COVID-19 transmission dynamics underlying epidemic waves in Kenya.
COVID-19 transmission dynamics underlying epidemic waves in Kenya.
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DOI：
10.1126/science.abk0414
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发表时间：
2021-11-19
期刊：
影响因子：
--
通讯作者：
Barasa E
中科院分区：
文献类型：
--
作者：
Brand SPC;Ojal J;Aziza R;Were V;Okiro EA;Kombe IK;Mburu C;Ogero M;Agweyu A;Warimwe GM;Nyagwange J;Karanja H;Gitonga JN;Mugo D;Uyoga S;Adetifa IMO;Scott JAG;Otieno E;Murunga N;Otiende M;Ochola-Oyier LI;Agoti CN;Githinji G;Kasera K;Amoth P;Mwangangi M;Aman R;Ng'ang'a W;Tsofa B;Bejon P;Keeling MJ;Nokes DJ;Barasa E
2021年6月，肯尼亚官方记录显示，确诊死亡人数不到4000人，确诊严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）病例18万例。这些数据往往反映出社会上经济上的中产阶级，他们买得起智能手机，并有机会获得医疗服务和检查。Brand等人开发了一个流行病学模型来估计大流行病在肯尼亚的影响，肯尼亚人口分为两个社会经济阶层。作者预测，到2021年6月，75%的肯尼亚人口（约3900万人）已暴露于病毒。如果在未来观察到第四波感染，它可能是由具有增强的传播性或自然免疫逃逸的变体驱动的。肯尼亚的SARS-COV-2感染浪潮是由社会经济环境和新出现的变种共同驱动的。关于COVID-19干预措施的政策决策应基于对严重急性呼吸系统综合征冠状病毒2（SARS-CoV-2）传播的地方、区域和国家理解。当限制被取消或遵守不严时，具有新表型特性的变异成功入侵，或感染扩散到易感亚群时，可能会导致流行病浪潮。在肯尼亚已经观察到三波COVID-19疫情。使用一个机械的数学模型，我们解释了前两个不同的波在高和低的社会经济群体的接触率的差异，和第三波的引入更高的传播性的变种。学校重新开放导致第二波和第三波之间的传播略有增加。社会经济和城乡人口结构是肯尼亚病毒传播的关键决定因素。
In June 2021, official records in Kenya showed fewer than 4000 confirmed deaths and 180,000 confirmed cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These data tend to reflect the economically advantaged strata of society who can afford smartphones and have access to medical attention and tests. Brand et al. developed an epidemiological model to estimate the impact of the pandemic in Kenya, the population of which was split into two socioeconomic strata. The authors predicted that 75% of the Kenyan population (about 39 million people) had been exposed to the virus by June 2021. If a fourth wave of infection is observed in the future, it would likely be driven by a variant with enhanced transmissibility or natural immune escape. —CA Waves of SARS-COV-2 infection in Kenya were driven by a combination of socioeconomic circumstance and emerging variants. Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or infection spreads to susceptible subpopulations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model, we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of higher-transmissibility variants. Reopening schools led to a minor increase in transmission between the second and third waves. Socioeconomic and urban–rural population structure are critical determinants of viral transmission in Kenya.
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期刊：
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