Characterising social contacts under COVID-19 control measures in Africa

Abstract

Background Early in the COVID-19 pandemic, countries adopted non-pharmaceutical interventions (NPIs) such as lockdowns to limit SARS-CoV-2 transmission. Social contact studies help measure the effectiveness of NPIs and estimate parameters for modelling SARS-CoV-2 transmission. However, few contact studies have been conducted in Africa. Methods We analysed nationally representative cross-sectional survey data from 19 African Union Member States, collected by the Partnership for Evidence-based Responses to COVID-19 (PERC) via telephone interviews at two time points (August 2020 and February 2021). Adult respondents reported contacts made in the previous day by age group, demographic characteristics, and their attitudes towards COVID-19. We described mean and median contacts across these characteristics and related contacts to Google Mobility reports and the Oxford Government Response Stringency Index for each country at the two time points. Results Mean reported contacts varied across countries with the lowest reported in Ethiopia (9, SD=16, median = 4, IQR = 8) in August 2020 and the highest in Sudan (50, SD=53, median = 33, IQR = 40) in February 2021. Contacts of people aged 18–55 represented 50% of total contacts, with most contacts in household and work or study settings for both surveys. Mean contacts increased for Ethiopia, Ghana, Liberia, Nigeria, Sudan, and Uganda and decreased for Cameroon, the Democratic Republic of Congo (DRC), and Tunisia between the two time points. Men had more contacts than women and contacts were consistent across urban or rural settings (except in Cameroon and Kenya, where urban respondents had more contacts than rural ones, and in Senegal and Zambia, where the opposite was the case). There were no strong and consistent variations in the number of mean or median contacts by education level, self-reported health, perceived self-reported risk of infection, vaccine acceptance, mask ownership, and perceived risk of COVID-19 to health. Mean contacts were correlated with Google mobility (coefficient 0.57, p=0.051 and coefficient 0.28, p=0.291 in August 2020 and February 2021, respectively) and Stringency Index (coefficient −0.12, p = 0.304 and coefficient −0.33, p=0.005 in August 2020 and February 2021, respectively). Conclusions These are the first COVID-19 social contact data collected for 16 of the 19 countries surveyed. We find a high reported number of daily contacts in all countries and substantial variations in mean contacts across countries and by gender. Increased stringency and decreased mobility were associated with a reduction in the number of contacts. These data may be useful to understand transmission patterns, model infection transmission, and for pandemic planning.

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon: A data-driven modelling

“…Since the onset of the outbreak of COVID-19 in Wuhan in December 2019, there have been numerous scientific works aimed at modelling, estimating, and forecasting the number of COVID-19 cases, deaths, recoveries, contacts and attack rates in various settings( Al-qaness et al, 2020 ; Benvenuto et al, 2020 ; Ceylan, 2020 ; Chakraborty & Ghosh, 2020 ; Dobreva et al, 2022 ; Musa et al, 2022 ; Toga et al, 2021 )–– ( Al-qaness et al, 2020 ; Benvenuto et al, 2020 ; Ceylan, 2020 ; Chakraborty & Ghosh, 2020 ; Dobreva et al, 2022 ; Musa et al, 2022 ; Toga et al, 2021 ). Most of these works were based on publicly available data, generally reported by public health authorities in an aggregated form.…”

Exploring COVID-19 vaccine hesitancy and uptake in Nairobi’s urban informal settlements: an unsupervised machine learning analysis of a longitudinal prospective cohort study from 2021 to 2022

“…We also found that most respondents had received more than one dose, although about 1 in 10 had only received the first dose, suggesting additional outreach is needed to make sure everyone is fully vaccinated. As vaccine immunity wanes and new variants emerge, continued messaging, new vaccinations, and uptake of other non-pharmaceutical interventions to prevent transmission will be critical 48 49. Studies to understand how to improve governance to increase vaccination and to determine optimal levels of vaccination are important to inform policy 50–52.…”

Different vaccination strategies for preventing coronavirus disease 2019 in Kenya: A dynamic modelling study of health impact and cost-effectiveness

“…Finally, we found that prioritizing booster vaccination for people aged 19-58 years avoided the largest infection cases and was the most cost-effective. Based on sociological surveys, 19-58 years old people were found that they have more unprotected close contact and are at greater risk of COVID-19 infection compared to those aged 0-18 and 58+ years due to work etc., [12] and the booster vaccination provides additional protection compared to one-dose vaccination. Thus, our conclusion is justified.…”