The secondary transmission pattern of Covid-19 based on contact tracing in Rwanda

Bibliographic Data

ID21879503
AuthorsMuhammed Semakula (0000-0002-9076-4922, University of Kigali, corresponding author), François Niragire (0000-0003-0473-387X, University of Rwanda), Angela Umutoni (Rwanda Biomedical Center), Sabin Nsanzimana (0000-0002-5946-0015, Rwanda Biomedical Center), Vedaste Ndahindwa (0000-0002-0782-354X, University of Rwanda), Edison Rwagasore (Rwanda Biomedical Center), Thierry Nyatanyi (0000-0003-2300-0479, Rwanda Biomedical Center), Eric Remera (0000-0002-6084-8877, Rwanda Biomedical Center), Christel Faes (0000-0002-1878-9869, Hasselt University)
Year2021
Volume6
Issue6
Pagese004885
Publication date2021-06-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBMJ Global Health (JOURNAL)
Journal identifiersISSN: 2059-7908 • E-ISSN: 2059-7908
PublisherBMJ (PUBLISHER • GB)
DOI10.1136/bmjgh-2020-004885
PMID34103325
OpenAlexW3166358696
LanguageEN
Citations received1
References cited20

INTRODUCTION: COVID-19 has shown an exceptionally high spread rate across and within countries worldwide. Understanding the dynamics of such an infectious disease transmission is critical for devising strategies to control its spread. In particular, Rwanda was one of the African countries that started COVID-19 preparedness early in January 2020, and a total lockdown was imposed when the country had only 18 COVID-19 confirmed cases known. Using intensive contact tracing, several infections were identified, with the majority of them being returning travellers and their close contacts. We used the contact tracing data in Rwanda for understanding the geographic patterns of COVID-19 to inform targeted interventions. METHODS: We estimated the attack rates and identified risk factors associated to COVID-19 spread. We used Bayesian disease mapping models to assess the spatial pattern of COVID-19 and to identify areas characterised by unusually high or low relative risk. In addition, we used multiple variable conditional logistic regression to assess the impact of the risk factors. RESULTS: The results showed that COVID-19 cases in Rwanda are localised mainly in the central regions and in the southwest of Rwanda and that some clusters occurred in the northeast of Rwanda. Relationship to the index case, being male and coworkers are the important risk factors for COVID-19 transmission in Rwanda. CONCLUSION: The analysis of contact tracing data using spatial modelling allowed us to identify high-risk areas at subnational level in Rwanda. Estimating risk factors for infection with SARS-CoV-2 is vital in identifying the clusters in low spread of SARS-CoV-2 subnational level. It is imperative to understand the interactions between the index case and contacts to identify superspreaders, risk factors and high-risk places. The findings recommend that self-isolation at home in Rwanda should be reviewed to limit secondary cases from the same households and spatiotemporal analysis should be introduced in routine monitoring of COVID-19 in Rwanda for policy making decision on real time

Confidence interval · Contact tracing · Credible interval · Disease · Environmental health · Geography · Logistic regression · Pandemic · Pathology · Preparedness · Psychological intervention · Spatial epidemiology · Computer Science · COVID-19 Digital Contact Tracing · COVID-19 epidemiological studies · Demography · Medicine · SARS-CoV-2 and COVID-19 Research · Epidemiology

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