The Project
With globalization and an increasing number of international traveling, the policy of global epidemic early warning has shifted from local epidemic controls to a joint defense strategy across countries and regions. However, the official epidemic early warning system in each country usually varies a lot, and the information transparency also serves at different level across systems and countries, which can result in a time lag in acquiring current epidemic status. Currently, the ProMED (Program for Monitoring Emerging Diseases), which was built and maintained by the International Society for Infectious Disease (ISID), is an online open platform that collects global real-time information on infectious diseases. Nevertheless, the enormous data quantity that ProMED provides does not balance with a well-classified structure for data retrieval, which make it challenge to be used for further analyses, such as estimating the spatio-temporal dynamics and risk of epidemic outbreaks. Also, there is a lack of research that integrates epidemic data with environmental parameters and risk factors to predict the transmission risk of infectious diseases. Moreover, besides an open platform for disease reports, building up a global early warning framework is a multi-disciplinary work that involves in human mobility and transport modeling, social media semantic analysis, and risk visualization to capture the global-scale epidemic trends and estimate the disease importation risk for Taiwan.
Therefore, by creating global epidemic transmission networks, the objective of this project is to build up an early warning framework for the international spread of infectious diseases and clarify the epidemic connection of Taiwan with the globe. We expect our results to provide a more comprehensive understanding of the disease importation risk and how global epidemics can affect or trigger potential disease outbreaks in Taiwan. Likewise, when Taiwan has a disease outbreak, how local epidemics may spread overseas and impact other countries that share close bonds with Taiwan such as the East Asia countries. As a transport hub of East Asia, the global early warning framework from Taiwan may demonstrate a novel and more comprehensive coping strategy for the international spread of infectious diseases and lead the society to a more sustainable future of public health.
The figure below shows the framework of the iGEAR project, from data, model to platform. It is a multi-disciplinary project that has developed into multiple research objectives with each in charge of a fundamental dimension of building up the early warning framework for estimating the international spread and the importation risk of infectious diseases in Taiwan. The research objectives of the iGEAR project is listed below:
Therefore, by creating global epidemic transmission networks, the objective of this project is to build up an early warning framework for the international spread of infectious diseases and clarify the epidemic connection of Taiwan with the globe. We expect our results to provide a more comprehensive understanding of the disease importation risk and how global epidemics can affect or trigger potential disease outbreaks in Taiwan. Likewise, when Taiwan has a disease outbreak, how local epidemics may spread overseas and impact other countries that share close bonds with Taiwan such as the East Asia countries. As a transport hub of East Asia, the global early warning framework from Taiwan may demonstrate a novel and more comprehensive coping strategy for the international spread of infectious diseases and lead the society to a more sustainable future of public health.
The figure below shows the framework of the iGEAR project, from data, model to platform. It is a multi-disciplinary project that has developed into multiple research objectives with each in charge of a fundamental dimension of building up the early warning framework for estimating the international spread and the importation risk of infectious diseases in Taiwan. The research objectives of the iGEAR project is listed below:
- Using machine-learning methods and satellites images for retrieving global epidemic information and environmental parameters.
- Using global-scale transportation data for estimating effective distances for epidemic spread in time and space.
- Early warning for epidemic spread using UGC and internet voice: a semantic interoperability perspective.
- Developing manifold-based algorithms for epidemic risk visualization: Linking transportation and social media data in time and space.
- Regional partitioning for epidemic control based on global disease surveillance and transportation data.
