Geospatial Analysis of DHF Surveillance Model in Si Sa Ket Province, Thailand using Geographic Information System

Abstract

Dengue Hemorrhagic Fever (DHF) is an enormous global health problem due to its morbidity and mortality across the world and Thailand. The Dengue virus presents four serotypes that contacted patients three or four times. The repeated outbreaks of DHF reported from every part of the world. The issue, we found is lacking proper surveillance. This requires a well-developed surveillance system to be developed by a surveillance organization. The purpose of this study was to investigate the issues and develop a surveillance system or model for DHF with the help of community participation and Geographic Information Systems (GIS). This study aimed to analyze the relationship between constituents that anticipated to the case of the disease. The methods are done with techniques, statistics, frequencies, percentage average, SD, odds ratio (OR), and logistic regression. Three parameters considered were  (HI), ) and Breteau Index (BI).  It was found that the selected villages associated with the higher levels of risk factors in the villages with HI≥5 (OR_adj = 5.02; 95% CI = 2.84-8.89), BI>50 (OR_adj = 4.84; 95% CI = 1.28-18.37), The flooded area (OR_adj = 2.47; 95% CI = 1.39-4.39), Patients with dengue 2013 (OR_adj = 2.31; 95% CI = 1.36-3.92), Patients with dengue 2014 (OR_adj = 1.81; 95% CI = 1.01-3.23) and area of housing> 100 rai (OR_adj = 1.73; 95% CI = 1.03-2.91). The logistic regression equation of the environments to estimate the risk of DHF was   . Using the proposed method, the study could find the number of infected people in 11 experimental villages and also infected with DHF outside the area. The index of mosquito larvae was reduced in short, middle and long-term periods while the levels of distance from the forest, and the number of the patient was increased. The outcome already showed GIS associated with vector index and DHF infection can map out the expected infected population. Therefore, the proposed method may be encouraged to support the system design of disease surveillance for DHF and may be applied in other areas.