Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Mahidol Vivax Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Malar J. 2018 Nov 16;17(1):428. doi: 10.1186/s12936-018-2574-0.
One challenge in moving towards malaria elimination is cross-border malaria infection. The implemented measures to prevent and control malaria re-introduction across the demarcation line between two countries require intensive analyses and interpretation of data from both sides, particularly in border areas, to make correct and timely decisions. Reliable maps of projected malaria distribution can help to direct intervention strategies. In this study, a Bayesian spatiotemporal analytic model was proposed for analysing and generating aggregated malaria risk maps based on the exceedance probability of malaria infection in the township-district adjacent to the border between Myanmar and Thailand. Data of individual malaria cases in Hlaingbwe Township and Tha-Song-Yang District during 2016 were extracted from routine malaria surveillance databases. Bayesian zero-inflated Poisson model was developed to identify spatial and temporal distributions and associations between malaria infections and risk factors. Maps of the descriptive statistics and posterior distribution of predicted malaria infections were also developed.
A similar seasonal pattern of malaria was observed in both Hlaingbwe Township and Tha-Song-Yang District during the rainy season. The analytic model indicated more cases of malaria among males and individuals aged ≥ 15 years. Mapping of aggregated risk revealed consistently high or low probabilities of malaria infection in certain village tracts or villages in interior parts of each country, with higher probability in village tracts/villages adjacent to the border in places where it could easily be crossed; some border locations with high mountains or dense forests appeared to have fewer malaria cases. The probability of becoming a hotspot cluster varied among village tracts/villages over the year, and some had close to no cases all year.
The analytic model developed in this study could be used for assessing the probability of hotspot cluster, which would be beneficial for setting priorities and timely preventive actions in such hotspot cluster areas. This approach might help to accelerate reaching the common goal of malaria elimination in the two countries.
在向消除疟疾迈进的过程中，跨境疟疾感染是一个挑战。为了防止和控制两国之间的疟疾病例再次传入，需要对双方的数据进行深入分析和解释，特别是在边境地区，以便做出正确和及时的决策。可靠的疟疾预测分布图有助于指导干预策略。在这项研究中，提出了一种贝叶斯时空分析模型，用于分析和生成基于缅甸和泰国边境相邻乡镇地区疟疾感染超概率的综合疟疾风险图。从常规疟疾监测数据库中提取了 2016 年 Hlaingbwe 镇和 Tha-Song-Yang 区的个体疟疾病例数据。开发了贝叶斯零膨胀泊松模型，以确定疟疾感染的时空分布以及与风险因素之间的关联。还开发了预测疟疾感染的描述性统计和后验分布的地图。
在雨季，Hlaingbwe 镇和 Tha-Song-Yang 区的疟疾呈现出相似的季节性模式。分析模型表明，男性和年龄≥15 岁的个体中疟疾病例较多。综合风险图显示，在两国内部的某些村庄或村庄中，疟疾感染的概率始终较高或较低，在边境附近的村庄或村庄中概率更高，在容易过境的地方；一些高山或茂密森林的边境地区似乎疟疾病例较少。热点群集的概率在一年内会在不同的村庄或村庄之间变化，有些村庄全年几乎没有病例。
本研究中开发的分析模型可用于评估热点群集的概率，这将有助于在这些热点群集地区确定优先事项和及时采取预防措施。这种方法可能有助于加速两国实现消除疟疾的共同目标。
