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Learning from Multi-Hazard Early Warning Systems to Respond to Pandemics (English)

Having a common framework for early action to cope with complex disasters can make it easier for authorities and other stakeholders, including populations at risk, to understand the full spectrum of a disaster’s secondary and tertiary effects and thus where to focus preparedness efforts, and how best to provide more targeted warnings and response services. Meteorological and hydrological services worldwide have developed and implemented Multi-Hazard Early Warning Systems (MHEWS) for weather-and climate-related hazards; these are now being expanded and transitioned toward Multi-Hazard Impact-Based Early Warning Systems (MHIEWS). While it is still early, it is becoming clear that this approach has useful lessons for the COVID-19 global pandemic, and some valuable insight to be gained in risk communication, risk analysis, and monitoring methodologies and approaches. The ability to understand and respond effectively to warnings through appropriate behaviors and actions is central to resilient societies and communities. By avoiding physical, societal, and economic harm to the greatest extent possible, recovery from a hazard is likely to be faster, less costly, and more complete. MHIEWS can be a common approach for all hazards and therefore is more likely to become a trusted tool that everyone can understand and use as a basic element of theirnational disaster risk management system. The interconnectedness of hazards and their impacts is a strong motivator for a common approach. One of the lessons from both the COVID-19 pandemic and extreme weather events is the need to understand the vulnerability of individuals, communities, and societies so as to provide reliable, targeted guidance and warnings and ensure the willingness and capacity to prepare for a reasonable worst-case scenario based on informed long-term planning. Meteorology and hydrology are making good progress in this direction, and the process can be readily applied to health and other sectors.


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    Rogers,David, Anderson Berry,Linda Jennette, Bogdanova,Anna-Maria, Fleming,Gerald J, Gitay,Habiba, Kahandawa,Suranga Sooriya Kumara, Md Kootval,Haleh Kootval, Staudinger,Michael, Suwa,Makoto, Tsirkunov,Vladimir V., Wang,Weibing

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    Learning from Multi-Hazard Early Warning Systems to Respond to Pandemics

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Rogers,David Anderson Berry,Linda Jennette Bogdanova,Anna-Maria Fleming,Gerald J Gitay,Habiba Kahandawa,Suranga Sooriya Kumara Md Kootval,Haleh Kootval Staudinger,Michael Suwa,Makoto Tsirkunov,Vladimir V. Wang,Weibing

Learning from Multi-Hazard Early Warning Systems to Respond to Pandemics (English). Washington, D.C. : World Bank Group.