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. 2021 Sep 9;37(17):2787-2788.
doi: 10.1093/bioinformatics/btab061.

Epidemiological modeling in StochSS Live!

Richard Jiang  1 , Bruno Jacob  2 , Matthew Geiger  3 , Sean Matthew  3 , Bryan Rumsey  3 , Prashant Singh  4 , Fredrik Wrede  4 , Tau-Mu Yi  5 , Brian Drawert  6 , Andreas Hellander  4 , Linda Petzold  1   2
Affiliations

Epidemiological modeling in StochSS Live!

Richard Jiang et al. Bioinformatics. .

Abstract

Summary: We present StochSS Live!, a web-based service for modeling, simulation and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters and analyze the results.

Availability and implementation: StochSS Live! is freely available at https://live.stochss.org/.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
Snapshot of the StochSS Live! web interface. (A) The user can explicitly define populations, parameters and reactions. (B) The preview window settings allow the user to preview simulation results for both deterministic and stochastic models. (C) Example of parameter sweep inference in StochSS Live! The blue lines are computed realizations obtained by the stochastic solver and the black lines correspond to the official data. Notice that, regardless of the fact that data from Buncombe and Santa Barbara counties have different scales and different levels of stochasticity, StochSS Live! is capable of modeling both cases. (D) Comparison of posteriors from Santa Barbara and Buncombe counties
See this image and copyright information in PMC

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