# Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System+
## 1 Introduction  
Malaria, as one of the most serious infectious diseases causing public health problems in the world, affects about two-thirds of the world population, with estimated resultant deaths close to a million annually [1]. Its prevalence can be significantly attributed to climate factors, usually worsened by human factors through poor sanitation, overwhelmed sewage and deforestation. These climatic factorswere found to contribute to the incidence of malaria [2], which apparently imposes a greater challenge to human life today. The effects of malaria are much more profound in third world countries due to very limited medical resources. When an intense outbreak occurs, most of these countries cannot cope with the high number of patients due to the lack of medicine, equipment and hospital facilities. The prevention or reduction of the risk factor of this disease is very challenging, especially in these countries, due to poverty, and economic insatiability. Technology can offer alternative solutions by providing early detection mechanisms that help to control the spread of the disease and allow the management of treatment facilities in advance to ensure a more timely health service, which can save thousands of lives. The availability of an early detection system will not only prevent or decrease the large spread of malaria by creating quarantine zones, but also help healthcare providers deliver the necessary medical care on time by managing resources and calling for international aid and support, if needed. In this study, we aim to design and deploy an _intelligent malaria outbreak early warning system_, which is a mobile application, that predicts malaria outbreak based on climatic factors using machine learning algorithms. The system will help hospitals, healthcare providers, and health organizations take precautions in time and utilize their resources in case of emergency. To our best knowledge, the system developed in this paper is the first publicly available application. As well as deploying a smart healthcare application, this paper also makes a significant contribution by identifying _hidden ecological factors_ of malaria (e.g., temperature, humidity, wind, location, drought, floods, etc.). Since confounding effects of climatic factors have a greater influence on the incidence of malaria, we have also conducted extensive research on exploring a new ecosystem model for the assessment of hidden ecological factors and identified three confounding factors that significantly contribute to the outbreak of malaria. In this paper, we use an efficient methodology, comprising four stages. In the first stage, we have collected data from some repositories. Unfortunately, most of this data was incomplete in terms of climate factors. We have completed the dataset with the climate variables using satellite-based meteorological data obtained from CFSR (Climate Forecast System Reanalysis). In the second stage, we have identified hidden ecological factors of malaria. The fundamental concept behind this emanated from the fact that a causal relationship exists among the climatic factors [3]. Some recent studies [4; 5] combined meteorological variables together with malaria incidence data and established time series models for predicting malaria incidence. Regression and correlation analysis modelling was applied and using meteorological variables the trend of malaria incidence was determined [6]. Also, one of the most recent studies presented in this direction [7] uses a hybrid approach for time-series modelling and lagged-regression analysis of climate data combined with reported malaria incidence cases. Their result showed that malaria incidence in the area studied has a significant association with relative humidity, whereas temperature and precipitation were found to have negligible effects. This finding might particularly reveal that malaria incidence can be strongly influenced by relative humidity alone.