The role of geospatial hotspots in the spatial spread of tuberculosis in rural Ethiopia: a mathematical model

Shaweno, Debebe, Trauer, James M., Denholm, Justin T., and McBryde, Emma S. (2018) The role of geospatial hotspots in the spatial spread of tuberculosis in rural Ethiopia: a mathematical model. Royal Society Open Science, 5 (9).

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Geospatial tuberculosis (TB) hotspots are hubs of TB transmission both within and across community groups. We aimed to quantify the extent to which these hotspots account for the spatial spread of TB in a high-burden setting. We developed spatially coupled models to quantify the spread of TB from geographical hotspots to distant regions in rural Ethiopia. The population was divided into three 'patches' based on their proximity to transmission hotspots, namely hotspots, adjacent regions and remote regions. The models were fitted to 5-year notification data aggregated by the metapopulation structure. Model fitting was achieved with a Metropolis-Hastings algorithm using a Poisson likelihood to compare model-estimated notification rate with observed notification rates. A cross-coupled metapopulation model with assortative mixing by region closely fit to notification data as assessed by the deviance information criterion. We estimated 45 hotspot-to-adjacent regions transmission events and and 2 hotspot-to-remote regions transmission events occurred for every 1000 hotspot-to-hotspot transmission events. Although the degree of spatial coupling was weak, the proportion of infections in the adjacent region that resulted from mixing with hotspots was high due to the high prevalence of TB cases in a hotspot region, with approximately 75% of infections attributable to hotspot contact. Our results suggest that the role of hotspots in the geospatial spread of TB in rural Ethiopia is limited, implying that TB transmission is primarily locally driven.

Item ID: 55940
Item Type: Article (Research - C1)
ISSN: 2054-5703
Keywords: hotspots, transmission, spatial analysis, tuberculosis, metapopulation models
Copyright Information: © 2018 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.
Date Deposited: 24 Oct 2018 09:34
FoR Codes: 42 HEALTH SCIENCES > 4202 Epidemiology > 420205 Epidemiological modelling @ 100%
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