March 27, 2015Print | PDF
Reducing the health impacts of extreme weather events will require new thinking, modeling and piloting of early warning systems.
Early warning systems are at a beginning stage in terms of predicting exact timing, location, scale and human impacts of extreme weather events.
Laurier’s Dr. James Orbinski, CIGI Chair of Global Health Governance at the Balsillie School of International Affairs (BSIA) is working with Julia Metelka, a master’s student in the Department of Geography, to map out the complex research questions.
Together they are conducting a literature review and gathering baseline data on current health, environmental monitoring, primary and secondary education infrastructure and systems in Malawi.
“I have been reviewing literature from different academic domains to gather the current state of knowledge,” said Metelka. “The intersection of this research will form the basis for a pilot project to link early warning of weather events to health-care systems in Malawi.”
Effective early warning systems are not simply technical networks that deliver warnings in the "last mile" before disaster, but are also cultural and political processes that engage traditional knowledge and community in the "first mile" of system design and use.
“This is quite a challenging topic considering the interconnectedness between systems – health, climate and weather variability, economy, education, and food systems all play an important role,” said Metelka. “One system does not solely depend on another in a linear fashion. I currently study the transmission of Japanese Encephalitis in Nepal, which exists in a similarly complex system. I hope to use some of these strategies to guide this research examining climate change adaptation and health in Malawi.”
Dr. Orbinski is excited to explore a pilot project in Malawi. “Such systems could also include ongoing crowd-sourced data and analysis, and be aligned with broader public health prevention, treatment and health surveillance strategies."
While these will improve as data reliability and modeling improve, there is enough knowledge now to both credibly imagine a seamless integrated warning system, and to guide development in a timely manner.
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