Modeling of Dengue by Cluster Analysis and Probability Distribution Functions in the State of Alagoas in Brazilian

Abstract

Dengue is a viral disease whose number of cases has increased in Brazil. This study aimed to characterize the spatio-temporal distribution patterns of the reported dengue infection cases in the state of Alagoas (AL), Northeastern Brazil (NEB). The data of the officially reported dengue cases from 2000 to 2015 was retrieved from the State Health Secretariat of Alagoas (SESAL), which captures national demographic and health data from the System for the Reporting of Notifiable Conditions (SINAN). After applying the Kernel Density Estimation (KDE) function, maps were generated based on the Inverse Distance Weighting (IDW) interpolation method. By using the clusters analysis (CA) technique, three homogeneous groups of dengue in AL were determined. Next, the LN (Lognormal), GUM (Gumbel) and GEV (Generalized Extreme Value) probability distributions were applied to monthly model dengue case data in AL, with the LN continuous probability distribution standing out. Maceió and Arapiraca have a higher number of dengue cases than other cities, being the main reason for their interpretation as separate groups. The coefficients of determination (R²) of dengue cases analysis as a function of month of each year for the studied years were low (between 0.03 and 0.63) and many regression slopes were not significant. Pearson's correlation coefficient (r) between dengue and the Human Development Index (HDI) of LA was considered moderate (0.53) and the correlation between dengue and demographic density was high (0.76). The importance of constant monitoring and assistance for these areas is reinforced.

Keywords:
Dengue cases; Health Geography; Urban space

HIGHLIGHTS

• A model to jointly assess the spatial distribution of reported dengue and severity.

• The utility of cluster analysis is demonstrated for PDF characterization;

• Cluster analysis identifies regions with similar PDF structure;

• Cluster analysis will be a straightforward model evaluation and analysis tool;

• Climate change affects human infectious disease.