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Penelitian ini dilakukan untuk menganalisis efek variasi sudut Doppler terhadap indeks velocimetry arteri karotis. Sudut Doppler yang dipilih adalah 20o, 40o, 60o dan 80o . Indeks yang ditentukan melalui hasil perhitungan dan pengamatan ini akan digunakan untuk melihat adanya kelainan (abnormalitas). Untuk membandingkan antara hasil perhitungan dan pengamatan digunakanlah sejumlah metoda statistika. Metoda teersebut adalah: an??lisis deskriptif, uji kesalahan pengukuran dan uji ANOVA one-way dan two-way. Tak ditemukan beda signifikan pada nilai rata-rata pengukuran besaran yang dihitung dan diamati. Namun, hasil an??lisis one-way ANOVA menunjukkan bahwa dengan mengubah sudut Doppler, ada perbedaan berarti pada besaran Systolic Peak Velocity (PSV) dan End Diastolic Velocity (EDV). Hasil analisis ANOVA two-way juga menunjukkan bahwa sudut Doppler mempengaruhi tekanan darah dan indeks velosimetri secara signifikan. Kajian ini menyimpulkan bahwa pengubahan sudut Doppler mempengaruhi PSV dan EDV. Secara spesifik, sudut Doppler 80o menyebabkan kelainan pada kecepatan aliran darah.

Penelitian ini dilakukan untuk menganalisis efek variasi sudut Doppler terhadap indeks velocimetry arteri karotis. Sudut Doppler yang dipilih adalah 20o, 40o, 60o dan 80o . Indeks yang ditentukan melalui hasil perhitungan dan pengamatan ini akan digunakan untuk melihat adanya kelainan (abnormalitas). Untuk membandingkan antara hasil perhitungan dan pengamatan digunakanlah sejumlah metoda statistika. Metoda teersebut adalah: an??lisis deskriptif, uji kesalahan pengukuran dan uji ANOVA one-way dan two-way. Tak ditemukan beda signifikan pada nilai rata-rata pengukuran besaran yang dihitung dan diamati. Namun, hasil an??lisis one-way ANOVA menunjukkan bahwa dengan mengubah sudut Doppler, ada perbedaan berarti pada besaran Systolic Peak Velocity (PSV) dan End Diastolic Velocity (EDV). Hasil analisis ANOVA two-way juga menunjukkan bahwa sudut Doppler mempengaruhi tekanan darah dan indeks velosimetri secara signifikan. Kajian ini menyimpulkan bahwa pengubahan sudut Doppler mempengaruhi PSV dan EDV. Secara spesifik, sudut Doppler 80o menyebabkan kelainan pada kecepatan aliran darah.

Using climate-based DHF prediction as warning system A three-month in advance warning system for upcoming DHF epidemics is developed. The system uses a simple predictive regression model based on past and present DHF cases, climate and meteorological observations as inputs to predict future DHF cases. Using Peirce score as a measure of prediction skill, the model only successfully predicts a moderately-severe epidemic at lead times of up to 6 months. Another model uses a discriminant method is also developed. This model gives much higher skill score and longer lead times than that of the regression model. The economic benefit of using the discriminant model's forecast to protect a family from an epidemic is also demonstrated. It is shown that families who are implementing such a prediction into their decision making process gain more benefit than those with un-informed decisions.

Developing, validating, and implementing climate-based statistical models for reducing DHF cases. Two simple models for predicting Dengue Hemorrhagic Fever (DHF) epidemic events from the city of Makassar are developed. The models are based on multiple regression and discriminant analysis techniques. Both models use past and present DHF cases, climate and meteorological observations as inputs. The inputs for each model are selected using a stepwise method to predict whether or not a DHF epidemic event will occur. Cross-validation test is performed for each model and its forecast quality is measured using a Peirce skill score. The value of model prediction is simulated and evaluated in a situation where the prediction is tailored into a decision making problem as to protect a family from DHF epidemics. When a DHF epidemic is predicted to occur, the family applies an insect-repellent product to avoid mosquito bites. On the other hand, the family abandon this mode of protection when an epidemic is not predicted to occur. Implementation of such a prediction in this particular situation resulting in lower expenses than those of other options such as: using a protection without any forecast and neglecting any protection. Both model predictions are valuable up to 3 month lead times.

The efficacy of two simple models for predicting dengue haemorrhagic fever (DHF) epidemics are evaluated. One model uses persistence while the other uses past dengue cases and climate factors to make predictions. It is shown that the efficacy of the models is not significantly different. The value of the prediction is also investigated when it is used to decide whether it can protect a household from epidemics. When the model predicts that a DHF epidemic is forthcoming, a highly effective but low-cost DEET product is applied by the whole family as protection against mosquito bites. It is found that the cost of implementing such a model for prediction is much cheaper than other options such as: (i) using protection without any forecast; and (ii) neglecting any protection. It is also found that the value of a forecast depends on the forecast skill and the cost-to-loss ratio.

A simple climate-based regression model for DHF prediction A statistical model for predicting monthly Dengue Hemorrhagic Fever (DHF) cases from the city of Makassar is developed and tested. The model uses past and present DHF cases, climate and meteorological observations as inputs. These inputs are selected using a stepwise regression method to predict future DHF cases. The model is tested independently and its skill assessed using two skill measures. Using the selected variables as inputs, the model is capable of predicting a moderately-severe epidemic at lead times of up to six months. The most important input variable in the prediction is the present number of DHF cases followed by the relative humidity three to four months previously. A prediction 1???6 months in advance is sufficient to initiate various activities to combat DHF epidemic. The model is suitable for warning and easily becomes an operational tool due to its simplicity in data requirement and computational effort.