A PREDICTIVE MODEL FOR DENGUE CASES IN SOUTHERN LAM DONG PROVINCE, VIETNAM, 2007-2017
Background: Dengue is arthropod-borne disease and one of major causes of morbidity and fatality in the Western Pacific Region. Climate is considered one of the main factors for dengue transmission. Objective of the study is to determine correlations between climatic factors and dengue, using reported cases in Southern Lam Dong Province, Vietnam, 2007-2017 and to validate the predictive model for number of dengue cases in 2017 using data from 2007 to 2016.
Materials and Methods: This was a retrospective quantitative study. Spearman’s Rank test was used to examine the correlation between each climatic factor and dengue reported cases. Seasonal Autoregressive Integrated Moving Average (SARIMA) models using the training data set from 2007 to 2016, correlative factors of dengue cases, the Bayes Information Criterion (BIC) and improved Box-Jenkins models, were applied to predict dengue cases during 2017. There is a wide range of potential confounders for annual dengue epidemics such as mosquito ecology, population density, population immunity and dengue cycle. Amongst these factors, population density was forced into the predictive model. Data analysis was done using Excel and SPSS version 16.
Result: There were significant correlations between dengue cases and climatic factors, consisting of minimum temperature (r = 0.384, p < 0.01) and relative humidity (r = 0.372, p < 0.01). The SARIMA (1,2,1) x (1,1,1)12 model at lag one month was the best fitted model for predicting dengue cases.
Conclusion: Predicted cases from time series model would be imperative for controlling and preventing the occurrence of dengue epidemics in the community. This study used secondary data, so it was difficult to control the occurrence of missing data point. Climatic and non-climate factors should be considered in predictive models for dengue epidemiology in the future.
Keywords: climatic factors, dengue cases, correlation, predictive model, Lam Dong Province