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COVID-19 modelling: How can you help?

Julia Gog, University of Cambridge, mathematical epidemiologist, explains in a Nature Physics review article, how physicists and mathematicians with no prior expertise in infectious diseases can contribute their modelling skills to the COVID-19 effort.

Gog first establishes where well-meaning contributors would not be of help. Such activities include recirculating older results as if new, or sending your first attempts at classical susceptible–infectious–recovered (SIR) epidemic modelling to epidemiologists – their inboxes are already full.

Don’t add to the noise

Instead, amplify the signal. The sheer volume of research appearing on preprint servers every day is beyond the reading of any one person. A valuable contribution would be sifting through the material already out there, too, for example, contrast different results and identify the root of the convergence. Or, if you come across useful results, distil the key findings and share them with colleagues or your wider network. Working collaboratively to compile summaries of what models or results are circulating is far more practical than individual contributions of models. Additionally, there is a demand for more COVID-19 peer-reviewers in most areas of expertise.

Communicate to the public

In the current infodemic where the public and media seek the reasoning of scientists, we are more vulnerable than ever to the voices of the loud, but unqualified, being amplified. For those that have the time and aptitude to understand epidemiology dynamics, analysis of control measures, and the complex models and methodologies, please communicate these with others. Such mathematically literature individuals can help to educate the public on the dangers of overly simplistic readings, that litter many news outlets, as well as the shortcomings of modelling as a whole in pandemic predictions.

Reconsider making your own models

Although your instinct to help by starting your own models or replicating the result of those published is well-meaning, it’s unlikely that they’ll be useful, without having prior experience in disease modelling. Resources may spring up in the coming weeks to help with such training, so it worth keeping an eye out. Consortiums, such as RAMP: Rapid Assistance in Modelling the Pandemic, are beginning to convene to pool together modelling expertise from a wide range of disciplines. Initiatives such as these are worth joining.

Recommended reading

Gog recommends a short list of epidemic modelling publications, that although not specific to COVID-19, are a good foundation for understanding the busy field.

  1. Modelling infectious disease dynamics in the complex landscape of global health (2015)
  2. Challenges in Modelling Infectious Disease Dynamics (2015)
  3. Contagion! The BBC Four Pandemic – The model behind the documentary (2018)
  4. Contacts in context: large-scale setting-specific social mixing matrices from the BBC Pandemic project (2020) Gog and colleagues work to process and share output from the BBC Pandemic project, the results of which are already being used in COVID-19 modelling

Image source: Corvallis Advocate

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