Mussina Kamilla, Kadyrov Shirali, Kashkynbayev Ardak, Yerdessov Sauran, Zhakhina Gulnur, Sakko Yesbolat, Zollanvari Amin, Gaipov Abduzhappar
Department of Medicine, Nazarbayev University School of Medicine, Astana, Kazakhstan.
Department of Mathematics and Natural Sciences, Suleyman Demirel University, Kaskelen, Kazakhstan.
HIV AIDS (Auckl). 2023 Jul 4;15:387-397. doi: 10.2147/HIV.S413876. eCollection 2023.
HIV is a growing public health burden that threatens thousands of people in Kazakhstan. Countries around the world, including Kazakhstan, are facing significant problems in predicting HIV infection prevalence. It is crucial to understand the epidemiological trends of infectious diseases and to monitor the prevalence of HIV in a long-term perspective. Thus, in this study, we aimed to forecast the prevalence of HIV in Kazakhstan for 10 years from 2020 to 2030 by using mathematical modeling and time series analysis.
We use statistical Autoregressive Integrated Moving Average (ARIMA) models and a nonlinear epidemic Susceptible-Infected (SI) model to forecast the HIV infection prevalence rate in Kazakhstan. We estimated the parameters of the models using open data on the prevalence of HIV infection among women and men (aged 15-49 years) in Kazakhstan provided by the Kazakhstan Bureau of National Statistics. We also predict the effect of pre-exposure prophylaxis (PrEP) control measures on the prevalence rate.
The ARIMA (1,2,0) model suggests that the prevalence of HIV infection in Kazakhstan will increase from 0.29 in 2021 to 0.47 by 2030. On the other hand, the SI model suggests that this parameter will increase to 0.60 by 2030 based on the same data. Both models were statistically significant by Akaike Information Criterion corrected (AICc) score and by the goodness of fit. HIV prevention under the PrEP strategy on the SI model showed a significant effect on the reduction of the HIV prevalence rate.
This study revealed that ARIMA (1,2,0) predicts a linear increasing trend, while SI forecasts a nonlinear increase with a higher prevalence of HIV. Therefore, it is recommended for healthcare providers and policymakers use this model to calculate the cost required for the regional allocation of healthcare resources. Moreover, this model can be used for planning effective healthcare treatments.
艾滋病毒给公共卫生带来的负担日益加重，威胁着哈萨克斯坦成千上万的人。包括哈萨克斯坦在内的世界各国在预测艾滋病毒感染率方面都面临重大问题。了解传染病的流行病学趋势并从长期角度监测艾滋病毒的流行情况至关重要。因此，在本研究中，我们旨在通过使用数学建模和时间序列分析来预测哈萨克斯坦2020年至2030年10年间的艾滋病毒流行情况。
我们使用统计自回归积分移动平均（ARIMA）模型和非线性传染病易感-感染（SI）模型来预测哈萨克斯坦的艾滋病毒感染率。我们利用哈萨克斯坦国家统计局提供的关于哈萨克斯坦15至49岁男女艾滋病毒感染率的公开数据来估计模型参数。我们还预测了暴露前预防（PrEP）控制措施对感染率的影响。
ARIMA（1,2,0）模型表明，哈萨克斯坦的艾滋病毒感染率将从2021年的0.29上升到2030年的0.47。另一方面，SI模型表明，基于相同数据，到2030年该参数将增至0.60。通过赤池信息准则校正（AICc）得分和拟合优度来看，这两个模型在统计学上都具有显著性。SI模型上PrEP策略下的艾滋病毒预防对降低艾滋病毒感染率显示出显著效果。
本研究表明，ARIMA（1,2,0）预测呈线性上升趋势，而SI预测呈非线性上升且艾滋病毒流行率更高。因此，建议医疗保健提供者和政策制定者使用该模型来计算区域医疗资源分配所需的成本。此外，该模型可用于规划有效的医疗治疗。
