Deterministic and Stochastic Dynamics of COVID-19: The Case Study of Italy and Spain

IF 0.9 Q3 MATHEMATICS, APPLIED Computational and Mathematical Methods Pub Date : 2022-02-13 DOI:10.1155/2022/5780719
Akhil Kumar Srivastav, Nico Stollenwerk, Maíra Aguiar
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Abstract

In December 2019, a severe respiratory syndrome (COVID-19) caused by a new coronavirus (SARS-CoV-2) was identified in China and spread rapidly around the globe. COVID-19 was declared a pandemic by the World Health Organization (WHO) in March 2020. With eventually substantial global underestimation, more than 225 million cases were confirmed by the end of August 2021, counting more than 4.5 million deaths. COVID-19 symptoms range from mild (or no symptoms) to severe illness, with disease severity and death occurring according to a hierarchy of risks, with age and preexisting health conditions enhancing the risks of disease severity manifestation. In this paper, a mathematical model for COVID-19 transmission is proposed and analyzed. The model stratifies the studied population into two groups, older and younger. Applied to the COVID-19 outbreaks in Spain and in Italy, we find the disease-free equilibrium and the basic reproduction number for each case study. A sensitivity analysis to identify the key parameters which influence the basic reproduction number, and hence regulate the transmission dynamics of COVID-19, is also performed. Finally, the model is extended to its stochastic counterpart to encapsulate the variation or uncertainty found in the transmissibility of the disease. We observe the variability of the infectious population finding its distribution at a given time, demonstrating that for small populations, stochasticity will play an important role.

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COVID-19的确定性和随机动力学:以意大利和西班牙为例
2019年12月,中国发现了由新型冠状病毒(SARS-CoV-2)引起的严重呼吸综合征(COVID-19),并在全球迅速传播。2020年3月,世界卫生组织(世卫组织)宣布新冠肺炎为大流行。在全球最终严重低估的情况下,到2021年8月底,确诊病例超过2.25亿例,死亡人数超过450万。COVID-19的症状范围从轻微(或无症状)到严重疾病,疾病严重程度和死亡的发生根据风险等级而定,年龄和既往健康状况增加了疾病严重程度表现的风险。本文提出并分析了COVID-19传播的数学模型。该模型将研究对象分为两组,老年人和年轻人。应用于西班牙和意大利的COVID-19疫情,我们找到了每个案例研究的无病平衡和基本繁殖数。此外,还进行了敏感性分析,以确定影响基本繁殖数从而调节COVID-19传播动态的关键参数。最后,将该模型扩展到随机对应模型,以封装在疾病传播性中发现的变化或不确定性。我们观察到传染病种群在给定时间内分布的变异性,表明对于小种群,随机性将起重要作用。
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