The COVID-19 pandemic highlighted the urgent need to strengthen public health systems.In response,the United Nations Disaster Risk Reduction(UNDRR) Public Health System Resilience Scorecard(Scorecard) was applied in workshops across multiple countries.The aim of our research was to explore the workshop findings to develop priority strategies for strengthening public health system resilience.We conducted a workshop from 14 to 16 March 2023,at the UNDRR Global Education and Training Institute in Incheon,Republic of Korea.A sequential modified Delphi method was utilized to develop a set of prioritized resilience strategies.These were drawn from 70 strategies identified from 13 distinct workshops in eight countries.After two surveys,23 strategies were finalized.Ten received ratings of "High" or Very High" from89% of participants.These related to the inclusion of public health risks in emergency plans,integrating multidisciplinary teams into public health,enabling local transport mechanisms,and improving the ability to manage an influx of patients.The Scorecard provides an adaptable framework to identify and prioritize strategies for strengthening public health system resilience.By leveraging this methodology,our study demonstrated how resilience strategies could inform disaster risk reduction funding,policies,and actions.

Rapid estimation of post-earthquake building damage and loss is very important in urgent response efforts.The current approach leaves much room for improvement in estimating ground motion and correctly incorporating the uncertainty and spatial correlation of the loss.This study proposed a new approach for rapidly estimating post-earthquake building loss with reasonable accuracy.The proposed method interpolates ground motion based on the observed ground motion using the Ground Motion Prediction Equation(GMPE) as the weight.It samples the building seismic loss quantile considering the spatial loss correlation that is expressed by Gaussian copula,and kriging is applied to reduce the dimension of direct sampling for estimation speed.The proposed approach was validated using three historical earthquake events in Japan with actual loss reports,and was then applied to predict the building loss amount for the March 2022 Fukushima Mw7.3 earthquake.The proposed method has high potential in future emergency efforts such as search,rescue,and evacuation planning.

Industrial accidents have shown that many people can be affected,such as in Seveso,Italy,in 1976.Industrial accidents in nuclear power plants have also led to fatalities and evacuations.To better guide preparedness against and mitigation of industrial accidents,an assessment is necessary to evaluate hazard exposure and the type of potentially vulnerable social groups that need to be taken into account.This study conducted a spatial assessment of three types of industrial facilities in Germany:facilities registered under the Seveso Directive,chemical parks,and nuclear power plants.The method consisted of a spatial assessment using a Geographic Information System of exposure around hazardous sites registered under the S eveso Directive in Germany and of census data to analyze social vulnerability.Hazards analyzed included industrial accidents and earthquakes.The results revealed that most industrial sites are in urban areas and that population density,the numbers of foreigners,and smaller housing unit sizes are higher in close proximity to these sites.The buffer zones analyzed in circles between 1 and 40 km show a decreasing vulnerability with more distance.This can guide emergency management planners and other stakeholders to better prepare for major accidents and better devise disaster risk reduction strategies specifically for different social groups.

Cultural relic conservation capability is an important issue in cultural relic conservation research,and it is critical to decrease the vulnerability of immovable cultural relics to rainfall hazards.Commonly used vulnerability assessment methods are subjective,are mostly applied to regional conditions,and cannot accurately assess the vulnerability of cultural relics.In addition,it is impossible to predict the future vulnerability of cultural relics.Therefore,this study proposed a machine learning-based vulnerability assessment method that not only can assess cultural relics individually but also predict the vulnerability of cultural relics under different rainfall hazard intensities.An extreme rainfall event in Henan Province in 2021was selected as an example,with a survey report on the damage to cultural relics as a database.The results imply that the back propagation(BP) neural network-based method of assessing the vulnerability of immovable cultural relics is reliable,with an accuracy rate higher than 92%.Based on this model to predict the vulnerability of Zhengzhou City's cultural relics,the vulnerability levels of cultural relics under different recurrence periods of heavy rainfall were obtained.Among them,the vulnerability of ancient sites is higher than those of other cultural relic types.The assessment model used in this study is suitable for predicting the vulnerability of immovable cultural relics to heavy rainfall hazards and can provide a technical means for cultural relic conservation studies.