Center for Excellence in Data Science, University of Rwanda - Kigali Campus, Kigali, Rwanda
Centre for Statistics, Hasselt Biostatistics and statistical Bioinformatics Center, Diepenbeek, Limburg, Belgium.
BMJ Glob Health. 2021 Jun;6(6). doi: 10.1136/bmjgh-2020-004885.
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.
简介：COVID-19 在全球范围内的传播速度异常之快，无论是在国与国之间，还是在国内。了解这种传染病传播的动态对于制定控制其传播的策略至关重要。特别是，卢旺达是非洲国家中最早于 2020 年 1 月初为 COVID-19 做好准备的国家之一，当该国仅发现 18 例已知 COVID-19 确诊病例时，就实施了全面封锁。通过密集的接触者追踪，发现了一些感染病例，其中大多数是返回的旅行者及其密切接触者。我们利用卢旺达的接触者追踪数据来了解 COVID-19 的地理模式，以便为有针对性的干预措施提供信息。
方法：我们估计了发病率，并确定了与 COVID-19 传播相关的危险因素。我们使用贝叶斯疾病制图模型来评估 COVID-19 的空间模式，并确定具有异常高或低相对风险的区域。此外，我们还使用多变量条件逻辑回归来评估危险因素的影响。
结果：结果表明，卢旺达的 COVID-19 病例主要集中在中部地区和卢旺达西南部，一些集群出现在卢旺达东北部。与首例病例的关系、男性和同事是卢旺达 COVID-19 传播的重要危险因素。
结论：使用空间建模分析接触者追踪数据使我们能够在卢旺达的国家以下级别确定高风险区域。估计感染 SARS-CoV-2 的危险因素对于确定 SARS-CoV-2 国家以下水平低传播的集群至关重要。了解首例病例和接触者之间的相互作用以识别超级传播者、危险因素和高风险地点至关重要。研究结果表明，卢旺达应重新考虑在家进行自我隔离，以限制同一家庭的继发病例，并在卢旺达引入时空分析，以便实时为 COVID-19 常规监测提供决策支持。
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