Benimana Theos Dieudonne, Lee Naae, Jung Seungpil, Lee Woojoo, Hwang Seung-Sik
Department of Public Health Science, Seoul National University, Seoul, Republic of Korea.
Glob Epidemiol. 2021 Nov;3:100058. doi: 10.1016/j.gloepi.2021.100058. Epub 2021 Aug 4.
The coronavirus disease 2019 (COVID-19) has taken millions of lives and disrupted living standards at individual, societal, and worldwide levels, causing serious consequences globally. Understanding its epidemic curve and spatio-temporal dynamics is crucial for the development of effective public health plans and responses and the allocation of resources. Thus, we conducted this study to assess the epidemiological dynamics and spatio-temporal patterns of the COVID-19 pandemic in Rwanda.
Using the surveillance package in R software version 4.0.2, we implemented endemic-epidemic multivariate time series models for infectious diseases to analyze COVID-19 data reported by Rwanda Biomedical Center under the Ministry of Health from March 15, 2020 to January 15, 2021.
The COVID-19 pandemic occurred in two waves in Rwanda and showed a heterogenous spatial distribution across districts. The Rwandan government responded effectively and efficiently through the implementation of various health measures and intervention policies to drastically reduce the transmission of the disease. Analysis of the three components of the model showed that the most affected districts displayed epidemic components within the area, whereas the effect of epidemic components from spatial neighbors were experienced by the districts that surround the most affected districts. The infection followed the disease endemic trend in other districts.
The epidemiological and spatio-temporal dynamics of COVID-19 in Rwanda show that the implementation of measures and interventions contributed significantly to the decrease in COVID-19 transmission within and between districts. This accentuates the critical call for continued intra- and inter- organization and community engagement nationwide to ensure effective and efficient response to the pandemic.
2019年冠状病毒病(COVID-19)已导致数百万人死亡,并在个人、社会和全球层面扰乱了生活水平,在全球造成了严重后果。了解其流行曲线和时空动态对于制定有效的公共卫生计划和应对措施以及资源分配至关重要。因此,我们开展了这项研究,以评估卢旺达COVID-19大流行的流行病学动态和时空模式。
我们使用R软件4.0.2版本中的监测软件包,对传染病实施地方病-流行病多变量时间序列模型,以分析卢旺达生物医学中心在2020年3月15日至2021年1月15日期间向卫生部报告的COVID-19数据。
COVID-19大流行在卢旺达分两波发生,且在各地区呈现出异质的空间分布。卢旺达政府通过实施各种卫生措施和干预政策,有效且高效地做出了回应,大幅减少了疾病传播。对模型的三个组成部分进行分析表明,受影响最严重的地区在其区域内呈现出流行成分,而受影响最严重地区周边的地区则受到来自空间邻域的流行成分的影响。在其他地区,感染情况遵循疾病的地方病趋势。
卢旺达COVID-19的流行病学和时空动态表明,措施和干预的实施对减少COVID-19在各地区内部和之间的传播做出了重大贡献。这凸显了在全国范围内持续开展组织内部和组织间以及社区参与的迫切需求,以确保对大流行做出有效且高效的应对。