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Inferring transmission trees to guide targeting of interventions against visceral leishmaniasis and post-kala-azar dermal leishmaniasis

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  • L.A.C. Chapman
  • S.E.F. Spencer
  • T.M. Pollington
  • C.P. Jewell
  • D. Mondal
  • J. Alvar
  • T.D. Hollingsworth
  • M.M. Cameron
  • C. Bern
  • G.F. Medley
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<mark>Journal publication date</mark>13/10/2020
<mark>Journal</mark>Proceedings of the National Academy of Sciences of the United States of America
Issue number41
Volume117
Number of pages9
Pages (from-to)25742-25750
Publication StatusPublished
Early online date24/09/20
<mark>Original language</mark>English

Abstract

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