Faculty of Medicine, University of Colombo, Colombo, Sri Lanka.
Ministry of Health, Colombo, Sri Lanka.
PLoS Negl Trop Dis. 2021 Apr 23;15(4):e0009346. doi: 10.1371/journal.pntd.0009346. eCollection 2021 Apr.
Leishmaniasis is a neglected tropical vector-borne disease, which is on the rise in Sri Lanka. Spatiotemporal and risk factor analyses are useful for understanding transmission dynamics, spatial clustering and predicting future disease distribution and trends to facilitate effective infection control.
The nationwide clinically confirmed cutaneous leishmaniasis and climatic data were collected from 2001 to 2019. Hierarchical clustering and spatiotemporal cross-correlation analysis were used to measure the region-wide and local (between neighboring districts) synchrony of transmission. A mixed spatiotemporal regression-autoregression model was built to study the effects of climatic, neighboring-district dispersal, and infection carryover variables on leishmaniasis dynamics and spatial distribution. Same model without climatic variables was used to predict the future distribution and trends of leishmaniasis cases in Sri Lanka.
A total of 19,361 clinically confirmed leishmaniasis cases have been reported in Sri Lanka from 2001-2019. There were three phases identified: low-transmission phase (2001-2010), parasite population buildup phase (2011-2017), and outbreak phase (2018-2019). Spatially, the districts were divided into three groups based on similarity in temporal dynamics. The global mean correlation among district incidence dynamics was 0.30 (95% CI 0.25-0.35), and the localized mean correlation between neighboring districts was 0.58 (95% CI 0.42-0.73). Risk analysis for the seven districts with the highest incidence rates indicated that precipitation, neighboring-district effect, and infection carryover effect exhibited significant correlation with district-level incidence dynamics. Model-predicted incidence dynamics and case distribution matched well with observed results, except for the outbreak in 2018. The model-predicted 2020 case number is about 5,400 cases, with intensified transmission and expansion of high-transmission area. The predicted case number will be 9115 in 2022 and 19212 in 2025.
The drastic upsurge in leishmaniasis cases in Sri Lanka in the last few year was unprecedented and it was strongly linked to precipitation, high burden of localized infections and inter-district dispersal. Targeted interventions are urgently needed to arrest an uncontrollable disease spread.
利什曼病是一种被忽视的热带病，在斯里兰卡呈上升趋势。时空和危险因素分析有助于了解传播动态、空间聚类以及预测未来疾病的分布和趋势，从而便于有效控制感染。
本研究收集了 2001 年至 2019 年全国范围内临床确诊的皮肤利什曼病和气候数据。使用层次聚类和时空交叉相关分析来衡量全区和局部（相邻地区之间）传播的同步性。建立混合时空回归自回归模型，研究气候、相邻地区扩散和感染遗留变量对利什曼病动态和空间分布的影响。同样的模型不包括气候变量，用于预测斯里兰卡未来利什曼病病例的分布和趋势。
2001 年至 2019 年，斯里兰卡共报告 19361 例临床确诊利什曼病病例。研究发现存在三个阶段：低传播阶段（2001-2010 年）、寄生虫种群建立阶段（2011-2017 年）和爆发阶段（2018-2019 年）。从时空动态相似性出发，将各地区分为三组。地区发病动态的全球平均相关系数为 0.30（95%CI 0.25-0.35），相邻地区的局部平均相关系数为 0.58（95%CI 0.42-0.73）。对七个发病率最高的地区进行风险分析表明，降水、相邻地区效应和感染遗留效应与地区发病动态显著相关。模型预测的发病动态和病例分布与观察结果吻合良好，除了 2018 年的疫情爆发。模型预测 2020 年病例数约为 5400 例，传播强度增加，高传播地区扩大。预计 2022 年病例数为 9115 例，2025 年病例数为 19212 例。
过去几年斯里兰卡利什曼病病例的急剧增加是前所未有的，与降水、局部感染负担高和地区间扩散密切相关。迫切需要采取有针对性的干预措施，以阻止疾病的不可控传播。
