Risk Analysis最新文献_第10页

Regional supply-chain impacts of Mississippi River fertilizer shipments disrupted by climate change. 气候变化对密西西比河化肥运输的区域供应链影响。

IF 3 3区医学

Risk Analysis Pub Date : 2025-07-13 DOI: 10.1111/risa.70064

Zhenhua Chen, Adam Rose, Fred Roberts, Andrew Tucci

引用次数: 0

Projecting stock market impacts of climate change via rational bubble models. 通过理性泡沫模型预测气候变化对股市的影响。

IF 3 3区医学

Risk Analysis Pub Date : 2025-07-10 DOI: 10.1111/risa.70078

John Fry

{"title":"Projecting stock market impacts of climate change via rational bubble models.","authors":"John Fry","doi":"10.1111/risa.70078","DOIUrl":"https://doi.org/10.1111/risa.70078","url":null,"abstract":"In this paper, we develop a rational bubble model to quantify the susceptibility of global stock markets to future temperature rises. The approach builds on existing theory incorporating the unpredictable timing of future Black-Swan events alongside price risks that increase in line with global temperature. An alternative specification where climate-change risks are instead linked to atmospheric carbon dioxide levels is also given. The approach offers simplicity, transparency and allows national-level effects to be estimated. In the short term, prices are artificially inflated and volatility artificially deflated as temperatures rise. This is in-line with previous work suggesting carbon-related risks are underpriced by markets. We use our model to estimate stock market exposure to future climate-change risks given future global temperature rises and increases in atmospheric <math> <semantics><mrow><mi>C</mi> <msub><mi>O</mi> <mn>2</mn></msub> </mrow> <annotation>$CO_2$</annotation></semantics> </math> . The potential effects are considerable once global temperatures increases beyond <math> <semantics> <mrow><msup><mn>2</mn> <mo>∘</mo></msup> <mi>C</mi></mrow> <annotation>$2^circ {rm C}$</annotation></semantics> </math> above preindustrial levels. We find that climate-change risks are priced in by certain G7 stock markets but not in smaller markets. Estimates of stock market losses directly attributable to global temperature rises up to <math> <semantics> <mrow><msup><mn>2</mn> <mo>∘</mo></msup> <mi>C</mi></mrow> <annotation>$2^circ {rm C}$</annotation></semantics> </math> above preindustrial levels are also given.","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144609251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natural disaster, ESG investing, and financial contagion. 自然灾害、ESG投资和金融传染。

IF 3 3区医学

Risk Analysis Pub Date : 2025-07-09 DOI: 10.1111/risa.70074

Haiying Wang, Ying Yuan, Tianyang Wang

引用次数: 0

Risk analysis of disinformation weaponized against critical networks. 针对关键网络的虚假信息武器化风险分析。

IF 3 3区医学

Risk Analysis Pub Date : 2025-07-07 DOI: 10.1111/risa.70062

Kash Barker, Elena Bessarabova, Sridhar Radhakrishnan, Andrés D González, Matthew S Weber, Jose E Ramirez Marquez, Yevgeniy Vorobeychik, John N Jiang

引用次数: 0

Managing infectious aerosols to counter engineered pandemics: Current recommendations and future research. 管理传染性气溶胶以对抗工程大流行：当前建议和未来研究。

IF 3 3区医学

Risk Analysis Pub Date : 2025-07-07 DOI: 10.1111/risa.70054

Adam Lerner, Gediminas Mainelis, William Hallman, Howard Kipen, Monica Magalhaes, Brian Buckley, José Guillermo Cedeño Laurent, Nir Eyal

{"title":"Managing infectious aerosols to counter engineered pandemics: Current recommendations and future research.","authors":"Adam Lerner, Gediminas Mainelis, William Hallman, Howard Kipen, Monica Magalhaes, Brian Buckley, José Guillermo Cedeño Laurent, Nir Eyal","doi":"10.1111/risa.70054","DOIUrl":"https://doi.org/10.1111/risa.70054","url":null,"abstract":"In the increasingly likely event of an engineered-virus outbreak or pandemic of catastrophic potential, managing infectious aerosols to reduce transmission will be crucial. Now is the time to start preparing our buildings, public opinion, and regulatory environments for the infectious aerosol management interventions necessary to protect the public. But which interventions should governments and institutions invest in the most? We review the leading candidate methods for infectious aerosol management and discuss their respective advantages, disadvantages, and suitable settings. There is strong emerging evidence that two recently explored technologies, direct exposure to far-ultraviolet-C (UVC) light and triethylene glycol, are particularly efficacious and safe, but there remain open questions about the long-term safety and efficacy of these interventions. In the meantime, we recommend other interventions-especially upper-room UVC and in-room air cleaners-for settings where most occupants regularly spend more than a small fraction of their day. We conclude by listing research questions about these interventions that still need to be researched in social science, product development, medicine, engineering, economics, and ethics.","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144584715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model averaging with logistic autoregressive conditional peak over threshold models for regional smog. 区域雾霾的logistic自回归条件峰值超过阈值模型平均。

IF 3 3区医学

Risk Analysis Pub Date : 2025-07-07 DOI: 10.1111/risa.70069

Chunli Huang, Xu Zhao, Fengying Zhang, Haiqing Chen, Ruoqi Song, Guangwen Ma, Weihu Cheng

{"title":"Model averaging with logistic autoregressive conditional peak over threshold models for regional smog.","authors":"Chunli Huang, Xu Zhao, Fengying Zhang, Haiqing Chen, Ruoqi Song, Guangwen Ma, Weihu Cheng","doi":"10.1111/risa.70069","DOIUrl":"https://doi.org/10.1111/risa.70069","url":null,"abstract":"We propose a novel dynamic generalized Pareto distribution (GPD) framework for modeling the time-dependent behavior of the peak over threshold (POT) in extreme smog (PM2.5) time series. First, unlike static GPD, three dynamic autoregressive conditional generalized Pareto (ACP) models are introduced. Specifically, in these three dynamic models, the exceedances of air pollutant concentration are modeled by a GPD with time-dependent scale and shape parameters conditioned on past PM2.5 and other air quality factors (SO2, NO2, CO) and weather factors (daily average temperature, average relative humidity, average wind speed). Second, unlike the recent studies of ACP models, we impose a logistic function autoregressive structure on the scale and shape parameters of the ACP models, which has simple calculation and flexible modeling for the scale and shape parameters, since the logistic function is used to mean that the changes in the long memory parameter occur in a continuous manner and often applied in time series models. Third, the model averaging method is applied to improve predictive performance using AIC and BIC criteria to select combined weights of the three ACP models. In addition, based on goodness-of-fit tests, the thresholds of the three ACP models are chosen by eight automatic threshold selection procedures to avoid subjectively assigning a certain value as the threshold. Maximum likelihood estimation (MLE) is employed to estimate parameters of the ACP models and its statistical properties are investigated. Various simulation studies and an example of real data in PM2.5 time series demonstrate the superiority of the proposed ACP models and the stability of the MLE.","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144584716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coupling wastewater-based epidemiology with data-driven machine learning for managing public health risks. 将基于废水的流行病学与数据驱动的机器学习相结合，以管理公共卫生风险。

IF 3 3区医学

Risk Analysis Pub Date : 2025-07-06 DOI: 10.1111/risa.70075

Sheree Pagsuyoin, Calvin Ng, Nerissa Molejon, Yan Luo

{"title":"Coupling wastewater-based epidemiology with data-driven machine learning for managing public health risks.","authors":"Sheree Pagsuyoin, Calvin Ng, Nerissa Molejon, Yan Luo","doi":"10.1111/risa.70075","DOIUrl":"https://doi.org/10.1111/risa.70075","url":null,"abstract":"Traditional health surveillance methods play a critical role in public health safety but are limited by the data collection speed, coverage, and resource requirements. Wastewater-based epidemiology (WBE) has emerged as a cost-effective and rapid tool for detecting infectious diseases through sewage analysis of disease biomarkers. Recent advances in big data analytics have enhanced public health monitoring by enabling predictive modeling and early risk detection. This paper explores the application of machine learning (ML) in WBE data analytics, with a focus on infectious disease surveillance and forecasting. We highlight the advantages of ML-driven WBE prediction models, including their ability to process multimodal data, predict disease trends, and evaluate policy impacts through scenario simulations. We also examine challenges such as data quality, model interpretability, and integration with existing public health infrastructure. The integration of ML WBE data analytics enables rapid health data collection, analysis, and interpretation that are not feasible in current surveillance approaches. By leveraging ML and WBE, decision makers can reduce cognitive biases and enhance data-driven responses to public health threats. As global health risks evolve, the synergy between WBE, ML, and data-driven decision-making holds significant potential for improving public health outcomes.","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Personalized risk score for post-COVID-19 condition: Bayesian directed acyclic graphic approach. covid -19后病情的个性化风险评分：贝叶斯有向无环图方法。

IF 3 3区医学

Risk Analysis Pub Date : 2025-07-06 DOI: 10.1111/risa.70072

Sam Li-Sheng Chen, Chen-Yang Hsu, Tin-Yu Lin, Amy Ming-Fang Yen, Tony Hsiu-Hsi Chen

{"title":"Personalized risk score for post-COVID-19 condition: Bayesian directed acyclic graphic approach.","authors":"Sam Li-Sheng Chen, Chen-Yang Hsu, Tin-Yu Lin, Amy Ming-Fang Yen, Tony Hsiu-Hsi Chen","doi":"10.1111/risa.70072","DOIUrl":"https://doi.org/10.1111/risa.70072","url":null,"abstract":"Post-COVID-19 condition (PCC) has gained traction currently in the post-pandemic era. To address this, we utilized a Bayesian directed acyclic graphic (DAG) model to develop a personalized composite risk score (CRS) for PCC, based on the tabular data derived from a comprehensive meta-analysis. Our risk assessment model incorporates 215 combinations of risk factors, including personal demographic and health-related profiles, across 41 studies involving over 860,000 COVID-19 cases. The CRS ranges from 0 to 500, categorizing patients into risk quartiles and estimating PCC probability across SARS-CoV-2 variants of concerns, including Wild/D614G/Alpha, Delta, and Omicron BA.1/BA.2. External validation demonstrated accurate predictions, though higher risk scores showed slight deviations, particularly in BA.5 Omicron subset. The risk assessment model is not only adaptable for incorporating new evidence as SARS-CoV-2 subvariants emerge but also very valuable in facilitating the optimal individualized medical care for PCC patients and prioritizing a spectrum of risk groups for early PCC diagnosis. Notably, the adaptability of Bayesian DAG model enhances PCC risk prediction, enabling data integration for evolving SARS-CoV-2 contexts and informing healthcare resource allocation for high-risk groups.","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144576194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Risk-aware autonomous search and rescue with multiagent reinforcement learning. 基于多智能体强化学习的风险感知自主搜索与救援。

IF 3 3区医学

Risk Analysis Pub Date : 2025-07-05 DOI: 10.1111/risa.70067

Aowabin Rahman, Salman Shuvo, Samrat Chatterjee, Mahantesh Halappanavar, Terje Aven

引用次数: 0

Contagious risk: Nexus of risk in climate, epidemic, geopolitics, and economic. 传染性风险：气候、流行病、地缘政治和经济风险的联系。

IF 3.3 3区医学

Risk Analysis Pub Date : 2025-07-01 Epub Date: 2024-12-11 DOI: 10.1111/risa.17687

Hailing Li, Xiaoyun Pei, Hua Zhang

{"title":"Contagious risk: Nexus of risk in climate, epidemic, geopolitics, and economic.","authors":"Hailing Li, Xiaoyun Pei, Hua Zhang","doi":"10.1111/risa.17687","DOIUrl":"10.1111/risa.17687","url":null,"abstract":"In recent years, \"black swan\" events have increasingly occurred across climate, epidemics, geopolitics, and economics, leading to a gradual coupling of different types of risk. Different from isolated shocks as a single type of risk affecting a specific industry, a nexus of risks allows one risk area to quickly relate to others, resulting in more catastrophic impacts. Utilizing an integrated framework, we investigate the contagion effects among climate policy uncertainty, the infectious disease equity market volatility tracker, geopolitical risk, and economic policy uncertainty using volatility, skewness, and kurtosis as risk measures. The results indicate that: (1) The contagion effect of different types of risk increases with higher order risk measures, suggesting that more extreme events are more likely to be contagious across domains. (2) Approximately two-thirds of risk contagion occurs contemporaneously, while about one-third occurs with a lag, indicating that risk contagion combines both immediacy and continuity. (3) Risk contagion exhibits significant time-varying and heterogeneous characteristics. Our study elucidates the inherent contagion characteristics between different types of risk, transforming the understanding of risk from a one-dimensional to a multidimensional perspective. This underscores that risk management should not be confined to a single domain; it is crucial to consider the potential impacts of risks from other industries on one's own.","PeriodicalId":21472,"journal":{"name":"Risk Analysis","volume":" ","pages":"1662-1682"},"PeriodicalIF":3.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0