Department of Mathematics, Howard University, Washington, DC, USA.
J Biol Dyn. 2021 Dec;15(1):523-562. doi: 10.1080/17513758.2021.1991497.
In a recent paper, Che et al. [5] used a continuous-time Ordinary Differential Equation (ODE) model with risk structure to study cholera infections in Cameroon. However, the population and the reported cholera cases in Cameroon are censored at discrete-time annual intervals. In this paper, unlike in [5], we introduce a discrete-time risk-structured cholera model with no spatial structure. We use our discrete-time demographic equation to 'fit' the annual population of Cameroon. Furthermore, we use our fitted discrete-time model to capture the annually reported cholera cases from 1987 to 2004 and to study the impact of vaccination, treatment and improved sanitation on the number of cholera infections from 2004 to 2019. Our discrete-time cholera model confirms the results of the ODE model in [5]. However, our discrete-time model predicts a decrease in the number of cholera cases in a shorter period of cholera intervention (2004-2019) as compared to the ODE model's period of intervention (2004-2022).
在最近的一篇论文中，Che 等人[5]使用具有风险结构的连续时间常微分方程（ODE）模型来研究喀麦隆的霍乱感染。然而，喀麦隆的人口和报告的霍乱病例在离散时间上以年度间隔进行了删失。在本文中，与[5]不同，我们引入了一个没有空间结构的离散时间风险结构霍乱模型。我们使用我们的离散时间人口方程来“拟合”喀麦隆的年度人口。此外，我们使用拟合的离散时间模型来捕获 1987 年至 2004 年每年报告的霍乱病例，并研究接种疫苗、治疗和改善卫生条件对 2004 年至 2019 年霍乱感染数量的影响。我们的离散时间霍乱模型证实了[5]中 ODE 模型的结果。然而，与 ODE 模型的干预期（2004-2022 年）相比，我们的离散时间模型预测在较短的霍乱干预期（2004-2019 年）内霍乱病例数量会减少。
