Sage advice from Julia Gog. How you can help with COVID-19 modelling?

Signal to noise.

Rather than adding noise, amplify the signal. The number of documents appearing on preprint servers is more than anyone can physically read, let alone the additional files being e-mailed and shared around. Perhaps you could contribute to sifting what is out there. For example, when different approaches give different results, you could attempt to identify exactly what is behind the divergence. 

When you see something particularly useful that has been overlooked, please help by distilling the key message and telling others about it — whether you do so by telling colleagues around you, sharing via social media or other routes. 

Alternatively, you could work with others to compile summaries and digests of what is out there with respect to some part of the data, models or results. If you have enough experience in the area, you could help address the need for more peer reviewers for COVID-19 work of all kinds. Research is being completed under immense time pressures, and preprints are being shared rapidly so they can be used by others and contribute quickly to policy decisions, but it remains as important as ever to maintain the quality and integrity of academic publishing, even during this unprecedented time.

Communicating to the public

The world wants to know what the science is behind the decisions, but there is great danger of misinformation when media interest is amplifying the voices of scientists, but not necessarily those most qualified to comment. 

You can learn the mathematical and scientific ideas from the broader literature, including some great textbooks. (Real-time papers are aimed at colleagues who know the literature already; reading only these will not be enough to get you up to speed.) If you have energy and time, share what you learn with people around you — and the wider public, if you have that gift. 

Communicate the ideas behind the models, the dynamics of epidemics, the explorations of control measures and the challenges of synthesizing data in real time. The mathematically literate community can identify and explain the dangers of overly simplistic readings of the raw data. 

For example, early in the epidemic many websites and media outlets reported the infection fatality ratio by dividing the current number of deaths by the current number of confirmed cases, even though it is known from previous epidemics that this naive approach is flawed. There will be other instances in the coming months for which the ‘obvious’ approaches are misleading, but their shortcomings might not be apparent to the general public. And, importantly, those with expertise in any modelling (in any area) can help communicate the limitations of modelling — it is not actually magic — and the ideas of uncertainties in predictions.

Cite this article

Gog, J.R. How you can help with COVID-19 modelling. Nat RevPhys (2020). https://doi.org/10.1038/s42254-020-0175-7