A data-driven interpretable ensemble framework based on tree models for forecasting the occurrence of COVID-19 in the USA.

This prevalence of coronavirus disease 2019 (COVID-19) has become one of the most serious public health crises. Tree-based machine learning methods, with the advantages of high efficiency, and strong interpretability, have been widely used in predicting diseases. A data-driven interpretable ensemble framework based on tree models was designed to forecast daily new cases of COVID-19 in the USA and to determine the important factors related to COVID-19. Based on a hyperparametric optimization technique, we developed three machine learning algorithms based on decision trees, including random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), and three linear ensemble models were used to integrate these outcomes for better prediction accuracy. Finally, the SHapley Additive explanation (SHAP) value was used to obtain the feature importance ranking. Our outcomes demonstrated that, among the three basic machine learners, the prediction accuracy was the following in descending order: LightGBM, XGBoost, and RF. The optimized LAD ensemble was the most precise prediction model that reduced the prediction error of the best base learner (LightGBM) by approximately 3.111%, while vaccination, wearing masks, less mobility, and government interventions had positive effects on the control and prevention of COVID-19.

Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study.

OBJECTIVE: The COVID-19 outbreak was first reported in Wuhan, China, and has been acknowledged as a pandemic due to its rapid spread worldwide. Predicting the trend of COVID-19 is of great significance for its prevention. A comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more accurate for anticipating the occurrence of COVID-19 in the USA. DESIGN: Time-series study. SETTING: The USA was the setting for this study. MAIN OUTCOME MEASURES: Three accuracy metrics, mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE), were applied to evaluate the performance of the two models. RESULTS: In our study, for the training set and the validation set, the MAE, RMSE and MAPE of the XGBoost model were less than those of the ARIMA model. CONCLUSIONS: The XGBoost model can help improve prediction of COVID-19 cases in the USA over the ARIMA model.

The correlation between temperature and the incidence of COVID-19 in four first-tier cities of China: a time series study.

The COVID-19 outbreak emerged in Wuhan, China, and was declared a global pandemic in March 2020. This study aimed to explore the association of daily mean temperature with the daily counts of COVID-19 cases in Beijing, Shanghai, Guangzhou, and Shenzhen, China. Data on daily confirmed cases of COVID-19 and daily mean temperatures were retrieved from the 4 first-tier cities in China. Distributed lag nonlinear models (DLNMs) were used to assess the association between daily mean temperature and the daily cases of COVID-19 during the study period. After controlling for the imported risk index and long-term trends, the distributed lag nonlinear model showed that there were nonlinear and lag relationships. The daily cumulative relative risk decreased for every 1.0 °C change in temperature in Shanghai, Guangzhou, and Shenzhen. However, the cumulative relative risk increased with a daily mean temperature below - 3 °C in Beijing and then decreased. Moreover, the delayed effects of lower temperatures mostly occurred within 6-7 days of exposure. There was a negative correlation between the cumulative relative risk of COVID-19 incidence and temperature, especially when the temperature was higher than - 3 °C. The conclusions from this paper will help government and health regulators in these cities take prevention and protection measures to address the COVID-19 crisis and the possible collapse of the health system in the future.

Spatiotemporal cluster analysis of COVID-19 and its relationship with environmental factors at the city level in mainland China.

This study sought to identify the spatial, temporal, and spatiotemporal clusters of COVID-19 cases in 366 cities in mainland China with the highest risks and to explore the possible influencing factors of imported risks and environmental factors on the spatiotemporal aggregation, which would be useful to the design and implementation of critical preventative measures. The retrospective analysis of temporal, spatial, and spatiotemporal clustering of COVID-19 during the period (January 15 to February 25, 2020) was based on Kulldorff's time-space scanning statistics using the discrete Poisson probability model, and then the logistic regression model was used to evaluate the impact of imported risk and environmental factors on spatiotemporal aggregation. We found that the spatial distribution of COVID-19 cases was nonrandom; the Moran's I value ranged from 0.017 to 0.453 (P < 0.001). One most likely cluster and three secondary likely clusters were discovered in spatial cluster analysis. The period from February 2 to February 9, 2020, was identified as the most likely cluster in the temporal cluster analysis. One most likely cluster and seven secondary likely clusters were discovered in spatiotemporal cluster analysis. Imported risk, humidity, and inhalable particulate matter PM2.5 had a significant impact on temporal and spatial accumulation, and temperature and PM10 had a low correlation with the spatiotemporal aggregation of COVID-19. The information is useful for health departments to develop a better prevention strategy and potentially increase the effectiveness of public health interventions.

Effects of climate variables on the transmission of COVID-19: a systematic review of 62 ecological studies.

The new severe acute respiratory syndrome coronavirus 2 was initially discovered at the end of 2019 in Wuhan City in China and has caused one of the most serious global public health crises. A collection and analysis of studies related to the association between COVID-19 (coronavirus disease 2019) transmission and meteorological factors, such as humidity, is vital and indispensable for disease prevention and control. A comprehensive literature search using various databases, including Web of Science, PubMed, and Chinese National Knowledge Infrastructure, was systematically performed to identify eligible studies from Dec 2019 to Feb 1, 2021. We also established six criteria to screen the literature to obtain high-quality literature with consistent research purposes. This systematic review included a total of 62 publications. The study period ranged from 1 to 8 months, with 6 papers considering incubation, and the lag effect of climate factors on COVID-19 activity being taken into account in 22 studies. After quality assessment, no study was found to have a high risk of bias, 30 studies were scored as having moderate risks of bias, and 32 studies were classified as having low risks of bias. The certainty of evidence was also graded as being low. When considering the existing scientific evidence, higher temperatures may slow the progression of the COVID-19 epidemic. However, during the course of the epidemic, these climate variables alone could not account for most of the variability. Therefore, countries should focus more on health policies while also taking into account the influence of weather.