This copy is for personal use only. To order printed copies, contact reprints@rsna.org I n P r e s s Abbreviations: AUC = area under the receiver operating characteristic curve CI = confidence interval COVID-19 = coronavirus disease 2019 COVNet = COVID-19 detection neural network CAP = community acquired pneumonia DICOM = digital imaging and communications in medicine Key Results:A deep learning method was able to identify COVID-19 on chest CT exams (area under the receiver operating characteristic curve, 0.96).A deep learning method to identify community acquired pneumonia on chest CT exams (area under the receiver operating characteristic curve, 0.95).There is overlap in the chest CT imaging findings of all viral pneumonias with other chest diseases that encourages a multidisciplinary approach to the final diagnosis used for patient treatment. Summary Statement:Deep learning detects coronavirus disease 2019 (COVID-19) and distinguish it from community acquired pneumonia and other non-pneumonic lung diseases using chest CT. I n P r e s s Abstract:Background: Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT.Purpose: To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. Materials and Methods:In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results:The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97). Conclusions:A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases.

Background: The ongoing new coronavirus pneumonia (Corona Virus Disease 2019, COVID-19) outbreak is spreading in China, but it has not yet reached its peak. Five million people emigrated from Wuhan before lockdown, potentially representing a source of virus infection. Determining case distribution and its correlation with population emigration from Wuhan in the early stage of the epidemic is of great importance for early warning and for the prevention of future outbreaks. Methods: The official case report on the COVID-19 epidemic was collected as of January 30, 2020. Time and location information on COVID-19 cases was extracted and analyzed using ArcGIS and WinBUGS software. Data on population migration from Wuhan city and Hubei province were extracted from Baidu Qianxi, and their correlation with the number of cases was analyzed. Results: The COVID-19 confirmed and death cases in Hubei province accounted for 59.91% (5806/9692) and 95.77% (204/213) of the total cases in China, respectively. Hot spot provinces included Sichuan and Yunnan, which are adjacent to Hubei. The time risk of Hubei province on the following day was 1.960 times that on the previous day. The number of cases in some cities was relatively low, but the time risk appeared to be continuously rising. The correlation coefficient between the provincial number of cases and emigration from Wuhan was up to 0.943. The lockdown of 17 cities in Hubei province and the implementation of nationwide control measures efficiently prevented an exponential growth in the number of cases. Conclusions: The population that emigrated from Wuhan was the main infection source in other cities and provinces. Some cities with a low number of cases showed a rapid increase in case load. Owing to the upcoming Spring Festival return wave, understanding the risk trends in different regions is crucial to ensure preparedness at both the individual and organization levels and to prevent new outbreaks.

23 BACKGROUND 24 The ongoing worldwide outbreak of the 2019-nCoV is markedly similar to the severe acute 25 respiratory syndrome (SARS) outbreak 17 years ago. During the 2002-2003 SARS outbreak, 26 healthcare workers formed a special population of patients. Although virus-specific IgG play 27 important roles in virus neutralization and prevention against future infection, limited information is 28 available regarding the long term persistence of IgG after infection with SARS-like coronavirus. 29 METHODS 30 A long-term prospective cohort study followed 34 SARS-CoV-infected healthcare workers from a 31 hospital with clustered infected cases during the 2002-2003 SARS outbreak in Guangzhou, China, 32 with a 13-year follow-up. Serum samples were collected annually from 2003-2015. Twenty 33 SARS-CoV-infected and 40 non-infected healthcare workers were enrolled in 2015, and their serum 34 samples were collected. All sera were tested for IgG antibodies with ELISA using whole virus and a 35 recombinant nucleocapsid protein of SARS-CoV, as a diagnostic antigen.36 RESULTS 37 Anti SARS-CoV IgG was found to persist for up to 12 years. IgG titers typically peaked in 2004, : medRxiv preprint SARS-CoV-infected healthcare workers remained at a significantly high level until 2015. Patients 40 treated with corticosteroids at the time of infection were found to have lower IgG titers than those 41 without. 42 CONCLUSIONS 43 IgG antibodies against SARS-CoV can persist for at least 12 years. The presence of SARS-CoV IgG 44 might provide protection against SARS-CoV and other betacoronavirus. This study provides valuable 45 information regarding humoral immune responses against SARS-CoV and the 2019-nCoV.46 47

While generally null results were found, long duration of unseasonable heat was associated with the increased risks for VSDs and ASDs, mainly in South and Northeast of the US. Further research to confirm our findings is needed.

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