Adekola Hafeez Aderinsayo, Adekunle Ibrahim Ayoade, Egberongbe Haneefat Olabimpe, Onitilo Sefiu Adekunle, Abdullahi Idris Nasir
Department of Microbiology Olabisi Onabanjo University Ago-Iwoye Ogun State Nigeria.
Department of Economics Olabisi Onabanjo University Ago-Iwoye Ogun State Nigeria.
J Public Aff. 2020 Nov;20(4):e2306. doi: 10.1002/pa.2306. Epub 2020 Aug 17.
In this study, we examined various forms of mathematical models that are relevant for the containment, risk analysis, and features of COVID-19. Greater emphasis was laid on the extension of the Susceptible-Infectious-Recovered (SIR) models for policy relevance in the time of COVID-19. These mathematical models play a significant role in the understanding of COVID-19 transmission mechanisms, structures, and features. Considering that the disease has spread sporadically around the world, causing large scale socioeconomic disruption unwitnessed in contemporary ages since World War II, researchers, stakeholders, government, and the society at large are actively engaged in finding ways to reduce the rate of infection until a cure or vaccination procedure is established. We advanced argument for the various forms of the mathematical model of epidemics and highlighted their relevance in the containment of COVID-19 at the present time. Mathematical models address the need for understanding the transmission dynamics and other significant factors of the disease that would aid policymakers to make accurate decisions and reduce the rate of transmission of the disease.
在本研究中，我们考察了与新冠病毒病的控制、风险分析及特征相关的各种数学模型形式。研究更着重于对易感-感染-康复（SIR）模型的扩展，以使其在新冠疫情期间具有政策相关性。这些数学模型在理解新冠病毒病的传播机制、结构及特征方面发挥着重要作用。鉴于该疾病已在全球范围内呈散发性传播，造成了自二战以来当代未见的大规模社会经济混乱，研究人员、利益相关者、政府及广大社会群体都在积极寻求降低感染率的方法，直至找到治愈方法或疫苗接种程序。我们对各种形式的流行病数学模型提出了论证，并强调了它们目前在新冠病毒病控制方面的相关性。数学模型满足了理解该疾病传播动态及其他重要因素的需求，这将有助于政策制定者做出准确决策并降低疾病传播率。
