Frontiers in Applied Mathematics and Statistics

Based on increasing ESG awareness, executives, and investment professionals consider ESG factors in strategic and operational decisions. Like other companies, insurers recognize that the ESG transition offers opportunities for long-term value creation. In our research, we evaluate how ESG corporate engagement impacts an insurance company's balance sheet in economic and financial terms, unlocking financial resources throughout the sustainability premium. We resort to the theoretical paradigms of behavioral finance applied to actuarial portfolio evaluations. Our findings show that ESG engagement is significantly performing since it determines higher expected profits. We offer our framework as a tool for the strategic decision-making process.

Maria Carannante

Valeria D'Amato

Maria Sole Staffa

How ESG corporate reputation affects sustainability premiums in the insurance industry

ESG企业声誉如何影响保险业的可持续性溢价

Early prediction of pest occurrences can enhance crop production, reduce input costs, and minimize environmental damage. Advances in machine learning algorithms facilitate the development of efficient pest alert systems. Furthermore, ensemble algorithms help in the utilization of several models rather than being dependent on a single model. This study introduces a dynamic ensemble model with absolute log error (ALE) and logistic error functions using four machine learning models—artificial neural networks (ANNs), support vector regression (SVR), k-nearest neighbors (kNN), and random forests (RF). Various abiotic factors such as minimum and maximum temperature, rainfall, and morning and evening relative humidity were incorporated into the model as exogenous variables. The proposed algorithms were compared with fixed-weighted and unweighted ensemble methods, and candidate machine learning models, using the pest population data for yellow stem borer (YSB) from two regions of India. Error metrics include the root mean square log error (RMSLE), root relative square error (RRSE), and median absolute error (MDAE), along with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. This study concluded that the proposed dynamic ensemble algorithm demonstrated better predictive accuracy in forecasting YSB infestation in rice crops.

Ankit Kumar Singh

Md Yeasin

Ranjit Kumar Paul

A. K. Paul

Anita Sarkar

Dynamic ensemble-based machine learning models for predicting pest populations

基于动态集成学习的害虫种群预测模型

The purpose of this note is to review certain recent results concerning the pseudospectra and the eigenvalues asymptotics of non-selfadjoint semiclassical pseudo-differential operators subject to small random perturbations.

Martin Vogel

Pseudospectra and eigenvalue asymptotics for disordered non-selfadjoint operators in the semiclassical limit

伪谱与无序非自伴算子在半经典极限下的本征值渐近性

In this study, we developed an optimal control deterministic model for the dynamics of bacterial meningitis disease. The objective was to investigate the efficiency and cost-effectiveness of the three controls (prevention, treatment, and screening) in curtailing the spread of bacterial meningitis. To accomplish this, we applied Pontryagin's maximum principle to derive the optimality system. We examined different combination strategies to investigate the effect of the interventions on the spread of bacterial meningitis. We used an incremental cost-effectiveness ratio (ICER) to examine which control technique was the most successful. The simulation results show that combining prevention and screening is the most cost-effective strategy. The objective function and the incremental cost-effectiveness ratio further support this result, indicating that maximum utilization of prevention and screening is required for the entire period.

Malede Atnaw Belay

Jeconia Okelo Abonyo

Haileyesus Tessema Alemneh

Habtamu Ayalew Engida

Melkamu Molla Ferede

Samuel Abebe Delnessaw

Optimal control and cost-effectiveness analysis for bacterial meningitis disease

细菌性脑膜炎疾病的最佳控制与成本效益分析

The growing global burden of communicable and non-communicable diseases necessitates innovative tools for understanding and managing health challenges. Mathematical modeling has emerged as a cornerstone in addressing these needs, offering deep insights into disease transmission, progression, and control. From guiding public health policies during pandemics to optimizing personalized treatment protocols, these models continue to shape the future of healthcare.The COVID-19 pandemic highlighted the power of mathematical models in predicting outbreaks, evaluating intervention strategies, and informing public health responses. These models help policymakers simulate "what-if" scenarios, enabling data-driven decisions that save lives and optimize resource allocation. Beyond infectious diseases, chronic conditions such as cancer, diabetes, and cardiovascular diseases have also benefited from mathematical approaches, which estimate disease burden, evaluate cost-effectiveness of interventions, and inform prevention strategies.Recent studies illustrate the diverse applications of mathematical modeling. For example, Zou et al. (2023) introduced a nomogram for predicting cardiovascular disease (CVD) risk in patients with erythrodermic psoriasis (EP). By leveraging clinical parameters, this study offered valuable insights into the relationship between psoriasis and cardiovascular health, enabling more effective patient care strategies. Similarly, Aga et al. ( 2024) developed a mathematical model to understand and control co-infections of pneumonia and COVID-19. Using numerical simulations and optimal control theory, they showed that combining treatment and prevention strategies is the most effective approach to minimize the impact of these diseases.Their model revealed that NPIs reduced disease transmission by approximately 43.7% from baseline levels but showed a subsequent increase in transmission rates by 32% upon relaxation of measures. This comprehensive analysis emphasized the critical role of NPIs in managing infectious diseases. Alemneh et al. (2024) developed a cholera transmission model that included pathogen shedding and environmental dynamics, highlighting that combining vaccination, water quality improvements, and treatment effectively reduces cholera prevalence.At the cellular level, mathematical models provide insights into disease mechanisms. Chakraborty et al. (2024) explored within-host dynamics of Mycobacterium tuberculosis (Mtb), offering a detailed overview of how Mtb interacts with the immune system. Their findings underscored the need for more sophisticated models to address gaps in understanding Mtb dynamics, which could inform better treatment strategies. Ganusov (2024) highlighted the importance of carefully considering assumptions in biological models, providing a critical lens for interpreting findings improving the reliability of these tools.The integration of population-and cellular-level data marks an exciting frontier in disease modeling. While population-level models capture macroscopic trends such as transmission rates and intervention impacts, cellular-level models reveal the molecular and immunological mechanisms driving these trends. A dual-scale approach can bridge these perspectives, enhancing our understanding of diseases like HIV, cancer, and future pandemics. For example, models that concurrently track viral spread and immune responses could refine strategies for both prevention and treatment.The evolution of mathematical modeling is closely tied to advancements in computational power, data availability, and artificial intelligence (AI). Machine learning techniques now enable real-time adaptation and optimization of models, making them invaluable during rapidly evolving scenarios like pandemics. These data-driven approaches complement traditional methods, opening new avenues for precision in disease management.In this journal, we welcome research that spans a diverse range of topics, from the dynamics of infectious and chronic diseases to the qualitative behavior of complex systems. We encourage submissions that explore innovative modeling techniques, including deterministic, stochastic, and agent-based approaches. Particularly, we aim to foster studies that integrate multi-scale data, addressing both macroscopic epidemiological trends and microscopic cellular processes. Mathematical modeling has become a cornerstone of modern disease management, offering comprehensive insights that inform policy, optimize treatment, and improve health outcomes. As computational and data-driven methodologies advance, the field holds immense promise for addressing the pressing challenges of global health. By integrating cutting-edge research and fostering interdisciplinary collaboration, we can unlock new strategies to combat diseases and enhance public health worldwide.

Preeti Dubey

Joseph Malinzi

Editorial: Mathematical modeling of diseases at population-level and cellular-level

编者按：人群水平和细胞水平的疾病数学建模

短名	Front. Appl. Math. Stat.
Journal Impact	1.24
国际分区	MATHEMATICS, INTERDISCIPLINARY APPLICATIONS(Q3)

ISSN	2297-4687
h-index	21

涉及主题	数学计算机科学物理人工智能经济统计生物量子力学机器学习工程类哲学心理学医学数学分析算法化学计量经济学
出版信息	出版商: Frontiers Media SA，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2015，原创研究文献占比: 93.50%，自引率:7.70%， Gold OA占比: 99.44%
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Frontiers in Applied Mathematics and Statistics

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How ESG corporate reputation affects sustainability premiums in the insurance industry

Dynamic ensemble-based machine learning models for predicting pest populations

Pseudospectra and eigenvalue asymptotics for disordered non-selfadjoint operators in the semiclassical limit

Optimal control and cost-effectiveness analysis for bacterial meningitis disease

Editorial: Mathematical modeling of diseases at population-level and cellular-level

Dynamic ensemble-based machine learning models for predicting pest populations

How ESG corporate reputation affects sustainability premiums in the insurance industry

Editorial: Mathematical modeling of diseases at population-level and cellular-level

Optimal control and cost-effectiveness analysis for bacterial meningitis disease

Pseudospectra and eigenvalue asymptotics for disordered non-selfadjoint operators in the semiclassical limit