activity
20202024
most citedTen simple rules for training scientists to make better software

5 citations · 9 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CY2024★ 5 cited

Ten simple rules for training scientists to make better software

Kit Gallagher, Richard Creswell, Ben Lambert +4

Computational methods and associated software implementations are central to every field of scientific investigation. Modern biological research, particularly within systems biolog…

q-bio.PE2023★ 1 cited

EpiGeoPop: A Tool for Developing Spatially Accurate Country-level Epidemiological Models

Lara Herriott, Henriette L. Capel, Isaac Ellmen +7

Mathematical models play a crucial role in understanding the spread of infectious disease outbreaks and influencing policy decisions. These models aid pandemic preparedness by pred…

math.ST2023

Understanding the impact of numerical solvers on inference for differential equation models

Richard Creswell, Katherine M. Shepherd, Ben Lambert +5

Most ordinary differential equation (ODE) models used to describe biological or physical systems must be solved approximately using numerical methods. Perniciously, even those solv…

stat.ME2022

Autocorrelated measurement processes and inference for ordinary differential equation models of biological systems

Ben Lambert, Chon Lok Lei, Martin Robinson +5

Ordinary differential equation models are used to describe dynamic processes across biology. To perform likelihood-based parameter inference on these models, it is necessary to spe…

stat.ME2020★ 3 cited

Using flexible noise models to avoid noise model misspecification in inference of differential equation time series models

Richard Creswell, Ben Lambert, Chon Lok Lei +2

When modelling time series, it is common to decompose observed variation into a "signal" process, the process of interest, and "noise", representing nuisance factors that obfuscate…