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SESSION 9.3 – Disaster and Disease A framework for assessing location-based personalized exposed risk of infectious disease transmission Ching-Shun Hsu.

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Presentation on theme: "SESSION 9.3 – Disaster and Disease A framework for assessing location-based personalized exposed risk of infectious disease transmission Ching-Shun Hsu."— Presentation transcript:

1 SESSION 9.3 – Disaster and Disease A framework for assessing location-based personalized exposed risk of infectious disease transmission Ching-Shun Hsu 1, Tzai-Hung Wen 2 2015.9.17 1 Graduate Student, Department of Geography, National Taiwan University, e-mail: b98208002@ntu.edu.tw 2 Associate Professor, Department of Geography, National Taiwan University, e-mail: wenthung@ntu.edu.tw

2  Personalized Exposure Assessment Motivation Kwan,2012 Steinle et al.,2012 Leyk et al.,2009 Qi and Du,2012 Health outcome Environmental exposure

3  In epidemiology, most of studies with analyzing human mobility data is to understand collective behaviors and spatial diffusion of infectious disease.  The risk you exposure will be affected by the health status of the people who surround with you.  The impact of the disease is more serious and quickly. Motivation Wesolowski et al,2012 Vazquez-Prokopec et al,2013 in 7 ~ 10 days

4  We have already known that  With GPS, we are able to collect high-resolution individual space-time data.  Tracking individual’s activity pattern is a proper way to understand the personal environmental pollution exposure.  Analyzing human behaviors and spatial diffusion of infectious disease.  Research Question  How about developing a personalized exposure assessment framework for infectious disease transmission ? Motivation

5 Objective Disease spread modeling Exposure assessment Course records Campus Buildings Students Flow Matrix Course-Taking Spatial Pattern Triggering an Epidemic Smart phone Apps Database Server Google Cloud Messaging 2 Risk assessment service Collect GPS logs data… alarm information GPS tracks Simulation results 1 Modeling collective mobility Case Report

6 National Taiwan University main campus 75 classroom buildings Methodology – Study Area and Data 編號建物名編號建物名 1 舊總圖書館 54 環境工程學研究所 2 計算機及資訊網路中心 55 工程科學及海洋工程學系 5 展書樓 56 建築與城鄉研究所 7 二號館 58 生物產業自動化教學及研究中心 13 新生大樓 59 農藝館 14 普通科目教室 60 獸醫學系 16 綜合教室 61 知武館 17 共同科目教室 62 中非大樓 18 體育館 63 生物產業機電學系 20 第一學生活動中心 65 造園館 21 鹿鳴堂 66 食品科技管 26 樂學館 67 台大動物醫院 27 人類學系 68 昆蟲學系 28 哲學系 69 動物科學技術學系 29 文學院 70 園藝學系 30 視聽教育館 71 森林環境暨資源學系 31 圖書資訊學系 72 農業化學系 32 一號館 73 生物環境系統工程學系 33 擬態科學研究中心 74 農化新館 34 全球變遷中心 75 農業綜合大樓 35 海洋研究所 76 水工試驗所 36 思亮館 78 管理學院一號館 38 數學系 79 管理學院二號館 39 化學系 81 電機一館 40 原子與分子科學研究所 82 資訊工程館 41 心理學系 83 博理館 42 地理環境資源學系 84 電機二館 43 大氣科學系 85 生化科學研究所 44 地質科學系 86 漁業科學研究所 45 社會與社工館 87 生命科學館 46 國家發展研究所 92 生物技術研究中心 47 新聞研究所 94 國青大樓 48 化學工程學系 110 明達館 49 土木工程學系 112 霖澤館 50 應用力學館 113 萬才館 51 志鴻館 115 土木研究大樓 52 工學院綜合大樓 999 綜合體育館 53 機械工程學系

7 Methodology – Study Area and Data 選課 190469 course enrollment 6059 classes 24975 students 3214 courses 75 classroom buildings Which courses he takes Where and When to go to the classes

8 Methodology – 1. Modeling collective mobility from course-taking records Gymnasium Building A Building B Building D Building C Monday morning Monday afternoon Tuesday morning Analyzing personal route on the campus from course-taking records.

9 Aggregating the route from all students to build the building network To From Building A Building B Building C Building D Building E Building F Gymnasium Building A 1 Building B 2141 Building C Building D 5274 Building E 13423724167 Building F 15130736364 Gymnasium 17663323 Methodology – 1. Modeling collective mobility from course-taking records Origin-Destination Matrix for each time slice Monday morning Monday afternoon Tuesday morning Tuesday afternoon Time Buildings Within-time relationships Cross-time relationships

10 Based on the metapopulation approach and SLIR stochastic model to simulate the disease spreads between classroom buildings Methodology – 2. Simulating the spread of diseases (Susceptible-Latent-Infectious-Recovery)

11 When the infectious disease outbreaks, we may want to know – How quickly the disease spreads ? How serious is it ? How long do we have to plan the disease control strategy ? – The disease outbreaks at Building A : Which buildings have close relationships with building A ? Simulating the spread of diseases Epidemic Curve Case Day susceptible latent Infectious recovery

12 Disease spread modeling Exposure assessment Course records Campus Buildings Students Flow Matrix Course-Taking Spatial Pattern Triggering an Epidemic Smart phone Apps Database Server Google Cloud Messaging 1 2 Course-taking records GPS logs, etc… alarm information GPS tracks Simulation results 0 Modeling collective mobility Case Report Methodology – 3. Developing Smart Phone Apps

13  1. Register  2. Collect personal GPS logs data  3. Personal risk query Methodology – 3. Developing Smart Phone Apps Register Risk Query GPS logs

14 Service Registration Alarm information To know the GPS logs belong to whom (or course-taking records) Phone unicode account Alarm information send code Methodology – 3. Developing Smart Phone Apps

15 Collect personal GPS logs data –GPS logs :  Collect by smart phone  Upload to the database –Course-taking records  if register with student ID Methodology – 3. Developing Smart Phone Apps

16 With integration of the exposed risk spatial patterns and personal route on the campus, we can assess the exposed risk of the infectious diseases. Monday morning Monday afternoon Tuesday morning Tuesday afternoon Buildings Personal route Time 1. Simulating the spread of diseases

17 How dangerous is the building I’m going to ? To understand the exposed risk pattern on the campus Personalized infection risk assessment Campus query What is my exposed risk score today ? Help to make better spatial decisions e.g. wear sanitary mask, see the doctor Personal query Campus Personal Course-taking GPS Based on course-taking Based on GPS logs Environment Risk Personal Risk

18 Real-time personalized exposed risk of infectious disease The study proposed a location-based framework for measuring real-time personalized exposed risk of infectious disease. Through installing and registering smart phone apps, each student at the campus could understand the spatial diffusion of disease transmission and make better spatial decisions based on personalized infection risk scores.

19 Thank you for your listening!! Questions or Comments ? Ching-Shun Hsu 1, Tzai-Hung, Wen 2 2015.9.17 1 Graduate Student, Department of Geography, National Taiwan University, e-mail: b98208002@ntu.edu.tw 2 Associate Professor, Department of Geography, National Taiwan University, e-mail: wenthung@ntu.edu.tw


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