Accurate Dengue Forecasting

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ABM-SIRTEM: A Hybrid Agent-Based and Epidemiological Model for Pandemic Response
Sep 16, 2026Sheryl Paul, Samuel Williams, Preetom K. Biswas +2Epidemiological Models Accurate Dengue Forecasting
University of Southern California Los Angeles, USA
A Hybrid Framework of Vision Transformer and Gated Recurrent Unit for Detection of Mosquito Diseases
Aug 12, 2026Danial Sharifrazi, Saadat Behzadi, Nouman Javed +3Accurate Dengue Forecasting Temporal Convolutional Networks
On-Device Multi-Species Malaria Detection with Uncertainty-Calibrated Slide-Level Aggregation
Aug 9, 2026Idaya Seidu, Ahmed Tahiru Issah, Charles B. Delahunt +1Accurate Dengue Forecasting Acute Myeloid Leukemia
Carnegie Mellon University Africa, Kigali Innovation City, Kigali, Rwanda · University of Washington, Seattle, Washington, USA
Unsupervised Consensus-Based Anomaly Detection for Spatiotemporal Malaria Incidence in Ghana
Jul 23, 2026T. Ansah-Narh, Y. Asare AfraneAccurate Dengue Forecasting Abnormality
Ghana Space Science and Technology Institute, Ghana Atomic Energy Commission, P. O. Box LG 80, Legon, Accra, Ghana · Department of Medical Microbiology, University of Ghana Medical School, University of Ghana, Accra, Ghana
Long-Memory Reservoir Computing for Data-Scarce Dengue Forecasting
Jul 13, 2026Rahul Goswami, Shinjini Paul, Palash Ghosh +1Reservoir Computing Long-Range Temporal Dependencies
Department of Mathematics, Indian Institute of Technology Guwahati, India · SAFIR, Sorbonne University Abu Dhabi, United Arab Emirates · Leiden University, Leiden, South Holland, Netherlands +1
The Epi-LLM Framework: probing LLM behavioral priors through epidemiological agent-based models
Jun 1, 2026Petra Ferencz, Ava Keeling, Tobias O'Keefe +4Epidemiological Models Agent-Based Model
Big Data Institute, Li Ka Shing Center for Health Information and Discovery, University of Oxford, Oxford, United Kingdom · Leverhulme Centre for Demographic Science, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom · Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom +3
Hybrid Probabilistic Forecasting of Under-Five Malaria Admissions in Ghana: A Gaussian Process Regression with Holt-Winters Smoothing
May 30, 2026T. Ansah-Narh, Y. Asare Afrane, J. Bremang TandohAccurate Dengue Forecasting Retrieval-Augmented Forecasting
Bayesian Inference of Nonlinear Malaria Dynamics in Ghana via an Ensemble Markov Chain Monte Carlo Sampler
May 30, 2026T. Ansah-Narh, Y. Asare Afrane, J. Bremang TandohAccurate Dengue Forecasting Bayesian Inference
Ghana Space Science and Technology Institute, Ghana Atomic Energy Commission, P. O. Box LG 80, Legon, Accra, Ghana · Department of Medical Microbiology, University of Ghana Medical School, University of Ghana, Accra, Ghana
Beyond Accuracy: Evaluating Efficiency, Robustness and Explainability in Deep Learning for Malaria Diagnosis
May 29, 2026Olivier Kanamugire, Kerol DjoumessiAccurate Dengue Forecasting Explainability
African Institute for Mathematical Sciences, Kigali, Rwanda · Hertie Institute for AI in Brain Health, University of Tübingen, Germany
A Logistic Regression Model to Predict Malaria Severity in Children
May 17, 2026Mary Opokua Ansong, Asare Yaw Obeng, Samuel King OpokuAccurate Dengue Forecasting Logistic Regression
AlphaEarth Satellite Embeddings for Modelling Climate Sensitive Diseases Towards Global Health Resilience
Apr 29, 2026Usman Nazir, I-Han Cheng, Sara KhalidAccurate Dengue Forecasting Climate
Planetary Health Informatics (PHI) Lab, University of Oxford

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ABM-SIRTEM: A Hybrid Agent-Based and Epidemiological Model for Pandemic Response

Sheryl Paul, Samuel Williams, Preetom K. Biswas, Giulia Pedrielli+1

University of Southern California Los Angeles, USA

Epidemiological Models Accurate Dengue Forecasting Compliance

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The COVID-19 pandemic has had profound impacts on global health, social structures, and economies. It disproportionately affected lower socioeconomic groups and those reliant on interaction-based jobs. Regulatory bodies faced the challenge of designing policies that preserve public health while limiting disruption to economic stability and productivity. Epidemiological models such as SIR and agent-based models (ABMs) have been used to study disease dynamics and the socioeconomic impacts of disease and interventions. Population-level models often simplify individual heterogeneity, while detailed ABMs can become computationally expensive as the numbers of agents and interactions increase. We propose ABM-SIRTEM, a hybrid model that incorporates occupation categories, economic productivity, and welfare at the individual level while dynamically modeling compliance with government interventions. We calibrate the model against historical positive and negative test counts from four U.S. states and examine the resulting compliance dynamics. This framework provides a basis for studying the interaction between disease spread and socioeconomic behavior in pandemic-response planning.

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