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Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence
- Publication Year :
- 2020
- Publisher :
- Elsevier, 2020.
-
Abstract
- Maps of disease burden are a core tool needed for the control and elimination of malaria. Reliable routine surveillance data of malaria incidence, typically aggregated to administrative units, is becoming more widely available. Disaggregation regression is an important model framework for estimating high resolution risk maps from aggregated data. However, the aggregation of incidence over large, heterogeneous areas means that these data are underpowered for estimating complex, non-linear models. In contrast, prevalence point-surveys are directly linked to local environmental conditions but are not common in many areas of the world. Here, we train multiple non-linear, machine learning models on Plasmodium falciparum prevalence point-surveys. We then ensemble the predictions from these machine learning models with a disaggregation regression model that uses aggregated malaria incidences as response data. We find that using a disaggregation regression model to combine predictions from machine learning models improves model accuracy relative to a baseline model.
- Subjects :
- Epidemiology
Computer science
Health, Toxicology and Mutagenesis
030231 tropical medicine
Geography, Planning and Development
High resolution
1117 Public Health and Health Services
03 medical and health sciences
0302 clinical medicine
parasitic diseases
Statistics
medicine
Prevalence
Humans
030212 general & internal medicine
Malaria, Falciparum
Disease burden
0707 Veterinary Sciences
Incidence
Contrast (statistics)
Regression analysis
Non linear model
medicine.disease
Regression
Malaria
Infectious Diseases
Malaria incidence
Nonlinear Dynamics
Subjects
Details
- Language :
- English
- ISSN :
- 18775845
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....dd39ddacb622f41afb070cd74ace863b