A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data

Abstract

We describe a statistical framework for reconstructing the sequence of transmission events between observed cases of an endemic infectious disease using genetic, temporal and spatial information. Previous approaches to reconstructing transmission trees have assumed all infections in the study area originated from a single introduction and that a large fraction of cases were observed. There are as yet no approaches appropriate for endemic situations in which a disease is already well established in a host population and in which there may be multiple origins of infection, or that can enumerate unobserved infections missing from the sample. Our proposed framework addresses these shortcomings, enabling reconstruction of partially observed transmission trees and estimating the number of cases missing from the sample. Analyses of simulated datasets show the method to be accurate in identifying direct transmissions, while introductions and transmissions via one or more unsampled intermediate cases could be identified at high to moderate levels of case detection. When applied to partial genome sequences of rabies virus sampled from an endemic region of South Africa, our method reveals several distinct transmission cycles with little contact between them, and direct transmission over long distances suggesting significant anthropogenic influence in the movement of infected dogs.

Sharka Epidemiology and Worldwide Management Strategies: Learning Lessons to Optimize Disease Control in Perennial Plants

“…These approaches consist of fitting epidemiological and microevolutionary models to spatiotemporal data on infected hosts and to the genetic sequence data of the pathogen (19,88,149). Up to now, these approaches have been applied only to diseases infecting mammals, but they could be advantageously transferred to sharka and other viral diseases of plants, especially to improve knowledge of dispersal function and latency duration in agricultural contexts.…”

Measurably evolving pathogens in the genomic era

“…The greater propensity for transmission between farms in spatial proximity can be accounted for by an additional term in the likelihood function that penalizes distant transmission events [20,71]. Analysing such space-time-genetic data is also possible in endemic regions where only a fraction of incident cases are observed and multiple introductions of the pathogen might have occurred [72]. Additional epidemiological information is sometimes available at the individual host level, further constraining the set of transmission trees that are consistent with the data.…”

Using genomics data to reconstruct transmission trees during disease outbreaks

“…The lack of comprehensive genetic datasets from actual outbreaks has not hindered development of these methods, however, as many of the most recently-published papers on this subject have concentrated on endemic disease (8, 9). This is an important development for epidemic analysis as well, because the testing of methods on real data of any sort is essential if inference is to be relied upon in an emergency situation, and because the problems involved in applying such procedures to endemic pathogens where the infected population is not well revealed are similar to those involved in handling epidemic sampling which is less than comprehensive.…”

“…As with Cottam et al, they were working on the 2001 FMDV outbreak and were able to include farm locations in the analysis. The work was extended by Mollentze et al (8), working instead on rabies samples from South Africa; this second paper extended the procedure to a situation of less consistent sampling by, as with outbreaker , allowing for multiple introductions to a study population and for the path of infection between two sampled individuals to pass through unsampled ones, although unlike outbreaker the procedure only indicates the presence of such indirect infections and does not enumerate them.…”

“…Work has been published reconstructing the tree for outbreaks of foot-and-mouth disease virus (1, 2, 3, 4), avian influenza (5, 6), and salmon infectious anaemia virus (7). However, as perfect sampling is unlikely in most circumstances, many of the most up-to-date methods are appropriate for imperfect and even quite sparse isolate collection (8, 9). The motivation for such work is often to design procedures to reconstruct the tree for endemic disease, but they are also appropriate for poorly-sampled outbreaks.…”