Maxime Fajgenblat | Searcharxiv

arXiv2026

Integrating Temporal Disaggregation and Distributed Lag Nonlinear Models for Bayesian Spatio-Temporal Disease Mapping with High-Resolution Environmental Exposures

Environmental conditions are major drivers of malaria transmission, but epidemiological analyses are often constrained by temporal misalignment between health outcomes reported at coarse time scales and environmental exposures available at finer resolutions. Conventional approaches aggregate environmental data to match health outcomes, potentially obscuring delayed and nonlinear relationships. We propose a Bayesian spatio-temporal framework that addresses this limitation through a latent daily disease process linked to observed monthly malaria counts by temporal disaggregation. The framework integrates distributed lag nonlinear models for climatic effects, spatio-temporal random effects, and intervention covariates within a unified hierarchical model. The methodology was applied to malaria surveillance data from 161 districts in Mozambique between 2017 and 2024, integrating temperature, precipitation, relative humidity, vegetation, elevation, and malaria interventions. Compared with a conventional monthly model, the proposed framework improved predictive accuracy and uncertainty quantification while exploiting the temporal resolution of environmental data. Estimated relationships showed nonlinear associations between climatic variability and malaria incidence, including an optimal temperature range, increasing risk with positive vegetation anomalies, and nonlinear precipitation effects. By avoiding temporal aggregation of environmental exposures, the framework provides a flexible approach for investigating delayed environmental effects from routine surveillance data and can be extended to other environmentally sensitive diseases with mismatched temporal resolutions.

stat.AP↗

arXiv2025

Backcasting biodiversity at high spatiotemporal resolution using flexible site-occupancy models for opportunistically sampled citizen science data

For many taxonomic groups, online biodiversity portals used by naturalists and citizen scientists constitute the primary source of distributional information. Over the last decade, site-occupancy models have been advanced as a promising framework to analyse such loosely structured, opportunistically collected datasets. Current approaches often ignore important aspects of the detection process and do not fully capitalise on the information present in these datasets, leaving opportunities for fine-grained spatiotemporal backcasting untouched. We propose a flexible Bayesian spatiotemporal site-occupancy model that aims to mimic the data-generating process that underlies common citizen science datasets sourced from public biodiversity portals, and yields rich biological output. We illustrate the use of the model to a dataset containing over 3M butterfly records in Belgium, collected through the citizen science data portal Observations.be. We show that the proposed approach enables retrospective predictions on the occupancy of species through time and space at high resolution, as well as inference on inter-annual distributional trends, range dynamics, habitat preferences, phenological patterns, detection patterns and observer heterogeneity. The proposed model can be used to increase the value of opportunistically collected data by naturalists and citizen scientists, and can aid the understanding of spatiotemporal dynamics of species for which rigorously collected data are absent or too costly to collect.

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