New microbes and new infections · 2026
Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.
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Cabrera-Rodríguez Romina, Reyes-Castañeda Iriome, Lorenzo-Sánchez Iria, Valenzuela-Fernández Agustin, Trujillo-González Rodrigo
Аннотация
BACKGROUND: Global health security is increasingly threatened by emerging viral syndemics, where infectious viruses interact synergistically with socio-economic-structural vulnerabilities. Traditional reactive surveillance is increasingly insufficient to address the complexity of these interconnected crises. This systematic review aims to synthesize evidence for the "Silicon Shield," a proactive computational framework integrating mathematical modelling and artificial intelligence (AI) to bridge a unified computational framework, and critical literature gaps for pandemic preparedness and response.
METHODS: Following updated PRISMA 2020 guidelines, we searched PubMed, Scopus, and Web of Science (January 1990-July 2026) using Medical Subject Headings (MeSH). We evaluated the convergence of mechanistic frameworks (SIR, SEIR, ABM) with deep learning architectures (CNNs, Transformers, pLMs). Analysis prioritized Uncertainty Quantification (UQ) via Bayesian updating and causal inference through Directed Acyclic Graphs (DAGs) to mitigate ecological biases when modelling biosocial determinants.
RESULTS: Synthesizing 364 sources, we formalize the "Silicon Shield" three-layer architecture: Data Pipes, Model Pipelines, and Decision Interfaces. Integrating real-time genomic and mobility data enables anomaly detection via ViraMiner and TCINet. EVEscape forecasts immune escape, while Social Vulnerability Indices (SVI) optimize resource allocation. The framework yields predictive performance, improved AUROC and probabilistic metrics including Weighted Interval Score (WIS) and Continuous Ranked Probability Score (CRPS). Explainable AI (xHAIM) translates outputs into clinical decision support.
CONCLUSIONS: The convergence of AI and mathematical modelling facilitates a fundamental transformation into a proactive global health defence system. Implementing the "Silicon Shield" requires interdisciplinary collaboration, standardized clinical validation, and a focus on global health equity to effectively mitigate future emerging virus syndemic threats.
METHODS: Following updated PRISMA 2020 guidelines, we searched PubMed, Scopus, and Web of Science (January 1990-July 2026) using Medical Subject Headings (MeSH). We evaluated the convergence of mechanistic frameworks (SIR, SEIR, ABM) with deep learning architectures (CNNs, Transformers, pLMs). Analysis prioritized Uncertainty Quantification (UQ) via Bayesian updating and causal inference through Directed Acyclic Graphs (DAGs) to mitigate ecological biases when modelling biosocial determinants.
RESULTS: Synthesizing 364 sources, we formalize the "Silicon Shield" three-layer architecture: Data Pipes, Model Pipelines, and Decision Interfaces. Integrating real-time genomic and mobility data enables anomaly detection via ViraMiner and TCINet. EVEscape forecasts immune escape, while Social Vulnerability Indices (SVI) optimize resource allocation. The framework yields predictive performance, improved AUROC and probabilistic metrics including Weighted Interval Score (WIS) and Continuous Ranked Probability Score (CRPS). Explainable AI (xHAIM) translates outputs into clinical decision support.
CONCLUSIONS: The convergence of AI and mathematical modelling facilitates a fundamental transformation into a proactive global health defence system. Implementing the "Silicon Shield" requires interdisciplinary collaboration, standardized clinical validation, and a focus on global health equity to effectively mitigate future emerging virus syndemic threats.