Intelligent Systems for Dengue, Chikungunya, and Zika Temporal and Spatio-Temporal Forecasting: A Contribution and a Brief Review

Abstract

Arboviruses are diseases transmitted by viruses which are maintained in the wild through a vertebrate host and a hematophagous arthropod, such as a mosquito. The transmitter vector of an arbovirus is the arthropod which transmits the virus from one vertebrate to the other through a bite. The biological transmission usually occurs when the hematophagous arthropod feeds on a viremic vertebrate and deposits infectious saliva during the feeding of the blood of another vertebrate. However, in some types of arboviruses, the biological transmission occurs directly in the human–mosquito cycle. Moreover, other forms of transmission have been reported, such as transmission from mother to child during pregnancy, blood transfusion, and through sexual intercourse. Demographic changes and the intense migratory flow from rural areas to urban areas have generated disorderly growth in cities. Deficiencies in basic sanitation also contribute to the vector’s proliferation in tropical and subtropical countries. Brazil, which is a tropical country, is very affected by arboviruses, such as dengue, malaria, and yellow fever. With climate change and the increase in the number and frequency of international flights, two new arboviruses transmitted by the Aedes aegypti mosquito appeared in Brazil: the chikungunya and the Zika virus. This situation brings new challenges regarding the control and vector monitoring. The advancement of Digital Epidemiology, together with the development of Data Mining and Machine Learning techniques, provided rapid monitoring, control, and simulation of the spread of diseases. With this in mind, the prediction tools are able to assist public health systems in controlling epidemics and behavioral factors that favor the vector of these diseases. In this sense, in this chapter we present a literature review to identify methods of predicting cases of arboviruses, as well as the prediction of breeding sites.