Naserpor Ahmad, Niakan Kalhori Sharareh R, Ghazisaeedi Marjan, Azizi Rasoul, Hosseini Ravandi Mohammad, Sharafie Sajad
Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
Department of Health Information Management, School of Allied Medical Sciences, Lorestan University of Medical Sciences, Khorramabad, Iran.
Healthc Inform Res. 2019 Jan;25(1):27-32. doi: 10.4258/hir.2019.25.1.27. Epub 2019 Jan 31.
The association between the spread of infectious diseases and climate parameters has been widely studied in recent decades. In this paper, we formulate, exploit, and compare three variations of the susceptible-infected-recovered (SIR) model incorporating climate data. The SIR model is a well-studied model to investigate the dynamics of influenza viruses; however, the improved versions of the classic model have been developed by introducing external factors into the model.
The modification models are derived by multiplying a linear combination of three complementary factors, namely, temperature (T), precipitation (P), and humidity (H) by the transmission rate. The performance of these proposed models is evaluated against the standard model for two outbreak seasons.
The values of the root-mean-square error (RMSE) and the Akaike information criterion (AIC) improved as they declined from 8.76 to 7.05 and from 98.12 to 93.01 for season 2013/14, respectively. Similarly, for season 2014/15, the RMSE and AIC decreased from 8.10 to 6.45 and from 117.73 to 107.91, respectively. The estimated values of () in the framework of the standard and modified SIR models are also compared.
Through simulations, we determined that among the studied environmental factors, precipitation showed the strongest correlation with the transmission dynamics of influenza. Moreover, the SIR+P+T model is the most efficient for simulating the behavioral dynamics of influenza in the area of interest.
近几十年来,传染病传播与气候参数之间的关联已得到广泛研究。在本文中,我们构建、运用并比较了三种纳入气候数据的易感-感染-康复(SIR)模型变体。SIR模型是一个用于研究流感病毒动态的经过充分研究的模型;然而,通过在模型中引入外部因素,已开发出经典模型的改进版本。
通过将温度(T)、降水量(P)和湿度(H)这三个互补因素的线性组合与传播率相乘来推导修正模型。针对两个疫情季节,对照标准模型评估这些提出的模型的性能。
对于2013/14季节,均方根误差(RMSE)值和赤池信息准则(AIC)值分别从8.76降至7.05以及从98.所提出的模型的性能针对两个爆发季节与标准模型进行评估。
2013/14年季节,均方根误差(RMSE)值和赤池信息准则(AIC)值分别从8.76降至7.05以及从98.12降至93.01,随着这些值下降,性能得到改善。同样,对于2014/15年季节,RMSE和AIC分别从8.10降至6.45以及从117.73降至107.91。还比较了标准和修正SIR模型框架下的()估计值。
通过模拟,我们确定在所研究的环境因素中,降水量与流感传播动态的相关性最强。此外,SIR+P+T模型在模拟感兴趣区域内流感的行为动态方面效率最高。 12降至93.01,随着这些值下降,性能得到改善。同样,对于2014/15年季节,RMSE和AIC分别从8.10降至6.45以及从117.73降至107.91。还比较了标准和修正SIR模型框架下的()估计值。
通过模拟,我们确定在所研究的环境因素中,降水量与流感传播动态的相关性最强。此外,SIR+P+T模型在模拟感兴趣区域内流感的行为动态方面效率最高。