Optimization of COVID-19 prevention and control measures during the Beijing 2022 Winter Olympics:a model-based study

Background:The continuous mutation of severe acute respiratory syndrome coronavirus 2 has made the coronavirus disease 2019(COVID-19)pandemic complicated to predict and posed a severe challenge to the Beijing 2022Winter Olympics and Winter Paralympics held in February and March 2022.Methods:During the preparations for the Beijing 2022 Winter Olympics,we established a dynamic model with pulsedetection and isolation efect to evaluate the efect of epidemic prevention and control measures such as entry policies,contact reduction,nucleic acid testing,tracking,isolation,and health monitoring in a closed-loop managementenvironment,by simulating the transmission dynamics in assumed scenarios.We also compared the importance ofeach parameter in the combination of intervention measures through sensitivity analysis.Results:At the assumed baseline levels,the peak of the epidemic reached on the 57th day.During the simulationperiod(100 days),13,382 people infected COVID-19.The mean and peak values of hospitalized cases were 2650and 6746,respectively.The simulation and sensitivity analysis showed that:(1)the most important measures to stopCOVID-19 transmission during the event were daily nucleic acid testing,reducing contact among people,and dailyhealth monitoring,with cumulative infections at 0.04%,0.14%,and 14.92%of baseline levels,respectively(2)strictlyimplementing the entry policy and reducing the number of cases entering the closed-loop system could delay thepeak of the epidemic by 9 days and provide time for medical resources to be mobilized;(3)the risk of environmentaltransmission was low.Conclusions:Comprehensive measures under certain scenarios such as reducing contact,nucleic acid testing,health monitoring,and timely tracking and isolation could efectively prevent virus transmission.Our research resultsprovided an important reference for formulating prevention and control measures during the Winter Olympics,andno epidemic spread in the closed-loop during the games indirectly proved the rationality of our research results.

Machine Learning-Based Temperature and Wind Forecasts in the Zhangjiakou Competition Zone during the Beijing 2022 Winter Olympic Games

Weather forecasting for the Zhangjiakou competition zone of the Beijing 2022 Winter Olympic Games is a challenging task due to its complex terrain.Numerical weather prediction models generally perform poorly for cold air pools and winds over complex terrains,due to their low spatiotemporal resolution and limitations in the description of dynamics,thermodynamics,and microphysics in mountainous areas.This study proposes an ensemble-learning model,named ENSL,for surface temperature and wind forecasts at the venues of the Zhangjiakou competition zone,by integrating five individual models—linear regression,random forest,gradient boosting decision tree,support vector machine,and artificial neural network(ANN),with a ridge regression as meta model.The ENSL employs predictors from the high-resolution ECMWF model forecast(ECMWF-HRES) data and topography data,and targets from automatic weather station observations.Four categories of predictors(synoptic-pattern related fields,surface element fields,terrain,and temporal features) are fed into ENSL.The results demonstrate that ENSL achieves better performance and generalization than individual models.The root-mean-square error(RMSE) for the temperature and wind speed predictions is reduced by 48.2% and 28.5%,respectively,relative to ECMWF-HRES.For the gust speed,the performance of ENSL is consistent with ANN(best individual model) in the whole dataset,whereas ENSL outperforms on extreme gust samples(42.7% compared with 38.7% obtained by ECMWF-HRES in terms of RMSE reduction).Sensitivity analysis of predictors in the four categories shows that ENSL fits their feature importance rankings and physical explanations effectively.