FUZZY EXPECTED VALUE IN EPIDEMIOLOGY MODEL SI AND SIS

The SI and SIS epidemic models are two models that are well known in epidemiology. Commonly, the disease transmission rate for both models are assumed to be constant. However, in reallity the transmission may depend on amount of pathogen in an infected body. To get insight into this, in this final p...

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Main Author: NURAFWA SOFHYA (NIM: 20116023), HERLINDA
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/27843
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:27843
spelling id-itb.:278432018-06-26T10:15:24ZFUZZY EXPECTED VALUE IN EPIDEMIOLOGY MODEL SI AND SIS NURAFWA SOFHYA (NIM: 20116023), HERLINDA Indonesia Theses INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/27843 The SI and SIS epidemic models are two models that are well known in epidemiology. Commonly, the disease transmission rate for both models are assumed to be constant. However, in reallity the transmission may depend on amount of pathogen in an infected body. To get insight into this, in this final project the disease transmission rate will be relaxed to contain uncertainty and is considered as fuzzy number. Our main focus will be paid into determination of fuzzy basic reproduction number through fuzzy expected value concept. It turns out that for SI model the endemic, that is represented by a fuzzy basic reproduction number, will occur when the pathogen number exceeds a certain value; as for the the classical SI model the endemic always occurs. While for the SIS models it turns out that a fuzzy basic reproduction number is greater than classical basic reproductionn number. It means that fuzzy SIS model can used as an early warning model for the endemic case. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description The SI and SIS epidemic models are two models that are well known in epidemiology. Commonly, the disease transmission rate for both models are assumed to be constant. However, in reallity the transmission may depend on amount of pathogen in an infected body. To get insight into this, in this final project the disease transmission rate will be relaxed to contain uncertainty and is considered as fuzzy number. Our main focus will be paid into determination of fuzzy basic reproduction number through fuzzy expected value concept. It turns out that for SI model the endemic, that is represented by a fuzzy basic reproduction number, will occur when the pathogen number exceeds a certain value; as for the the classical SI model the endemic always occurs. While for the SIS models it turns out that a fuzzy basic reproduction number is greater than classical basic reproductionn number. It means that fuzzy SIS model can used as an early warning model for the endemic case.
format Theses
author NURAFWA SOFHYA (NIM: 20116023), HERLINDA
spellingShingle NURAFWA SOFHYA (NIM: 20116023), HERLINDA
FUZZY EXPECTED VALUE IN EPIDEMIOLOGY MODEL SI AND SIS
author_facet NURAFWA SOFHYA (NIM: 20116023), HERLINDA
author_sort NURAFWA SOFHYA (NIM: 20116023), HERLINDA
title FUZZY EXPECTED VALUE IN EPIDEMIOLOGY MODEL SI AND SIS
title_short FUZZY EXPECTED VALUE IN EPIDEMIOLOGY MODEL SI AND SIS
title_full FUZZY EXPECTED VALUE IN EPIDEMIOLOGY MODEL SI AND SIS
title_fullStr FUZZY EXPECTED VALUE IN EPIDEMIOLOGY MODEL SI AND SIS
title_full_unstemmed FUZZY EXPECTED VALUE IN EPIDEMIOLOGY MODEL SI AND SIS
title_sort fuzzy expected value in epidemiology model si and sis
url https://digilib.itb.ac.id/gdl/view/27843
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