Risk Modeling Research Articles

Research on Intelligent Control Model of Laboratory Safety Risks in Colleges and Universities Based on Digital Twins

This paper focuses on developing an intelligent management model for safety risks in university laboratories based on digital twin technology to improve safety management efficiency and accuracy. The virtual simulation environment of the laboratory is constructed by using digital twin technology, which is combined with the DEMATEL-ISM method for risk identification and analysis. Fault tree analysis (FTA) method was utilized to construct a laboratory safety accident fault tree to identify and assess potential risk factors. It was found that safety risks in laboratories can be effectively identified and controlled by digital twin technology. The risk assessment showed that unregulated drug storage, lack of monitoring and warning devices, insufficient safety awareness, inadequate systems and unreasonable layout of water, electricity and gas pipelines were the main risk factors. The empirical analysis of 11 university laboratories revealed that most of the laboratories were at a “relatively safe” level. The intelligent management model of safety risk in university laboratories based on digital twins can effectively identify and assess the safety risk, provide a scientific basis for the formulation of safety management measures, and thus improve the efficiency and accuracy of laboratory safety management.

Credit risk modelling within the euro area in the COVID‐19 period: Evidence from an ICAS framework

AbstractThis paper develops a logistic regression model in an in‐house credit assessment system (ICAS) framework for predicting corporate defaults in the Greek economy. We consider the impact of the COVID‐19 pandemic and the associated government financial support schemes, aiming to protect against financial vulnerabilities, on the probability of default of non‐financial firms, as well as the relevant sectoral and firm‐size effects. In developing the ICAS framework, we address methodological issues such as the predictive performance of statistical versus machine learning approaches and the imbalanced dataset problem, indicating ways to evaluate such models with strong predictive power. Our findings suggest that the effect of the financial support measures dominates the pandemic shocks, thus substantially reducing the probability of firms' default, while the size‐ and industry‐based models show that firms in the micro and services sectors benefited the most. Furthermore, using a random forest model, our findings highlight the trade‐off between the transparency of traditional statistical models and the predictive value of machine learning models.

Enhancing river floodplain management with nature‐based solutions: Overcoming barriers and harnessing enablers

AbstractFloodplains are regularly inundated areas of land that provide ecosystem benefits for the entire catchment area, along with numerous benefits for communities making them ideal for human settlement. The management of floodplains involves an ongoing dilemma; how best to balance the need to protect urban settlements from significant flood events with the benefits that inundation brings to the environmental and cultural values of the floodplain. These flood events have been traditionally addressed using technical flood protection measures. However, nature‐based solutions (NbS), provide a potential alternative approach. In this research, we analyze 29 global case studies centered on river floodplain management to investigate the barriers and enablers that affect the implementation of NbS, including floodplain restoration, mangroves, riparian forest restoration, wetland green infrastructure, among other strategies. The aim of this paper is to gain insights into improving floodplain management through the assessment of barriers and enablers evident in these case studies. The barriers and enablers were divided into six categories: social, technical, financial, political, institutional and economic and further into several sub‐divided categories. Social and technical categories were the most commonly described barriers and enablers closely followed by the institutional category. The discussion is centered around four topics: understanding community and decision‐maker risk perceptions, increased focus on stakeholders, managing multiple conflicting objectives, and consideration of multiple spatial scales. Further research on the uptake of NbS needs to focus on addressing risk communication and modeling, managing trade‐offs, and demonstration of delayed benefits.This article is categorized under: Water and Life > Conservation, Management, and Awareness Science of Water > Water and Environmental Change

System risk modelling and decision-making – Reflections and common pitfalls

Since its foundation, the Joint Committee on Structural Safety (JCSS) has been engaged in the discussion of methods for determining the reliability of components, calibration of standards, as well as risk modelling of systems. In publications, it is regularly explained which methods have which advantages. In the literature, the drawbacks and pitfalls that challenge rational decisions and help to develop and find more appropriate methods for practice are often not documented.Such problems can lead to decisions, which are not rational from a decision-theoretic point of view, some of which are worse than a random decision. Especially events, with a very small probability of occurrence hardly give any feedback possibilities from reality and evidence-based analysis of decisions is not possible. Careful selection of methods and knowledge/information of the assumptions is crucial to rational decisions.This paper will discuss some of the identified pitfalls based on the discussions in the JCSS. It will span from aspects in the uncertainty quantification, uncertainty propagation, consequence assessment as well as approaches that are found and used in practice for decision-making (e.g. probability interpretations, risk aversion, risk matrices and FN diagrams). This paper can be seen as a documentation of outtakes from the discussions which led to the joint understanding and approach of the JCSS. The paper does not claim to be complete concerning all the possible pitfalls in risk assessments and system identification. But it does provide important reflections and indicates where the eyes must be kept open. Further, the paper points to a way of rational decision-making accounting for the uncertainties in information.

The effect of room air cleaners on infection control in day care centres

The aim of this study was to assess the effectiveness of air cleaning in reducing the risk of respiratory infection in two day care centres using a simple and robust calculation model. Additionally, we aimed to identify potential hotspots for infections in indoor setting and focus countermeasures accordingly. Initial results from an interventional clinical study are provided as proof-of-concept for the model. We constructed a mathematical model to assess the number of persons at risk for airborne infection transmission in day care. Utilizing the model, we used portable air cleaners in two day care units (A and B, number of children participating in the study n = 43) and compared infection incidents between the two intervention units to the rest of the units in city of Helsinki (n = 607). The intervention buildings had mechanical supply and exhaust air ventilation. The risk modelling suggests that the use of air cleaners reduced the expected number of persons at infection transmission risk significantly. At day care centre A the average reduction was 60% (range 52% - 88%) and at day care centre B 53% (range 14% - 59%). During the approximately six month study period, we observed a significant difference in the days absent from day care due to infections between the intervention and reference day care units. On average, the parents were absent from work due to child’s illness in reference day care centers for 5.53 days and 3.77 days in intervention day care centers during the study period (p=0.009). In relative terms the reduction was approximately 32%. Our study offers compelling evidence to support increasing non-infectious air flow rates in daycare centers during periods requiring infection risk management. This can be implemented with portable air cleaners as an effective and cost-efficient strategy for mitigating the spread of respiratory infections among children. The clinical results support the findings suggested by the theoretical model. Implications and impacts–Air cleaning seems to be an effective way to reduce infection risk in day cares–Identification of infection risk hotspot will help in desgining the preventive methods–Portable air cleaners offer an affordable and versatile solution also in buildings where existing ventilation is insufficient for infection risk management”

Using predictive risk modelling to identify patients with hidden health needs in an Aboriginal and Torres Strait Islander health service.

In partnership with an Aboriginal and Torres Strait Islander community-controlled health service, we explored the use of a machine learning tool to identify high-needs patients for whom services are harder to reach and, hence, who do not engage with primary care. Using deidentified electronic health record data, two predictive risk models (PRMs) were developed to identify patients who were: (1) unlikely to have health checks as an indicator of not engaging with care; and (2) likely to rate their wellbeing as poor, as a measure of high needs. According to the standard metrics, the PRMs were good at predicting health checks but showed low reliability for detecting poor wellbeing. Results and feedback from clinicians were encouraging. With additional refinement, informed by clinic staff feedback, a deployable model should befeasible.

Predictive Intelligence: The Impact of Risk Modeling on Organizational Resilience

The importance of risk modelling in strategic planning and decision-making cannot be emphasized enough in an age where uncertainty is constant. This paper explores the crucial function of risk modelling as a vital resource for people and organizations trying to understand and reduce potential risks. The emergence of big data and technology has made it possible to create complex risk models that offer more insights into possible hazards and improve the efficacy of risk management choices. In order to properly measure and manage risks, this article highlights the value of risk models across a range of industries. Risk models are essential for regulatory compliance, especially in sectors like healthcare, where following laws like GDPR and HIPAA are required. These models offer a consistent method for evaluating risk, guaranteeing adherence and averting costly fines. Additionally covered in the article is how risk models improve decision-making by lowering uncertainty and increasing openness, which in turn fosters stakeholder trust. The paper's main body describes a thorough approach to risk modelling, which begins with identifying potential risks and progresses to risk assessment through the use of methods like fault tree analysis (FTA) and event tree analysis (ETA). It then goes over how to quantify risks using both quantitative and qualitative approaches, and it ends with adding up every potential threat to provide an overall risk profile for the company. This paper concludes by highlighting the need for risk modelling in the intricate corporate world. It offers a thorough process for creating risk models, matching them to organizational goals, and utilizing them as a preventative measure for resilience and long-term performance.

A drink equals how many cigarettes? Equating mortality risks from alcohol and tobacco use in Canada.

To quantify and communicate risk equivalencies for alcohol-and tobacco-attributable mortality by comparing per standard drinks consumed to per number of cigarettes smoked in Canada. Alcohol-and tobacco-attributable premature deaths (≤75 years of age) and years of life lost (YLL) were estimated using a lifetime risk modeling approach. Alcohol-attributable death statistics were obtained from the 2023 Canadian Guidance on Alcohol and Health data source. Tobacco-attributable death statistics were derived from the Mortality Population Risk Tool (MPoRT) model. The risk of alcohol use on premature death and YLL increased non-linearly with the number of drinks consumed, while the risk for tobacco use on these two measures increased linearly with the number of cigarettes smoked. Males who consumed 5 drinks/day-a standard drink contains 13.45 grams of alcohol in Canada-had an equivalent risk as smoking 4.9 cigarettes/day (when modeling for premature death) and 5.1 cigarettes/day (when modeling for YLL). Females who consumed 5 drinks/day experienced an equivalent risk as smoking 4.2 cigarettes/day for premature deaths and YLL. At all levels of alcohol consumption females and males who consumed <5 drinks/day have less risks from consuming a standard drink than from smoking a cigarette. For males who consumed 5 drinks/day, the increased risks of death from per drink consumed and per cigarette smoked were equal. Risk equivalencies comparing alcohol use to tobacco use could help people who drink improve their knowledge and understanding of the mortality risks associated with increased number of drinks consumed per day.

Polychlorinated dibenzodioxins/furans and dioxin-like polychlorinated biphenyls in fish and crustaceans of a recreationally fished estuary, following targeted remediation

Polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (dl-PCBs) are a suite of harmful chemicals (hereafter collectively referred to as ‘dioxins’), and their emission into aquatic habitats leads to persistent contamination of sediments, aquatic food-webs, and seafoods. Quantifying contaminant levels in seafood species is important for the ongoing management of exposure risk by fishers, particularly after any remediation actions. We present dioxin concentrations in four seafood species (Yellowfin Bream Acanthopagrus australis, Sea Mullet Mugil cephalus, Eastern School Prawn Metapenaeus macleayi, and Eastern King Prawn Penaeus plebejus) in a recreationally fished estuary, in relation to a contaminated site that has undergone a remediation process, partially removing contaminated sediments (Homebush Bay, Port Jackson, New South Wales, Australia). Dioxin concentrations in these species were measured before (2005/6) and after (2015/16) remediation at a range of locations in and around the remediated site. Dioxin concentrations and congener profiles differed substantially among taxa, and concentrations were frequently higher than Australian screening criteria. The two prawn species showed evidence of a decline in dioxin concentrations after remediation, but the fish species only showed a declining dioxin concentration with distance from the contaminated site (not between periods). There were some minor changes in the congener profile for some species following remediation. While there was evidence for greatly reduced dioxin concentrations in prawn species following remediation, the complex patterns for fish were likely affected by environmental changes, species-specific and temporal changes in lipid content, and animal movement patterns. Future monitoring may aid interpretation of the patterns and modelling of exposure risk associated with seafood consumption into the future.

Investigating the sensitivity of losses to time-dependent components of seismic risk modeling

Conventional earthquake risk modeling involves several notable simplifications, which neglect: (1) the effects on seismicity of interactions between adjacent faults and the long-term elastic rebound behavior of faults; (2) short-term hazard increases associated with aftershocks; and (3) the accumulation of damage in assets due to the occurrence of multiple earthquakes in a short time window, without repairs. Several recent earthquake events (e.g. 2010–2011 Canterbury earthquakes, New Zealand; 2019 Ridgecrest earthquakes, USA; and 2023 Turkey–Syria earthquakes) have emphasized the need for risk models to account for the aforementioned short- and long-term time-dependent characteristics of earthquake risk. This study specifically investigates the sensitivity of monetary loss (i.e. a possible earthquake-risk-model output) to these time dependencies, for a case-study portfolio in Central Italy. The investigation is intended to provide important insights for the catastrophe risk insurance and reinsurance industry. In addition to salient catastrophe risk insurance features, the end-to-end approach for time-dependent earthquake risk modeling used in this study incorporates recent updates in long-term time-dependent fault modeling, aftershock forecasting, and vulnerability modeling that accounts for damage accumulation. The sensitivity analysis approach presented may provide valuable guidance on the importance and appropriate treatment of time dependencies in regional (i.e. portfolio) earthquake risk models. We find that the long-term fault and aftershock occurrence models are the most crucial features of a time-dependent seismic risk model to constrain, at least for the monetary loss metrics examined in this study. Accounting for damage accumulation is also found to be important, if there is a high insurance deductible associated with portfolio assets.

Examining Injustices: Transfusion Medicine and Race

Race and ethnicity are sociopolitical and not biological constructs, and assertions that these population descriptors have scientific meaning has caused significant harm. A critical assessment of the transfusion medicine literature is an important aspect of promoting race-conscious as opposed to race-based medicine. Utilizing current definitions and health equity frameworks, this review will provide a critical appraisal of transfusion medicine studies at the intersection of race and healthcare disparities, with a focus on larger methodological challenges facing the transfusion medicine community. Moving forward, risk modelling accounting for upstream factors, patient input, as well as an expert consensus on how to critically conduct and evaluate this type of literature are needed. Further, when using race and ethnicity in research contexts, investigators must be aware of existing guidelines for such reporting.

Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS

Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.

Fast hybrid methods for modeling landslide susceptibility in Ardal County

Recently, machine learning models have received huge attention for environmental risk modeling. One of these applications is landslide susceptibility mapping which is a necessary primary step for dealing with the landslide risk in prone areas. In this study, a conventional machine learning model called multi-layer perceptron (MLP) neural network is built upon advanced optimization algorithms to achieve a firm prediction of landslide susceptibility in Ardal County, West of Iran. The used geospatial dataset consists of fourteen conditioning factors and 170 landslide events. The used optimizers are electromagnetic field optimization (EFO), symbiotic organisms search (SOS), shuffled complex evolution (SCE), and electrostatic discharge algorithm (ESDA) that contribute to tuning MLP’s internal parameters. The competency of the models is evaluated using several statistical methods to provide a comparison among them. It was discovered that the EFO-MLP and SCE-MLP enjoy much quicker training than SOS-MLP and ESDA-MLP. Further, relying on both accuracy and time criteria, the EFO-MLP was found to be the most efficient model (time = 1161 s, AUC = 0.879, MSE = 0.153, and R = 0.657). Hence, the landslide susceptibility map of this model is recommended to be used by authorities to provide real-world protective measures within Ardal County. For helping this, a random forest-based model showed that Elevation, Lithology, and Land Use are the most important factors within the studied area. Lastly, the solution discovered in this study is converted into an equation for convenient landslide susceptibility prediction.

Taxonomy-Driven Graph-Theoretic Framework for Manufacturing Cybersecurity Risk Modeling and Assessment

Abstract Identifying, analyzing, and evaluating cybersecurity risks are essential to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. However, a manufacturing-specific quantitative approach is lacking to effectively model threat events and evaluate the unique cybersecurity risks in discrete manufacturing systems. In response, this paper introduces the first taxonomy-driven graph-theoretic model and framework to formally represent this unique cybersecurity threat landscape and identify vulnerable manufacturing assets requiring prioritized control. First, the proposed framework characterizes threat actors’ techniques, tactics, and procedures using taxonomical classifications of manufacturing-specific threat attributes and integrates these attributes into cybersecurity risk modeling. This facilitates the systematic generation of comprehensive and generalizable cyber-physical attack graphs for discrete manufacturing systems. Second, using the attack graph formalism, the proposed framework enables concurrent modeling and analysis of a wide variety of cybersecurity threats comprising varying attack vectors, locations, vulnerabilities, and consequences. The risk model captures the cascading attack impact of varying attack methods through different cyber and physical entities in manufacturing systems, leading to specific consequences. Then, the constructed cyber-physical attack graphs are analyzed to comprehend threat propagation through the discrete manufacturing value chain and identify potential attack paths. Third, a quantitative risk assessment approach is presented to evaluate the cybersecurity risk associated with potential attack paths. It also identifies the attack path with the maximum likelihood of success, pointing out critical manufacturing assets requiring prioritized control. Finally, the proposed risk modeling and assessment framework is demonstrated using an illustrative example.

Enhanced osteoporotic fracture prediction in postmenopausal women using Bayesian optimization of machine learning models with genetic risk score.

This study aimed to enhance the fracture risk prediction accuracy in major osteoporotic fractures (MOFs) and hip fractures (HFs) by integrating genetic profiles, machine learning (ML) techniques, and Bayesian optimization. The genetic risk score (GRS), derived from 1,103 risk single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS), was formulated for 25,772 postmenopausal women from the Women's Health Initiative dataset. We developed four ML models: Support Vector Machine (SVM), Random Forest, XGBoost, and Artificial Neural Network (ANN) for binary fracture outcome and 10-year fracture risk prediction. GRS and FRAX clinical risk factors (CRFs) were used as predictors. Death as a competing risk was accounted for in ML models for time-to-fracture data. ML models were subsequently fine-tuned through Bayesian optimization, which displayed marked superiority over traditional grid search. Evaluation of the models' performance considered an array of metrics such as accuracy, weighted F1 Score, the area under the precision-recall curve (PRAUC), and the area under the receiver operating characteristic curve (AUC) for binary fracture predictions, and the C-index, Brier score, and dynamic mean AUC over a 10-year follow-up period for fracture risk predictions. We found that GRS-integrated XGBoost with Bayesian optimization is the most effective model, with an accuracy of 91.2% (95% CI: 90.4-92.0%) and an AUC of 0.739 (95% CI: 0.731-0.746) in MOF binary predictions. For 10-year fracture risk modeling, the XGBoost model attained a C-index of 0.795 (95% CI: 0.783-0.806) and a mean dynamic AUC of 0.799 (95% CI: 0.788-0.809). Compared to FRAX, the XGBoost model exhibited a categorical net reclassification improvement (NRI) of 22.6% (P= .004). A sensitivity analysis, which included BMD but lacked GRS, reaffirmed these findings. Furthermore, portability tests in diverse non-European groups, including Asians and African Americans, underscored the model's robustness and adaptability. This study accentuates the potential of combining genetic insights and optimized ML in strengthening fracture predictions, heralding new preventive strategies for postmenopausal women.

Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk

Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot discover hidden nonlinearities and interactions within the data. This study uses discrete-time survival models with embedded neural networks as estimators of time to default. This provides flexibility to express nonlinearities and interactions between variables and hence allows for models with better overall model fit. Additionally, the neural networks are used to estimate age–period–cohort (APC) models so that default risk can be decomposed into time components for loan age (maturity), origination (vintage), and environment (e.g., economic, operational, and social effects). These can be built as general models or as local APC models for specific customer segments. The local APC models reveal special conditions for different customer groups. The corresponding APC identification problem is solved by a combination of regularization and fitting the decomposed environment time risk component to macroeconomic data since the environmental risk is expected to have a strong relationship with macroeconomic conditions. Our approach is shown to be effective when tested on a large publicly available US mortgage dataset. This novel framework can be adapted by practitioners in the financial industry to improve modeling, estimation, and assessment of credit risk.

A Bayesian approach for estimating the post-earthquake recovery trajectories of electric power systems in Japan

ABSTRACT Post-disaster recovery modelling of engineering systems has become an important facet of catastrophe risk modelling and management for natural hazards. The post-disaster recovery trajectory of a civil infrastructure system can be quantified using (a) the initial post-disaster functionality level, Q o ; (b) rapidity, h (i.e., the rate of functionality restoration); and (c) recovery time, R t . This study uses a Bayesian estimation approach to derive a set of probabilistic models to estimate Q o , R t , and h of electric power networks (EPNs) using post-earthquake recovery data from 16 large earthquakes in Japan between 2003 and 2022. The considered predictor (explanatory) variables include earthquake magnitude, year of occurrence, seismic intensity, and exposed population (PEX). Apart from being a simple and efficient stand-alone tool, the proposed data-driven models can be a useful benchmarking tool for simulation-based approaches for EPN recovery modelling.

PANDORA: Deep Graph Learning Based COVID-19 Infection Risk Level Forecasting

Coronavirus disease 2019 (COVID-19) as a global pandemic causes a massive disruption to social stability that threatens human life and the economy. An effective forecasting system is arguably important to provide an early signal of the risk of COVID-19 infection so that the authorities are ready to protect the people from the worst. However, making a good forecasting model for infection risks in different cities or regions is not an easy task, because it has a lot of influential factors that are difficult to be identified manually. To address the current limitations, we propose a deep graph learning model, called PANDORA, to predict the infection risks of COVID-19, by considering all essential factors and integrating them into a geographical network. The framework uses geographical position relationships and transportation frequency as higher order structural properties formulated by higher order network structures (i.e., network motifs). Moreover, four significant node attributes (i.e., multiple features of a particular area, including climate, medical condition, economy, and human mobility) are also considered. We propose three different aggregators to better aggregate node attributes and structural features, namely, Hadamard, Summation, and Connection. Experimental results over real data show that PANDORA outperforms the baseline methods with higher accuracy and faster convergence speed, no matter which aggregator is chosen.

ASSESSMENT OF URBAN FLOOD RISKS OF THE CITIES USING ENTROPY-VIKOR METHODS IN TÜRKİYE

In recent years, there is growing interest for evaluation of urban flood risks of cities over the past decade due to rapid urbanization and climate change. The optimal flood risk assessment is strategically achieved not only with classical risk modelling approaches but also with holistic and comprehensive framework. This paper focuses on a detailed flood assessment providing risk database for policymakers and urban planners to decide the flood prone areas in Turkey. In this context, the Entropy based VIKOR (VIseKriterijumska Optimizacija Kompromisno Resenje) was provided to evaluate a range of flood risk criteria named number of floods, population density and number of buildings, flood protection area which are under the concept of risk dimension including “hazard, exposure and vulnerability” aspects. Computational results demonstrate that the provinces of Şanlıurfa, Ordu, Zonguldak and Van are assigned with higher urban risk values, respectively and the ranking of the cities was presented with different q values. The findings should support practitioners and researchers for land use planning and risk reduction works as the detailed flood risk evaluation was presented in terms of the flood management.

Spatio-temporal analysis and risk modeling of foot-and-mouth disease outbreaks in China

FMD is an acute contagious disease that poses a significant threat to the health and safety of cloven-hoofed animals in Asia, Europe, and Africa. The impact of FMD exhibits geographical disparities within different regions of China. The present investigation undertook an exhaustive analysis of documented occurrences of bovine FMD in China, spanning the temporal range from 2011 to 2020. The overarching objective was to elucidate the temporal and spatial dynamics underpinning these outbreaks. Acknowledging the pivotal role of global factors in FMD outbreaks, advanced machine learning techniques were harnessed to formulate an optimal prediction model by integrating comprehensive meteorological data pertinent to global FMD. Random Forest algorithm was employed with top three contributing factors including Isothermality(bio3), Annual average temperature(bio1) and Minimum temperature in the coldest month(bio6), all relevant to temperature. By encompassing both local and global factors, our study provides a comprehensive framework for understanding and predicting FMD outbreaks. Furthermore, we conducted a phylogenetic analysis to trace the origin of Foot-and-mouth disease virus (FMDV), pinpointing India as the country posing the greatest potential hazard by leveraging the spatio-temporal attributes of the collected data. Based on this finding, a quantitative risk model was developed for the legal importation of live cattle from India to China. The model estimated an average probability of 0.002254% for FMDV-infected cattle imported from India to China. TA sensitivity analysis identified two critical nodes within the model: he possibility of false negative clinical examination in infected cattle at destination (P5) and he possibility of false negative clinical examination in infected cattle at source(P3). This comprehensive approach offers a thorough evaluation of FMD landscape within China, considering both domestic and global perspectives, thereby augmenting the efficacy of early warning mechanisms.