Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

Abstract

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.

A Decade of Progress Accelerating Malaria Control in Mali: Evidence from the West Africa International Center of Excellence for Malaria Research

“…36,37 The WA-ICEMR studies also described seasonal spatiotemporal clusters in the distribution of P. falciparum infection and diseases that may provide opportunities for new control strategies such as reactive, community-based, self-administered treatments against residual malaria transmission or targeted vector control strategies such as larval control of vector breeding and during the dry season. 26,38 These studies also showed that spatial analyses and cluster detection approaches have considerable utility for guiding targeted intervention strategies.…”

Trends in malaria epidemiological factors following the implementation of current control strategies in Dangassa, Mali

“…During the past decade, Mali and most SSA countries started the progressive implementation of proven malaria prevention and control strategies, such as the distribution of insecticide-treated bed nets (ITNs), indoor residual spraying (IRS) in targeted geographic areas, improved access to healthcare with early diagnosis and prompt treatment, prevention and rapid management of malaria epidemics and surveillance, provision of intermittent preventive therapy to pregnant women (IPTp), and seasonal malaria chemoprevention (SMC) for children under 5 years old. The Malian Government and its partners have been offering free malaria diagnostics and treatment to children under 5 years old and pregnant women since 2008 [ 2 – 4 ].…”

Collaboration between meteorology and public health: Predicting the dengue epidemic in Guangzhou, China, by meteorological parameters

“…Generalized Additive Models (GAMs) are commonly used to set a predictive model for infectious diseases ( Hart et al., 2009 ; Gu et al., 2015 ; Ateba et al., 2020 ). Lin et al.…”

“…Generalized Additive Models (GAMs) are commonly used to set a predictive model for infectious diseases (Hart et al, 2009;Gu et al, 2015;Ateba et al, 2020). Lin et al (2016) used GAMs to assess the effectiveness of a community-based integrated intervention of dengue in Guangzhou and acheived significant effects to control outbreak in areas.…”