Malaria and dengue hemorrhagic fever （DHF） are infectious diseases prevalent in many tropical countries, including Thailand. Thailand is located geographically in a tropical zone and the transmission of malaria and ...Malaria and dengue hemorrhagic fever （DHF） are infectious diseases prevalent in many tropical countries, including Thailand. Thailand is located geographically in a tropical zone and the transmission of malaria and DHF is common, particularly in the upper Northern region of the country. The objective of this study is to identify the patterns of hospital-diagnosed Malaria and DHF incidences by using the previous monthly or quarterly periods of incidences occurring in the upper Northern region of Thailand. The authors use additive plus multiplicative regression models to describe these patterns. The models can be used to forecast malaria and DHF incidences, thus predicting where epidemics are likely to occur. This information can be used to prevent disease outbreaks occurring. Graphical displays showing district and period effects are presented. The results of this study show that historical malaria and DHF incidence rates can be used to provide a useful model for forecasting future epidemics. The graphical display shows the improvement of risk prediction brought about by model. The model, even if based purely on statistical data analysis, can provide a useful basis for allocation of resources for disease prevention.展开更多
文摘Malaria and dengue hemorrhagic fever （DHF） are infectious diseases prevalent in many tropical countries, including Thailand. Thailand is located geographically in a tropical zone and the transmission of malaria and DHF is common, particularly in the upper Northern region of the country. The objective of this study is to identify the patterns of hospital-diagnosed Malaria and DHF incidences by using the previous monthly or quarterly periods of incidences occurring in the upper Northern region of Thailand. The authors use additive plus multiplicative regression models to describe these patterns. The models can be used to forecast malaria and DHF incidences, thus predicting where epidemics are likely to occur. This information can be used to prevent disease outbreaks occurring. Graphical displays showing district and period effects are presented. The results of this study show that historical malaria and DHF incidence rates can be used to provide a useful model for forecasting future epidemics. The graphical display shows the improvement of risk prediction brought about by model. The model, even if based purely on statistical data analysis, can provide a useful basis for allocation of resources for disease prevention.
