School of Mathematical Sciences, College of Science, Rochester Institute of Technology, Rochester, NY, USA.
Department of Mathematics, Howard University, Washington, DC, USA.
J Biol Dyn. 2023 Dec;17(1):2246496. doi: 10.1080/17513758.2023.2246496.
Worldwide, the recent SARS-CoV-2 virus disease outbreak has infected more than 691,000,000 people and killed more than 6,900,000. Surprisingly, Sub-Saharan Africa has suffered the least from the SARS-CoV-2 pandemic. Factors that are inherent to developing countries and that contrast with their counterparts in developed countries have been associated with these disease burden differences. In this paper, we developed data-driven COVID-19 mathematical models of two 'extreme': Cameroon, a developing country, and New York State (NYS) located in a developed country. We then identified critical parameters that could be used to explain the lower-than-expected COVID-19 disease burden in Cameroon versus NYS and to help mitigate future major disease outbreaks. Through the introduction of a 'disease burden' function, we found that COVID-19 could have been much more severe in Cameroon than in NYS if the vaccination rate had remained very low in Cameroon and the pandemic had not ended.
全球范围内,最近的 SARS-CoV-2 病毒病爆发已感染超过 6.91 亿人,造成超过 690 万人死亡。令人惊讶的是,撒哈拉以南非洲地区受 SARS-CoV-2 大流行的影响最小。发展中国家固有的、与发达国家相对的因素与这些疾病负担差异有关。在本文中,我们为两个“极端”国家建立了数据驱动的 COVID-19 数学模型:一个是发展中国家喀麦隆,另一个是位于发达国家的纽约州(NYS)。然后,我们确定了关键参数,可以用来解释 COVID-19 在喀麦隆的疾病负担低于纽约州的原因,并帮助减轻未来的重大疾病爆发。通过引入“疾病负担”函数,我们发现如果喀麦隆的疫苗接种率保持非常低,且大流行尚未结束,那么 COVID-19 在喀麦隆可能比在 NYS 严重得多。