Estimating force of infection from serologic surveys with imperfect tests

  1. Neal Alexander
  2. Mabel Carabali
  3. Jacqueline K. Lim

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Abstract

The force of infection, or the rate at which susceptible individuals become infected, is an important public health measure for assessing the extent of outbreaks and the impact of control programs.

Methods and findings

We present Bayesian methods for estimating force of infection using serological surveys of infections which produce a lasting immune response, accounting for imperfections of the test, and uncertainty in such imperfections. In this estimation, the sensitivity and specificity can either be fixed, or belief distributions of their values can be elicited to allow for uncertainty. We analyse data from two published serological studies of dengue, one in Colombo, Sri Lanka, with a single survey and one in Medellin, Colombia, with repeated surveys in the same individuals. For the Colombo study, we illustrate how the inferred force of infection increases as the sensitivity decreases, and the reverse for specificity. When 100% sensitivity and specificity are assumed, the results are very similar to those from a standard analysis with binomial regression. For the Medellin study, the elicited distribution for sensitivity had a lower mean and higher variance than the one for specificity. Consequently, taking uncertainty in sensitivity into account resulted in a wide credible interval for the force of infection.

Conclusions

These methods can make more realistic estimates of force of infection, and help inform the choice of serological tests for future serosurveys.

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  1. Version published to 10.1371/journal.pone.0247255
  2. ScreenIT

    SciScore for 10.1101/2020.06.09.20125724: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We adapted this to a spreadsheet in Microsoft Excel (Appendix 1).
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Another limitation is the assumption that each individual has long-lasting immunity, so that apparent seroreversions are due to test errors rather than waning immunity. Depending on the infection in question, the validity of this assumption may depend on factors such as age and immunocompetence. In conclusion, the methods presented here can make more realistic estimates of force of infection, and can help inform the choice of serological tests for future serosurveys.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.06.09.20125724v1 on medRxiv