivity when seasonal trends in non-influenza ILI are identified using an alternative statistical model.

    [Download PDF]

Stay Connected to Science Translational Medicine

Navigate This Article