Aug 2026· Frontiers in Digital Health· Vol 8· 0 citations· 58 references
Medicine
TL;DR
It is argued that artificial intelligence encompassing machine learning and deep learning can help shift malaria control from reactive reporting toward predictive, precision public health, while cautioning that it is one enabler among many rather than a stand-alone solution.
Abstract
Malaria elimination has stalled globally despite decades of investment, with traditional surveillance constrained by retrospective reporting and limited capacity to integrate high-dimensional, non-linear data. In this perspective, we argue that artificial intelligence (AI) encompassing machine learning and deep learning can help shift malaria control from reactive reporting toward predictive, precision public health, while cautioning that it is one enabler among many rather than a stand-alone solution. We examine AI across three interconnected domains: surveillance (understood broadly as case detection, entomological and intervention-coverage monitoring, and the data-to-response loop), prediction (outbreak forecasting and spatial risk mapping), and control (resource stratification and intervention optimisation). Reported AI diagnostics can exceed 90% accuracy, and forecasting models offer useful lead times by integrating climatic, entomological and epidemiological data. However, realising this potential depends on resolving data-quality limitations, algorithmic bias, weak digital infrastructure, and absent governance frameworks, and on locally adapted rather than uniformly generalised models. We contend that demand-driven integration into national strategies, local capacity building, and prospective trials not algorithmic novelty will determine whether AI meaningfully accelerates progress toward elimination.
Malaria endures a significant part in public health concern, especially in tropical and subtropical regions. Traditional malaria control methods often face limitations with surveillance, diagnosis and efficient resource allocation. This review explores the role of Artificial Intelligence (AI) in augmenting data-driven decision-making for malaria control and elimination efforts, focusing on surveillance systems, enhancing the effectiveness of intervention strategies and optimizing the resource allocations. AI technologies, mainly machine learning algorithms and computer vision systems, demonstrate significant potential in improving malaria control outcomes. Key findings include increased accuracy in outbreak prediction, improved diagnostic precision through automated microscopy and optimized resource allocation reducing response times. Additionally, deep learning models are emerging as promising tools in identifying drug resistance patterns and personalizing treatment protocols. AI integration in malaria control programs offers substantial benefits for public health decision-making. In this article, we conducted a comprehensive review of peer-reviewed literature, analyzing AI applications in malaria control across key domains such as surveillance, diagnosis, treatment and resource management. However, effective implementation requires robust data infrastructure, ethical frameworks addressing algorithmic bias and sustained international collaboration. Future directions prioritize equitable access, capacity building and development of standardized evaluation metrics for evaluating AI-driven interventions.
Sweta Bhan, Ayushi Singh, Pankaj U. Ramteke et al.· International Journal of Com...· 0 citations
The development and assessment of a machine learning-driven early warning system for infectious disease prediction using geospatial big data from South-Western Nigeria, at the level of Local Government Area outperform conventional surveillance systems in developing countries.
I. Adewumi, N. Bakare, W. Ajayi et al.· London Journal of Physics· 0 citations
Infectious disease surveillance is a critical component of global public health systems, enabling the detection, monitoring, and prevention of disease outbreaks. Traditional surveillance methods often rely on manual reporting and laboratory confirmation, which can lead to delays, incomplete data, and limited real-time analysis. In recent years, artificial intelligence (AI) has emerged as a transformative approach to overcoming these limitations by enabling faster, more accurate, and data-driven surveillance systems. This article explores the role of AI in infectious disease surveillance, highlighting its applications in early outbreak detection, real-time monitoring, predictive modeling, genomic surveillance, and digital data analysis. AI technologies such as machine learning, deep learning, and natural language processing allow the analysis of large and diverse datasets from sources including electronic health records, social media, mobile data, and global health databases. These systems have been effectively used in identifying early signals of outbreaks such as COVID-19, tracking disease spread during Ebola, and predicting seasonal influenza trends. Despite these advantages, challenges such as data privacy concerns, algorithmic bias, data quality issues, and infrastructure limitations in developing regions remain significant barriers. Overall, AI holds great potential to enhance global disease surveillance by improving early detection, increasing accuracy, and enabling real-time insights. With proper ethical frameworks and global collaboration, AI can play a vital role in strengthening future public health preparedness and response systems.
Muhammad Naveed Aslam, Hussna Khan· Electronic Journal of Medica...· 0 citations
The escalating threat of viral pandemics, dramatically illustrated by the COVID-19 crisis, has exposed the critical shortcomings of conventional reactive virology in addressing rapidly evolving pathogens. This review introduces predictive virology (PV) as an artificial intelligence (AI)-driven discipline within broader epidemic intelligence and public health surveillance that uses advanced computational tools to forecast viral threats and accelerate countermeasure design. The current review systematically examines how AI-driven approaches (e.g., machine learning and deep learning) are reshaping virology by integrating vast genomic datasets, multimodal surveillance signals, and advanced computational models to anticipate viral emergence and evolution before widespread transmission occurs. Core pillars of PV discussed include zero-shot mutational fitness and antigenic escape prediction using large protein language models; multimodal early-warning systems that fuse wastewater monitoring, digital epidemiology, mobility data, and social media; neural differential equation-based transmission modeling; generative AI for de novo design of broad-spectrum antivirals and vaccines; and ecological risk assessment of zoonotic spillovers. In retrospective benchmarks against deep mutational scanning experiments and real-world epidemiological outcomes (SARS-CoV-2 variants, influenza, and other outbreaks), several AI-powered tools have demonstrated performance comparable to or exceeding traditional methods, although prospective validation at scale remains limited. Despite remarkable progress, significant challenges persist, including data bias, overfitting to historical patterns, lack of prospective validation, and limited generalizability across settings. In addition, there are concerns about mechanistic interpretability, equitable global data integration, and responsible deployment. This review also critically addresses the ethical, governance, and equity implications of deploying predictive capabilities at a global scale. By consolidating cutting-edge AI methodologies with virological insights and acknowledging current limitations, this work provides a comprehensive framework for transitioning virology from a reactive to a truly predictive discipline, ultimately strengthening global health security and pandemic preparedness.
M. Farrag· Vector Borne and Zoonotic Di...· 0 citations
Disease surveillance is fundamental to public health, enabling timely outbreak detection, efficient resource allocation, and evidence-based policymaking. In Nigeria, the Integrated Disease Surveillance and Response (IDSR) framework, while structured, is hampered by inconsistent data quality, limited private sector participation, and infrastructural constraints. Machine Learning (ML), a powerful subset of Artificial Intelligence (AI), offers transformative potential through its capacity for predictive analytics, real-time data processing, and automated pattern recognition to enhance surveillance capabilities. Despite global advancements in ML for disease forecasting and syndromic surveillance, its adoption within Nigeria’s IDSR system lags considerably. This paper addresses this critical gap by investigating how ML can overcome Nigeria-specific barriers, such as fragmented data systems and rural connectivity deficits. We provide enhanced technical depth on ML methodologies, comparing supervised and unsupervised learning, and detailing relevant architectures, including Decision Trees, Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), suited for time-series epidemiological forecasting. We present a methodological illustration of malaria surveillance in Northern Nigeria using synthetic data, benchmarking five ML models (Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Machine) under temporal validation, and employing SHapley Additive exPlanations (SHAP) for robust model interpretability. A sensitivity analysis further examines the stability of model performance under coefficient perturbations. A benchmarking analysis compares Nigeria’s ML adoption against Rwanda and Kenya. Finally, we propose a tiered strategic framework encompassing policy, infrastructure, and capacity-building recommendations, complemented by a cost-benefit perspective emphasizing potential Disability-Adjusted Life Years (DALYs) averted and significant economic returns, aiming to foster a more resilient and equitable public health system in Nigeria. Not applicable.
L. Aliyu, Abbas B. Umar, Saifuddeen K. Sani et al.· BMC Artificial Intelligence· 1 citation
A comprehensive review of CI models for outbreak prediction, comparing supervised and unsupervised methods such as Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and hybrid models.
Z. Abdullahi· International Journal of App...· 0 citations
