SESSION 9.3 – Disaster and Disease

A framework for assessing location-based personalized

exposed risk of infectious disease transmission

Ching-Shun Hsu1, Tzai-Hung Wen2

2015.9.17

1Graduate Student, Department of Geography, National Taiwan University, e-mail: b98208002@ntu.edu.tw

2Associate Professor, Department of Geography, National Taiwan University, e-mail: wenthung@ntu.edu.tw

Personalized Exposure Assessment

Motivation

Kwan,2012

Steinle et al.,2012

Leyk et al.,2009

Qi and Du,2012

Healthoutcome

Environmentalexposure

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

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

ObjectiveDisease spread modelingExposure

assessmentCourse records

CampusBuildings

Students FlowMatrix

Course-TakingSpatial Pattern

Triggering an Epidemic

Smart phone Apps

Database Server

Google Cloud Messaging

2

Risk assessment serviceCollect GPS logs data…

alarm information

GPS tracks

Simulation results

1 Modeling collective mobility

CaseReport

• 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

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

Methodology –

1. Modeling collective mobility from course-taking records

Gymnasium

Gymnasium

Building A

Building A

Building B

Building B

Building D

Building D

Building C

Building C

Monday morning

Monday afternoon

Tuesday morning

Analyzing personal route on the campus from course-taking records.

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 21 4 1

Building C

Building D 52 7 4

Building E 1 34 237 241 67

Building F 1 51 307 363 64

Gymnasium 17 66 33 23

Methodology –

1. Modeling collective mobility from course-taking records

Origin-Destination Matrixfor each time slice

Monday morning

Monday afternoon

Tuesday morning

Tuesday afternoon

TimeBuildings

Within-time relationships

Cross-time relationships

Based on the metapopulation approach and SLIR stochastic model to simulate the disease spreads between classroom buildings

𝐝𝐒𝒊 ( 𝐭 )𝐝𝐭

=−𝛃𝐒𝒊 ( 𝐭 ) 𝐈𝒊 (𝐭 )

𝑵 𝒊−∑

𝐢≠ 𝒋𝛃𝐒𝒊 (𝐭 ) 𝐖 ( 𝐣 , 𝒊 )

∑𝐖 ( 𝐣 ,𝒂𝒍𝒍 )𝐈 𝒋 (𝐭 )𝐍 𝒋

𝐝𝐋𝒊 (𝐭 )𝐝𝐭

=𝛃𝐒𝒊 ( 𝐭 ) 𝐈𝒊 (𝐭 )

𝑵 𝒊

+∑𝐢≠ 𝒋

𝛃𝐒𝒊 (𝐭 ) 𝐖 ( 𝐣 ,𝒊 )𝑾 ( 𝒋 ,𝒂𝒍𝒍)

𝐈 𝒋 (𝐭 )𝐍 𝒋

−𝛉𝐋𝒊 (𝐭 )

𝐝𝐈𝒊 ( 𝐭 )𝐝𝐭

=𝛉𝐋𝒊 (𝐭 )−𝛄 𝐈𝒊 ( 𝐭 )

𝐝𝐑𝒊(𝐭)𝐝𝐭

=𝛄 𝐈𝒊 ( 𝐭 )

Force of infection :

Methodology –

2. Simulating the spread of diseases

(Susceptible-Latent-Infectious-Recovery)

• 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

Ca

se

Daysusceptible latent Infectious recovery

Disease spread modelingExposure assessment

Course records

CampusBuildings

Students FlowMatrix

Course-TakingSpatial Pattern

Triggering an Epidemic

Smart phone Apps

Database Server

Google Cloud Messaging

1

2

Course-taking recordsGPS logs, etc…

alarm information

GPS tracks

Simulation results

0 Modeling collective mobility

CaseReport

Methodology – 3. Developing Smart Phone Apps

1. Register

2. Collect personal GPS logs data

3. Personal risk query

Methodology –

3. Developing Smart Phone Apps

Register

Risk Query

GPS logs

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

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

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

Force of infection :

1. Simulating the spread of diseases

How dangerous is the building I’m going to ?To understand the exposed risk pattern on the campus

Personalized infection risk assessment

Campusquery

What is my exposed risk score today ?Help to make better spatial decisions e.g. wear sanitary mask, see the doctor

Personalquery

Campus

Personal Personal

Course-takingGPS

Based on course-taking

Based on GPS logs

Environment Risk

Personal Risk

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.

Thank you for your listening!!

Questions or Comments ?Ching-Shun Hsu1, Tzai-Hung, Wen2

2015.9.17

1Graduate Student, Department of Geography, National Taiwan University, e-mail: b98208002@ntu.edu.tw

2Associate Professor, Department of Geography, National Taiwan University, e-mail: wenthung@ntu.edu.tw