ArticleMathematical biosciences2022
Learning transmission dynamics modelling of COVID-19 using comomodels.
Article in Mathematical biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- EpiGeoPop: a tool for developing spatially accurate country-level epidemiological models.Scientific reports · 2025Article
- The time-dependent reproduction number for epidemics in heterogeneous populations.Journal of the Royal Society, Interface · 2025Article
- The Mathematics of Serocatalytic Models With Applications to Public Health Data.Statistics in medicine · 2025Article
- Quantifying age-specific household contacts in Aotearoa New Zealand for infectious disease modelling.Royal Society open science · 2024Article
- Understanding the impact of numerical solvers on inference for differential equation models.Journal of the Royal Society, Interface · 2024Article
- Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model.Infectious Disease Modelling · 2024Article
- Heterologous vaccination with inactivated vaccine and mRNA vaccine augments antibodies against both spike and nucleocapsid proteins of SARS-CoV-2: a local study in Macao.Frontiers in immunology · 2023Article
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Authors and funding
14 authors.
Funding
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Abstract
The COVID-19 epidemic continues to rage in many parts of the world. In the UK alone, an array of mathematical models have played a prominent role in guiding policymaking. Whilst considerable pedagogical material exists for understanding the basics of transmission dynamics modelling, there is a substantial gap between the relatively simple models used for exposition of the theory and those used in practice to model the transmission dynamics of COVID-19. Understanding these models requires considerable prerequisite knowledge and presents challenges to those new to the field of epidemiological modelling. In this paper, we introduce an open-source R package, comomodels, which can be used to understand the complexities of modelling the transmission dynamics of COVID-19 through a series of differential equation models. Alongside the base package, we describe a host of learning resources, including detailed tutorials and an interactive web-based interface allowing dynamic investigation of the model properties. We then use comomodels to illustrate three key lessons in the transmission of COVID-19 within R Markdown vignettes.
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