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|Title:||Climatic factors influencing dengue hemorrhagic fever in Kolaka district, Indonesia|
Faculty of Environment and Resource Studies, Mahidol University
University of Halu Oleo Kendari
|Citation:||Environment and Natural Resources Journal. Vol.16, No.2 (2018), 1-10|
|Abstract:||© 2018, Faculty of Environment and Resource Studies,Mahidol University. All rights reserved. Dengue hemorrhagic fever in Indonesia is one of the serious health problems and requires understanding the occurrence of this disease. Climate Factors have a role that needs attention in the prevention of DHF disease. Understanding of disease patterns will benefit the health surveillance system and provide a way to tackle this problem. The records of dengue fever cases and climate data for the years 2010-2015 were obtained from the Health Office Kolaka District, southeast Sulawesi province and Meteorology, Climatology and Geophysics Agency in Southeast Sulawesi province, respectively. Data for the period 2010 to 2014 were used for model development through multiple linear regressions. The prediction model was used to forecast dengue cases in 2015 and the predicted results were compared with reported dengue cases in Kolaka in the past and forecasting period. Rainfall, humidity, temperature average, minimum temperature, and maximum temperature are significantly correlated with monthly cases of dengue fever. Predicted results showed a good performance where the model was able to predict 3 out of 5 epidemic outbreak events that occurred in January-March 2015 and November-December 2015. The sensitivity of detecting the outbreaks was estimated to be 60%, the specificity was 100%, positive and negative predictive value were estimated to be 100% and 77.8%, respectively. Climate has a major influence on the occurrence of dengue hemorrhagic fever infection in Kolaka district. Although the predictive model has some limitations in predicting the number of cases of monthly dengue fever, it can estimate the possibility of an outbreak three months in advance with a fairly high accuracy. The predictive model can be used to explain the incident rate of DHF of approximately 71%.|
|Appears in Collections:||Scopus 2018|
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