The importance of saturating density dependence for population-level predictions of SARS-CoV-2 resurgence compared with density-independent or linearly density-dependent models, England, 23 March to 31 July 2020

  1. Emily S Nightingale
  2. Oliver J Brady
  3. CMMID Covid-19 working group
  4. Laith Yakob

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Abstract

Population-level mathematical models of outbreaks typically assume that disease transmission is not impacted by population density (‘frequency-dependent’) or that it increases linearly with density (‘density-dependent’).

Aim

We sought evidence for the role of population density in SARS-CoV-2 transmission.

Methods

Using COVID-19-associated mortality data from England, we fitted multiple functional forms linking density with transmission. We projected forwards beyond lockdown to ascertain the consequences of different functional forms on infection resurgence.

Results

COVID-19-associated mortality data from England show evidence of increasing with population density until a saturating level, after adjusting for local age distribution, deprivation, proportion of ethnic minority population and proportion of key workers among the working population. Projections from a mathematical model that accounts for this observation deviate markedly from the current status quo for SARS-CoV-2 models which either assume linearity between density and transmission (30% of models) or no relationship at all (70%). Respectively, these classical model structures over- and underestimate the delay in infection resurgence following the release of lockdown.

Conclusion

Identifying saturation points for given populations and including transmission terms that account for this feature will improve model accuracy and utility for the current and future pandemics.

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  1. Version published to 10.2807/1560-7917.es.2021.26.49.2001809
  2. Version published to 10.1101/2020.08.28.20183921v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.08.28.20183921: (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
    Full model specification and sources for its parameterization can be found in the Supplementary Materials, and the Python (v3.8) code is freely available from https://github.com/lwyakob/COVIDsaturates.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Supplementary Materials and Methods: Google Scholar search by year ‘2020’ on June 19th 2020: coronavirus, OR covid19, OR covid-19 “‘(mathematical OR simulation OR transmission) model’“ 613 results were returned and ordered by relevance.
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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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  4. Version published to 10.1101/2020.08.28.20183921v1 on medRxiv