Spatial prediction of Crimean Congo hemorrhagic fever virus seroprevalence among livestock in Uganda

• Type of Material: Article
• Authors: Carson Telford, University of North Carolina, Chapel Hill; Luke Nyakarahuka, Makerere University; Lance Waller, Emory University; Uriel Kitron, Emory University; Trevor Shoemaker, Centers for Disease Control and Prevention
• Language: English
• Date: 2023-06-12
• Publisher: Elsevier
• Publication Version: Final Published Version
• Copyright Statement: © 2023 The Authors
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): https://doi.org/10.1016/j.onehlt.2023.100576
• Title of Journal or Parent Work: One Health
• Volume: 17
• Start Page: 100576
• Keywords: Livestock; Geostatistical model; Seroprevalence prediction; Epidemiology; Crimean-Congo hemorrhagic fever
• Research Categories: Health Sciences, Public Health; Health Sciences, Epidemiology

Abstract

Crimean-Congo Hemorrhagic Fever (CCHF) is a viral disease that can infect humans via contact with tick vectors or livestock reservoirs and can cause moderate to severe disease. The first human case of CCHF in Uganda was identified in 2013. To determine the geographic distribution of the CCHF virus (CCHFV), serosampling among herds of livestock was conducted in 28 Uganda districts in 2017. A geostatistical model of CCHF seroprevalence among livestock was developed to incorporate environmental and anthropogenic variables associated with elevated CCHF seroprevalence to predict CCHF seroprevalence on a map of Uganda and estimate the probability that CCHF seroprevalence exceeded 30% at each prediction location. Environmental and anthropogenic variables were also analyzed in separate models to determine the spatially varying drivers of prediction and determine which covariate class resulted in best prediction certainty. Covariates used in the full model included distance to the nearest croplands, average annual change in night-time light index, percent sand soil content, land surface temperature, and enhanced vegetation index. Elevated CCHF seroprevalence occurred in patches throughout the country, being highest in northern Uganda. Environmental covariates drove predicted seroprevalence in the full model more than anthropogenic covariates. Combination of environmental and anthropogenic variables resulted in the best prediction certainty. An understanding of the spatial distribution of CCHF across Uganda and the variables that drove predictions can be used to prioritize specific locations and activities to reduce the risk of future CCHF transmission.