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

被引:6
|
作者
Telford, Carson [1 ,2 ]
Nyakarahuka, Luke [3 ,4 ]
Waller, Lance [5 ]
Kitron, Uriel [5 ]
Shoemaker, Trevor [1 ]
机构
[1] Ctr Dis Control & Prevent, Viral Special Pathogens Branch, 1600 Clifton Rd, Atlanta, GA 30333 USA
[2] Univ N Carolina, Gillings Sch Global Publ Hlth, 135 Dauer Dr, Chapel Hill, NC 27599 USA
[3] Uganda Virus Res Inst, 51-59 Nakiwogo Rd, Entebbe, Uganda
[4] Makerere Univ, Dept Biosecur Ecosyst & Vet Publ Hlth, 7062 Univ Rd, Kampala, Uganda
[5] Emory Univ, Rollins Sch Publ Hlth, 1518 Clifton Rd, Atlanta, GA 30322 USA
关键词
Crimean-Congo hemorrhagic fever; Geostatistical model; Seroprevalence prediction; Epidemiology; Livestock; LAND-USE CHANGE; TRANSMISSION; OUTBREAK; HUMANS;
D O I
10.1016/j.onehlt.2023.100576
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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.
引用
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页数:8
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