FireLossRate: An R package to estimate the loss rate of residential structures affected by wildfires at the Wildland Urban Interface.

To inform proactive management actions supporting community resilience to wildfires, we developed a new software package called FireLossRate. This package in R helps the user to compute wildfire impacts on residential structures at the Wildland Urban Interface (WUI). The package integrates spatial information about exposed structures, empirical equations that estimate the loss rate of structures affected by wildfires as a function of fireline intensity and distance from fire edge with fire growth modeling outputs from fire simulation software and burn probability models. FireLossRate helps to quantify and produce spatially explicit data on structural exposure and loss for single and multiple fires. The package automates post hoc analyses on simulations that include single or multiple wildfires and enables result mapping when combined with other packages available in R. In this paper, we describe the functionality of the FireLossRate package and introduce users to the interpretation of impact indicators of wildfires at the WUI. FireLossRate is available for download at https://github.com/LFCFireLab/FireLossRate.•FireLossRate allows the computation of wildfire impacts indicators on residential structures at the Wildland Urban Interface in support of community fire risk management.

A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis.

COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool's predictions and illustrate how it can support managers in their daily decisions concerning the system's capacity and ensure patients the access the resources they require.