Background: To contain the outbreak of coronavirus disease 2019 in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restoration of operational capability of metropolitan medical service in China.Methods: Real-time data of COVID-19 and population mobility data were extracted from open sources. SEIR (Susceptible, Exposed, Infectious, Recovered) and neural network models (NNs) were built to model disease trends in Wuhan, Beijing, Shanghai and Guangzhou. Combined with public transportation data, Autoregressive Integrated Moving Average (ARIMA) model was used to estimate the accumulated demands for nonlocal hospitalization during the epidemic period in Beijing, Shanghai and Guangzhou. Results: The number of infected people and deaths would increase by 45% and 567% respectively, given that the government only has implemented traffic control in Wuhan without additional medical professionals. The epidemic of Wuhan (measured by cumulative confirmed cases) was predicted to reach turning point at the end of March and end in later April, 2020. The outbreak in Beijing, Shanghai and Guangzhou was predicted to end at the end of March and the medical service could be fully back to normal in middle of April. During the epidemic, the number of nonlocal inpatient hospitalizations decreased by 69.86%, 57.41% and 66.85% in Beijing, Shanghai and Guangzhou respectively. After the end of epidemic, medical centers located in these metropolises may face 58,799 (95% CI 48926-67,232) additional hospitalization needs in the first month.

Background The early life gut microbiome is crucial in maintaining host metabolic and immune homeostasis. Though neonates with critical congenital heart disease (CCHD) are at substantial risks of malnutrition and immune imbalance, the microbial links to CCHD pathophysiology remain poorly understood. In this study, we aimed to investigate the gut microbiome in neonates with CCHD in association with metabolomic traits. Moreover, we explored the clinical implications of the host-microbe interactions in CCHD. Methods Deep metagenomic sequencing and metabolomic profiling of paired fecal samples from 45 neonates with CCHD and 50 healthy controls were performed. The characteristics of gut microbiome were investigated in three dimensions (microbial abundance, functionality, and genetic variation). An in-depth analysis of gut virome was conducted to elucidate the ecological interaction between gut viral and bacterial communities. Correlations between multilevel microbial features and fecal metabolites were determined using integrated association analysis. Finally, we conducted a subgroup analysis to examine whether the interactions between gut microbiota and metabolites could mediate inflammatory responses and poor surgical prognosis. Results Gut microbiota dysbiosis was observed in neonates with CCHD, characterized by the depletion of Bifidobacterium and overgrowth of Enterococcus, which was highly correlated with metabolomic perturbations. Genetic variations of Bifidobacterium and Enterococcus orchestrate the metabolomic perturbations in CCHD. A temperate core virome represented by Siphoviridae was identified to be implicated in shaping the gut bacterial composition by modifying microbial adaptation. The overgrowth of Enterococcus was correlated with systemic inflammation and poor surgical prognosis in subgroup analysis. Mediation analysis indicated that the overgrowth of Enterococcus could mediate gut barrier impairment and inflammatory responses in CCHD. Conclusions We demonstrate for the first time that an aberrant gut microbiome associated with metabolomic perturbations is implicated in immune imbalance and adverse clinical outcomes in neonates with CCHD. Our data support the importance of reconstituting optimal gut microbiome in maintaining host metabolic and immunological homeostasis in CCHD.

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