Prediction of dengue incidence using auto-regression models: case study of Tainan City
碩士 === 崑山科技大學 === 環境工程研究所 === 105 === Monthly time series data of dengue infection cases was collected from 1998 to 2015 in Tainan. Regression technique applied to predict dengue incidence rate by using weather, social-economic and climate change parameters. Auto-regression (AR) was then embedded in...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2016
|
Online Access: | http://ndltd.ncl.edu.tw/handle/18098158906467833008 |
id |
ndltd-TW-103KSUT0515040 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-103KSUT05150402016-12-31T04:08:30Z http://ndltd.ncl.edu.tw/handle/18098158906467833008 Prediction of dengue incidence using auto-regression models: case study of Tainan City 運用自回歸分析預測登革熱發病率:以台南市為例 Qi-Ren Chen 陳啓仁 碩士 崑山科技大學 環境工程研究所 105 Monthly time series data of dengue infection cases was collected from 1998 to 2015 in Tainan. Regression technique applied to predict dengue incidence rate by using weather, social-economic and climate change parameters. Auto-regression (AR) was then embedded into IR models, and to significantly improve the predictability of dengue for Tainan City. The integration models of AR would be used to assist an efficient dengue control. The predictive power and robustness of predictive models would be improved with additional data over longer time periods. Capturing all aspects of the disease is a daunting task, but newer techniques may help overcome the difficulties. In particular, this study discussed several significant outbreaks in Tainan City with better performance. Lee,Chih-Sheng 李志賢 2016 學位論文 ; thesis 35 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 崑山科技大學 === 環境工程研究所 === 105 === Monthly time series data of dengue infection cases was collected from 1998 to 2015 in Tainan. Regression technique applied to predict dengue incidence rate by using weather, social-economic and climate change parameters. Auto-regression (AR) was then embedded into IR models, and to significantly improve the predictability of dengue for Tainan City. The integration models of AR would be used to assist an efficient dengue control. The predictive power and robustness of predictive models would be improved with additional data over longer time periods. Capturing all aspects of the disease is a daunting task, but newer techniques may help overcome the difficulties. In particular, this study discussed several significant outbreaks in Tainan City with better performance.
|
author2 |
Lee,Chih-Sheng |
author_facet |
Lee,Chih-Sheng Qi-Ren Chen 陳啓仁 |
author |
Qi-Ren Chen 陳啓仁 |
spellingShingle |
Qi-Ren Chen 陳啓仁 Prediction of dengue incidence using auto-regression models: case study of Tainan City |
author_sort |
Qi-Ren Chen |
title |
Prediction of dengue incidence using auto-regression models: case study of Tainan City |
title_short |
Prediction of dengue incidence using auto-regression models: case study of Tainan City |
title_full |
Prediction of dengue incidence using auto-regression models: case study of Tainan City |
title_fullStr |
Prediction of dengue incidence using auto-regression models: case study of Tainan City |
title_full_unstemmed |
Prediction of dengue incidence using auto-regression models: case study of Tainan City |
title_sort |
prediction of dengue incidence using auto-regression models: case study of tainan city |
publishDate |
2016 |
url |
http://ndltd.ncl.edu.tw/handle/18098158906467833008 |
work_keys_str_mv |
AT qirenchen predictionofdengueincidenceusingautoregressionmodelscasestudyoftainancity AT chénqǐrén predictionofdengueincidenceusingautoregressionmodelscasestudyoftainancity AT qirenchen yùnyòngzìhuíguīfēnxīyùcèdēnggérèfābìnglǜyǐtáinánshìwèilì AT chénqǐrén yùnyòngzìhuíguīfēnxīyùcèdēnggérèfābìnglǜyǐtáinánshìwèilì |
_version_ |
1718405824269778944 |