COVID-Net: A deep learning based and interpretable predication model for the county-wise trajectories of COVID-19 in the United States

The cases of COVID-19 have been reported in the United States since January 2020. We propose a COVINet by combining the architecture of both Long Short-Term Memory and Gated Recurrent Unit. First, we use the 10-fold cross-validation to train and assess different prediction models for which all counties serve alternatively as the training and test counties. Then, we focus on the prediction for the 10 severest counties. We employ the Mean Relative Errors (MREs) to measure the performance of the COVINet in predicting confirmed cases and deaths. Two COVINet models with 26 and 19 input variables, respectively, are trained. We estimate their respective MREs in the last 30 days before January 23, 2021, by the 10-fold CV, which are 0.0898 and 0.1068 for the number of confirmed cases, and 0.0694 and 0.0724 for the number of deaths. The MREs are also small for all predictions of the events in the last 7 or 30 days before January 23, 2021. The COVINet uses features including workforce driving alone to work, traffic volume, income inequality, and longitude and latitude of infected counties to predict the trajectories of COVID-19 in counties of the United States. The increasing awareness of how predictors affect the pandemic helps policymakers develop plans to mitigate the spread of COVID-19.

Risk factors associated with mortality of COVID-19 in 2692 counties of the United States

BackgroundThe number of cumulative confirmed cases of COVID-19 in the United States has risen sharply since March 2020. A county health ranking and roadmaps program has been established to identify factors associated with disparity in mobility and mortality of COVID-19 in all counties in the United States. MethodsTo find out the risk factors associated with county-level mortality of COVID-19 with various levels of prevalence, a negative binomial design was applied to the county-level mortality counts of COVID-19 as of August 27, 2020 in the United States. In this design, the infected counties were categorized into three levels of infections using clustering analysis based on time-varying cumulative confirmed cases from March 1 to August 27, 2020. COVID-19 patients were not analyzed individually but were aggregated at the county-level, where the county-level deaths of COVID-19 confirmed by the local health agencies. Results3125 infected counties were assigned into three classes corresponding to low, median, and high prevalence levels of infection. Several risk factors were significantly associated with the mortality counts of COVID-19, where higher level of air pollution (0.153, P<0.001) increased the mortality in the low prevalence counties and elder individuals were more vulnerable in both the median and high prevalence counties. The segregation between non-Whites and Whites and higher Hispanic population had higher likelihood of risk of the deaths in all infected counties. ConclusionsThe mortality of COVID-19 depended on sex, race/ethnicity, and outdoor environment. The increasing awareness of the impact of these significant factors may lead to the reduction in the mortality of COVID-19.

Data-driven analysis on the simulations of the spread of COVID-19 under different interventions of China

Since February 2020, COVID-19 has spread rapidly to more than 200 countries in the world. During the pandemic, local governments in China have implemented different interventions to efficiently control the spread of the epidemic. Characterizing transmission of COVID-19 under some typical interventions is essential to help countries develop appropriate interventions. Based on the pre-symptomatic transmission patterns of COVID-19, we established a novel compartmental model: Baysian SIHR model with latent Markov structure, which treated the numbers of infected and infectious individuals without isolation to be the latent variables and allowed the effective reproduction number to change over time, thus the effects of policies could be reasonably estimated. By using the epidemic data of Wuhan, Wenzhou and Shenzhen, we migrated the corresponding estimated policy modes to South Korea, Italy, and the United States and simulated the potential outcomes for these countries when they adopted similar policy strategies of three cities in China. We found that the mild interventions implemented in Shenzhen were effective to control the epidemic in the early stage, while more stringent policies which were issued in Wuhan and Wenzhou were necessary if the epidemic was more severe and needed to be controlled in a short time.

The diagnostic value of lupus anticoagulant ratio, D-dimer and soluble endothelial protein C receptor for early deep vein thrombosis / 中国医师进修杂志

Objective:To investigate the early predictive value of lupus anticoagulant (LA) ratio, D-dimer (D-D) and soluble endothelial protein C receptor (sEPCR) on deep vein thrombosis (DVT).Methods:Thirty hundred and fifty patients who performed surgery for lower extremity fracture and suspected DVT in Zhejiang Rongjun Hospital from October 2018 to October 2019 were enrolled. With deep vein contrast of the lower extremity as the gold standard, 82 patients with confirmed DVT were treated as the observation group and 268 patients without DVT as the control group. The levels of LA, D-D and sEPCR of two groups were detected by coagulation, immunoturbidimetry and enzyme linked immunosorbent assay—sandwich technique respectively. Indexes of the two groups were compared. Pearson correlation was used to analyze the relationship between plasma levels of LA, D-D and sEPCR, and the predictive value of plasma sEPCR, LA ratio and D-D level on DVT was evaluated by receiver operator characteristic (ROC) curve.Results:The plasma sEPCR, LA ratio and D-D levels in the observation group were significantly higher than those in the control group [(143.30 ± 11.28) μg/L vs.(112.56 ± 14.62) μg/L, 1.51 ± 0.24 vs. 1.22 ± 0.18, (1 013.00 ± 319.54) μg/L vs. (425.17 ± 100.36) μg/L] with statistically significant differences ( P < 0.05). There was no significant differences in activated partial thromboplastin time (APTT), prothrombin time (PT) and thrombin time (TT) between the two groups ( P > 0.05). In the observation group, plasma sEPCR level was positively correlated with LA ratio and D-D level ( r = 0.280, P = 0.011; r = 0.563, P < 0.001), and LA ratio was positively correlated with D-D level( r = 0.741, P < 0.001). The area under curve (AUC) of D-D in diagnosis of DVT was 0.940, and the sensitivity and specificity were 87.80% and 87.69% when the cut-off value was 569.43 μg/L. The AUC of LA ratio in the diagnosis of DVT was the smallest, which was 0.912, the sensitivity and specificity were 87.80% and 91.25% when the cut-off value was 1.23. The sensitivity was 95.12% and specificity was 95.00% of sEPCR and LA ratio combined with DD in diagnosis of DVT. Conclusions:LA and D-D combined with sEPCR has high predictive value for DVT.