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The second paper of January 2025 in Computo, by Daphné Giorgi, Sarah Kaakai and Vincent Lemaire, introduces the R package IBMPopSim, which facilitates the simulation of the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs).

The package relies on a unified mathematical framework, based on thinning of Poisson measures, for the simulation of IBMs where individuals are represented by their birth date, death date (+∞ if none), and a collection of features. It uses Rcpp for efficiency.

The paper introduces this mathematical framework, gives a detailed overview of the IBMPopSim package, and illustrates it on two use cases, one from actuarial sciences and the other from population genetics.

On this last example, the authors show that their randomized algorithm is one or two orders of magnitudes faster than the full algorithm.

The paper is available with (of course) R code at: doi.org/10.57750/sfxn-1t05

doi.orgEfficient simulation of individual-based population modelsThis document provides a full description of the Stochastic Individual-Based Models (IBMs) that can be implemented in the IBMPopSim package. A unified mathematical and simulation framework is given, with a detailed description of the simulation algorithm. Examples of applications for the package are also provided, showing the performance and flexibility of IBMPopSim.

📢In 2021, @MPIDR hosted a global conference on "Pandemic Babies? The Covid-19 pandemic and its impact on fertility and family dynamics." Recently, the special issue of Population and Development Review, based on this conference, has been published!

Explore the open-access papers on how the pandemic shaped fertility, partnerships, and family dynamics, co-funded by MPIDR and the Population Council.

demogr.mpg.de/en/news_events_6

This #tutorial explores the oscillatory #PopulationDynamics of generalized #IntegrateAndFire (GIF) neurons simulated with #NESTSimulator. The GIF #NeuronModel is a biophysically detailed model that captures the essential features of spiking neurons, including #SpikeFrequencyAdaptation and #DynamicThreshold behavior:

🌍 fabriziomusacchio.com/blog/202