Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models.
Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models.
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DOI：
10.3390/ijerph17176339
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发表时间：
2020-08-31
影响因子：
--
通讯作者：
Doumbia S
中科院分区：
文献类型：
--
作者：
Ateba FF;Febrero-Bande M;Sagara I;Sogoba N;Touré M;Sanogo D;Diarra A;Magdalene Ngitah A;Winch PJ;Shaffer JG;Krogstad DJ;Marker HC;Gaudart J;Doumbia S
马里的目标是到下一个十年达到消灭疟疾前的阶段。这项研究使用函数回归模型来预测疟疾发病率作为过去气象模式的函数，以更好地预防和积极主动地应对即将爆发的疟疾。所有数据都是在五年内（2012-2017年）从1400名在Dangassa社区卫生中心寻求治疗的人中收集的。收集了降雨量、温度、湿度和风速变量。函数广义谱加性模型（FGSAM），函数广义线性模型（FGLM），和函数广义核加性模型（FGKAM）被用来预测疟疾发病率作为一个函数的模式的气象指标在连续的18周前的一周的利益。他们各自的结果进行了比较的预测能力。结果表明：（1）疟疾发病率最高值出现在空气湿度> 65%、连续两次或两次以上降雨、平均风速<1.8m/s的模式出现后10 ~ 12周，（2）在3种模式中，FGLM的预测效果最好;（3）FGSAM是FGLM和FGKAM之间的一个很好的折衷，具有灵活性和简单性。这些模型表明，某些气象条件可能为检测未来疟疾暴发提供依据。本文中开发的模型是有用的实施预防战略，利用过去的气象和过去的疟疾发病率。
Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.
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影响因子：
8.1
作者：
Killeen GF;Marshall JM;Kiware SS;South AB;Tusting LS;Chaki PP;Govella NJ
通讯作者：
Govella NJ
影响因子：
1.3
作者：
Febrero-Bande, Manuel;Gonzalez-Manteiga, Wenceslao;Oviedo de la Fuente, Manuel
通讯作者：
Oviedo de la Fuente, Manuel
影响因子：
1.3
作者：
Febrero-Bande, Manuel;Gonzalez-Manteiga, Wenceslao
通讯作者：
Gonzalez-Manteiga, Wenceslao
影响因子：
3.7
作者：
Oviedo de la Fuente, Manuel;Febrero-Bande, Manuel;Dominguez, Angela
通讯作者：
Dominguez, Angela
影响因子：
3
作者：
Diakite, Seidina A. S.;Traore, Karim;Diakite, Mahamadou
通讯作者：
Diakite, Mahamadou