Analisis Ketidakpastian dalam Model Matematika pada Fenomena Biologi

• Main Author: Andriani Br Sebayang, Afrina
• Format: Theses
• Language: Indonesia
• Subjects: Probabilitas & matematika terapan
• Online Access: https://digilib.itb.ac.id/gdl/view/32143
• Institution: Institut Teknologi Bandung

One of the most important things that is needed to be reviewed of biology phenomena such as regulation and disease transmission is uncertainty. These uncertainties usually appear because noise in observation data or limitations of observed components. The limitations of observed data will lead to uncertainty in predicting the parameter values of the biological model used to represent data observation. These uncertainties also cause uncertainty in determining basic reproduction number, representing biology model, and predicting the out put of mathematical models that been observed. In epidemic model, basic reproduction number is one of the most important things used to predict the out put of biological model and control the spread of diseases. The aim of this research is to develop new methods that can be alternative solution to determine the uncertainties in model biology based on data observation. In this research, maximum interval width of basic reproduction number will be determined which is obtained from parameters that are quite good to represent observation data. Hopefully, these methods will produce more accurate prediction in mathematical model, especially for biological models. Deterministic models of disease transmission for the type of direct infection, such as Spanish Flu will be used to apply the methods. Two biology models are used, SEIR model and Complex SEIR model. Furthermore, the biology models that are used in this research are the models which have many equations with many parameters. Heuristic algorithm will be used to generate the parameter prediction values and obtain the maximum interval width of basic reproduction number of epidemic models. This study will useful to give more accurate information in preventing and controlling disease transmission.