A Spread Study for Dengue fever through SIR-SI compartmental model and fuzzy systems using cellular automata with stochastic approach

Gabriel Valdez, Luis Salgueiro, Christian E. Schaerer


Dengue fever is an endemic disease that continues to cause health problems in the world population. There are thousands of cases of dengue fever infections per year and for each detected case there are approximately five undetected. Methods used to deal with dengue fever includes cleaning and elimination of possible hatcheries for the vector (mosquito of the Aedes family) and the use of insecticides for fumigation to reduce vector population to an acceptable limit. Nonetheless, dengue fever resurfaces after a certain time. The aim of this work is to model dengue fever spread for the prediction of new cases. To this end, a SIR-SI model is used coupled to transition rules as part of a fuzzy system which includes probabilistic parameters for determining the changes in the agent status. This allows having an estimation of possible dengue cases in such a way that the health units can take necessary prevention actions and measure to corroborate the prediction. [...]

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