A framework for assessing location-based personalized exposure risk of infectious disease transmission
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Ching-Shun, Hsu
Human mobility is an important risk factor affecting disease transmission. Therefore, understanding detailed spatial behaviors and interactions among individuals is a fundamental issue. Past studies using high-resolution human contacts data from smart phones with GPS logs have captured spatial-temporal heterogeneity and daily contact patterns among individuals. However, measuring personalized exposed risk of infectious disease transmission is still under development. The purpose of the study is to establish a location-based framework for assessing personalized exposed risk of infectious disease transmission. The framework consists of three components: the first is client-side smart phone-based risk assessment module. We developed Android application for collecting real-time location data and displaying the personalized exposed risk score. The second component is the server-side epidemic simulation model. The simulation model calculated the personalized exposed risk score based on real-time GPS logs and individual mobility data from the client-side Android application. The last component is the disease alarm device for triggering the service-side epidemic simulation model. We installed infrared sensors in people-gathering areas as the alarm device to monitor human body temperature for detecting fever syndrome. We used NTU main campus as a pilot study to demonstrate the feasibility of the framework. We analyzed the records of students’ taking course and modeled the spatial interaction relationships among classroom buildings due to students’ mobility around the campus. Someone who got a fever is detected by the sensor and the server-side epidemic simulation is triggered. Each student who installed the client-side risk assessment module in his/her smart phone receives the real-time personalized exposed risk score when an epidemic outbreak on the NTU campus. The study proposed a location-based framework for measuring real-time personalized exposed risk. Each student at the campus could understand the spatial diffusion of disease transmission and make better spatial decisions based on personalized exposed risk scores.
 Good afternoon ladies and gentlemen. My name is Xu Jingshun, I come from National Taiwan University. The topic of my presentation today is a framework for assessing location-based personalized exposed risk of infectious disease transmission. When we try to understand the relationship between our health outcome and environmental exposure, more and more studies focus on personal exposure assessment. Because where and when people spend their time from different from individual to individual, it is important to assess exposed risk based on their behavior patterns instead of where they live. Meanwhile, with the development of GPS, some variable sensors or personal monitoring device, we can collect personal mobility data and environment data more currently. And integrate with GIS to estimate the personal exposure risk. Some studies about these issues are talking about... sorry. Some studies about these issues are talking about air pollution exposure or pesticide exposure and so on. And in epidemiology, most of your studies with analyzing human mobility data try to understand collective behaviors and special diffusion of infectious disease. For example, how does the disease spread between cities? Or what is the impact of variable mobility routines on infectious disease dynamic? They seldom focus on the perspective on personal exposure risk of infectious disease. However, there are some distinctive features between infectious disease and environmental pollution. For example, your risk will be affected by the people surround with you. The more people, the more think people surround with you, the more risk you are. That is the source of infection could good faster than environmental pollution. On the other hand, the impact of the disease is more serious and quickly. To sum up my introduction, with GPS or positioning function in the wearable device, we can collect high resolution individual space time data. Based on this data, we are able to understand the personal environmental pollution exposure. But in epidemiology, the perspective on personal exposure assessment of infectious disease is still unclear. So the purpose of this study is to establish a framework for assessing personalized exposure risk assessment to assessing personal exposure risk of infectious disease transmission. The framework consists of two components. The first is client-side smartphone application. We developed an Android application for collecting personal GPS logs data and providing users with personal exposure risk assessment service. The second is server-side epidemic simulation model. The model simulates the spatial diffusion of disease and integrate with personal mobility data to calculate the personal risk score. In this model, we analyze students' course-taking records to understand how the students work on the campus. We use school campus as a blood study to demonstrate the facility of this framework. We analyze students' course-taking records to understand how these students work on the campus and to model the spatial interactions between classroom buildings. And with this relationship, we can model the spatial diffusion of the disease on the campus. That is my study area, National Taiwan University Main Campus. There are 75 classroom buildings. Next is students' element database. With query function, we can know the students' course-taking records from individual to individual. For example, which course he takes and when and where to go to the class. For example, these students will go to gymnasium on Monday morning and then go to building A and from building A to building B and so on. So with this information, we can analyze his route, routes on the campus. And we divide these routes into two times slides, morning and afternoon. Then we aggregate all of the students' routes on the campus to establish the building's network. It is an origin-distination matrix for each time slide. So we can understand the spatial interactions between classroom buildings here half-day. And we integrate the relationship with meta-population approach and the SR-IRS stochastic model to simulate the spatial diffusion of disease on the campus. With the model, we can know the dynamic infection patterns by building's peer-half-day and based on the definition in epidemiology, the force of infection, we calculate this index as a risk score for each building. So far, we can understand the dynamic of disease diffusion and can answer some questions. For example, with a platinely-epidemic curve of this outbreak, we can realize how long do we have to plan the disease control strategy. By visualizing the risk score of each building, we can know which building is the most serious one at a specific day. And to point out the other buildings that have close relationship with. Close relationship means there are a lot of students come and go between two buildings. If one building's outbreak and the disease would spread to the other one quickly. And based on this information, we can plan the strategy more precisely. Next, we are going to look at the function of the smartphone application. This app is developed in Android environment, which consists of three functions including registering an account, collecting personal GPS logs data, and the clearing personal risk of infectious disease transmission. First, registration is to create an account in database and provide users with risk assessment and a lot of information service. Once there is a case report on the campus, the app will receive a message from the server to tell the users there is a disease outbreak. Secondly, we collect personal GPS logs data with position in functions on the smartphone and upload this data to the database. Meanwhile, we developed a simple GPS log data mining method to guess which buildings the user has visited. Otherwise, if he registers with his student's ID, we can also know his behavior patterns on the campus by visualizing his cross-taking records of information. And with simulating the spread of disease, we can understand the special patterns of each of exposed risk and the overlay and overlay these patterns with personal routes on the campus. Based on this idea, we developed an indicator to measure how dangerous the user is as a personal exposed risk score. Finally, at the query user interface, we decided two types of query. The first one is campus query for environment risk. When a disease outbreak as a student, he may want to know how dangerous is the building he is going to. And with the query, users can understand the special patterns of the risk on the campus. On the other side, personal query provides the risk score information to each user. The risk score is based on their own mobility patterns. They are about to know the risk they have encountered or are going to face. We support this information can help users make better special decisions such as avoid visiting high-risk place to reduce the probability of getting sick or if he doesn't feel well and he found that he has exposed to high risk. It is better for him to see a doctor or wear a sanitary mask. In conclusion, we propose a framework for personal exposed risk of infectious disease transmission which consists of smartphone apps and the epidemic simulation model. With information about the environmental risk or personal risk, we may make better decisions when a disease outbreak. How would you identify patient number one? How do you know when there is an infectious disease in the area that you would start deploying this scenario? On the campus, there is health centers where students get think they would go to the centers first to see the data in the school. Once the students get disease, this is an infectious disease. We support that the center would report this case to the school or CDC and so on. Any other questions? Oh, that's me. I have a question about the exposure risk score today. Is it how to make a value to risk score? How to make a value? Is it the numbers 10 or 11 or 13, 14, 19 is a risk score? We think this score is a relative index. So he can know which buildings are more dangerous. And with comparison, he can choose the less serious one. Is it the red one is a high risk point? The red one is the personal route on the campus and the north is buildings. This is his first list of buildings on Monday morning. We also know each building's risk score. So we can know which buildings he has visited. And we calculate the risk of the buildings to calculate his personal risk score. Thank you very much.
 Good afternoon ladies and gentlemen. My name is Xu Jingshun, I come from National Taiwan  University. The topic of my presentation today is a framework for assessing location-based  personalized exposed risk of infectious disease transmission. When we try to understand the  relationship between our health outcome and environmental exposure, more and more studies  focus on personal exposure assessment. Because where and when people spend their time from  different from individual to individual, it is important to assess exposed risk based on their  behavior patterns instead of where they live. Meanwhile, with the development of GPS, some  variable sensors or personal monitoring device, we can collect personal mobility data and environment  data more currently. And integrate with GIS to estimate the personal exposure risk. Some studies  about these issues are talking about... sorry.  Some studies about these issues are talking about air pollution exposure or pesticide  exposure and so on. And in epidemiology, most of your studies with analyzing human mobility data  try to understand collective behaviors and special diffusion of infectious disease. For  example, how does the disease spread between cities? Or what is the impact of variable  mobility routines on infectious disease dynamic? They seldom focus on the perspective on personal  exposure risk of infectious disease. However, there are some distinctive features between  infectious disease and environmental pollution. For example, your risk will be affected by  the people surround with you. The more people, the more think people surround with you, the  more risk you are. That is the source of infection could good faster than environmental pollution.  On the other hand, the impact of the disease is more serious and quickly. To sum up my introduction,  with GPS or positioning function in the wearable device, we can collect high resolution individual  space time data. Based on this data, we are able to understand the personal environmental  pollution exposure. But in epidemiology, the perspective on personal exposure assessment  of infectious disease is still unclear. So the purpose of this study is to establish a framework  for assessing personalized exposure risk assessment to assessing personal exposure risk  of infectious disease transmission. The framework consists of two components. The first is  client-side smartphone application. We developed an Android application for collecting personal  GPS logs data and providing users with personal exposure risk assessment service. The second  is server-side epidemic simulation model. The model simulates the spatial diffusion of disease  and integrate with personal mobility data to calculate the personal risk score. In this model,  we analyze students' course-taking records to understand how the students work on the  campus. We use school campus as a blood study to demonstrate the facility of this framework.  We analyze students' course-taking records to understand how these students work on the campus  and to model the spatial interactions between classroom buildings. And with this relationship,  we can model the spatial diffusion of the disease on the campus.  That is my study area, National Taiwan University Main Campus. There are 75 classroom buildings.  Next is students' element database. With query function, we can know the students'  course-taking records from individual to individual. For example, which course he takes  and when and where to go to the class. For example, these students will go to gymnasium  on Monday morning and then go to building A and from building A to building B and so on.  So with this information, we can analyze his route, routes on the campus. And we divide these routes  into two times slides, morning and afternoon. Then we aggregate all of the students' routes  on the campus to establish the building's network. It is an origin-distination matrix for each time  slide. So we can understand the spatial interactions between classroom buildings here half-day.  And we integrate the relationship with meta-population approach and the SR-IRS  stochastic model to simulate the spatial diffusion of disease on the campus. With the model, we can  know the dynamic infection patterns by building's peer-half-day and based on the definition in  epidemiology, the force of infection, we calculate this index as a risk score for each building.  So far, we can understand the dynamic of disease diffusion and can answer some questions. For  example, with a platinely-epidemic curve of this outbreak, we can realize how long do we have to  plan the disease control strategy. By visualizing the risk score of each building, we can know  which building is the most serious one at a specific day. And to point out the other buildings  that have close relationship with. Close relationship means there are a lot of students  come and go between two buildings. If one building's outbreak and the disease would spread to the  other one quickly. And based on this information, we can plan the strategy more precisely.  Next, we are going to look at the function of the smartphone application.  This app is developed in Android environment, which consists of three functions including  registering an account, collecting personal GPS logs data, and the clearing personal risk of  infectious disease transmission. First, registration is to create an account in database and provide  users with risk assessment and a lot of information service. Once there is a case report on the campus,  the app will receive a message from the server to tell the users there is a disease outbreak.  Secondly, we collect personal GPS logs data with position in functions on the smartphone  and upload this data to the database. Meanwhile, we developed a simple GPS log data mining method  to guess which buildings the user has visited. Otherwise, if he registers with his student's ID,  we can also know his behavior patterns on the campus by visualizing his cross-taking records  of information. And with simulating the spread of disease, we can understand the special patterns  of each of exposed risk and the overlay and overlay these patterns with personal routes on the campus.  Based on this idea, we developed an indicator to measure how dangerous the user is as a personal  exposed risk score. Finally, at the query user interface, we decided two types of query.  The first one is campus query for environment risk. When a disease outbreak as a student,  he may want to know how dangerous is the building he is going to. And with the query,  users can understand the special patterns of the risk on the campus. On the other side,  personal query provides the risk score information to each user. The risk score is based on their  own mobility patterns. They are about to know the risk they have encountered or are going to face.  We support this information can help users make better special decisions such as avoid  visiting high-risk place to reduce the probability of getting sick or if he doesn't feel well and  he found that he has exposed to high risk. It is better for him to see a doctor or wear a sanitary mask.  In conclusion, we propose a framework for personal exposed risk of infectious disease transmission  which consists of smartphone apps and the epidemic simulation model. With information  about the environmental risk or personal risk, we may make better decisions when a disease outbreak.  How would you identify patient number one? How do you know when there is an infectious  disease in the area that you would start deploying this scenario?  On the campus, there is health centers where students get think they would go to the centers  first to see the data in the school. Once the students get disease,  this is an infectious disease. We support that the center would report this case to  the school or CDC and so on.  Any other questions?  Oh, that's me. I have a question about the exposure risk score today.  Is it how to make a value to risk score?  How to make a value? Is it the numbers 10 or 11 or 13, 14, 19 is a risk score?  We think this score is a relative index. So he can know which buildings are more dangerous.  And with comparison, he can choose the less serious one.  Is it the red one is a high risk point?  The red one is the personal route on the campus and the north is buildings.  This is his first list of buildings on Monday morning. We also know each building's risk score.  So we can know which buildings he has visited. And we calculate the risk of the buildings to  calculate his personal risk score.  Thank you very much.
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