Researcher's Publications

Permanent URI for this communityhttp://erepository.kibu.ac.ke/handle/123456789/9616

Browse

Browsing Researcher's Publications by Author "Agumba, John Onyango"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    KESOZI Digital Twin: Physics-Informed Neural Network for Independent Estimation and Prediction of Childhood Diarrheal Disease Burden in Kenya, Somaliland, and Zimbabwe
    (2026-06-24) Agumba, John Onyango; Namusonge, Lucy Natecho; Ogendo, Joshua Ondura; Takavarasha, Musiiwa; Mohamad Ahmed Hassan; Senghor, Morris Shisanya; Waswa, Lydia; Pembere, Anthony
    Childhood diarrheal disease remains a leading cause of morbidity and mortality among children under five years in sub-Saharan Africa, particularly in settings affected by inadequate sanitation, climate variability, malnutrition, and limited healthcare access. Conventional forecasting approaches are often constrained by sparse surveillance data, weak spatial representation, and limited incorporation of mechanistic disease dynamics. This study presents a Physics-Informed Multimodal Artificial Intelligence Digital Twin framework that integrates Physics-Informed Neural Networks, Graph Neural Networks, diffusion-reaction epidemiological modeling, multimodal fusion learning, and Digital Twin simulation to estimate and predict childhood diarrheal disease burden in Kenya, Somaliland, and Zimbabwe. Using public epidemiological, environmental, climate, sanitation, and synthetic proof-of-concept datasets, the framework modeled temporal disease dynamics, spatial transmission, pathogen-attributed burden, and outbreak trajectories while enforcing epidemiological consistency through physics-informed optimization. Results demonstrated robust forecasting performance, enhanced spatial transmission modeling, uncertainty-aware predictions, and realistic outbreak simulations across the three countries. Rotavirus, Shigella, and Cryptosporidium were identified as major contributors to modeled mortality burden, while unsafe water exposure, poor sanitation, malnutrition, and climate-sensitive transmission substantially increased disease risk. Compared with a Bayesian baseline model, the multimodal framework achieved superior nonlinear risk characterization, geospatial learning, and temporal prediction. These findings highlight the potential of scientific machine learning and digital twin systems for infectious disease surveillance, outbreak forecasting, climate-health analytics, and evidence-based public health decision-making in low-resource African settings.
  • No Thumbnail Available
    Item
    The AFRIDIARRHEA multimodal fusion framework for Estimating the Burden of Diarrheal Diseases Among Children Under Five in Kenya, Zimbabwe, and Somaliland
    (2026-06-09) Agumba, John Onyango; Namusonge, Lucy Natecho; Ogendo, Joshua Ondura; Takavarasha, Musiiwa; Hassan, Mohamad Ahmed; Shisanya, Morris Senghor; Waswa, Lydia
    Background: Accurate estimation of childhood diarrheal disease burden in Africa remains challenging because of limited surveillance, incomplete mortality data, pathogen-attribution uncertainty, and complex environmental and socioeconomic drivers. This study developed the African Diarrheal Disease Integrated Risk Intelligence and Burden Estimation Architecture (AFRIDIARRHEA), a multimodal fusion framework for estimating under-five diarrheal burden in resource-constrained settings. Methods: AFRIDIARRHEA integrates Bayesian epidemiological modeling, machine learning, temporal forecasting, geospatial analytics, pathogen attribution, environmental intelligence, and uncertainty quantification within a unified framework. Synthetic datasets representing Kenya, Zimbabwe, and Somaliland were used to evaluate mortality, morbidity, hospitalization burden, pathogen-attributed mortality, and predictive performance. Results: The framework identified substantial heterogeneity in disease burden across countries, with Zimbabwe exhibiting the highest modeled mortality and morbidity burden and Somaliland the highest hospitalization burden. Rotavirus and Shigella were the dominant contributors to pathogen-attributed mortality. The multimodal fusion model outperformed the Bayesian baseline and individual component models, achieving improved predictive accuracy, robust uncertainty calibration, and strong agreement with benchmark estimates. Conclusions: AFRIDIARRHEA demonstrates the potential of multimodal fusion modeling for integrated estimation of childhood diarrheal burden, pathogen attribution, and uncertainty in African settings. The framework provides a scalable, transparent, and policy-relevant approach for supporting vaccine prioritization, WASH investments, outbreak preparedness, and child survival programs in data-limited environments.