Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials
2.1.1. Study Areas
2.1.2. Data Source
Study Population and Data Collection
- (1)
- Malaria Data
- (2)
- Meteorological and Environmental Data
2.2. Statistical Methods
- FGLM:
- FGSAM: with being smooth functions of , the score of the kth principal component of the ith covariate.
- FGKAM: with being a general function computed from the functional covariate using a Gaussian kernel approximation.
3. Result
3.1. Descriptive Analysis of the Functional Data
3.2. Constructing a Functional Regression Model (FGSAM) for Malaria Incidence Rate
3.3. Comparing Different Functional Models for Dangassa Data
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ACTs | Artemisinin-based Combination Therapies |
EOSDIS | Earth Observing System Data and Information System |
IPTp | Intermittent Preventive Treatment during pregnancy |
LLINS | Long Lasting Insecticidal Nets |
NASA | National Aeronautics and Space Administration |
FDA | Functional Data Analysis |
FGLM | Functional Generalized Linear Model |
FGSAM | Functional Generalized Spectral Additive Models |
FGKAM | Functional Generalized Kernel Additive Models |
MSPE | Mean Squared Prediction Error |
NDVI | Normalized Difference Vegetation Index |
PCA | Principal Components Analysis |
PCD | Passive Detection Case |
RDT | Rapid Diagnostic Test |
SMC | Seasonal Malaria Chemoprevention |
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Functional Covariates | Incidence (n + 1) | Incidence (n + 2) |
---|---|---|
fIncidence (n − 17, …, n) | 0.256 | 0.220 |
fWindspeed (n − 17, …, n) | 0.357 | 0.350 |
fRainNb (n − 17, …, n) | 0.363 | 0.390 |
fTemperature (n − 17, …, n) | 0.267 | 0.240 |
fHumidity (n − 17, …, n) | 0.404 | 0.420 |
Functional Covariates | fIncidence | fTemperature | fHumidity | fRainNb | fWindspeed |
---|---|---|---|---|---|
fIncidence | 1.000 | 0.457 | 0.556 | 0.604 | 0.585 |
fTemperature | 0.457 | 1.000 | 0.519 | 0.430 | 0.387 |
fHumidity | 0.556 | 0.519 | 1.000 | 0.887 | 0.705 |
fRainNb | 0.604 | 0.430 | 0.887 | 1.000 | 0.691 |
fWindspeed | 0.585 | 0.387 | 0.705 | 0.691 | 1.000 |
Curves | Edf | Ref.df | F | p-Value |
---|---|---|---|---|
s(fHumidity.PC1) | 3.126 | 4.003 | 3.865 | 0.005 |
s(fWindspeed.PC2) | 2.000 | 2.536 | 2.259 | 0.075 |
s(fRainNb.PC1) | 3.304 | 4.199 | 9.457 | <0.001 |
s(fRainNb.PC2) | 1.000 | 1.000 | 7.840 | 0.006 |
s(fIncidence.PC1) | 8.544 | 8.910 | 4.551 | <0.001 |
s(fIncidence.PC3) | 1.000 | 1.000 | 2.885 | 0.091 |
Goodness-of-Fit Measures of the Functional Models | FGKAM | FGLM | FGSAM |
---|---|---|---|
Adjusted R-sq (%) | 65.70 | 57.90 | 67.30 |
Dev. Explained (%) | 75.10 | 61.20 | 72.40 |
MSPE | 7.52 | 7.50 | 11.38 |
Pred. coverage (%) | 90.00 | 95.00 | 92.50 |
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Share and Cite
Ateba, F.F.; Febrero-Bande, M.; Sagara, I.; Sogoba, N.; Touré, M.; Sanogo, D.; Diarra, A.; Magdalene Ngitah, A.; Winch, P.J.; Shaffer, J.G.; et al. Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models. Int. J. Environ. Res. Public Health 2020, 17, 6339. https://doi.org/10.3390/ijerph17176339
Ateba FF, Febrero-Bande M, Sagara I, Sogoba N, Touré M, Sanogo D, Diarra A, Magdalene Ngitah A, Winch PJ, Shaffer JG, et al. Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models. International Journal of Environmental Research and Public Health. 2020; 17(17):6339. https://doi.org/10.3390/ijerph17176339
Chicago/Turabian StyleAteba, François Freddy, Manuel Febrero-Bande, Issaka Sagara, Nafomon Sogoba, Mahamoudou Touré, Daouda Sanogo, Ayouba Diarra, Andoh Magdalene Ngitah, Peter J. Winch, Jeffrey G. Shaffer, and et al. 2020. "Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models" International Journal of Environmental Research and Public Health 17, no. 17: 6339. https://doi.org/10.3390/ijerph17176339