Understanding of spatiotemporal transmission of infectious diseases has improved significantly in recent years. Advances in Bayesian inference methods for individual-level geo-located epidemiological data have enabled reconstruction of transmission trees and quantification of disease spread in space and time, while accounting for uncertainty in missing data. However, these methods have rarely been applied to endemic diseases or ones in which asymptomatic infection plays a role, for which additional estimation methods are required. Here, we develop such methods to analyze longitudinal incidence data on visceral leishmaniasis (VL) and its sequela, post–kala-azar dermal leishmaniasis (PKDL), in a highly endemic community in Bangladesh. Incorporating recent data on VL and PKDL infectiousness, we show that while VL cases drive transmission when incidence is high, the contribution of PKDL increases significantly as VL incidence declines (reaching 55% in this setting). Transmission is highly focal: 85% of mean distances from inferred infectors to their secondary VL cases were <300 m, and estimated average times from infector onset to secondary case infection were <4 mo for 88% of VL infectors, but up to 2.9 y for PKDL infectors. Estimated numbers of secondary cases per VL and PKDL case varied from 0 to 6 and were strongly correlated with the infector’s duration of symptoms. Counterfactual simulations suggest that prevention of PKDL could have reduced overall VL incidence by up to 25%. These results highlight the need for prompt detection and treatment of PKDL to achieve VL elimination in the Indian subcontinent and provide quantitative estimates to guide spatiotemporally targeted interventions against VL.

近年来，人们对传染性疾病时空传播的认识有了很大提高。用于个体水平地理位置流行病学数据的贝叶斯推断方法的进步使得能够重建传播树并量化疾病在空间和时间上的传播，同时考虑了缺失数据的不确定性。但是，这些方法很少用于地方病或无症状感染起作用的疾病，因此需要其他评估方法。在这里，我们开发了这样的方法来分析孟加拉国一个高度流行的社区内脏利什曼病（VL）及其后遗症，黑热病后皮肤利什曼病（PKDL）的纵向发病率数据。结合有关VL和PKDL传染性的最新数据，我们显示，虽然VL病例在发病率较高时驱动传播，但PKDL的贡献随着VL发病率的下降而显着增加（在这种情况下达到55％）。传播是高度集中的：从推断出的感染者到其继发性VL病例的平均距离的85％为<300 m，并且对于88％的VL感染者，从感染者发病到继发性病例的估计平均时间为<4 mo，但最长为2.9 y用于PKDL感染者。每个VL和PKDL病例继发病例的估计数量从0到6不等，并且与感染者症状的持续时间密切相关。反事实模拟表明，预防PKDL可以使整体VL发生率降低多达25％。