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Comparability of different methods for estimating influenza infection rates over a single epidemic wave.

ABSTRACT: Estimation of influenza infection rates is important for determination of the extent of epidemic spread and for calculation of severity indicators. The authors compared estimated infection rates from paired and cross-sectional serologic surveys, rates of influenza like illness (ILI) obtained from sentinel general practitioners (GPs), and ILI samples that tested positive for influenza using data from similar periods collected during the 2009 H1N1 epidemic in Singapore. The authors performed sensitivity analyses to assess the robustness of estimates to input parameter uncertainties, and they determined sample sizes required for differing levels of precision. Estimates from paired seroconversion were 17% (95% Bayesian credible interval (BCI): 14, 20), higher than those from cross-sectional serology (12%, 95% BCI: 9, 17). Adjusted ILI estimates were 15% (95% BCI: 10, 25), and estimates computed from ILI and laboratory data were 12% (95% BCI: 8, 18). Serologic estimates were least sensitive to the risk of input parameter misspecification. ILI-based estimates were more sensitive to parameter misspecification, though this was lessened by incorporation of laboratory data. Obtaining a 5-percentage-point spread for the 95% confidence interval in infection rates would require more than 1,000 participants per serologic study, a sentinel network of 90 GPs, or 50 GPs when combined with laboratory samples. The various types of estimates will provide comparable findings if accurate input parameters can be obtained.

SUBMITTER: Lee VJ

PROVIDER: S-EPMC3148265 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature

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Comparability of different methods for estimating influenza infection rates over a single epidemic wave.

Lee Vernon J VJ Chen Mark I MI Yap Jonathan J Ong Jocelyn J Lim Wei-Yen WY Lin Raymond T P RT Barr Ian I Ong Jimmy B S JB Mak Tze Minn TM Goh Lee Gan LG Leo Yee Sin YS Kelly Paul M PM Cook Alex R AR

American journal of epidemiology 20110630 4

Estimation of influenza infection rates is important for determination of the extent of epidemic spread and for calculation of severity indicators. The authors compared estimated infection rates from paired and cross-sectional serologic surveys, rates of influenza like illness (ILI) obtained from sentinel general practitioners (GPs), and ILI samples that tested positive for influenza using data from similar periods collected during the 2009 H1N1 epidemic in Singapore. The authors performed sensi ...[more]

PMID: 21719743

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Project description:ObjectiveThere has been much discussion and debate around the underreporting of COVID-19 infections and deaths in India. In this short report we first estimate the underreporting factor for infections from publicly available data released by the Indian Council of Medical Research on reported number of cases and national seroprevalence surveys. We then use a compartmental epidemiologic model to estimate the undetected number of infections and deaths, yielding estimates of the corresponding underreporting factors. We compare the serosurvey based ad hoc estimate of the infection fatality rate (IFR) with the model-based estimate. Since the first and second waves in India are intrinsically different in nature, we carry out this exercise in two periods: the first wave (April 1, 2020-January 31, 2021) and part of the second wave (February 1, 2021-May 15, 2021). The latest national seroprevalence estimate is from January 2021, and thus only relevant to our wave 1 calculations.ResultsBoth wave 1 and wave 2 estimates qualitatively show that there is a large degree of "covert infections" in India, with model-based estimated underreporting factor for infections as 11.11 (95% credible interval (CrI) 10.71-11.47) and for deaths as 3.56 (95% CrI 3.48-3.64) for wave 1. For wave 2, underreporting factor for infections escalate to 26.77 (95% CrI 24.26-28.81) and to 5.77 (95% CrI 5.34-6.15) for deaths. If we rely on only reported deaths, the IFR estimate is 0.13% for wave 1 and 0.03% for part of wave 2. Taking underreporting of deaths into account, the IFR estimate is 0.46% for wave 1 and 0.18% for wave 2 (till May 15). Combining waves 1 and 2, as of May 15, while India reported a total of nearly 25 million cases and 270 thousand deaths, the estimated number of infections and deaths stand at 491 million (36% of the population) and 1.21 million respectively, yielding an estimated (combined) infection fatality rate of 0.25%. There is considerable variation in these estimates across Indian states. Up to date seroprevalence studies and mortality data are needed to validate these model-based estimates.

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Comparability of different methods for estimating influenza infection rates over a single epidemic wave.

Publications

Comparability of different methods for estimating influenza infection rates over a single epidemic wave.

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