Global Evidence of Temperature Acclimation of COVID-19 D614G Linage.

The novel D614G linage is becoming the dominating species of SARS-CoV-2. The impact of meteorological and geographical factors on SARS-CoV-2 pandemic are presently not well understood. This research article presents a retrospective case series. Pandemic and meteorological data from 30 countries and 49 states from USA are collected as of June 10th, 2020. The primary outcome are the coefficients of correlations between meteorological factors and pandemic data. Hierarchical clustering analysis are used on SARS-CoV-2 genome, meteorological factors, and pandemic. Disseminating velocity of SARS-CoV-2 is negatively correlated with average temperature in majority of included countries and states from USA. Proportion of the GR clade is positively associated with temperature, but is negatively correlated with altitude in countries-set. Virus disseminating velocities in states from cluster A (Overwhelming proportion of G + GR + GH clades, GH > 60%) and C (Overwhelming proportion of G + GR + GH clades, G 20-30%) both has negative correlations with temperature, while cluster C has more significant negative correlation than cluster A. Climate and geographical environment are revealed to affect virus spreading. GH and GR clades of SARS-CoV-2 are probably acquiring higher temperature tolerance, while G clade may retain high temperature intolerance.

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

Risk factors for developing into critical COVID-19 patients in Wuhan, China: A multicenter, retrospective, cohort study.

BACKGROUND: The ferocious global assault of COVID-19 continues. Critically ill patients witnessed significantly higher mortality than severe and moderate ones. Herein, we aim to comprehensively delineate clinical features of COVID-19 and explore risk factors of developing critical disease. METHODS: This is a Mini-national multicenter, retrospective, cohort study involving 2,387 consecutive COVID-19 inpatients that underwent discharge or death between January 27 and March 21, 2020. After quality control, 2,044 COVID-19 inpatients were enrolled. Electronic medical records were collected to identify the risk factors of developing critical COVID-19. FINDINGS: The severity of COVID-19 climbed up straightly with age. Critical group was characterized by higher proportion of dyspnea, systemic organ damage, and long-lasting inflammatory storm. All-cause mortality of critical group was 85•45%, by contrast with 0•58% for severe group and 0•18% for moderate group. Logistic regression revealed that sex was an effect modifier for hypertension and coronary heart disease (CHD), where hypertension and CHD were risk factors solely in males. Multivariable regression showed increasing odds of critical illness associated with hypertension, CHD, tumor, and age ≥ 60 years for male, and chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), tumor, and age ≥ 60 years for female. INTERPRETATION: We provide comprehensive front-line information about different severity of COVID-19 and insights into different risk factors associated with critical COVID-19 between sexes. These results highlight the significance of dividing risk factors between sexes in clinical and epidemiologic works of COVID-19, and perhaps other coronavirus appearing in future. FUNDING: 10.13039/100000001 National Science Foundation of China.