| Title:
|
Estimating the relationship between mobility non-pharmaceutical interventions and COVID-19 transmission in Ghana |
| Abstract:
|
Governments around the world have implemented non-pharmaceutical interventions (NPIs) e.g. physical distancing and travel restrictions to limit the transmission of COVID-19. While lockdowns and physical distancing have proven effective for reducing COVID-19 transmission there is still limited understanding of the degree to which these interventions impact disease transmission and how they are reflected in measures of human behaviour. Further there is a lack of understanding about how new sources of data can be used to monitor NPIs where these data have the potential to augment existing disease surveillance and modelling efforts. 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 in subnational districts of Ghana using a multilevel generalised linear mixed model. We demonstrate a relationship between reductions in human mobility and decreases in R(t) during the early stages of the COVID-19 epidemic in Ghana and show how reductions in human mobility relate to increasing stringency of NPIs. We demonstrate the utility of combining local disease surveillance data with large scale human mobility data to augment existing surveillance capacity to estimate and monitor the effect of NPI policies. |
| Published:
|
2021-11-02 |
| Journal:
|
medRxiv |
| DOI:
|
10.1101/2021.11.01.21265660 |
| DOI_URL:
|
http://doi.org/10.1101/2021.11.01.21265660 |
| Author Name:
|
Gibbs Hamish |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/gibbs_hamish |
| Author Name:
|
Liu Yang |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/liu_yang |
| Author Name:
|
Abbott Sam |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/abbott_sam |
| Author Name:
|
Baffoe Nyarko Isaac |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/baffoe_nyarko_isaac |
| Author Name:
|
Laryea Dennis O |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/laryea_dennis_o |
| Author Name:
|
Akyereko Ernest |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/akyereko_ernest |
| Author Name:
|
Kuma Aboagye Patrick |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/kuma_aboagye_patrick |
| Author Name:
|
Asante Ivy |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/asante_ivy |
| Author Name:
|
Mitj Oriol |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/mitj_oriol |
| Author Name:
|
Ampofo William |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/ampofo_william |
| Author Name:
|
Asiedu Bekoe Franklin |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/asiedu_bekoe_franklin |
| Author Name:
|
Marks Michael |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/marks_michael |
| Author Name:
|
Eggo Rosalind M |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/eggo_rosalind_m |
| sha:
|
4db29a42ae270e1254c74f4073aa36602b499ecb |
| license:
|
cc-by |
| license_url:
|
https://creativecommons.org/licenses/by/4.0/ |
| source_x:
|
MedRxiv; Medline; PMC; WHO |
| source_x_url:
|
https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/https://www.who.int/ |
| pubmed_id:
|
34751275 |
| pubmed_id_url:
|
https://www.ncbi.nlm.nih.gov/pubmed/34751275 |
| pmcid:
|
PMC8575146 |
| pmcid_url:
|
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8575146 |
| url:
|
https://www.ncbi.nlm.nih.gov/pubmed/34751275/
http://medrxiv.org/cgi/content/short/2021.11.01.21265660v1?rss=1
https://doi.org/10.1101/2021.11.01.21265660 |
| has_full_text:
|
TRUE |
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https://doi.org/10.1101/2021.11.01.21265660 doi
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| Extracted Text Content in Record:
|
First 5000 Characters:Governments around the world have implemented non-pharmaceutical interventions (NPIs), e.g. physical distancing and travel restrictions, to limit the transmission of COVID-19. While lockdowns and physical distancing have proven effective for reducing transmission, there is still limited understanding of the degree to which these interventions impact disease transmission, and how they are reflected in measures of human behaviour.
Further, there is a lack of understanding about how new sources of data can be used to monitor NPIs, where these data have the potential to augment existing disease surveillance and modelling efforts. 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 in subnational districts of Ghana using a multilevel generalised linear mixed model. We demonstrate a relationship between reductions in human mobility and decreases in R t during the early stages of the COVID-19 epidemic in Ghana, and show how reductions in human mobility relate to increasing stringency of NPIs. We demonstrate the utility of combining local disease surveillance data with large scale human mobility data to augment existing surveillance capacity to estimate and monitor the effect of NPI policies.
What is already known? NPI measures including physical distancing, reduction of travel, and use of personal protective equipment have been demonstrated to reduce COVID-19 transmission. Much of the existing research focuses on comparisons of NPI stringency with COVID-19 transmission among different countries, or on high-income countries.
What are the new findings?
We show how human mobility and NPI stringency were associated with changes in R t using
Nations around the world introduced a range of non-pharmaceutical interventions (NPIs) to limit the spread of COVID-19 in the early phases of the epidemic 1 One approach used by researchers and policymakers to measure the impact of NPIs during the COVID-19 pandemic has been to observe changes in measurements of human behaviour under individual interventions or under a combination of interventions [8] [9] [10] [11] [12] .
Perhaps the most common way to quantify varying patterns of human behaviour is the use of human mobility datasets, which measure the locations of individuals using GPS or Call Detail Records (CDRs) 13, 14 . These mobility datasets have been made available by a variety of network service and mobile application providers [15] [16] [17] . Mobility data has been used widely during the COVID-19 pandemic to predict the introduction of COVID-19 cases, and to monitor and estimate adherence to NPIs including travel restrictions 8, 9, [18] [19] [20] , but questions remain about how patterns of mobility and NPI stringency relate to transmission in LMIC settings.
Previous research has been conducted in Africa on the implications of mobility patterns for disease transmission 21, 22 and during the COVID-19 epidemic, analysis of movement patterns in Ghana has been conducted to inform policy makers about the volume of reductions coinciding with lockdown interventions in Accra and Kumasi 23 . These indicators may be used as a proxy for social contact 13 and therefore, for potential COVID-19 transmission, although the "link" between movement and disease transmission may decrease due to NPI 4 . CC-BY 4.0 International license It is made available under a perpetuity.
is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint measures (for example, greater adherence to social distancing or personal protective equipment guidelines) 24 .There remain questions about how mobility indicators can be used to estimate COVID-19 transmission and how these indicators reflect behavioural responses to NPI measures, particularly in an LMIC context. In this paper, we combine human mobility and NPI data to estimate their relationship to the progression of the COVID-19 epidemic in Ghana.
The first cases of COVID-19 in Ghana were reported on the 12th March 2020 25 . These cases were detected in Accra, the capital city of Ghana and were imported via international travel 25 .
Following the announcement of the first COVID-19 cases, the Ghanaian government announced the suspension of international travel and the closure of land borders to reduce the risk of further introduction 26 . Domestic case numbers grew in March and April 2020, leading to the closure of universities and high schools and the announcement of a partial lockdown in the Ashanti and Greater Accra regions, the two most populous regions of Ghana 26 . This lockdown introduced a stay at home order except for essential travel including shopping, healthcare, and use of public toilets. Almost all COVID-19 NPI restrictions were lifted by July, although restrictions on international travel and mandated use of facemasks remained in place until September 2020.
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| Keywords Extracted from PMC Text:
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Figure 4a
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| Extracted PMC Text Content in Record:
|
First 5000 Characters:Nations around the world introduced a range of non-pharmaceutical interventions (NPIs) to limit the spread of COVID-19 in the early phases of the epidemic1. These NPIs have been diverse, and have included the use of personal protective measures, environmental measures, physical distancing, restricting movement, and limiting the gathering of people. NPIs have been implemented at different times in relation to the progression of local and national disease outbreaks, with some put in place before transmission was established, and others reactive to rises in cases. NPI measures have also overlapped one another in the timing of their application1,2. Previous research has attempted to quantify the relative effectiveness on COVID-19 transmission of different NPIs3–6, but modelling the impact of different intervention strategies includes uncertainty about how different strategies are implemented in practice. Additionally, statistical approaches for estimating the impact of individual interventions can be confounded by the overlapping nature of NPI policies and the different mechanisms that interventions use to reduce disease transmission. There remain significant open questions about methods for reliably isolating and quantifying the individual effect of each intervention7.
One approach used by researchers and policymakers to measure the impact of NPIs during the COVID-19 pandemic has been to observe changes in measurements of human behaviour under individual interventions or under a combination of interventions 8–12. Perhaps the most common way to quantify varying patterns of human behaviour is the use of human mobility datasets, which measure the locations of individuals using GPS or Call Detail Records (CDRs)13,14. These mobility datasets have been made available by a variety of network service and mobile application providers15–17. Mobility data has been used widely during the COVID-19 pandemic to predict the introduction of COVID-19 cases, and to monitor and estimate adherence to NPIs including travel restrictions8,9,18–20, but questions remain about how patterns of mobility and NPI stringency relate to transmission in LMIC settings.
Previous research has been conducted in Africa on the implications of mobility patterns for disease transmission21,22 and during the COVID-19 epidemic, analysis of movement patterns in Ghana has been conducted to inform policy makers about the volume of reductions coinciding with lockdown interventions in Accra and Kumasi 23. These indicators may be used as a proxy for social contact13 and therefore, for potential COVID-19 transmission, although the "link" between movement and disease transmission may decrease due to NPI measures (for example, greater adherence to social distancing or personal protective equipment guidelines)24.There remain questions about how mobility indicators can be used to estimate COVID-19 transmission and how these indicators reflect behavioural responses to NPI measures, particularly in an LMIC context. In this paper, we combine human mobility and NPI data to estimate their relationship to the progression of the COVID-19 epidemic in Ghana.
The first cases of COVID-19 in Ghana were reported on the 12th March 202025. These cases were detected in Accra, the capital city of Ghana and were imported via international travel25. Following the announcement of the first COVID-19 cases, the Ghanaian government announced the suspension of international travel and the closure of land borders to reduce the risk of further introduction26. Domestic case numbers grew in March and April 2020, leading to the closure of universities and high schools and the announcement of a partial lockdown in the Ashanti and Greater Accra regions, the two most populous regions of Ghana26. This lockdown introduced a stay at home order except for essential travel including shopping, healthcare, and use of public toilets. Almost all COVID-19 NPI restrictions were lifted by July, although restrictions on international travel and mandated use of facemasks remained in place until September 2020.
Here, we used surveillance data in a sample of 27 districts collected by the Ghana Health Service on PCR confirmed COVID-19 patients at the district level (administrative level 2, 261 total districts) in 11 of the 16 regions of Ghana between March and December 2020 to produce individual estimates of Rt, the real-time reproduction number, for individual districts. Rt is a time-varying parameter describing the average number of infections derived from a single infection and indicates whether an epidemic is growing (Rt > 1) or decreasing (Rt < 1). We combined estimates of Rt with subnational human movement data from Vodafone Ghana and Google. These data measure the volume of movement activity (a proxy for social contact), in each district. We then modelled the relationship between human mobility indicators and Rt using a multilevel generalised linear mixed model to assess whether changes in mobility and NPI |
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