Predictiveness of Disease Risk in a Global Outreach Tourist Setting in Thailand Using Meteorological Data and Vector-Borne Disease Incidences (original) (raw)
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2014
Abstract: Dengue and malaria are vector-borne diseases and major public health problems worldwide. Changes in climatic factors influence incidences of these diseases. The objective of this study was to investigate the relationship between vector-borne disease incidences and meteorological data, and hence to predict disease risk in a global outreach tourist setting. The retrospective data of dengue and malaria incidences together with local meteorological factors (temperature, rainfall, humidity) registered from 2001 to 2011 on Koh Chang, Thailand were used in this study. Seasonal distribution of disease incidences and its correlation with local climatic factors were analyzed. Seasonal patterns in disease transmission differed between dengue and malaria. Monthly meteorological data and reported disease incidences showed good predictive ability of disease transmission patterns. These findings provide a rational basis for identifying the predictive ability of local meteorological facto...
Association between climate variables and dengue incidence in Nakhon Si Thammarat Province, Thailand
Geospatial Health
The tropical climate of Thailand encourages very high mosquito densities in certain areas and is ideal for dengue transmission, especially in the southern region where the province Nakhon Si Thammarat is located. It has the longest dengue fever transmission duration that is affected by some important climate predictors, such as rainfall, number of rainy days, temperature and humidity. We aimed to explore the relationship between weather variables and dengue and to analyse transmission hotspots and coldspots at the district-level. Poisson probability distribution of the generalized linear model (GLM) was used to examine the association between the monthly weather variable data and the reported number of dengue cases from January 2002 to December 2018 and geographic information system (GIS) for dengue hotspot analysis. Results showed a significant association between the environmental variables and dengue incidence when comparing the seasons. Temperature, sea-level pressure and wind s...
Weather factors influencing the occurrence of dengue fever in Nakhon Si Thammarat, Thailand
2013
This study explored the impact of weather variability on the transmission of dengue fever in Nakhon Si Thammarat, Thailand. Data on monthly-notified cases of dengue fever, over the period of January 1981 - June 2012 were collected from the Bureau of Epidemiology, Department of Disease Control, Ministry of Public Health. Weather data over the same period were obtained from the Thai Meteorological Department. Spearman correlation analysis and time-series adjusted Poisson regression analysis were performed to quantify the relationship between weather and the number of dengue cases. The results showed that maximum and minimum temperatures at a lag of zero months, the amount of rainfall, and relative humidity at a lag of two months were significant predictors of dengue incidence in Nakhon Si Thammarat. The time series Poisson regression model demonstrated goodness-of-fit with a correlation between observed and predicted number of dengue incidence rate of 91.82%. This model could be used ...
Distribution, seasonal variation & dengue transmission prediction in Sisaket, Thailand
The Indian Journal of Medical Research
Background & objectives : Environmental factors including weather variables may play a significant role in the transmission of dengue. This study investigated the effect of seasonal variation on the abundance of Aedes aegypti and Ae. albopictus larvae and explored the impact of weather variability on dengue transmission in Sisaket, Thailand. Methods : The monthly mosquito larval surveys were carried out in urban and rural areas in Sisaket, Thailand from January to December 2010. Data on monthly-reported cases of dengue fever over the period 2004-2010 were obtained from the Ministry of Public Health. Weather data over the same period were obtained from the Thai Meteorological Department. Chi-square test was used to find the differences relating to seasonal variability, areas of study, and mosquito species factors using entomological survey data. Time series Poisson regression analysis was performed using data on monthly weather variables and dengue cases. Results : There were more Ae...
BMC Infectious Diseases, 2020
BackgroundIn 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.MethodsThe 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.ResultsThe 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.ConclusionThis 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.
Climatic variability and dengue virus transmission in Chiang Rai, Thailand
Objective: The objective of this study was to assess the impact of climate variation on the dengue virus transmission in Chiang Rai, Thailand. Materials and Methods: We obtained population – based information on monthly variation in monthly dengue cases and climatic factors. A time series analysis was conducted by using cross-correlation function and seasonal auto-regressive integrated moving average (SARIMA) mode-ling. Results and Conclusions: Our findings indicate that rainfall and minimum temperature seem to have played an important role in the transmission of dengue in Chiang Rai. Such model may be used to assist public health decision – making and environmental health risk management. Early warning based on forecasts could assist in improving vector control, community intervention, and personal protection.
Weather as an effective predictor for occurrence of dengue fever in Taiwan
Acta Tropica, 2007
We evaluated the impacts of weather variability on the occurrence of dengue fever in a major metropolitan city, Kaohsiung, in southern Taiwan using time-series analysis. Autoregressive integrated moving average (ARIMA) models showed that the incidence of dengue fever was negatively associated with monthly temperature deviation (β = −0.126, p = 0.044), and a reverse association was also found with relative humidity (β = −0.025, p = 0.048). Both factors were observed to present their most prominent effects at a time lag of 2 months. Meanwhile, vector density record, a conventional approach often applied as a predictor for outbreak, did not appear to be a good one for diseases occurrence.
Climatic Factors Affecting Dengue Haemorrhagic Fever Incidence in Southern Thailand
2005
jmullica@wu.ac.th; " +66 75 672 005; Fax: +66 75 672 004 This study investigated the climatic factors associated with dengue haemorrhagic fever (DHF) incidence in southern Thailand. The climatic factors comprised rainfall, rainy days, relative humidity and maximum, minimum and mean temperatures. Pearson's correlation coefficient was used to explore the primary association between the DHF incidence and all climatic factors. Step-wise regression technique was then used to fit the statistical model. The result indicated that the mean temperature, rainfall and relative humidity were associated with the DHF incidence in the areas bordering on the Andaman Sea, while minimum temperature, rainy days and relative humidity were associated with the DHF incidence on the Gulf of Thailand side of the southern peninsula.
2015
Background: Weather variables affect dengue transmission. This study aimed to identify a dengue weather correlation pattern in Kandy, Sri Lanka, compare the results with results of similar studies, and establish ways for better control and prevention of dengue. Method: We collected data on reported dengue cases in Kandy and midyear population data from 2003 to 2012, and calculated weekly incidences. We obtained daily weather data from two weather stations and converted it into weekly data. We studied correlation patterns between dengue incidence and weather variables using the wavelet time series analysis, and then calculated cross-correlation coefficients to find magnitudes of correlations. Results: We found a positive correlation between dengue incidence and rainfall in millimeters, the number of rainy and wet days, the minimum temperature, and the night and daytime, as well as average, humidity, mostly with a five-to seven-week lag. Additionally, we found correlations between dengue incidence and maximum and average temperatures, hours of sunshine, and wind, with longer lag periods. Dengue incidences showed a negative correlation with wind run. Conclusion: Our results showed that rainfall, temperature, humidity, hours of sunshine, and wind are correlated with local dengue incidence. We have suggested ways to improve dengue management routines and to control it in these times of global warming. We also noticed that the results of dengue weather correlation studies can vary depending on the data analysis.