Pandemic lockdown, isolation, and exit policies based on machine learning predictions
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Abstract
The widespread lockdowns imposed in many countries at the beginning of the COVID‐19 pandemic elevated the importance of research on pandemic management when medical solutions such as vaccines are unavailable. We present a framework that combines a standard epidemiological SEIR (susceptible–exposed–infected–removed) model with an equally standard machine learning classification model for clinical severity risk, defined as an individual's risk of needing intensive care unit (ICU) treatment if infected. Using COVID‐19–related data and estimates for France as of spring 2020, we then simulate isolation and exit policies. Our simulations show that policies considering clinical risk predictions could relax isolation restrictions for millions of the lowest risk population months earlier while consistently abiding by ICU capacity restrictions. Exit policies without risk predictions, meanwhile, would considerably exceed ICU capacity or require the isolation of a substantial portion of population for over a year in order to not overwhelm the medical system. Sensitivity analyses further decompose the impact of various elements of our models on the observed effects. Our work indicates that predictive modeling based on machine learning and artificial intelligence could bring significant value to managing pandemics. Such a strategy, however, requires governments to develop policies and invest in infrastructure to operationalize personalized isolation and exit policies based on risk predictions at scale. This includes health data policies to train predictive models and apply them to all residents, as well as policies for targeted resource allocation to maintain strict isolation for high‐risk individuals.
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SciScore for 10.1101/2020.04.29.20084707: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Several caveats should be noted. First, epidemic models - and the conclusions they may support - rely on a number of parameters, for example virus incubation and recovery times and the basic reproduction number , while the effects of policies also depend on healthcare system factors such as the availability of relevant resources (e.g., trained personnel). Second, these parameters are uncertain and evolve dynamically;23 the resultant policies …
SciScore for 10.1101/2020.04.29.20084707: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Several caveats should be noted. First, epidemic models - and the conclusions they may support - rely on a number of parameters, for example virus incubation and recovery times and the basic reproduction number , while the effects of policies also depend on healthcare system factors such as the availability of relevant resources (e.g., trained personnel). Second, these parameters are uncertain and evolve dynamically;23 the resultant policies are therefore contingent. Observing an ICU demand that is closer to an upper boundary of the confidence interval may require the next wave to be delayed or involve a smaller release percentage than out current simulations built from day zero suggest. Third, policy decisions require careful context-specific robustness analysis; however, using risk prediction models can at worst make no significant difference while at best improve policies by a significant margin, fixing all other conditions. Fourth, risk prediction models cannot be used when, or for people, for whom the necessary data is unavailable. In this case, simple models (e.g., based only on age and some reliable chronic disease data) may need to be used, which may limit the benefits of the approach. Finally, risk-predictions based policies using epidemic simulations should be developed taking into account behavioural aspects that may prove any model predictions and policy actions wrong; ethical issues, fear, widespread non-compliance to isolation measures, and the likes. In conclus...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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