Age-structured Impact of Mitigation Strategies on COVID-19 Severity and Deaths in Kenya

Author:

Mwalili Samuel1ORCID,Kimathi Mark E. M.2,Ojiambo Viona N.1,Gathungu Duncan K.1,Achia Thomas N. O.3

Affiliation:

1. Jomo Kenyatta University of Agriculture and Technology

2. Machakos University

3. University of the Witwatersrand

Abstract

Abstract Introduction: COVID-19, a coronavirus disease 2019, is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There have been a lot of attempts to model this pandemic from a global perspective. The Novel Coronavirus is still spreading quickly in several countries and the peak has not yet been reached in many countries. We developed age-structured model for describing the COVID-19 pandemic in Kenya under different non-pharmaceutical interventions. The first case in Kenya was identified in March 13, 2020 with the pandemic increasing to 465 confirmed cases by end of 3rd May, 2020. We fitted an age-structured deterministic mathematical model in Kenyan context.Methods: We model the COVID-19 situation in Kenya using Age-structured Susceptible Exposed Infectious Recovered compartmental model. These compartments follow a cascade of the disease from the Susceptible to Exposed individuals who in return are either symptomatic or asymptomatic. The symptomatic depict mild signs, which can develop to severe symptoms warranting hospitalization or can otherwise recover. The severe cases can recover with some developing critical condition. The critical are admitted at intensive care units. The resulting age-dependent ordinary differential equations from the model are solved using fourth order Runge-Kutta methods. We controlled for school closure, social distancing and lockdown in terms of movement restrictionsResults: The model shows varying epidemic peak by age-structure and the mitigation scenarios. The peak dates for unmitigated (UM), the 45% NPI (M45) and School closure-curfew-partial lockdown NPI (SCL) are May 21st, October 17th and December 13th 2020, respectively. Their respective cumulative infections peaks are 43M, 24M and 25M. The daily reported severe cases, critical cases and death proportionately increased with age. Conclusions: The cumulative number of infections reduces greatly with introduction of school closure, social distancing and restricted movement in highly affected counties. The degree of COVID-19 severity increases with age. However, it is not immediately clear when these restrictions can be lifted.

Publisher

Research Square Platform LLC

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3