A Location-Based Client-Server Framework for Assessing Personal Exposure to the Transmission Risks of Contagious Diseases

Human mobility is an important risk factor affecting contagious disease transmission. Therefore, understanding spatial behaviors and interactions among individuals is a fundamental issue. Past studies using high-resolution human contacts data with sequential location data from global positioning systems (GPS) receivers have captured spatial-temporal heterogeneity and daily contact patterns among individuals. However, how to measure effectively personalized exposure to the risk of contagious disease transmission is still under development. The purpose of the study is to establish a location-based client-server framework for assessing personalized exposure to the transmission risk of contagious disease. The location-based framework consists of two major components: one is a client-side smartphone-based risk assessment module. We developed an Android application for collecting course-attending records and real-time location data for displaying personalized exposure scores. The other component is a server-side epidemic simulation model. The simulation model calculated the personalized exposure score based on GPS logs and individual mobility data from the client-side Android application. We used National Taiwan University (NTU) main campus as a pilot study to demonstrate the feasibility of the framework. The records of students attending courses and GPS logs were used for capturing mobility of students around the campus. We then generated a space-time mobility network based on individual mobility trajectories. Based on the epidemic simulation model with individual mobility network, each student who uses his/her smartphone as a personalized platform, can understand the epidemic progression and make better decisions, such as wearing a face mask or reducing the contact frequency, based on personalized exposure scores from the server-side computation. The proposed location-based framework presents complex interactions among personal risk reception, behavioral changes, and epidemic progression. More scenarios can be implemented in future studies for quantifying the effects of risk reception or behavioral changes with epidemic progression.

[1]  Chun Lin,et al.  Personal exposure monitoring of PM2.5 in indoor and outdoor microenvironments. , 2015, The Science of the total environment.

[2]  Luc Int Panis,et al.  Impact of time–activity patterns on personal exposure to black carbon , 2011 .

[3]  Alessandro Vespignani,et al.  Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model , 2010, J. Comput. Sci..

[4]  Corby K. Martin,et al.  Smartloss: A Personalized Mobile Health Intervention for Weight Management and Health Promotion , 2016, JMIR mHealth and uHealth.

[5]  Panos G Georgopoulos,et al.  Modeling of Personal Exposures to Ambient Air Toxics in Camden, New Jersey: An Evaluation Study , 2009, Journal of the Air & Waste Management Association.

[6]  M. Brauer,et al.  The impact of daily mobility on exposure to traffic-related air pollution and health effect estimates , 2011, Journal of Exposure Science and Environmental Epidemiology.

[7]  Derek A T Cummings,et al.  Outbreak of 2009 pandemic influenza A (H1N1) at a New York City school. , 2009, The New England journal of medicine.

[8]  Alessandro Vespignani,et al.  Modeling Contact and Mobility Based Social Response to the Spreading of Infectious Diseases , 2013 .

[9]  Uriel Kitron,et al.  The Role of Human Movement in the Transmission of Vector-Borne Pathogens , 2009, PLoS neglected tropical diseases.

[10]  Mei-Po Kwan,et al.  From place-based to people-based exposure measures. , 2009, Social science & medicine.

[11]  John Mark Ansermino,et al.  The PAediatric Risk Assessment (PARA) Mobile App to Reduce Postdischarge Child Mortality: Design, Usability, and Feasibility for Health Care Workers in Uganda , 2016, JMIR mHealth and uHealth.

[12]  Stefan Leyk,et al.  Spatial modeling of personalized exposure dynamics: the case of pesticide use in small-scale agricultural production landscapes of the developing world , 2009, International journal of health geographics.

[13]  Hai-Ying Liu,et al.  Mobile phone tracking: in support of modelling traffic-related air pollution contribution to individual exposure and its implications for public health impact assessment , 2013, Environmental Health.

[14]  Kazuyuki Aihara,et al.  Safety-Information-Driven Human Mobility Patterns with Metapopulation Epidemic Dynamics , 2012, Scientific Reports.

[15]  Lisa Sattenspiel,et al.  The Geographic Spread of Infectious Diseases , 2017 .

[16]  Yongmei Lu,et al.  Personal real-time air pollution exposure assessment methods promoted by information technological advances , 2012, Ann. GIS.

[17]  Istvan Z. Kiss,et al.  Incorporating Human Behaviour in Epidemic Dynamics: A Modelling Perspective , 2013 .

[18]  Antonio Krüger,et al.  Applying indoor and outdoor modeling techniques to estimate individual exposure to PM2.5 from personal GPS profiles and diaries: a pilot study. , 2009, The Science of the total environment.

[19]  David Ben-Arieh,et al.  Modeling infection spread and behavioral change using spatial games , 2015 .

[20]  Geert Wets,et al.  Personal exposure to Black Carbon in transport microenvironments , 2012 .

[21]  Andrew Molineux,et al.  Mobile Technology for Empowering Health Workers in Underserved Communities: New Approaches to Facilitate the Elimination of Neglected Tropical Diseases , 2016, JMIR public health and surveillance.

[22]  Michael Small,et al.  The impact of awareness on epidemic spreading in networks , 2012, Chaos.

[23]  C. Watkins,et al.  The spread of awareness and its impact on epidemic outbreaks , 2009, Proceedings of the National Academy of Sciences.

[24]  Funda Samanlioglu,et al.  A Susceptible-Exposed-Infected-Removed (Seir) Model For The 2009-2010 A/H1N1 Epidemic In Istanbul , 2012, 1205.2497.

[25]  Alessandro Vespignani,et al.  Modeling human mobility responses to the large-scale spreading of infectious diseases , 2011, Scientific reports.

[26]  Stefan Reis,et al.  Assessment of personal exposure to air pollutants in Scotland - an integrated approach using personal monitoring data , 2011 .

[27]  U C de Silva,et al.  A preliminary analysis of the epidemiology of influenza A(H1N1)v virus infection in Thailand from early outbreak data, June-July 2009. , 2009, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[28]  L. Mao,et al.  Modeling triple-diffusions of infectious diseases, information, and preventive behaviors through a metropolitan social network—An agent-based simulation , 2014, Applied Geography.

[29]  Chung-Yuan Huang,et al.  Integrating epidemic dynamics with daily commuting networks: building a multilayer framework to assess influenza A (H1N1) intervention policies , 2011, Simul..

[30]  Simon Watkins,et al.  Personalized Exposure Assessment: Promising Approaches for Human Environmental Health Research , 2005, Environmental health perspectives.

[31]  Y. Kestens,et al.  Conceptualization and measurement of environmental exposure in epidemiology: accounting for activity space related to daily mobility. , 2013, Health & place.