Soft computing forecasting of cardiovascular and respiratory incidents based on climate change scenarios

Abstract

Climate change is one of the most serious threats for modern societies. It contributes to the fluctuation of air pollutants' concentrations which affects the number of respiratory and cardiovascular incidents. This research initially determines the contributing meteorological features for the maximization of air pollutants on a seasonal basis. In the second stage it employs Fuzzy Cognitive Maps (FCMs) to model and forecast the level of morbidity and mortality due to the above health problems, which are intensified from the changes in minimum and maximum meteorological values. This research effort takes into consideration the climate change scenarios for the period up to 2100. The assessment of the proposed model is done on historical meteorological, pollution and nursing data from the prefecture of Thessaloniki, for the period 2000–2013.