Assessing Strategies Against Gambiense Sleeping Sickness Through Mathematical Modeling.
Assessing Strategies Against Gambiense Sleeping Sickness Through Mathematical Modeling.
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DOI：
10.1093/cid/ciy018
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发表时间：
2018-06-01
期刊：
影响因子：
--
通讯作者：
Keeling MJ
中科院分区：
文献类型：
--
作者：
Rock KS;Ndeffo-Mbah ML;Castaño S;Palmer C;Pandey A;Atkins KE;Ndung'u JM;Hollingsworth TD;Galvani A;Bever C;Chitnis N;Keeling MJ
冈比亚昏睡病的控制主要依靠对人群的被动和主动筛查，然后进行治疗。数学模型探索了高传播率和低传播率环境中 3 种互补干预措施的潜力。预计包括病媒控制在内的干预策略将最快阻止传播。据估计，有针对性的主动筛查（覆盖范围更广、更有针对性）以及加强的被动监测（改善诊断和治疗的机会）都可以避免许多新的感染，但单独使用时，不太可能在 2030 年之前阻止高风险环境中的传播。尽管由于模型内不同的流行病学假设导致定量预测之间存在差异，但对所研究的 3 种补充干预措施的排名存在普遍的模型共识。虽然这些预测为改善控制提供了一般性见解，但任何情况下最有效的策略都取决于该地区的具体流行病学和相关成本。
Control of gambiense sleeping sickness relies predominantly on passive and active screening of people, followed by treatment. Mathematical modeling explores the potential of 3 complementary interventions in high- and low-transmission settings. Intervention strategies that included vector control are predicted to halt transmission most quickly. Targeted active screening, with better and more focused coverage, and enhanced passive surveillance, with improved access to diagnosis and treatment, are both estimated to avert many new infections but, when used alone, are unlikely to halt transmission before 2030 in high-risk settings. There was general model consensus in the ranking of the 3 complementary interventions studied, although with discrepancies between the quantitative predictions due to differing epidemiological assumptions within the models. While these predictions provide generic insights into improving control, the most effective strategy in any situation depends on the specific epidemiology in the region and the associated costs.
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影响因子：
3.8
作者：
Jamonneau V;Ilboudo H;Kaboré J;Kaba D;Koffi M;Solano P;Garcia A;Courtin D;Laveissière C;Lingue K;Büscher P;Bucheton B
通讯作者：
Bucheton B
影响因子：
3.2
作者：
Hollingsworth TD;Adams ER;Anderson RM;Atkins K;Bartsch S;Basáñez MG;Behrend M;Blok DJ;Chapman LA;Coffeng L;Courtenay O;Crump RE;de Vlas SJ;Dobson A;Dyson L;Farkas H;Galvani AP;Gambhir M;Gurarie D;Irvine MA;Jervis S;Keeling MJ;Kelly-Hope L;King C;Lee BY;Le Rutte EA;Lietman TM;Ndeffo-Mbah M;Medley GF;Michael E;Pandey A;Peterson JK;Pinsent A;Porco TC;Richardus JH;Reimer L;Rock KS;Singh BK;Stolk W;Swaminathan S;Torr SJ;Townsend J;Truscott J;Walker M;Zoueva A;NTD Modelling Consortium
通讯作者：
NTD Modelling Consortium
影响因子：
3
作者：
Eckhoff PA
通讯作者：
Eckhoff PA
影响因子：
15.8
作者：
Griffin, Jamie T.;Hollingsworth, T. Deirdre;Ghani, Azra C.
通讯作者：
Ghani, Azra C.
影响因子：
4.9
作者：
Lumbala, Crispin;Simarro, Pere P.;Jannin, Jean G.
通讯作者：
Jannin, Jean G.