Dana A. Focks Mohammad Juffrie Dana A. Focks
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EarlyWarning System (EWS) for Dengue in Indonesia and Thailand Dana A. Focks, Mohammad Juffrie, Dana A. Focks
Journal of the Medical Sciences (Berkala Ilmu Kedokteran) Vol 41, No 03 (2009)
Publisher : Journal of the Medical Sciences (Berkala Ilmu Kedokteran)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (268.089 KB)

Abstract

Background: Dengue virus infection is an acute febrile disease caused by 4 sero-type viruses. The transmission via mosquito vector Ae. Aegypti. The morbidity of dengue virus infection is quite high and the mortality below 5%. The most dangerous form is dengue shock syndrome, the mortality is very high. The effort to reduce morbidity and mortality is improvement of the clinical management and control of vector. Today, most dengue control efforts are based on suppression of Aedes aegypti (L.) and not eradication. EWS would provide significant utility where mitigation methods were available. EWSs were possible for three reasons, an extensive time series on the disease incidence the available, dengue being a vector-borne disease, is significantly influenced by weather, in many sub-regions of SE Asia, weather anomalies are significantly influenced by and lag behind several months, sea surface temperature (SST) anomalies. Methods: Analytic cross sectional study was conducted. The dependant variable in this analysis, Epi.yr. is dichotomous and indicates whether an epidemic occurred during a particular year. The two independent (predictor) variables are sea surface temperature anomalies as reported by the Japanese Meteorological Association (JMA) and previous cases. The monthly number of cases were dengue and DHF in Yogyakarta, Indonesia and the metropolitan area of Bangkok, Thailand. Results: Yogyakarta, many years were very near the epidemic cutoff of 278 cases, yet only one year, 1992 with 237 cases, was incorrectly labeled. The false positive in 1992, had a probability of 0.64 of epidemic and 0.36 of no epidemic. Bangkok, the best three-month prediction gave 6 false indication in 35 years, 5 false negatives, 1 false positive. For two month prediction, 3 errors in 35 years were made, 2 false negatives, 1 false positive. Conclusion: The results presented in this study is very use full for predicting the incidence of dengue virus infection using weather data. This method would only require a simple calculator, or preferably a PC using the derived equation. Key words: dengue -incidence -early warning -weather - probability
EarlyWarning System (EWS) for Dengue in Indonesia and Thailand Dana A. Focks, Mohammad Juffrie, Dana A. Focks
Journal of the Medical Sciences (Berkala Ilmu Kedokteran) Vol 41, No 03 (2009)
Publisher : Journal of the Medical Sciences (Berkala ilmu Kedokteran)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (268.089 KB)

Abstract

Background: Dengue virus infection is an acute febrile disease caused by 4 sero-type viruses. The transmission via mosquito vector Ae. Aegypti. The morbidity of dengue virus infection is quite high and the mortality below 5%. The most dangerous form is dengue shock syndrome, the mortality is very high. The effort to reduce morbidity and mortality is improvement of the clinical management and control of vector. Today, most dengue control efforts are based on suppression of Aedes aegypti (L.) and not eradication. EWS would provide significant utility where mitigation methods were available. EWSs were possible for three reasons, an extensive time series on the disease incidence the available, dengue being a vector-borne disease, is significantly influenced by weather, in many sub-regions of SE Asia, weather anomalies are significantly influenced by and lag behind several months, sea surface temperature (SST) anomalies. Methods: Analytic cross sectional study was conducted. The dependant variable in this analysis, Epi.yr. is dichotomous and indicates whether an epidemic occurred during a particular year. The two independent (predictor) variables are sea surface temperature anomalies as reported by the Japanese Meteorological Association (JMA) and previous cases. The monthly number of cases were dengue and DHF in Yogyakarta, Indonesia and the metropolitan area of Bangkok, Thailand. Results: Yogyakarta, many years were very near the epidemic cutoff of 278 cases, yet only one year, 1992 with 237 cases, was incorrectly labeled. The false positive in 1992, had a probability of 0.64 of epidemic and 0.36 of no epidemic. Bangkok, the best three-month prediction gave 6 false indication in 35 years, 5 false negatives, 1 false positive. For two month prediction, 3 errors in 35 years were made, 2 false negatives, 1 false positive. Conclusion: The results presented in this study is very use full for predicting the incidence of dengue virus infection using weather data. This method would only require a simple calculator, or preferably a PC using the derived equation. Key words: dengue -incidence -early warning -weather - probability