Modelación matemática para la predictibilidad del dengue en Ecuador

Abstract

The emergence of vector diseases, especially those transmitted by the Aedes aegypti mosquitoe, have become a problem in public health, especially due to the continuous population movement, growth in urban areas and even the monthly variability in precipitation and temperature. Thanks to Bayesian theory and the generation of Markov Chains, the aim is to merge a priori knowledge of the researcher and some criteria acquired in the study of vectors to approximate reality to a set of factors that explain the spread of the disease concentrating at the level of circuits with monthly temporality. Supported by this toolkit, the relative risk for each year in the period 2010 to 2015 gives us an idea of how this indicator changes according to climatic fluctuations and the inclusion of housing and health conditions, typical of each locality. The results show that the increase in the number of dengue cases is related to areas that still have deficiencies such as limitation in the collection of garbage, access to water and inadequate sewage system, which in combination with the increase of rain in the months of February to May, July and December are ideal scenarios for the production of mosquitoe breeding sites and their reproduction