The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017)

Abstract

Background: In Thailand, dengue fever is one of the most well-known public health problems. The objective of this study was to examine the epidemiology of dengue and determine the seasonal pattern of dengue and its associate to climate factors in Bangkok, Thailand, from 2003 to 2017.

Methods: The dengue cases in Bangkok were collected monthly during the study period. The time-series data were extracted into the trend, seasonal, and random components using the seasonal decomposition procedure based on loess. The Spearman correlation analysis and artificial neuron network (ANN) were used to determine the association between climate variables (humidity, temperature, and rainfall) and dengue cases in Bangkok.

Results: The seasonal-decomposition procedure showed that the seasonal component was weaker than the trend component for dengue cases during the study period. The Spearman correlation analysis showed that rainfall and humidity played a role in dengue transmission with correlation efficiency equal to 0.396 and 0.388, respectively. ANN showed that precipitation was the most crucial factor. The time series multivariate Poisson regression model revealed that increasing 1% of rainfall corresponded to an increase of 3.3% in the dengue cases in Bangkok. There were three models employed to forecast the dengue case, multivariate Poisson regression, ANN, and ARIMA. Each model displayed different accuracy, and multivariate Poisson regression was the most accurate approach in this study.

Conclusion: This work demonstrates the significance of weather in dengue transmission in Bangkok and compares the accuracy of the different mathematical approaches to predict the dengue case. A single model may insufficient to forecast precisely a dengue outbreak, and climate factor may not only indicator of dengue transmissibility.

Keywords: ARIMA; Artificial neuron network; Dengue fever; Poisson regression.

Fig. 1

a: The number of dengue incidence rate per 100,000 population in Bangkok from 2003 to 2017. b: Monthly box plot distribution of dengue incidence rate

Fig. 2

The decomposition plot of the time-series dengue case in Bangkok from 2003 to September 2017. a) The decomposition plot of raw data set; b) The decomposition plot of adjusted data set; The other layers show the decomposed components, representing the seasonal, trend, and random component, respectively

Fig. 3

Histogram of adjusted dengue incidence data, $Y_t^{\ast }$ in Bangkok from 2003 to 2017, classified by quarterly

Fig. 4

a: The dengue incidence rate per 100,000 population between real number in 2004-2016, the train set. b: The actual number in 2017 and the predicted number (MDR, ANN, and ARIMA), the test set