VISUALIZATION AND PREDICTION OF SARS-COV-1 SPREADING BASED OF MOBILIZED URBANIZATION PATTERN USING NAÏVE BAYES THEOREM

Silitonga, Marsheyla Olivia Debora Br (2022) VISUALIZATION AND PREDICTION OF SARS-COV-1 SPREADING BASED OF MOBILIZED URBANIZATION PATTERN USING NAÏVE BAYES THEOREM. Other thesis, Universitas Katholik Soegijapranata Semarang.

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Abstract

Epidemiology is a study and analysis of distribution, pattern and determinant of health based on a disease form in a population. Epidemiology offers a strong data to calculate how risk factor and intervention can affect population’s health in a crisis condition. One of the key matric in epidemiological is the base reproduction of virus transmigration, which means one sick individual can infect multiple other individuals. Time series data in epidemiology is a critical aspect to be used for analyzation and visualization to predict on-coming waves of epidemic waves. Data collected in a time-series format are crucial keys to prevent further spreading of the virus as it is dependent on the time which the event took place. Forecasting these data helps to detect future epidemics by understanding the spread of disease related by factors such as environments.

Item Type: Thesis (Other)
Subjects: 000 Computer Science, Information and General Works > 004 Data processing & computer science
Divisions: Faculty of Computer Science > Department of Informatics Engineering
Depositing User: mr AM. Pudja Adjie Sudoso
Date Deposited: 26 Oct 2022 09:12
Last Modified: 26 Oct 2022 09:12
URI: http://repository.unika.ac.id/id/eprint/30010

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