论文部分内容阅读
ABSTRACT Objective:To study the number of leptospirosis cases in relations to the seasonal patt, and its association with climate factors.Methods:Time series analysis was used to study the time variations in the number of leptospirosis cases.TheAutoregressiveIntegratedMovingAverage (ARIMA) model was used in data curve fitting and predicting the next leptospirosis cases. Results:We found that the amount of rainfall was correlated to leptospirosis cases in both regions of interest, namely the north and northeast region ofThailand, while the temperature played a role in the northeast region only.The use of multivariateARIMA(ARIMAX) model showed that factoring in rainfall(with an8 months lag) yields the best model for the north region while the model, which factors in rainfall(with a10 months lag) and temperature(with an8 months lag) was the best for the northeast region.Conclusions:The models are able to show the trend in leptospirosis cases and closely fit the recorded data in both regions.The models can also be used to predict the next seasonal peak quite accurately.