The impact of COVID-19 control measures on social contacts and transmission in Kenyan informal settlements
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Abstract
Background
Many low- and middle-income countries have implemented control measures against coronavirus disease 2019 (COVID-19). However, it is not clear to what extent these measures explain the low numbers of recorded COVID-19 cases and deaths in Africa. One of the main aims of control measures is to reduce respiratory pathogen transmission through direct contact with others. In this study, we collect contact data from residents of informal settlements around Nairobi, Kenya, to assess if control measures have changed contact patterns, and estimate the impact of changes on the basic reproduction number ( R 0 ).
Methods
We conducted a social contact survey with 213 residents of five informal settlements around Nairobi in early May 2020, 4 weeks after the Kenyan government introduced enhanced physical distancing measures and a curfew between 7 pm and 5 am. Respondents were asked to report all direct physical and non-physical contacts made the previous day, alongside a questionnaire asking about the social and economic impact of COVID-19 and control measures. We examined contact patterns by demographic factors, including socioeconomic status. We described the impact of COVID-19 and control measures on income and food security. We compared contact patterns during control measures to patterns from non-pandemic periods to estimate the change in R 0 .
Results
We estimate that control measures reduced physical contacts by 62% and non-physical contacts by either 63% or 67%, depending on the pre-COVID-19 comparison matrix used. Masks were worn by at least one person in 92% of contacts. Respondents in the poorest socioeconomic quintile reported 1.5 times more contacts than those in the richest. Eighty-six percent of respondents reported a total or partial loss of income due to COVID-19, and 74% reported eating less or skipping meals due to having too little money for food.
Conclusion
COVID-19 control measures have had a large impact on direct contacts and therefore transmission, but have also caused considerable economic and food insecurity. Reductions in R 0 are consistent with the comparatively low epidemic growth in Kenya and other sub-Saharan African countries that implemented similar, early control measures. However, negative and inequitable impacts on economic and food security may mean control measures are not sustainable in the longer term.
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SciScore for 10.1101/2020.06.06.20122689: (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
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study has a number of limitations. In the absence of baseline contact data (i.e. before control measures were put in place), we use empirical matrices from a different area of Kenya and synthetic matrices based on adjusting contact surveys from higher income countries to household and other characteristics in Kenya. Although we adjust these datasets by the age structure of the Kenyan population, other factors such as household …
SciScore for 10.1101/2020.06.06.20122689: (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
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study has a number of limitations. In the absence of baseline contact data (i.e. before control measures were put in place), we use empirical matrices from a different area of Kenya and synthetic matrices based on adjusting contact surveys from higher income countries to household and other characteristics in Kenya. Although we adjust these datasets by the age structure of the Kenyan population, other factors such as household size were not reported and may influence number of contacts and therefore pathogen transmission. The pre-COVID-19 setting of Kiti et al. is very different to this sample, not least as estimates place population density around 24 times greater in informal settlements (Kibera: 55000 persons/km2) compared to urban Kilifi (2325 persons/km2)[28]. Because we would expect contacts to be greater in more densely populated areas, the true reduction in contacts may be more than we estimate here. Although we have a range of background data on respondents from using existing sampling frames, households in the AGI-K and NITISU cohorts were initially selected as having an adolescent residing there in 2015 and 2018, respectively. Other social contact surveys have used a prospective study design, asking respondents to record contacts in a daily diary[29]. Because we asked respondents to recall contacts from the previous day, these data may be subject to recall bias, although it is not clear in which direction this may act. Furthermore, we impute adjusted child cont...
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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