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Publications

Peer-reviewed

  1. Amaral, AVR, Wolffram, D, Moraga, P, and Bracher, J (2025). Post-processing and weighted combination of infectious disease nowcasts. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1012836.
  2. Zhong, R, Amaral, AVR, and Moraga, P (2024). Spatial data fusion adjusting for preferential sampling using INLA and SPDE. Journal of the Royal Statistical Society (Series A). https://doi.org/10.1093/jrsssa/qnae058.
  3. Amaral, AVR, Rubio, FJ, Quaresma, M, Rodríguez-Cortés, FJ, and Moraga, P (2024). Extended Excess Hazard Models for Spatially Dependent Survival Data. Statistical Methods in Medical Research. https://doi.org/10.1177/09622802241233767.
  4. Amaral, AVR, Krainski, ET, Zhong, R, and Moraga, P (2023). Model-based Geostatistics under Spatially Varying Preferential Sampling. Journal of Agricultural, Biological and Environmental Statistics. https://doi.org/10.1007/s13253-023-00571-0.
  5. Amaral, AVR, González, JA, and Moraga, P (2022).  Spatio-temporal modeling of infectious diseases by integrating compartment and point process models. Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-022-02354-4.
  6. Mahmood, M, Amaral, AVR, Mateu, J, and Moraga, P (2022). Modeling infectious disease dynamics: Integrating contact tracing-based stochastic compartment and spatio-temporal risk models. Spatial Statistics. https://doi.org/10.1016/j.spasta.2022.100691.

Preprint

  1. Steyn, N, Unwin, HJT, Ponmattam, J, Villaveces, A, Martins, L, Sherr, L, Blenkinsop, A, Semenova, E, Stuart-Brown, A, Amaral, AVR, Ratmann, O, Schnekenberg, RP, Cluver, L, Hillis, S, Rawlings, L, Barberia, L, Souza, AS, Castro, MC, and Flaxman, S (2025). Regional and national estimates of children affected by all-cause and COVID-19-associated orphanhood and caregiver death in Brazil, by age and family circumstance. medRxiv preprint. https://doi.org/10.1101/2025.01.31.25321479.
  2. Amaral AVR, Sykulski AM, Cavan E, and Fielding, S (2024). Navigating Challenges in Spatio-temporal Modelling of Antarctic Krill Abundance: Addressing Zero-inflated Data and Misaligned Covariates. arXiv preprint. https://doi.org/10.48550/arXiv.2412.01399.