Stephen Jun Villejo

Stephen Jun Villejo

Postdoctoral Research Associate in Biostatistics

Posts by Collection

portfolio

publications

Gaussian process for modelling the space environment from sparse data collected by massively distributed femtosatellite networks

Published in 75th International Astronautical Congress (IAC), Milan, Italy, 14 -- 18 October 2024, 2024

Recommended citation: Teale, C., Colombo, P., Villejo, S. J., & McInnes, C. (2024, October 14–18). Gaussian process for modelling the space environment from sparse data collected by massively distributed femtosatellite networks [Conference presentation]. 75th International Astronautical Congress (IAC), Milan, Italy.
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Development of a clinical prediction model for the diagnosis of tuberculous pleural effusion in a resource-limited setting

Published in European Respiratory Journal , 2025

Recommended citation: Villanueva, C. A., Cruz, A. M., Cruz, K. M., Uy, T. C., Villejo, S. J., & Santiaguel, J. (2025). Development of a clinical prediction model for the diagnosis of tuberculous pleural effusion in a resource-limited setting. European Respiratory Journal, 66(Suppl 69), PA783. https://doi.org/10.1183/13993003.congress-2025.PA783
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talks

Block aggregation modelling approach for spatial models

Published:

This work develops a spatial modelling approach for areal data when predictors are available at finer grid‑based resolutions. The method treats area‑level response data as aggregates of an underlying latent spatial process. The aggregation involves an appropriate functional depending on the likelihood or sampling model. The proposed method provides a unified framework for understanding response-covariate relationship, doing spatial disaggregation, and predicting the latent process at arbitrary block configurations. A simulation study evaluates performance under varying sampling proportions, spatial correlation ranges, and marginal variances, and compares the proposed block‑aggregation model with two alternatives: a traditional geostatistical modelling approach and a Markov random field (MRF) approach. Results show that for Gaussian data, the proposed approach and the first model comparator perform similarly, while the MRF performs poorly when spatial range is smaller than area size. For Poisson data, the proposed method yields more accurate latent‑field predictions and reduced parameter bias. Two real‑data applications illustrate the approaches: modelling wastewater viral concentrations across England during the COVID-19 pandemic using population density raster as covariate, and modelling the number of cardiovascular hospitalisations at local authority level in England using socio-demographic variables at fine-scale administrative units as covariates.

teaching

University of the Philippines

Lecture, School of Statistics, University of the Philippines Diliman, 0000

I taught several courses from 2013 to 2019 in my capacity as an Assistant Professor and Lecturer at the University of the Philippines Diliman.

University of Glasgow

Lecture, School of Mathematics and Statistics, College of Science and Engineering, 0000

I am doing lectures and tutorials in several courses both in the undergraduate and graduate programs from 2021 to 2025 as a MacLaurin Scholar.

Imperial College London

Lecture, School of Public Health, Faculty of Medicine, 0000

I am doing lectures in the course on Bayesian reasoning and methods for spatio-temporal data for Term 2 AY 2025-2026.