Estimating epidemiological quantities from repeated cross-sectional prevalence measurements

  1. Sam Abbott
  2. Sebastian Funk

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Abstract

Background

Repeated measurements of cross-sectional prevalence of Polymerase Chain Reaction (PCR) positivity or seropositivity provide rich insight into the dynamics of an infection. The UK Office for National Statistics (ONS) Community Infection Survey publishes such measurements for SARS-CoV-2 on a weekly basis based on testing enrolled households, contributing to situational awareness in the country. Here we present estimates of time-varying and static epidemiological quantities that were derived from the estimates published by ONS.

Methods

We used a gaussian process to model incidence of infections and then estimated observed PCR prevalence by convolving our modelled incidence estimates with a previously published PCR detection curve describing the probability of a positive test as a function of the time since infection. We refined our incidence estimates using time-varying estimates of antibody prevalence combined with a model of antibody positivity and waning that moved individuals between compartments with or without antibodies based on estimates of new infections, vaccination, probability of seroconversion and waning.

Results

We produced incidence curves of infection describing the UK epidemic from late April 2020 until early 2022. We used these estimates of incidence to estimate the time-varying growth rate of infections, and combined them with estimates of the generation interval to estimate time-varying reproduction numbers. Biological parameters describing seroconversion and waning, while based on a simple model, were broadly in line with plausible ranges from individual-level studies.

Conclusions

Beyond informing situational awareness and allowing for estimates using individual-level data, repeated cross-sectional studies make it possible to estimate epidemiological parameters from population-level models. Studies or public health surveillance methods based on similar designs offer opportunities for further improving our understanding of the dynamics of SARS-CoV-2 or other pathogens and their interaction with population-level immunity.

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    SciScore for 10.1101/2022.03.29.22273101: (What is this?)

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    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    As currently implemented, our method suffers from a number of limitations that risk biasing the results. Several of the key parameters in our model, especially the estimates of PCR positivity over time from infection, generation interval distributions, are fixed and based on estimates derived from wildtype virus in a particular cohort of healthcare workers and may well be incorrect for other circulating variants or populations. Furthermore, generation times have been shown to change over time due to behavioural changes and epidemiological dynamics, which would affect our reproduction number estimates (Champredon & Dushoff, 2015; Hart et al., 2021; Park et al., 2021). PCR detection probabilities as a function of time since infection were based on independent normal distributions, whereas in reality they are likely to be correlated over time. We modelled the growth of infections as a stationary Gaussian process, whereas in reality variation over time has changed between periods of stability and rapid change due to changes in contact behaviour in response to the epidemic. Lastly, we assumed that antibody waning was exponential, and ignored any consequences of multiple rounds of vaccination or infection apart from converting those without detectable antibodies to having detectable antibodies. Future directions of this work should help address some of these limitations, for example by including more detail on antibody levels, or by including antibody measurements that may be able ...

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2022.03.29.22273101v1 on medRxiv