Assessment of the risks of losing investments aimed at the development of Smart city systems

The article proposes a model for the computational core of the decision support system (DSS) in assessing the risks of investment loss during the dynamic planning (DP) of Smart City development. In contrast to the existing solutions, the proposed model provides specific recommendations when assessing the risks of loss. In case of an unsatisfactory risk forecast, it is possible to flexibly adjust the parameters of the investment process in order for the parties to achieve an acceptable financial result. The scientific novelty of the results is that for the first time it is proposed to apply a new class of bilinear multistep games. This class allowed us to adequately describe the process of assessing the risks of investment loss, using the example of dynamic planning for the placement of financial resources of players in Smart City projects. A distinctive feature of the considered approach is the use of tools based on the solution of a bilinear multistep game of both quality with several terminal surfaces, and a game of degree solved in the class of mixed strategies. Computational experiments were carried out in the Maple mathematical modeling package, and a DSS was developed in which a risk assessment model was implemented. The developed DSS allows to reduce the discrepancies between the data for predicting the risks of investment loss during the Smart City DP and the real return on investment.

Sovereign Default Forecasting in the Era of the COVID-19 Crisis

The COVID-19 crisis has revealed the economic vulnerability of various countries and, thus, has instigated the systematic exploration and forecasting of sovereign default risks. Multivariate statistical and stochastic process-based sovereign default risk forecasting has a 50-year developmental history. This article describes a continuous, non-homogeneous Markov chain method as the basis for a COVID-19-related sovereign default risk forecast model. It demonstrates the estimation of sovereign probabilities of default (PDs) over a five-year horizon period with the developed model reflecting the impact of the COVID-19 crisis. The COVID-19-adopted Markov model estimates PDs for most countries, including those that are advanced with AAA and AA ratings, to suggest that no sovereign nation’s economy is secure from the financial impact of the COVID-19 pandemic. The dynamics of the estimated PDs are indicative of contemporary evidence as experienced in the recent financial crisis. The empirical results of this article have policy implications for foreign investors, sovereign lenders, export finance institutions, foreign trade experts, risk management professionals, and policymakers in the field of finance. The developed model can be used to timely recognize potential problems with sovereign entities in the current COVID-19 crisis and to take appropriate mitigating actions.

Integrating Resilience in Time-based Dependency Analysis: A Large-Scale Case Study for Urban Critical Infrastructures

As critical systems shall withstand different types of perturbations affecting their functionalities and their service level, resilience is a very important requirement. Especially in an urban critical infrastructures where the occurrence of natural events may influence the state of other dependent infrastructures from various different sectors, the overall resilience of such infrastructures against large scale failures is even more important. When a perturbation occurs in a system, the quality (level) of the service provided by the affected system will be reduced and a recovery phase will be triggered to restore the system to its normal operation level. According to the implemented recovery controls, the restoration phase may follow a different growth model. This paper extends a previous time-based dependency risk analysis methodology by integrating and assessing the effect of recovery controls. The main goal is to dynamically assess the evolution of recovery over time, in order to identify how the expected recovery plans will eventually affect the overall risk of the critical paths. The proposed recovery-aware time-based dependency analysis methodology was integrated into the CIPCast Decision Support System that enables risk forecast due to natural events to identify vulnerable and disrupted assets (e.g., electric substations, telecommunication components) and measure the expected risk paths. Thus, CIPCast can be valuable to Critical Infrastructure Operators and other Emergency Managers involved in a crisis assessment to evaluate the effect of natural and anthropic threats affecting critical assets and plan proper countermeasures to reduce the overall risk of degradation of services. The proposed methodology is evaluated in a real scenario, which utilizes several infrastructures and Points of Interest of the city of Rome.

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.

The social risk forecast indicator of the road traffic accidents in the territory of Khabarovsk and the Khabarovskiy krai

The paper analyzes the road traffic safety in the territory of Khabarovsk in 2011-2020 according to the following indicators: the severity of the accidents, the severity of the consequences, the social risk, the forecast indicator of the social risk in the city of Khabarovsk by 2024. The analysis is based on the exponential smoothing method using the statistical data of 2011-2020. The forecast indicator of the social risk is compared to the value established by the Road Safety Strategy in the Russian Federation for 2018 - 2024, as a target by 2024. An assessment of achieving the mortality rate reduction in the Khabarovskiy krai is also given. The impact of the pandemic consequences and the socio-economic situation on reducing the road accidents mortality are examined. The objective data on the costs increasing to support the population and business being evaluated, the target values are set by the national project in 2020 and the subsequent years. For the analysis, the empirical methods are used, such as: examining the results of the previous activities; the expert assessments; the methods of studying an object in time: retrospective, forecasting. The socio-economic factors are considered the most susceptible to reduce the traffic accidents mortality.

WILDFIRES FORECAST PERFORMANCE IN ALBANIA DURING SUMMER 2020

"The Wildfire Risk Forecast (WRF) remains a daily procedure conducted by the National Centre for Forecast and Monitoring of Natural Hazards (NCFMNH), which is part of the Institute of Geosciences, Energy, Water and Environment (IGEWE) of Albania. WRF is generated on daily basis, by the country’s administrative unit (prefecture) and disseminated to the General Directorate of Civil Emergencies (GDCE) in order to help better coordinate fire-fighting activities. This study investigates the accuracy of the Wildfire Risk Forecasts during the 2020 summer season by analysing fire occurrences over each prefecture of Albania for two components of wildfire forecast Performance, i.e., The Prefecture Hit Probability (PHP) and the Average Fires per Hit (AFH). The study has revealed a “VERY GOOD” Performance for the “High Risk Level” forecast alerts, “GOOD” Performance for the “Moderate Risk Level” and “Low Risk Level” forecast alerts, and VERY GOOD” Performance for the “No Risk Level”"