REDISSE in Benin and Lessons Learned for the Management of Public Health Emergencies after Five Years of Implementation

Introduction: Benin was embarked on phase 3 of the REDISSE Benin project (Regional Disease Surveillance Systems Enhancement) which began in 2018. The objectives were in five key components namely, Surveillance and health information;Laboratory capacity building;Emergency preparedness and response;Human resources management for effective disease surveillance and epidemic preparedness;and Institutional Capacity Building, Project Management, Coordination and Advocacy. After five years of implementation, this study aimed at the documentation of lessons learned and best practices. Methods: A descriptive cross-sectional study. Apart from individual semi-structured interviews, a thematic workshops bringing together the project’s main stakeholders recruited on an exhaustive way by component to identify and validate lessons learned, good practices and propose improvement mechanisms to be taken into account by the sector. Criteria were set up and used to validate best practices and lessons learned. Results: A total 54 (Surveillance workshop), 47 (Preparedness & response workshop), 53 (Human Resources workshop), 26 (Laboratories workshop) participated to the thematic workshops, and five interviews. The good practices (33: 9 for animal health, 7 for human health and 17 crosscutting) and lessons learned (10: 3 for animal health and 7 for human health) have been identified and have been the subject, depending on the case, of proposals for improvement or conditions necessary for their maintenance. Discussion: The richness of a project lies not only in the immediate achievement of its results, but also and above all, in its usefulness for similar interventions, whether in the local, regional, national or international context. It is in this context that the REDISSE project has set out to make public the various lessons learned and best practices from the implementation of its activities over a period of some five consecutive years.

A Method for Determining the Importance of Critical Emergency Indicators Based on Multi-granularity Uncertain Language

In view of the complexity of emergencies and the subjectivity of decision-makers,a method of determining key emergency indicators based on multi-granularity uncertainty language is proposed.Firstly,decision members use preferred uncertain language phrases to represent the importance of each key indicator and use transformation functions to carry out the consistent transformation of this multi-granularity uncertain language information.Secondly,the group evaluation vector is obtained by using the extended weighted average operator of uncertainty,and then the weight vector of each key index is obtained by using the decision theory of uncertain language.Finally,an example is given to verify the practicability and effectiveness of the proposed method.

Water consumption and biodiversity:Responses to global emergency events

Given that it was a once-in-a-century emergency event,the confinement measures related to the coronavirus disease 2019(COVID-19)pandemic caused diverse disruptions and changes in life and work patterns.These changes significantly affected water consumption both during and after the pandemic,with direct and indirect consequences on biodiversity.However,there has been a lack of holistic evaluation of these responses.Here,we propose a novel framework to study the impacts of this unique global emergency event by embedding an environmentally extended supply-constrained global multi-regional input-output model(MRIO)into the drivers-pressure-state-impact-response(DPSIR)framework.This framework allowed us to develop scenarios related to COVID-19 confinement measures to quantify country-sector-specific changes in freshwater consumption and the associated changes in biodiversity for the period of 2020-2025.The results suggest progressively diminishing impacts due to the implementation of COVID-19 vaccines and the socio-economic system’s self-adjustment to the new normal.In 2020,the confinement measures were estimated to decrease global water consumption by about 5.7% on average across all scenarios when compared with the baseline level with no confinement measures.Further,such a decrease is estimated to lead to a reduction of around 5% in the related pressure on biodiversity.Given the interdependencies and interactions across global supply chains,even those countries and sectors that were not directly affected by the COVID-19 shocks experienced significant impacts:Our results indicate that the supply chain propagations contributed to 79% of the total estimated decrease in water consumption and 84%of the reduction in biodiversity loss on average.Our study demonstrates that the MRIO-enhanced DSPIR framework can help quantify resource pressures and the resultant environmental impacts across supply chains when facing a global emergency event.Further,we recommend the development of more locally based water conservation measures—to mitigate the effects of trade disruptions—and the explicit inclusion of water resources in post-pandemic recovery schemes.In addition,innovations that help conserve natural resources are essential for maintaining environmental gains in the post-pandemic world.

Model and algorithm for optimization of rescue center location of emergent catastrophe

The location of rescue centers is a key problem in optimal resource allocation and logistics in emergency response.We propose a mathematical model for rescue center location with the considerations of emergency oc-currence probability,catastrophe diffusion function and rescue function.Because the catastrophe diffusion and res-cue functions are both nonlinear and time-variable,it cannot be solved by common mathematical programming methods.We develop a heuristic embedded genetic algorithm for the special model solution.The computation based on a large number of examples with practical data has shown us satis-factory results.

Similarity-based emergency event detection in social media

Emergency events need early detection,quick response,and accuracy recover.In the era of big data,the use of social media platforms is being popularized.Social media users can be seen as social sensors to monitor real time emergency events.In this paper,a similarity-based method is proposed to early detect all kinds of emergency events in social media,including natural disasters,accidents,public health events and social security events.The method focuses on clustering social media texts based on the 3 W attribute information(What,When,and Where)of events.First,with the two-step classification,emergency related messages are detected and divided into different types from the massive and irrelevant data.Second,the time and location information are respectively extracted with the regular expression matching and the BiLSTM model.Finally,the text similarity is calculated using the type,time and location information,based on which social media texts are clustered into different events.The experiments on Sina Weibo data demonstrate the superiority of the proposed framework.Case studies on some real emergency events show the proposed framework has good performance and high timeliness.As the attribute information of events is extracted during the algorithm flow,it can be described what emergency,and when and where it happened.