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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.

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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 detailed COVID-19 surveillance and human mobility data from districts in Ghana. We further demonstrate how this association was strongest in the early COVID-19 outbreak in Ghana, decreasing after the relaxation of national restrictions. What do the new findings imply?: The change in association between human mobility, NPI stringency, and R _t may reflect a "decoupling" of NPI stringency and human mobility from disease transmission in Ghana as the COVID-19 epidemic progressed. This has implications for public responses to the early stages of epidemic outbreaks and our understanding of the utility of mobility data for predicting the spread of COVID-19.

SUBMITTER: Gibbs H

PROVIDER: S-EPMC8575146 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRestricting human mobility is an effective strategy used to control disease spread. However, whether mobility restriction is a proportional response to control the ongoing COVID-19 pandemic is unclear. We aimed to develop a model that can quantify the potential effects of various intracity mobility restrictions on the spread of COVID-19.MethodsIn this modelling study, we used anonymous and aggregated mobile phone sightings data to build a susceptible-exposed-infectious-recovered transmission model for COVID-19 based on the city of Shenzhen, China. We simulated how disease spread changed when we varied the type and magnitude of mobility restrictions in different transmission scenarios, with variables such as the basic reproductive number (R 0), length of infectious period, and the number of initial cases.Findings331 COVID-19 cases distributed across the ten regions of Shenzhen were reported on Feb 7, 2020. In our basic scenario (R 0 of 2·68), mobility reduction of 20-60% within the city had a notable effect on controlling COVID-19 spread: a flattening of the peak number of cases by 33% (95% UI 21-42) and delay to the peak number by 2 weeks with a 20% restriction, 66% (48-75) reduction and 4 week delay with a 40% restriction, and 91% (79-95) reduction and 14 week delay with a 60% restriction. The effects of mobility restriction were increased when combined with reductions of 25% or 50% in transmissibility of the virus. In specific analyses of mobility restrictions for individuals with symptomatic infections and for high-risk regions, these measures also had substantial effects on reducing the spread of COVID-19. For example, the peak of the epidemic was delayed by 2 weeks if the proportion of individuals with symptomatic infections who could move freely was maintained at 20%, and by 4 weeks if two high-risk regions were locked down. The simulation results were also affected by various transmission parameters.InterpretationOur model shows the effects of various types and magnitudes of mobility restrictions on controlling COVID-19 outbreaks at the city level in Shenzhen, China. The model could help policy makers to establish the optimal combinations of mobility restrictions during the COVID-19 pandemic, especially to assess the potential positive effects of mobility restriction on public health in view of the potential negative economic and societal effects.FundingGuangdong Medical Science Fund, and National Natural Science Foundation of China.

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Estimating the relationship between mobility, non-pharmaceutical interventions, and COVID-19 transmission in Ghana.

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