arXiv subjects

Julien Riou

Publications and source records attributed to Julien Riou.

3 recordsLinked to original sources

arXiv2023

Bayesian ensemble modelling to monitor excess deaths during summer 2022 in Switzerland

Switzerland experienced one of the warmest summers during 2022. Extreme heat has been linked to increased mortality. Monitoring the mortality burden attributable to extreme heat is crucial to inform policies, such as heat warnings, and prevent heat-related deaths. In this study, we evaluate excess mortality during summer 2022, identify vulnerable populations and estimate temperature thresholds for heat warnings. We use nationwide mortality and population data in Switzerland during 2011-2022 by age, sex, day and canton. We develop a Bayesian ensemble modelling approach with dynamic population to predict expected mortality in summer 2022 and calculate excess by comparing expected with observed mortality. We account for covariates associated with mortality such as ambient temperature, national holidays and spatiotemporal random effects to improve predictions. After accounting for the effect of the COVID-19 pandemic, we found a 3% (95% credible interval: 0%-6%) excess mortality during summer 2022. We observed a total of 456 (5-891) excess deaths during summer 2022 in people older than 80 years. There was weak evidence of excess mortality in the other age groups. The highest excess mortality was observed in July (12%: 4%-19%), the hottest month in 2022. We also found that for heatwaves longer than four days, the minimum excess mortality temperature threshold in the oldest age group is the 70th percentile of the temperature. To reduce future summer excess mortality in Switzerland, we propose targeted heat warnings to older populations and reducing the temperature threshold when weather forecasts predict periods of extreme heat of four days or longer.

stat.AP↗

arXiv2021

Bayesian workflow for disease transmission modeling in Stan

This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the SARS-CoV-2 pandemic and other infectious diseases in a Bayesian framework. Bayesian modeling provides a principled way to quantify uncertainty and incorporate both data and prior knowledge into the model estimates. Stan is an expressive probabilistic programming language that abstracts the inference and allows users to focus on the modeling. As a result, Stan code is readable and easily extensible, which makes the modeler's work more transparent. Furthermore, Stan's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means the user can verify whether the obtained inference is reliable. In this tutorial, we demonstrate how to formulate, fit, and diagnose a compartmental transmission model in Stan, first with a simple Susceptible-Infected-Recovered (SIR) model, then with a more elaborate transmission model used during the SARS-CoV-2 pandemic. We also cover advanced topics which can further help practitioners fit sophisticated models; notably, how to use simulations to probe the model and priors, and computational techniques to scale-up models based on ordinary differential equations.

stat.CO↗

arXiv2020

Accounting for Uncertainty During a Pandemic

We discuss several issues of statistical design, data collection, analysis, communication, and decision making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This paper does not purport to be a comprehensive survey of the research literature; rather, we use examples to illustrate statistical points that we think are important.

stat.AP↗