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Coronavirus disease 2019 (COVID-19) has rapidly spread not only in China but throughout the world. Children with kidney failure (chronic kidney disease (CKD) stage 5) are at significant risk for COVID-19. In turn, a set of recommendations for the prevention and control of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and COVID-19 in pediatric hemodialysis (HD) centers and in home peritoneal dialysis (PD) settings have been proposed. The recommendations are based on the epidemiological features of the SARS-CoV-2 virus and COVID-19 disease, susceptibility factors, and preventive and control strategies. These recommendations will be updated as new information regarding SARS-CoV-2 and COVID-19 becomes available.

The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.

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