Abstract
BackgroundMalaria is the world’s most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality.MethodsWe describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as LOW, MEDIUM or HIGH, where these classes are defined as a total of 0–2, 3–16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak.ResultsModel accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7–8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the MEDIUM class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3.ConclusionsA previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict LOW, MEDIUM or HIGH cases 7–8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.
Highlights
Malaria is the world’s most prevalent vector-borne disease
Variables used for input into the prediction model include epidemiological, environmental, and socioeconomic data found to be useful in previous studies
Linthicum et al [13] noted that malaria outbreaks in Republic of Korea (ROK) were possibly a result of infected mosquitoes entering from Democratic People’s Republic of Korea (DPRK)
Summary
Malaria is the world’s most prevalent vector-borne disease. Malaria is a parasitic disease of humans and is transmitted via the bite of infectious female Anopheles mosquitoes. The adult female mosquito requires a blood meal in order to obtain the proteins necessary to complete the development of her eggs [1]. The salivary glands of infectious mosquitoes carry parasites belonging to the genus Plasmodium, with P. vivax and P. falciparum being the most common pathogens for humans. Uninfected mosquitoes acquire the parasite when they feed on infected hosts, thereby completing the transmission cycle. If the infectious mosquito doesn’t survive longer than this, malaria won’t be transmitted. These mosquitoes often breed in stagnant water commonly found in ditches, rice fields, and impounded waters associated
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