The Global Conflict Risk Index (GCRI): Regression model, data ingestion, processing and output methods

The GCRI is a quantitative conflict risk model, developed by the JRC and based solely on open source data, providing quantitative input to the EU early warning framework, one input to the EU Conflict Early Warning System (EWS), developed by the European External Action Service (EEAS) in close partnership with the European Commission to enhance the EU's conflict prevention capacities. The GCRI distinguishes between three types of violent conflict a state may experience: civil war over national power, subnational conflicts over secession, autonomy, or resources, and conflicts in the international sphere. While the latter are not currently modelled by GCRI, for the first two the index quantifies the probability and the intensity respectively of national and subnational conflicts occurring in the next one to four years. Relying on historical data and a statistical model that includes political, socio-economic, environmental and security variables, it assesses the level and likelihood of future conflicts The GCRI is composed of two statistical models: the regression model and the composite model. Both models are based on twenty-four individual variables. This report presents the work done between February 2017 and September 2017, specifically focused on improving the documentation on the regression model. The present report describes on the one hand the regression model, including the input data and the model itself. On the other hand, it presents the statistical significance test and the matrix of confusion that have been performed, in order to get a highly detailed analysis of the performances of the model. The results of these analyses are presented in chapter 4 and 5. This report is part of a series of documentations produced in 2017 aiming at improving the GCRI models with greater transparency and robustness. This work contributes to enhancing the GCRI performance.JRC.E.1-Disaster Risk Managemen

The JRC DIONE model version II

The DIONE cost model is used to assess the costs and benefits of European Union road vehicle CO2 standards, from the perspective of vehicle users, vehicle producers, and the society. The model has been developed and employed at the European Commission’s Joint Research Centre (JRC) since 2014, and has recently undergone major extensions in its scope as well as updates in its computational implementation. The present report documents the second, fully revamped version of this model.JRC.C.4 - Sustainable, Smart and Safe Mobilit

Global Conflict Risk Index: New variables in 2018

The Global Conflict Risk Index (GCRI) is an early warning system designed to give policy makers a global risk assessment based on economic, social, environmental, security and political factors. The GCRI is composed of two statistical models: the regression model, that quantifies the probability and the intensity of national and subnational conflicts occurring in the next one to four years, and the composite model, whose aim is to provide an overview of the factors contributing to conflict at country level. Both models are based on twenty-four individual variables, whose raw data are open-source. While it is generally agreed that political and social variables are the most relevant ones for conflict risk modelling, other variables and their linkages with armed conflicts have received growing attention from both academics and policy makers in recent years, e.g. climate variability. Indeed, the nature of conflict is evolving and the diversity of conflict drivers has been acknowledged. In this report new triggers of instability, such as climate variability, levels of resilience, and displaced people are explored as drivers of conflicts. The aim is to improve the accuracy of the regression model, and further develop the GCRI with new variables.JRC.E.1-Disaster Risk Managemen

Risk assessment for the 2025 In-Service Verification (ISV) of CO2 emissions of Light-Duty Vehicles

Article 13 of Regulation (EU) 2019/631 requires the type-approval authorities to verify the CO2 emission and fuel consumption values of light-duty vehicles in-service. Commission Delegated Regulation (EU) 2023/2867 sets out the guiding principles and criteria for defining the procedures for that verification, while Commission Implementing Regulation (EU) 2023/2866 determines the actual verification procedures. Article 3(4) of that Implementing Regulation requires the Commission to set out a methodology for assessing the risk that in-service verification (ISV) families may include vehicles with a deviation in the CO2 emission values and to publish each year a report describing that methodology and listing those families with the highest risk of including such vehicles. JRC has been tasked to perform the risk assessment on behalf of the Commission. This is the second annual report describing the methodology for the assessment, and the main findings. The risk assessment methodology described was built upon the approach established in last year’s report, using the concept of the Composite Risk Index (CRI). The CRI combines the probability and severity of a specific occurrence. Probability levels are determined based on the total number of new vehicles from the in-service verification family that have been placed on the Union market. For the severity determination, the data collected pursuant to Article 14 of Implementing Regulation (EU) 2021/392 and the real-world data, as referred to in Article 3(3)(e) of Implementing Regulation (EU) 2023/2866 have been used. In addition, tests performed through the Commission’s market surveillance test campaigns and from the in-service conformity tests pursuant to Regulation (EU) 2017/1151 have been part of this year’s risk assessment. This report identifies the ISV families with the highest risk of including vehicles with a deviation in CO2 emissions values. Based on the risk assessment and random selection, 333 unique interpolation families, representing 250 unique ISV families, have been identified as having such high risk. Additionally, some interpolation families were reported as part of the annual CO2 monitoring for light-duty vehicles, but could not be found amongst those reported to the Commission under Article 14 of Implementing Regulation (EU) 2021/392. As a result, a number (24) of those missing interpolation families with the highest vehicle registration numbers in the last three years and manufacturers with the highest percentage of missing families, has been selected and included in the list of high risk families for the 2025 in-service verification. In addition, and to fill the gap between the 2025 ISV testing needs and to cover all manufacturers, the final list of families includes also 13 interpolation families selected based on medium risk or the highest registration volumes. In total, the ISV 2025 testing plan comprises 370 unique interpolation families. To further support the vehicle selection for the 2025 in-service verification, this report also links potential risks associated with ISV families flagged as high risk to chassis-dynamometer testing, road load tests, or the implementation of artificial strategies. Consequently, each of the listed ISV families was marked for specific types of tests based on the outcomes of this risk assessment.JRC.C.4 - Sustainable, Smart and Safe Mobilit

Posttransplant cyclophosphamide as GVHD prophylaxis in patients receiving mismatched unrelated HCT: the PHYLOS trial

Posttransplant high-dose cyclophosphamide (PTCy) is effective in overcoming the negative impact of HLA disparity in the haploidentical setting. In light of these results, we investigated the efficacy of PTCy, in improving clinical outcomes of hematopoietic stem cell transplantation (HSCT) from a mismatched unrelated donor (MMUD) in patients with acute myeloid malignancies by reducing the incidence and severity of acute graft-versus-host disease (aGVHD). A prospective, single-arm, phase 2 study (PHYLOS) was conducted by the Gruppo Italiano Trapianto di Midollo Osseo. The ethical committees of the participating centers approved the study (EURODRACT 2017-003530-85). A total of 77 consecutive patients (acute myeloid leukemia: 64; myelodysplastic syndrome: 13) were enrolled at 26 Italian transplant centers (January 2020-November 2022). Median age of the patients was 53 (range, 19-65) years. The 100-day cumulative incidence of grades 2 to 4 aGVHD was 18.2% (95% CI, 10.6-27.6) and of grades 3 to 4 was 6.5% (95% CI, 3.1-15.1). Seventy-one patients (92%) had full-donor chimerism with complete neutrophil engraftment by day +30. One-year cumulative incidence of chronic GVHD was 13.4% (95% CI, 6.9-22.1). One-year cumulative incidence of nonrelapse mortality was 9.1% (95% CI, 4.0-16.9), and the relapse rate was 23.8% (95% CI, 14.9-33.9). One-year overall survival and graft relapse-free survival were 78.6% (95% CI, 67.4-86.3) and 55.3% (95% CI, 43.4-65.7), respectively. Our study in a homogeneous patient cohort suggests that PTCy leads to a low rate of aGVHD and improves clinical outcomes of HSCT from MMUD. This trial was registered at www.clinicaltrials.gov as #NCT03270748

Long-term outcomes of the global tuberculosis and COVID-19 co-infection cohort

Background: Longitudinal cohort data of patients with tuberculosis (TB) and coronavirus disease 2019 (COVID-19) are lacking. In our global study, we describe long-term outcomes of patients affected by TB and COVID-19. Methods: We collected data from 174 centres in 31 countries on all patients affected by COVID-19 and TB between 1 March 2020 and 30 September 2022. Patients were followed-up until cure, death or end of cohort time. All patients had TB and COVID-19; for analysis purposes, deaths were attributed to TB, COVID-19 or both. Survival analysis was performed using Cox proportional risk-regression models, and the log-rank test was used to compare survival and mortality attributed to TB, COVID-19 or both. Results: Overall, 788 patients with COVID-19 and TB (active or sequelae) were recruited from 31 countries, and 10.8% (n=85) died during the observation period. Survival was significantly lower among patients whose death was attributed to TB and COVID-19 versus those dying because of either TB or COVID-19 alone (p<0.001). Significant adjusted risk factors for TB mortality were higher age (hazard ratio (HR) 1.05, 95% CI 1.03-1.07), HIV infection (HR 2.29, 95% CI 1.02-5.16) and invasive ventilation (HR 4.28, 95% CI 2.34-7.83). For COVID-19 mortality, the adjusted risks were higher age (HR 1.03, 95% CI 1.02-1.04), male sex (HR 2.21, 95% CI 1.24-3.91), oxygen requirement (HR 7.93, 95% CI 3.44-18.26) and invasive ventilation (HR 2.19, 95% CI 1.36-3.53). Conclusions: In our global cohort, death was the outcome in >10% of patients with TB and COVID-19. A range of demographic and clinical predictors are associated with adverse outcomes

Worldwide Effects of Coronavirus Disease Pandemic on Tuberculosis Services, January–April 2020

Coronavirus disease has disrupted tuberculosis services globally. Data from 33 centers in 16 countries on 5 continents showed that attendance at tuberculosis centers was lower during the first 4 months of the pandemic in 2020 than for the same period in 2019. Resources are needed to ensure tuberculosis care continuity during the pandemic

Modelling conflict resilience in the Global Conflict Risk Index

The Global Conflict Risk Index (GCRI) is a quantitative method for the prediction of conflict risk in a country in the next 1-4 years. It is modelled on 24 variables, in five dimensions: political, economical, social, environmental and the country's security status. In this paper, we explore a definition of conflict resilience as the capacity of a country to demonstrate a decreased conflict intensity, than what would be expected given its specific structural profile. We investigate the relationship between the two GCRI variables, conflict intensity and political repression, under the assumption that the eruption of a conflict in a country takes place when the population of that specific state is deprived of its fundamental rights. We note that there exists a correlation between repression and the eruption of a conflict. The correlation matrix shows that repression (REPRESS) and internal conflict (CON_INT) variables have a correlation coefficient of 0.62, which is overall one of the highest coefficients in GCRI. We then observe which countries are resilient to conflicts when they have to bear a high or increasing level of repression. The study of the regression trend lines, allows us to classify the country's type of conflict resilience according to the coefficient of the regression line (high m, low m) and the intercepts (q>0 q<0). We theorize resilience conflict as: i) stability in the face of conflict risk due to the increased level of repression, and therefore absorptive capacity, and ii) resilience as flexibility, and therefore adaptive capacity. We also observe a number of countries with reduced conflict resilience to armed conflict in the face of political repression. In the future, we plan to test resilience as the transformative capacity after the eruption of armed conflict, and the recovery rate of a country to an improved conflict risk status, using the GCRI historical data.JRC.E.1 - Disaster Risk Managemen