/superspreading_testing

Primary LanguageRMIT LicenseMIT

Superspreading testing

This repository contains code and data for the analyses in the preprint 'Disentangling the drivers of heterogeneity in SARS-CoV-2 transmission from data on viral load and daily contact rates'. The aim of this work was to combine data from contact surveys and viral load studies to estimate the secondary infection distribution of SARS-CoV-2 over the course of the pandemic in the UK in 2020 and the effectiveness of targeted testing strategies for reducing superspreading events.

Installation

Clone/download this project onto your machine using the green button at the top right of this page.

Data

We used contact data from the BBC Pandemic [1] and CoMix [2] contact surveys and viral load trajectory parameter estimates from the literature [3] to simulate viral load trajectories. We converted viral load to infectiousness using laboratory data on the probability of culturing virus at different viral loads [4]. All data required for running the analyses is available in the data folder or the linked repositories.

Running the code

The main analysis can be run by setting the working directory to where the code was downloaded and running:

source("scripts/main.R")

The results can then be visualised by running:

source("scripts/results.R")

which will generate the plots showing the mean, R, and overdispersion, k, of the secondary infection distribution over time; the overdispersion over time with and without hetereogeneity in contacts and/or viral load; the impact of LFT testing for regular and pre-event testing; and the sensitivity analysis for R and k when imputing a heavier tail in the BBC Pandemic contact distribution (Fig. S3).

The other scripts in the scripts folder can be used to generate the other plots in the paper:

  • contact_plots.R produces the figure of the variation in contact rates over time in the BBC Pandemic and CoMix surveys, and the plots of the household and non-household contact distributions for the different time periods (Fig. S1)
  • curve_plot.R produces the figure showing how viral load is converted into infectivity
  • duration.R generates the contact duration distribution plot for household and non-household contacts (Fig. S2)

Output

The simulations output and results plots are saved in the results folder.

Built With

Authors

Citation

Quilty B.J., Chapman L.A.C., Munday J.D., Wong K.L.M., Gimma A., Pickering S., Neil S.J.D., Galao R-P., Edmunds W.J., Jarvis C.I., Kucharski A.J., CMMID COVID-19 Working Group. Disentangling the drivers of heterogeneity in SARS-CoV-2 transmission from data on viral load and daily contact rates. medRxiv 2024.08.15.24311977; doi: https://doi.org/10.1101/2024.08.15.24311977

References

  1. Klepac, P. et al. Contacts in Context: Large-Scale Setting-Specific Social Mixing Matrices from the BBC Pandemic Project. medRxiv (2020). http://doi.org/10.1101/2020.02.16.20023754

  2. Gimma, A. et al. Changes in social contacts in England during the COVID-19 pandemic between March 2020 and March 2021 as measured by the CoMix survey: A repeated cross-sectional study. PLoS Med. 19, e1003907 (2022). https://doi.org/10.1371/journal.pmed.1003907

  3. Kissler, S. M. et al. Densely sampled viral trajectories suggest longer duration of acute infection with B.1.1.7 variant relative to non-B.1.1.7 SARS-CoV-2. medRxiv (2021). https://doi.org/10.1101/2021.02.16.21251535

  4. Pickering, S. et al. Comparative performance of SARS-CoV-2 lateral flow antigen tests and association with detection of infectious virus in clinical specimens: a single-centre laboratory evaluation study. Lancet Microbe 2, e461–e471 (2021). https://doi.org/10.1016/S2666-5247(21)00143-9