莊坤達/資料科學與防疫應用的結合 : 以登革熱防治為例

Biosketch

Current—

Past— Synopsys, Inc. (2006-2011) Graduated from NTUEE 2006

Research Interest — Data Mining, Database and Data Management

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Agenda•

• (Data Science for Epidemic Prevention)

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My Story

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Advanced Network Database Lab前瞻網路資料庫實驗室

• 我的認知 (Before 2015) – 新聞講得很嚴重,所以應該很恐怖 – 但好像也沒很恐怖, 隔壁的阿伯還穿汗衫在

公園打球,我媽說不要自己嚇自己 – 沒錯, 我就是

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我與登革熱不熟

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• 有一天

• 很不幸

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我與登革熱

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• 怎麼辦??

• 我以為Google大神會告訴我 –你完蛋了, 你住家附近是重災區, 你

應該去快篩 –安心去睡覺~

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我與登革熱

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Advanced Network Database Lab前瞻網路資料庫實驗室 8

身為我們自己造一個

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– http://real.taiwanstat.com/dengue-spatial-temporal/

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http://www.taiwanstat.com/

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2015 Tainan Outbreak

防疫顧問團成立

https://www.facebook.com/chinghua.huang.9/media_set?set=a.1038930549460172.1073741835.100000297104540&type=3

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Photo Credit:

(Dengue Fever)



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Photo Credit:

(Dengue Fever)



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Photo Credit:

(Dengue Fever)



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Photo Credit:

(Dengue Fever)


Research Spectrum @ NCKU

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Challenges in Prevention

Summary Strategies in Epidemiology

VaccinationHospital-side Control (Antibody)

InterventionGenetically Modified Mosquitos

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(Data Science for Epidemic Prevention)

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Data Science to Human Science

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資料科學 vs.

傳統GIS防疫系統

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GIS

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https://whatsthebigdata.com/2016/07/08/the-new-data-scientist-venn-diagram/

? ? ?

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1. 2. 3.TIME

3 94 7 8 1110 12 1-2

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7. 8.

••

• GIS••

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• GIS••

• GIS

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••••

• •• Ovitrap••

2015

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?

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• 9

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••••• ….

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防治規劃從多天減少為

1天

小區域熱區打擊

熱區偵測與追踪

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IT

GIS

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App Demo

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twitter @hosimaki

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+

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5050

Google Analytics ?

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Data Science for Epidemic Computing

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http://showbar.ctitv.com.tw/v/video/vcecddo201509081417305562.htm

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http://showbar.ctitv.com.tw/v/video/vcecddo201509081417305562.htm

http://udn.com/news/story/8504/1223693- -

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http://udn.com/news/story/8504/1223693-%E7%99%BB%E9%9D%A9%E7%86%B1%E7%97%85%E6%82%A3%E5%A1%9E%E7%88%86%E5%8D%97%E5%B8%82%E9%86%AB%E9%99%A2-%E7%94%A2%E5%A9%A6%E7%9D%A1%E8%B5%B0%E5%BB%8A





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Epidemic struck

Solution 1

Under normal circumstances

Resource Allocation

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Epidemic struck

Solution 2

Under normal circumstances

Temporal Medical Stations

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…

Patients

Hospitals

Resource

Selection Temporal Medical Locations

Resource Allocation

Patient Preferences

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Perfect Service: the number of patients that do not need to wait for the beds

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• k: # of temporary medical stations

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den dengte60

k0 20 12

k1 18 12

k2 20 10

k3 18 10

k4 14 10

den dengte60

p0 13 4

p1 14 6

p2 8 3

p3 6 1

p4 1 0

Green:2015interventionin9/14~9/15Gray:Top-5resultsofoursystem.

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Acknowledgement

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莊坤達/資料科學與防疫應用的結合 : 以登革熱防治為例

Data & Analytics