Pan African University Institute for Basic Science, Technology and Innovation (PAUSTI), Nairobi, Kenya.
Jomo Kenyatta University of Agriculture and Technology, Department of Statistics and Actuarial Sciences, Nairobi, Kenya.
PLoS One. 2019 Mar 1;14(3):e0212934. doi: 10.1371/journal.pone.0212934. eCollection 2019.
Visceral Leishmaniasis is a very dangerous form of leishmaniasis and, shorn of appropriate diagnosis and handling, it leads to death and physical disability. Depicting the spatiotemporal pattern of disease is important for disease regulator and deterrence strategies. Spatiotemporal modeling has distended broad veneration in recent years. Spatial and spatiotemporal disease modeling is extensively used for the analysis of registry data and usually articulated in a hierarchical Bayesian framework. In this study, we have developed the hierarchical spatiotemporal Bayesian modeling of the infected rate of Visceral leishmaniasis in Human (VLH). We applied the Stochastics Partial Differential Equation (SPDE) approach for a spatiotemporal hierarchical model for Visceral leishmaniasis in human (VLH) that involves a GF and a state process is associated with an autoregressive order one temporal dynamics and the spatially correlated error term, along with the effect of land shield, metrological, demographic, socio-demographic and geographical covariates in an endemic area of Amhara regional state, Ethiopia. The model encompasses a Gaussian Field (GF), affected by an error term, and a state process described by a first-order autoregressive dynamic model and spatially correlated innovations. A hierarchical model including spatially and temporally correlated errors was fit to the infected rate of Visceral leishmaniasis in human (VLH) weekly data from January 2015 to December 2017 using the R package R-INLA, which allows for Bayesian modeling using the stochastic partial differential equation (SPDE) approach. We found that the mean weekly temperature had a significant positive association with infected rate of VLH. Moreover, net migration rate, clean water coverage, average number of households, population density per square kilometer, average number of persons per household unit, education coverage, health facility coverage, mortality rate, and sex ratio had a significant association with the infected rate of visceral leishmaniasis (VLH) in the region. In this study, we investigated the dynamic spatiotemporal modeling of Visceral leishmaniasis in Human (VLH) through a stochastic partial differential equation approach (SPDE) using integrated nested Laplace approximation (INLA). Our study had confirmed both metrological, demographic, sociodemographic and geographic covariates had a significant association with the infected rate of visceral leishmaniasis (VLH) in the region.
内脏利什曼病是一种非常危险的利什曼病形式，如果没有适当的诊断和处理，它会导致死亡和身体残疾。描述疾病的时空模式对于疾病调节剂和威慑策略非常重要。近年来，时空建模得到了广泛的关注。空间和时空疾病建模广泛用于分析登记数据，通常以分层贝叶斯框架表达。在这项研究中，我们开发了一种用于人类内脏利什曼病（VLH）感染率的分层时空贝叶斯模型。我们应用随机偏微分方程（SPDE）方法为人类内脏利什曼病（VLH）建立了一个时空分层模型，该模型涉及一个高斯场（GF）和一个状态过程，与一个自回归一阶时间动态和空间相关的误差项相关，以及土地盾、气象、人口、社会人口和地理协变量在埃塞俄比亚阿姆哈拉地区的一个流行地区的影响。该模型包括一个高斯场（GF），受误差项的影响，以及一个由一阶自回归动态模型和空间相关创新描述的状态过程。使用 R 包 R-INLA 对 2015 年 1 月至 2017 年 12 月每周的人类内脏利什曼病（VLH）感染率数据进行了包括空间和时间相关误差的分层模型拟合，该模型允许使用随机偏微分方程（SPDE）方法进行贝叶斯建模。我们发现，每周平均温度与 VLH 的感染率呈显著正相关。此外，净迁移率、清洁水覆盖率、平均家庭数量、每平方公里人口密度、每户平均人口、教育覆盖率、卫生设施覆盖率、死亡率和性别比与该地区内脏利什曼病（VLH）的感染率有显著关联。在这项研究中，我们通过使用综合嵌套拉普拉斯逼近（INLA）的随机偏微分方程方法（SPDE），研究了人类内脏利什曼病（VLH）的动态时空建模。我们的研究证实了气象、人口、社会人口和地理协变量与该地区内脏利什曼病（VLH）的感染率有显著关联。
