Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/40419
Title: Spatio-temporal dynamic of the COVID-19 epidemic and the impact of imported cases in Rwanda
Authors: SEMAKULA, Muhammed 
Niragire, Francois
Nsanzimana, Sabin
Remera, Eric
FAES, Christel 
Issue Date: 2023
Publisher: BMC
Source: BMC PUBLIC HEALTH, 23 (1) (Art N° 930)
Abstract: IntroductionAfrica was threatened by the coronavirus disease 2019 (COVID-19) due to the limited health care infrastructure. Rwanda has consistently used non-pharmaceutical strategies, such as lockdown, curfew, and enforcement of prevention measures to control the spread of COVID-19. Despite the mitigation measures taken, the country has faced a series of outbreaks in 2020 and 2021.In this paper, we investigate the nature of epidemic phenomena in Rwanda and the impact of imported cases on the spread of COVID-19 using endemic-epidemic spatio-temporal models. Our study provides a framework for understanding the dynamics of the epidemic in Rwanda and monitoring its phenomena to inform public health decision-makers for timely and targeted interventions.ResultsThe findings provide insights into the effects of lockdown and imported infections in Rwanda's COVID-19 outbreaks. The findings showed that imported infections are dominated by locally transmitted cases. The high incidence was predominant in urban areas and at the borders of Rwanda with its neighboring countries. The inter-district spread of COVID-19 was very limited due to mitigation measures taken in Rwanda.ConclusionThe study recommends using evidence-based decisions in the management of epidemics and integrating statistical models in the analytics component of the health information system.
Notes: Semakula, M (corresponding author), Hasselt Univ, I BioStat, Hasselt, Belgium.; Semakula, M (corresponding author), Univ Rwanda, Coll Business & Econ, Ctr Excellence Data Sci, Biostat, Kigali, Kigali, Rwanda.; Semakula, M (corresponding author), Minist Hlth, Rwanda Biomed Ctr, Kigali, Kigali, Rwanda.
semakulam@gmail.com
Keywords: COVID-19;Spatio-temporal models;Epidemiology
Document URI: http://hdl.handle.net/1942/40419
e-ISSN: 1471-2458
DOI: 10.1186/s12889-023-15888-1
ISI #: 000993874800003
Rights: The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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