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.
Background: The dengue fever is a mosquito-borne viral disease and a regular epidemic in Thailand. The peak of the dengue epidemic period is around June to August during the rainy season. It is believed that the climate is an important factor for dengue transmission.Method: A mathematical model for vector-host infectious disease was used to calculate the impacts of climate to the transmission of dengue virus. In this study, the data of climate and dengue fever cases were derived from Chiang Mai during 2008-2015, Thailand. The value of seasonal reproduction number was calculated to evaluate the potential, severity and persistence of dengue infection.Results: The mosquito population was increasing exponentially from the start of the rainy season in early May and reached its the peak in late June. The simulations suggest that the greatest potential for the dengue transmission occurs when the temperature is 28.9ºC. The seasonal reproduction numbers were larger than one from late March to end of August and reaching the peak in June. The highest incidences occurred in August due to the delay of transmission humans-mosquito-humans. Increasing mean temperature by 1.2ºC, the number of incidences increases 43.7%. However, a very high or very low temperature reduces the number of infection.Discussion and Conclusion: The results show that the dengue infection depends on the seasonal variation of the climate. The rainfall provides places for the mosquitoes to lay eggs and develop to adult stage. The temperature plays an important role in the life cycle and behavior of the mosquitoes. A very high or very low temperature reduces the risk of the dengue infection.
Background. Dengue fever is a mosquito-borne viral disease and a regular epidemic in Thailand. The peak of the dengue epidemic period is around June to August during the rainy season. It is believed that climate is an important factor for dengue transmission.Method. A mathematical model for vector–host infectious disease was used to calculate the impacts of climate to the transmission of dengue virus. In this study, the data of climate and dengue fever cases were derived from Chiang Mai during 2004–2014, Thailand. The value of seasonal reproduction number was calculated to evaluate the potential, severity and persistence of dengue infection.Results. The mosquito population was increasing exponentially from the start of the rainy season in early May and reached its the peak in late June. The simulations suggest that the greatest potential for the dengue transmission occurs when the temperature is 28.9 °C. The seasonal reproduction numbers were larger than one from late March to end of August and reaching the peak in June. The highest incidences occurred in August due to the delay of transmission humans-mosquito-humans. Increasing mean temperature by 1 °C, the number of incidences increases 28.1%. However, a very high or very low temperature reduces the number of infection.Discussion and Conclusion. The results show that the dengue infection depends on the seasonal variation of the climate. The rainfall provides places for the mosquitoes to lay eggs and develop to the adult stage. The temperature plays an important role in the life cycle and behavior of the mosquitoes. A very high or very low temperature reduces the risk of the dengue infection.
Our results provided a fundamental step toward estimation of the risk of the secondary transmission of DENV in non-epidemic countries via travelers, which can serve as an early warning of a dengue outbreak. The highest infective person-day is associated with the rainy season in Thailand. The increasing number of overseas travelers may increase the risk of global transmission of the DENV. Better understanding of the virus transmission dynamics will enable further quantitative predictions of epidemic risk.
The objective of this study is to find the correlation between climate factors and dengue incidence rate in Bangkok and Singapore during 2009-2015. Spearman's rank correlation tests with time-lag are performed to investigate the overall correlation between dengue incidence rates and climate factors , minimum, mean, and maximum temperatures, DTR, and rainfall. A Linear and Poisson regression analysis was performed. Spearman's rank correlation tests show that in Bangkok monthly rainfall (r=0.451, p<0.001), the number of rainy days (r=0.411, p<0.001) are positive correlation with 2 month lag time. DTR (r=-0.335, p<0.001) is negative correlation with 2 month lag time. Maximum (r=0.256, p<0.001), mean (r=0.304, p<0.001) and minimum (r=0.323, p<0.001) temperature are positive correlation with 4 month lag time. In Singapore, only minimum temperature (r=-0.299, p<0.001) with 1 month lag time is negative correlation and DTR (r=-0.289, p<0.001) with zero month lag time is positive correlation. The rest has no statically significance (p>0.05). This study concluded, climate factors play moderate role in dengue incidence in Bangkok. There is no statistical significance between rainfall and dengue incidence rate and temperature play a marginal role in Singapore.
Background: The dengue fever is a mosquito-borne viral disease and a regular epidemic in Thailand. The peak of the dengue epidemic period is around June to August during the rainy season. It is believed that the climate is an important factor for dengue transmission.Method: A mathematical model for vector-host infectious disease was used to
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