Sarker Indrani, Karim Md Rezaul, E-Barket Sefat, Hasan Mehedi
Department of Statistics and Data Science Jahangirnagar University Dhaka Bangladesh.
Health Sci Rep. 2024 May 27;7(6):e2154. doi: 10.1002/hsr2.2154. eCollection 2024 Jun.
Epidemics of the dengue virus can trigger widespread morbidity and mortality along with no specific treatment. Examining the spatial autocorrelation and variability of dengue prevalence throughout Bangladesh's 64 districts was the focus of this study.
The spatial autocorrelation is evaluated with the help of Moran and Geary . Local Moran was used to detect hotspots and cold spots, whereas local Getis Ord was used to identify only spatial hotspots. The spatial heterogeneity has been detected using various conventional and spatial models, including the Poisson-Gamma model, the Poisson-Lognormal Model, the Conditional Autoregressive (CAR) model, the Convolution model, and the BYM2 model, respectively. These models are implemented using Gibbs sampling and other Bayesian hierarchical approaches to analyze the posterior distribution effectively, enabling inference within a Bayesian context.
The study's findings show that Moran and Geary analysis provides a substantial clustering pattern of positive spatial autocorrelation of dengue fever (DF) rates between surrounding districts at a 90% confidence interval. The Local Indicators of Spatial Autocorrelation cluster mapped spatial clusters and outliers based on prevalence rates, while the local Getis-Ord displayed a thorough breakdown of high or low rates, omitting outliers. Although Chattogram had the most dengue cases (15,752), Khulna district had a higher prevalence rate (133.636) than Chattogram (104.796). The BYM2 model, determined to be well-fitted based on the lowest Deviance Information Criterion value (527.340), explains a significant association between spatial heterogeneity and prevalence rates.
This research pinpoints the district with the highest prevalence rate for dengue and the neighboring districts that also have high risk, allowing government agencies and communities to take the necessary precautions to mollify the risk effect of DF.
登革热病毒流行可引发广泛的发病和死亡，且尚无特效治疗方法。本研究重点考察孟加拉国64个地区登革热患病率的空间自相关性和变异性。
借助莫兰指数（Moran's I）和Geary's C系数评估空间自相关性。局部莫兰指数用于检测热点和冷点，而局部Getis-Ord Gi*用于仅识别空间热点。分别使用各种传统模型和空间模型检测空间异质性，包括泊松-伽马模型、泊松-对数正态模型、条件自回归（CAR）模型、卷积模型和BYM2模型。这些模型通过吉布斯采样和其他贝叶斯分层方法实现，以有效分析后验分布，从而在贝叶斯框架内进行推断。
研究结果表明，莫兰指数和Geary's C系数分析在90%置信区间显示出周边地区登革热（DF）发病率呈显著正空间自相关的聚类模式。空间自相关局部指标聚类图根据患病率绘制了空间聚类和离群值，而局部Getis-Ord Gi*显示了高发病率或低发病率的详细分类，忽略了离群值。尽管吉大港的登革热病例最多（15752例），但库尔纳地区的患病率（133.636）高于吉大港（104.796）。基于最低偏差信息准则值（527.340）确定拟合良好的BYM2模型解释了空间异质性与患病率之间的显著关联。
本研究确定了登革热患病率最高的地区以及同样具有高风险的周边地区，使政府机构和社区能够采取必要的预防措施，以减轻登革热的风险影响。
