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Pandemic lockdown, isolation, and exit policies based on machine learning predictions.

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.

SUBMITTER: Evgeniou T

PROVIDER: S-EPMC9115407 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:COVID-19 pandemic had expanded to the US since early 2020 and has caused nationwide economic loss and public health crisis. Until now, although the US has the most confirmed cases in the world and are still experiencing an increasing pandemic, several states insisted to re-open business activities and colleges while announced strict control measures. To provide a quantitative reference for official strategies, predicting the near future trend based on finer spatial resolution data and presumed scenarios are urgently needed. In this study, the first attempted COVID-19 case predicting model based on county-level demographic, environmental, and mobility data was constructed with multiple machine learning techniques and a hybrid framework. Different scenarios were also applied to selected metropolitan counties including New York City, Cook County in Illinois, Los Angeles County in California, and Miami-Dade County in Florida to assess the impact from lockdown, Phase I, and Phase III re-opening. Our results showed that, for selected counties, the mobility decreased substantially after the lockdown but kept increasing with an apparent weekly pattern, and the weekly pattern of mobility and infections implied high infections during the weekend. Meanwhile, our model was successfully built up, and the scenario assessment results indicated that, compared with Phase I re-opening, a 1-week and a 2-week lockdown could reduce 4%-29% and 15%-55% infections, respectively, in the future week, while 2-week Phase III re-opening could increase 16%-80% infections. We concluded that the mandatory orders in metropolitan counties such lockdown should last longer than one week, the effect could be observed. The impact of lockdown or re-opening was also county-dependent and varied with the local pandemic. In future works, we expect to involve a longer period of data, consider more county-dependent factors, and employ more sophisticated techniques to decrease the modeling uncertainty and apply it to counties nationally and other countries.

Project description:BackgroundDietary restriction (DR) is the most studied pro-longevity intervention; however, a complete understanding of its underlying mechanisms remains elusive, and new research directions may emerge from the identification of novel DR-related genes and DR-related genetic features.ResultsThis work used a Machine Learning (ML) approach to classify ageing-related genes as DR-related or NotDR-related using 9 different types of predictive features: PathDIP pathways, two types of features based on KEGG pathways, two types of Protein-Protein Interactions (PPI) features, Gene Ontology (GO) terms, Genotype Tissue Expression (GTEx) expression features, GeneFriends co-expression features and protein sequence descriptors. Our findings suggested that features biased towards curated knowledge (i.e. GO terms and biological pathways), had the greatest predictive power, while unbiased features (mainly gene expression and co-expression data) have the least predictive power. Moreover, a combination of all the feature types diminished the predictive power compared to predictions based on curated knowledge. Feature importance analysis on the two most predictive classifiers mostly corroborated existing knowledge and supported recent findings linking DR to the Nuclear Factor Erythroid 2-Related Factor 2 (NRF2) signalling pathway and G protein-coupled receptors (GPCR). We then used the two strongest combinations of feature type and ML algorithm to predict DR-relatedness among ageing-related genes currently lacking DR-related annotations in the data, resulting in a set of promising candidate DR-related genes (GOT2, GOT1, TSC1, CTH, GCLM, IRS2 and SESN2) whose predicted DR-relatedness remain to be validated in future wet-lab experiments.ConclusionsThis work demonstrated the strong potential of ML-based techniques to identify DR-associated features as our findings are consistent with literature and recent discoveries. Although the inference of new DR-related mechanistic findings based solely on GO terms and biological pathways was limited due to their knowledge-driven nature, the predictive power of these two features types remained useful as it allowed inferring new promising candidate DR-related genes.

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Pandemic lockdown, isolation, and exit policies based on machine learning predictions.

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