Integrated Computational Viromics & Digital Health Analytics
ICV Analytics is a digital health research and computational science platform focused on viromics, infectious disease modeling, clinical decision support systems, and population-level vulnerability prediction. We integrate molecular epidemiology, machine learning, and real-world health data to advance precision public health in low- and middle-income countries.
Our Digital Engines
PhenoGenX (PGX)
Dual-engine HIV drug resistance interpretation platform integrating rule-based algorithms and machine learning models trained on genotype–phenotype datasets. Designed for research, surveillance, and advanced resistance profiling.
SynDx-Clinical Decision Support for Syndromic STI Management
AI-assisted clinical decision support system for syndromic management of sexually transmitted infections. Integrates symptom modeling, probabilistic inference, and treatment recommendation pathways.
SVE-Syndemic Vulnerability Engine
A population-level predictive modeling engine that quantifies multidimensional vulnerability using demographic, behavioral, psychosocial, and health system indicators. Designed for geospatial and national-level strategic planning.
Data Science & Analytical Infrastructure
Data Science & Analytical Infrastructure
- Machine learning (ensemble models, LASSO, calibration modeling)
- Large-scale DHS data integration (>500,000 records)
- Viral sequence analysis and mutation profiling
- Phenotypic susceptibility modeling
- Geospatial vulnerability mapping
- Reproducible pipelines using Python, R, and cloud-based environments
Research & Global Collaboration
ICV Analytics supports multi-country research initiatives across Africa and Asia, contributing to HIV drug resistance surveillance, antimicrobial stewardship modeling, cervical cancer elimination strategies, and digital health innovation.
Our work bridges molecular epidemiology, public health intelligence, and digital decision support systems.
