Association between mobility, non-pharmaceutical interventions, and COVID-19 transmission in Ghana: A modelling study using mobile phone data

  1. Hamish Gibbs
  2. Yang Liu
  3. Sam Abbott
  4. Isaac Baffoe-Nyarko
  5. Dennis O. Laryea
  6. Ernest Akyereko
  7. Patrick Kuma-Aboagye
  8. Ivy Asantewaa Asante
  9. Oriol Mitjà
  10. LSHTM CMMID COVID-19 Working Group
  11. William Ampofo
  12. Franklin Asiedu-Bekoe
  13. Michael Marks
  14. Rosalind M. Eggo

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Abstract

Governments around the world have implemented non-pharmaceutical interventions to limit the transmission of COVID-19. Here we assess if increasing NPI stringency was associated with a reduction in COVID-19 cases in Ghana. While lockdowns and physical distancing have proven effective for reducing COVID-19 transmission, there is still limited understanding of how NPI measures are reflected in indicators of human mobility. Further, there is a lack of understanding about how findings from high-income settings correspond to low and middle-income contexts. In this study, we assess the relationship between indicators of human mobility, NPIs, and estimates of R t , a real-time measure of the intensity of COVID-19 transmission. We construct a multilevel generalised linear mixed model, combining local disease surveillance data from subnational districts of Ghana with the timing of NPIs and indicators of human mobility from Google and Vodafone Ghana. We observe a relationship between reductions in human mobility and decreases in R t during the early stages of the COVID-19 epidemic in Ghana. We find that the strength of this relationship varies through time, decreasing after the most stringent period of interventions in the early epidemic. Our findings demonstrate how the association of NPI and mobility indicators with COVID-19 transmission may vary through time. Further, we demonstrate the utility of combining local disease surveillance data with large scale human mobility data to augment existing surveillance capacity to monitor the impact of NPI policies.

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  1. Version published to 10.1371/journal.pgph.0000502
  2. Version published to 10.1101/2021.11.01.21265660v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.11.01.21265660: (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
    Rt estimates were calculated at the district level using the EpiNow2 R package (1.3.2) using MCMC, as implemented in Stan27,28, based on weekly reported cases29.
    EpiNow2
    suggested: None
    Unlike in Abbott et. al.30 we modelled Rt explicitly with the gaussian process and not as a first order difference.
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    Google Mobility Indicator: We used mobility data from Google as a second measure of human movement16.
    Google
    suggested: (Google, RRID:SCR_017097)

    Results from OddPub: Thank you for sharing your code and data.

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  4. Version published to 10.1101/2021.11.01.21265660v1 on medRxiv