Predicting covid-19 resurgence: do it locallyBMJ 2020; 370 doi: https://doi.org/10.1136/bmj.m2731 (Published 09 July 2020) Cite this as: BMJ 2020;370:m2731
- Arnaud Chiolero, epidemiologist and professor of public health
This week, 13 June
The debate on how to use mathematical simulations to predict the impact of covid-19 on population health and healthcare needs is ongoing.1 While some researchers have predicted a huge number of deaths and overwhelmed healthcare systems,2 others have forecast a much smaller burden.3 Now, predictions aim to estimate the probability and the scale of a second wave.1 Many prediction models share the problem of being built on fragile epidemiological data, especially early in the course of a pandemic, and are open to uncertainties.4 The public are stressed by these predictions and policymakers are sceptical about using this information for decision making.
I would like to raise one point and make one proposal.
My point is that there is a cultural gap between the worlds of simulation and of public health surveillance. Surveillance consists of transforming health related data into useful information for decision makers.5 Indeed, to make data useful for decision making, they must be translated. Scientists modelling predictions, unless active in applied public health, may not be trained to do that. Additionally, policymakers struggle to understand the strengths and limitations of prediction models.
My proposal is for simulation at a local scale—for example, at the level of hospital or healthcare facilities in a given jurisdiction. The key is to put data scientists, healthcare providers, and policymakers in the same room. One advantage is that models are built accounting for the true needs of the healthcare system, based on actual and directly available data. Further, the predictions can be directly discussed with policymakers and adapted as needed, increasing the probability of having an impact on decisions.
Competing interests: None declared.
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