![]() ![]() Recovery from infection (primary infection) by one serotype provides lifelong immunity against that serotype and temporary for the other. Dengue virus has four different serotypes (DENV 1-4) that can transmit to humans. Dengue symptoms can be classified into three categories depending on the clinical syndromes, from mild to severe, dengue fever (DF), dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). Severe cases can be massive bleeding, shock, and death. The symptoms of dengue fever individuals range from no signs, mild fever, high fever, pain behind eyes, headache, vomiting, and muscle pains. ![]() Approximately a third of the world population are living in dengue-endemic areas, the significant disease burden being in tropical and subtropical regions, which are mostly developing countries. The dengue virus is a single positive-stranded RNA virus of the family Flaviviridae genus Flavivirus. Dengue virus is the cause of dengue fever. A single model may insufficient to forecast precisely a dengue outbreak, and climate factor may not only indicator of dengue transmissibility.ĭengue fever is one of the most common infectious diseases in Thailand and one of the top threats to global public health. 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. Each model displayed different accuracy, and multivariate Poisson regression was the most accurate approach in this study. There were three models employed to forecast the dengue case, multivariate Poisson regression, ANN, and ARIMA. 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. ANN showed that precipitation was the most crucial factor. 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. 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 and artificial neuron network (ANN) were used to determine the association between climate variables (humidity, temperature, and rainfall) and dengue cases in Bangkok. The time-series data were extracted into the trend, seasonal, and random components using the seasonal decomposition procedure based on loess. The dengue cases in Bangkok were collected monthly during the study period. 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. In Thailand, dengue fever is one of the most well-known public health problems. ![]()
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