Climate-driven potential for tularemia in East Africa: skill testing and ecological consistency of a transferred risk model

IF 1.7 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Komi Mensah Agboka , Allan Muohi Ngángá , Bonoukpoè Mawuko Sokame , Steve Soh Bernard Baleba , Tobias Landmann , Elfatih M. Abdel-Rahman , Chrysantus M. Tanga , Souleymane Diallo
{"title":"Climate-driven potential for tularemia in East Africa: skill testing and ecological consistency of a transferred risk model","authors":"Komi Mensah Agboka ,&nbsp;Allan Muohi Ngángá ,&nbsp;Bonoukpoè Mawuko Sokame ,&nbsp;Steve Soh Bernard Baleba ,&nbsp;Tobias Landmann ,&nbsp;Elfatih M. Abdel-Rahman ,&nbsp;Chrysantus M. Tanga ,&nbsp;Souleymane Diallo","doi":"10.1016/j.sste.2025.100756","DOIUrl":null,"url":null,"abstract":"<div><div>Tularemia, a neglected zoonosis, remains underreported in Africa despite growing concern over its climate-driven expansion. This study aims to quantify the specific contribution of climate to tularemia risk using a climate attribution framework. We trained a Least Squares Dummy Variable (LSDV) fixed-effects panel model on United States (U.S.) county-level tularemia incidence data from 2011–2020 (n = 500, R² = 0.90), incorporating only climatic predictors: cumulative temperature, cumulative precipitation, and their respective variabilities. The climate-only model explained 86% of variance in the training data, demonstrating strong climate influence on tularemia disease dynamics. We then applied the model to East Africa, using environmental similarity analysis to assess transferability. Results show moderate-to-high climatic analogues in northern Kenya, eastern Uganda, and South Sudan. Between 2017 and 2020, predicted tularemia suitability increased by a median of +0.18 compared to the 2012–2015 baseline, particularly in arid and semi-arid zones. Low interannual variability suggests persistent climatic suitability. A thermal plausibility test showed strong agreement (r = 0.82) between predicted risk and the Gaussian thermal profile of <em>Francisella tularensis</em>. Our findings suggest that climate alone can spatially explain tularemia risk across Africa’s drylands. This method provides a transferable framework for early warning in data-poor regions and supports anticipatory surveillance in the context of climate change.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"55 ","pages":"Article 100756"},"PeriodicalIF":1.7000,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spatial and Spatio-Temporal Epidemiology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1877584525000474","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0

Abstract

Tularemia, a neglected zoonosis, remains underreported in Africa despite growing concern over its climate-driven expansion. This study aims to quantify the specific contribution of climate to tularemia risk using a climate attribution framework. We trained a Least Squares Dummy Variable (LSDV) fixed-effects panel model on United States (U.S.) county-level tularemia incidence data from 2011–2020 (n = 500, R² = 0.90), incorporating only climatic predictors: cumulative temperature, cumulative precipitation, and their respective variabilities. The climate-only model explained 86% of variance in the training data, demonstrating strong climate influence on tularemia disease dynamics. We then applied the model to East Africa, using environmental similarity analysis to assess transferability. Results show moderate-to-high climatic analogues in northern Kenya, eastern Uganda, and South Sudan. Between 2017 and 2020, predicted tularemia suitability increased by a median of +0.18 compared to the 2012–2015 baseline, particularly in arid and semi-arid zones. Low interannual variability suggests persistent climatic suitability. A thermal plausibility test showed strong agreement (r = 0.82) between predicted risk and the Gaussian thermal profile of Francisella tularensis. Our findings suggest that climate alone can spatially explain tularemia risk across Africa’s drylands. This method provides a transferable framework for early warning in data-poor regions and supports anticipatory surveillance in the context of climate change.
气候驱动的东非兔热病潜在风险:技能测试和转移风险模型的生态一致性
图拉雷米亚是一种被忽视的人畜共患病,尽管人们对其气候驱动的扩张日益关注,但在非洲仍未得到充分报道。本研究旨在利用气候归因框架量化气候对兔热病风险的具体贡献。我们对2011-2020年美国(us)县级兔热病发病率数据(n = 500, R²= 0.90)训练了一个最小二乘虚拟变量(LSDV)固定效应面板模型,其中仅包含气候预测因子:积温、累积降水及其各自的变量。仅气候模型解释了训练数据中86%的方差,表明气候对土拉菌病动态有很强的影响。然后,我们将该模型应用于东非,使用环境相似性分析来评估可转移性。结果显示,肯尼亚北部、乌干达东部和南苏丹的气候类似于中高气候。2017年至2020年期间,与2012-2015年基线相比,预测的兔热病适宜性中位数增加了+0.18,特别是在干旱和半干旱地区。年际变率低表明气候适宜性持久。热合理性检验显示,预测风险与土拉弗朗西斯菌的高斯热分布之间具有很强的一致性(r = 0.82)。我们的研究结果表明,气候本身可以在空间上解释非洲旱地的土拉热病风险。这种方法为数据匮乏地区的早期预警提供了一个可转移的框架,并支持气候变化背景下的预见性监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Spatial and Spatio-Temporal Epidemiology
Spatial and Spatio-Temporal Epidemiology PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
5.10
自引率
8.80%
发文量
63
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信