Spatial Interactions and the Spread of COVID-19: A Network Perspective

We use unique data on the travel history of confirmed patients at a daily frequency across 31 provinces in China to study how spatial interactions influence the geographic spread of pandemic COVID-19. We develop and simultaneously estimate a structural model of dynamic disease transmission network formation and spatial interaction. This allows us to understand what externalities the disease risk associated with a single place may create for the entire country. We find a positive and significant spatial interaction effect that strongly influences the duration and severity of pandemic COVID-19. And there exists heterogeneity in this interaction effect: the spatial spillover effect from the source province is significantly higher than from other provinces. Further counterfactual policy analysis shows that targeting the key province can improve the effectiveness of policy interventions for containing the geographic spread of pandemic COVID-19, and the effect of such targeted policy decreases with an increase in the time of delay.

Spatial Interactions and the Spread of COVID-19: A Network Perspective

We use unique data on the travel history of confirmed patients at a daily frequency across 31 provinces in China to study how spatial interactions influence the geographic spread of pandemic COVID-19. We develop and simultaneously estimate a structural model of dynamic disease transmission network formation and spatial interaction. This allows us to understand what externalities the disease risk associated with a single place may create for the entire country. We find a positive and significant spatial interaction effect that strongly influences the duration and severity of pandemic COVID-19. And there exists heterogeneity in this interaction effect: the spatial spillover effect from the source province is significantly higher than from other provinces. Further counterfactual policy analysis shows that targeting the key province can improve the effectiveness of policy interventions for containing the geographic spread of pandemic COVID-19, and the effect of such targeted policy decreases with an increase in the time of delay.

Broad Impacts of Coronavirus Disease 2019 (COVID-19) Pandemic on Acute Respiratory Infections in China: An Observational Study.

BACKGROUND: To combat the coronavirus disease 2019 (COVID-19) pandemic, nonpharmaceutical interventions (NPIs) were implemented worldwide, which impacted a broad spectrum of acute respiratory infections (ARIs). METHODS: Etiologically diagnostic data from 142 559 cases with ARIs, who were tested for 8 viral pathogens (influenza virus [IFV], respiratory syncytial virus [RSV], human parainfluenza virus [HPIV], human adenovirus [HAdV], human metapneumovirus [HMPV], human coronavirus [HCoV], human bocavirus [HBoV], and human rhinovirus [HRV]) between 2012 and 2021, were analyzed to assess the changes in respiratory infections in China during the first COVID-19 pandemic year compared with pre-pandemic years. RESULTS: Test-positive rates of all respiratory viruses decreased during 2020, compared to the average levels during 2012-2019, with changes ranging from -17.2% for RSV to -87.6% for IFV. Sharp decreases mostly occurred between February and August when massive NPIs remained active, although HRV rebounded to the historical level during the summer. While IFV and HMPV were consistently suppressed year-round, RSV, HPIV, HCoV, HRV, and HBoV resurged and went beyond historical levels during September 2020-January 2021, after NPIs were largely relaxed and schools reopened. Resurgence was more prominent among children <18 years and in northern China. These observations remain valid after accounting for seasonality and long-term trend of each virus. CONCLUSIONS: Activities of respiratory viral infections were reduced substantially in the early phases of the COVID-19 pandemic, and massive NPIs were likely the main driver. Lifting of NPIs can lead to resurgence of viral infections, particularly in children.

Etiological and epidemiological features of acute respiratory infections in China.

Nationwide prospective surveillance of all-age patients with acute respiratory infections was conducted in China between 2009‒2019. Here we report the etiological and epidemiological features of the 231,107 eligible patients enrolled in this analysis. Children <5 years old and school-age children have the highest viral positivity rate (46.9%) and bacterial positivity rate (30.9%). Influenza virus, respiratory syncytial virus and human rhinovirus are the three leading viral pathogens with proportions of 28.5%, 16.8% and 16.7%, and Streptococcus pneumoniae, Mycoplasma pneumoniae and Klebsiella pneumoniae are the three leading bacterial pathogens (29.9%, 18.6% and 15.8%). Negative interactions between viruses and positive interactions between viral and bacterial pathogens are common. A Join-Point analysis reveals the age-specific positivity rate and how this varied for individual pathogens. These data indicate that differential priorities for diagnosis, prevention and control should be highlighted in terms of acute respiratory tract infection patients' demography, geographic locations and season of illness in China.

The changing pattern of enteric pathogen infections in China during the COVID-19 pandemic: a nation-wide observational study.

BACKGROUND: Non pharmaceutical interventions (NPI) including hand washing directives were implemented in China and worldwide to combat the COVID-19 pandemic, which are likely to have had impacted a broad spectrum of enteric pathogen infections. METHODS: Etiologically diagnostic data from 45 937 and 67 395 patients with acute diarrhea between 2012 and 2020, who were tested for seven viral pathogens and 13 bacteria respectively, were analyzed to assess the changes of enteric pathogen infections in China during the first COVID-19 pandemic year compared to pre-pandemic years. FINDINGS: Test positive rates of all enteric viruses decreased during 2020, compared to the average levels during 2012-2019, with a relative decrease of 71•75% for adenovirus, 58•76% for norovirus, 53•50% for rotavirus A, and 72•07% for the combination of other four uncommon viruses. In general, a larger reduction of positive rate in viruses was seen among adults than pediatric patients. A rebound of rotavirus A was seen after September 2020 in North China rather than South China. Test positive rates of bacteria decreased during 2020, compared to the average levels during 2012-2019, excepting for nontyphoidal Salmonella and Campylobacter coli with 66•53% and 90•48% increase respectively. This increase was larger for pediatric patients than for adult patients. INTERPRETATION: The activity of enteric pathogens changed profoundly alongside the NPIs implemented during the COVID-19 pandemic in China. Greater reductions of the test positive rates were found for almost all enteric viruses than for bacteria among acute diarrhea patients, with further large differences by age and geography. Lifting of NPIs will lead to resurgence of enteric pathogen infections, particularly in children whose immunity may not have been developed and/or waned. FUNDING: China Mega-Project on Infectious Disease Prevention; National Natural Science Funds.

A Preliminary Study on the Architectural Design of Chinese Hospitals in Virus Era

After the new Corona Virus Disease 2019 (COVID-2019), we have many new ideas for architectural design, more emphasis on epidemic prevention issues, designers in the functional design to meet the needs of medical staff and patients to use the design of the hospital, The assembly of hospital buildings is not only a transformation of construction methods, but also an upgrade of design concepts, which is a direction for future hospital development.

Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19. METHODS: 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community-acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19. RESULTS: Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity; 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P < 0.001), had longer incubation period (P < 0.001), were more likely to have abnormal laboratory findings (P < 0.05), and be in severity status (P < 0.001). More lesions (including larger volume of lesion, consolidation, and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P < 0.001). More lesions were found on CT images in patients with more comorbidities. The median volumes of lesion, consolidation, and ground-glass opacity in diabetes mellitus group were largest among the groups with single comorbidity that had the incidence rate of top three. CONCLUSIONS: Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions (including GGO and consolidation) were found in CT images of cases with comorbidity. The more comorbidities patients have, the more lesions CT images show.