Vaccination status for mild and asymptomatic infections with SARS-CoV-2 Omicron BA.2 variant in Shanghai.

This study aimed to evaluate the effects of different vaccine regimens on mild and asymptomatic infections with SARS-CoV-2 Omicron BA.2 variant in Shanghai. All asymptomatic patients and those with mild symptoms of Omicron infections were recruited from three major Fangcang shelter hospitals between March 26, 2022 and May 20, 2022. Nucleic acid for SARS-CoV-2 by real-time reverse-transcription polymerase chain reaction methods in nasopharyngeal swabs was assessed every day during the hospitalization. The value of cycle threshold lower than 35 was considered as positive result of SARS-CoV-2. A total of 214 592 cases were included in this study. The proportion of the asymptomatic patients was 76.90% and 23.10% of the recruited patients had mild symptoms. The median (interquartile range [IQR]: 25-75) duration of viral shedding (DVS) was 7 (5-10) days among all participants. The DVS varied greatly among different age groups. Children and the elderly had longer DVS compared with the adults. The booster shot of inactivated vaccine contributed to the shorter DVS in patients aged ≥70 years compared with the unvaccinated patients (8 [6-11] vs. 9 [6-12] days, p = 0.002]. Full inactivated vaccine regimen contributed to the shorter DVS in patients aged 3-6 years (7 [5-9] vs. 8 [5-10] days, p = 0.001]. In conclusion, the full inactivated vaccine regimen on children aged 3-6 years and booster inactivated vaccine regimen on the elderly aged ≥70 years appeared to be effective in reducing DVS. The booster vaccine regimen should be rigorously promoted and implemented.

A retrospective study of Pupingqinghua prescription versus Lianhuaqingwen in Chinese participants infected with SARS-CoV-2 Omicron variants.

Background: Coronavirus disease (COVID-19) seriously endangers global public health. Pupingqinghua prescription (PPQH) is an herbal formula from traditional Chinese medicine used for treatment of SARS-CoV-2 infection. This study aims to evaluate the clinical efficacy and safety of PPQH in Chinese participants infected with the SARS-CoV-2 Omicron variant. Methods: A total of 873 SARS-CoV-2 (Omicron)-infected patients were included. Among them, the patients were divided into the PPQH group (653 cases) and LHQW group (220 cases) according to different medications. The effectiveness indicators (hematological indicators, Ct values of novel Coronavirus nucleic acid tests, and viral load-shedding time) and safety indicators (liver and kidney function and adverse events) were analyzed. Results: There was no significant difference in baseline characteristics between the PPQH group and the LHQW group, except the gender; After the treatment, the levels of IL-5, IL-6, IL-10, NK cells, and INF-α of the patients in the PPQH group showed a downward trend (p < 0.05); The viral load shedding time was 5.0 (5.0, 7.0) in the PPQH group and 5.0 (4.0, 7.0) in the LHQW group; both PPQH and LHQW can shorten the duration of symptoms of fever, cough, and sore throat. The re-positive rate of COVID-19 test was 1.5 % in the PPQH group and 2.3 % in the LHQW group. In terms of safety, the levels of γ-GTT decreased significantly (p < 0.01); gastrointestinal reaction was the primary adverse reaction, and the reaction rate was 4.7 % in the PPQH group and 9.5 % in the LHQW group. Conclusion: PPQH can shorten the length of hospital stay and improve clinical symptoms of patients with SARS-COV-2 (Omicron), and it also has a good safety profile.

Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China.

Introduction: In confronting the sudden COVID-19 epidemic, China and other countries have been under great pressure to block virus transmission and reduce fatalities. Converting large-scale public venues into makeshift hospitals is a popular response. This addresses the outbreak and can maintain smooth operation of a country or region's healthcare system during a pandemic. However, large makeshift hospitals, such as the Shanghai New International Expo Center (SNIEC) makeshift hospital, which was one of the largest makeshift hospitals in the world, face two major problems: Effective and precise transfer of patients and heterogeneity of the medical care teams. Methods: To solve these problems, this study presents the medical practices of the SNIEC makeshift hospital in Shanghai, China. The experiences include constructing two groups, developing a medical management protocol, implementing a multi-dimensional management mode to screen patients, transferring them effectively, and achieving homogeneous quality of medical care. To evaluate the medical practice performance of the SNIEC makeshift hospital, 41,941 infected patients were retrospectively reviewed from March 31 to May 23, 2022. Multivariate logistic regression method and a tree-augmented naive (TAN) Bayesian network mode were used. Results: We identified that the three most important variables were chronic disease, age, and type of cabin, with importance values of 0.63, 0.15, and 0.11, respectively. The constructed TAN Bayesian network model had good predictive values; the overall correct rates of the model-training dataset partition and test dataset partition were 99.19 and 99.05%, respectively, and the respective values for the area under the receiver operating characteristic curve were 0.939 and 0.957. Conclusion: The medical practice in the SNIEC makeshift hospital was implemented well, had good medical care performance, and could be copied worldwide as a practical intervention to fight the epidemic in China and other developing countries.

Medical implementation practice and its medical performance evaluation of a giant makeshift hospital during the COVID-19 pandemic: An innovative model response to a public health emergency in Shanghai, China

Introduction In confronting the sudden COVID-19 epidemic, China and other countries have been under great pressure to block virus transmission and reduce fatalities. Converting large-scale public venues into makeshift hospitals is a popular response. This addresses the outbreak and can maintain smooth operation of a country or region's healthcare system during a pandemic. However, large makeshift hospitals, such as the Shanghai New International Expo Center (SNIEC) makeshift hospital, which was one of the largest makeshift hospitals in the world, face two major problems: Effective and precise transfer of patients and heterogeneity of the medical care teams. Methods To solve these problems, this study presents the medical practices of the SNIEC makeshift hospital in Shanghai, China. The experiences include constructing two groups, developing a medical management protocol, implementing a multi-dimensional management mode to screen patients, transferring them effectively, and achieving homogeneous quality of medical care. To evaluate the medical practice performance of the SNIEC makeshift hospital, 41,941 infected patients were retrospectively reviewed from March 31 to May 23, 2022. Multivariate logistic regression method and a tree-augmented naive (TAN) Bayesian network mode were used. Results We identified that the three most important variables were chronic disease, age, and type of cabin, with importance values of 0.63, 0.15, and 0.11, respectively. The constructed TAN Bayesian network model had good predictive values;the overall correct rates of the model-training dataset partition and test dataset partition were 99.19 and 99.05%, respectively, and the respective values for the area under the receiver operating characteristic curve were 0.939 and 0.957. Conclusion The medical practice in the SNIEC makeshift hospital was implemented well, had good medical care performance, and could be copied worldwide as a practical intervention to fight the epidemic in China and other developing countries.

Epidemiological, clinical, and household transmission characteristics of children and adolescents infected with SARS-CoV-2 Omicron variant in Shanghai, China: a retrospective, multicenter observational study.

OBJECTIVES: To describe the epidemiological, clinical, and household transmission characteristics of pediatric COVID-19 cases in Shanghai, China. METHODS: Pediatric patients with COVID-19 hospitalized in Shanghai from March-May 2022 were enrolled in this retrospective, multicenter cohort study. The symptoms and the risk factors associated with disease severity were analyzed. RESULTS: In total, 2620 cases (age range, 24 days-17 years) were enrolled in this study. Of these, 1011 (38.6%) were asymptomatic, whereas 1415 (54.0%), 190 (7.3%), and 4 (0.2%) patients developed mild, moderate, and severe illnesses, respectively. Household infection rate was negatively correlated with household vaccination coverage. Children aged 0-3 years, those who are unvaccinated, those with underlying diseases, and overweight/obese children had a higher risk of developing moderate to severe disease than children aged 12-17 years, those who were vaccinated, those without any underlying disease, and those with normal weight, respectively (all P <0.05). A prolonged duration of viral shedding was associated with disease severity, presence of underlying diseases, vaccination status, and younger age (all P <0.05). CONCLUSION: Children aged younger than 3 years who were not eligible for vaccination had a high risk of developing moderate to severe COVID-19 with a prolonged duration of viral shedding. Vaccination could protect children from COVID-19 at the household level.

The machine learning model based on trajectory analysis of ribonucleic acid test results predicts the necessity of quarantine in recurrently positive patients with SARS-CoV-2 infection.

Background: SARS-CoV-2 patients re-experiencing positive nucleic acid test results after recovery is a concerning phenomenon. Current pandemic prevention strategy demands the quarantine of all recurrently positive patients. This study provided evidence on whether quarantine is required in those patients, and predictive algorithms to detect subjects with infectious possibility. Methods: This observational study recruited recurrently positive patients who were admitted to our shelter hospital between May 12 and June 10, 2022. The demographic and epidemiologic data was collected, and nucleic acid tests were performed daily. virus isolation was done in randomly selected cases. The group-based trajectory model was developed based on the cycle threshold (Ct) value variations. Machine learning models were validated for prediction accuracy. Results: Among the 494 subjects, 72.04% were asymptomatic, and 23.08% had a Ct value under 30 at recurrence. Two trajectories were identified with either rapid (92.24%) or delayed (7.76%) recovery of Ct values. The latter had significantly higher incidence of comorbidities; lower Ct value at recurrence; more persistent cough; and more frequently reported close contacts infection compared with those recovered rapidly. However, negative virus isolation was reported in all selected samples. Our predictive model can efficiently discriminate those with delayed Ct value recovery and infectious potentials. Conclusion: Quarantine seems to be unnecessary for the majority of re-positive patients who may have low transmission risks. Our predictive algorithm can screen out the suspiciously infectious individuals for quarantine. These findings may assist the enaction of SARS-CoV-2 pandemic prevention strategies regarding recurrently positive patients in the future.

Can the nucleic acid Ct value of discharged patients infected with SARS-CoV-2 Omicron variant be 35?--A retrospective study on fluctuation of nucleic acid Ct values in SNIEC mobile cabin hospital.

Objective: To explore the meaning of cycle threshold (Ct) value fluctuation and the appropriateness of setting the discharge Ct value to 35, which is the current standard in Chinese guidelines. Method: A retrospective study was conducted on 95 patients with Ct value fluctuation (Ct value below 35 on day 3; group A) and 97 patients with a normal discharge process (control; group B). Their clinical characteristics and follow-up data were collected. Results: (1) There was no significant difference between the groups in age, gender distribution, number of vaccinations, initial ORF-Ct value, and initial N-Ct value. The proportion of patients complicated with chronic internal disorders, respiratory symptoms, and abnormal chest radiology in group A was significantly higher than that in group B. (2) Between the two groups, there was no significant difference in the ORF-Ct or N-Ct value on day 1, but the ORF-Ct and N-Ct values of group B on days 2 to 4 were significantly higher than those of group A. (3) There was no significant difference between the groups in the ORF-Ct value at discharge, but there was a significant difference in the N-Ct value at discharge. Seven days after discharge, almost 100% of the patients had been cured. The mean negative conversion interval of nucleic acid of the patients in group A was 14.5 ± 4.6 days, which was longer than that of the patients in group B (11.8 ± 4 days). (4) Logistic regression analysis showed that the ORF-Ct value on day 2 was the key factor influencing the Ct value fluctuation. Conclusion: The fluctuation of Ct value is only a normal phenomenon in the recovery period of the disease, and there is no need for excessive intervention. It is reasonable to set the Ct value of the discharge standard to 35 and retest the nucleic acid on the 10th day after discharge for patients with underlying diseases or symptoms.

The machine learning model based on trajectory analysis of ribonucleic acid test results predicts the necessity of quarantine in recurrently positive patients with SARS-CoV-2 infection

Background SARS-CoV-2 patients re-experiencing positive nucleic acid test results after recovery is a concerning phenomenon. Current pandemic prevention strategy demands the quarantine of all recurrently positive patients. This study provided evidence on whether quarantine is required in those patients, and predictive algorithms to detect subjects with infectious possibility. Methods This observational study recruited recurrently positive patients who were admitted to our shelter hospital between May 12 and June 10, 2022. The demographic and epidemiologic data was collected, and nucleic acid tests were performed daily. virus isolation was done in randomly selected cases. The group-based trajectory model was developed based on the cycle threshold (Ct) value variations. Machine learning models were validated for prediction accuracy. Results Among the 494 subjects, 72.04% were asymptomatic, and 23.08% had a Ct value under 30 at recurrence. Two trajectories were identified with either rapid (92.24%) or delayed (7.76%) recovery of Ct values. The latter had significantly higher incidence of comorbidities;lower Ct value at recurrence;more persistent cough;and more frequently reported close contacts infection compared with those recovered rapidly. However, negative virus isolation was reported in all selected samples. Our predictive model can efficiently discriminate those with delayed Ct value recovery and infectious potentials. Conclusion Quarantine seems to be unnecessary for the majority of re-positive patients who may have low transmission risks. Our predictive algorithm can screen out the suspiciously infectious individuals for quarantine. These findings may assist the enaction of SARS-CoV-2 pandemic prevention strategies regarding recurrently positive patients in the future.

Predictive Modeling for Epidemic Outbreaks: A New Approach and COVID-19 Case Study

Since the onset of the COVID-19 outbreak in Wuhan, China, numerous forecasting models have been proposed to project the trajectory of coronavirus infection cases. Most of these forecasts are based on epidemiology models that utilize deterministic differential equations and have resulted in widely varying predictions. We propose a new discrete-time Markov chain model that directly incorporates stochastic behavior and for which parameter estimation is straightforward from available data. Using such data from China’s Hubei province (for which Wuhan is the provincial capital city and which accounted for approximately 82% of the total reported COVID-19 cases in the entire country), the model is shown to be flexible, robust, and accurate. As a result, it has been adopted by the first Shanghai assistance medical team in Wuhan’s Jinyintan Hospital, which was the first designated hospital to take COVID-19 patients in the world. The forecast has been used for preparing medical staff, intensive care unit (ICU) beds, ventilators, and other critical care medical resources and for supporting real-time medical management decisions.

A retrospective study of Pupingqinghua prescription versus Lianhuaqingwen in Chinese participants infected with SARS-CoV-2 Omicron variants

Background: Coronavirus disease (COVID-19) seriously endangers global public health. Pupingqinghua prescription (PPQH) is an herbal formula from traditional Chinese medicine used for treatment of SARS-CoV-2 infection. This study aims to evaluate the clinical efficacy and safety of PPQH in Chinese participants infected with the SARS-CoV-2 Omicron variant. Methods: A total of 873 SARS-CoV-2 (Omicron)-infected patients were included. Among them, the patients were divided into the PPQH group (653 cases) and LHQW group (220 cases) according to different medications. The effectiveness indicators (hematological indicators, Ct values of novel Coronavirus nucleic acid tests, and viral load-shedding time) and safety indicators (liver and kidney function and adverse events) were analyzed. Results: There was no significant difference in baseline characteristics between the PPQH group and the LHQW group, except the gender;After the treatment, the levels of IL-5, IL-6, IL-10, NK cells, and INF-α of the patients in the PPQH group showed a downward trend (p < 0.05);The viral load shedding time was 5.0 (5.0, 7.0) in the PPQH group and 5.0 (4.0, 7.0) in the LHQW group;both PPQH and LHQW can shorten the duration of symptoms of fever, cough, and sore throat. The re-positive rate of COVID-19 test was 1.5 % in the PPQH group and 2.3 % in the LHQW group. In terms of safety, the levels of γ-GTT decreased significantly (p < 0.01);gastrointestinal reaction was the primary adverse reaction, and the reaction rate was 4.7 % in the PPQH group and 9.5 % in the LHQW group. Conclusion: PPQH can shorten the length of hospital stay and improve clinical symptoms of patients with SARS-COV-2 (Omicron), and it also has a good safety profile.

Vaccination, symptomatic infection and negative conversion of viral RNA by body mass index, diabetes, and age: An observational study.

BACKGROUND: The associations of doses of vaccine received with symptomatic infection with SARS-CoV-2 and negative conversion rate of viral RNA by BMI, diabetes, and age are unclear. METHODS: Included were adult cases of SARS-CoV-2 infection hospitalized at a makeshift hospital in Shanghai (N = 38,592). Each case received a real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test every day until discharge. Symptomatic cases had ≥1 pre-specified symptoms. Negative conversion time (NCT) was the duration between the specimen collection date associated with the first positive RT-PCR test and the first test date of the two consecutive negative tests at least 24 h apart. BMI-, diabetes- and age-stratified multivariable logistic and Poisson regressions were applied. FINDINGS: Coexistence of overweight/obesity and diabetes was associated with a higher risk of symptomatic infection and a longer NCT compared with coexistence of normal weight and without diabetes, but this association was primarily attributed to underlying comorbidities. Compared with absence of vaccination, booster vaccination, but not full vaccination, was consistently associated with a 42 %-56 % lower odds of symptomatic infection and ∼1.6-1.8 days of shorter NCT across different strata separately for weight and diabetes. Age-stratified analyses found that the effectiveness of booster vaccination did not attenuate with age, except for preventing symptomatic infection among adults with diabetes. INTERPRETATION: BMI and diabetes co-determined their associations with symptomatic infection and NCT. Booster vaccination but not full vaccination was associated a lower risk of symptomatic infection, a shorter NCT or both regardless of BMI, diabetes status and age.

The predicting roles of carcinoembryonic antigen and its underlying mechanism in the progression of coronavirus disease 2019.

BACKGROUND: The coronavirus disease 2019 (COVID-19) has induced a worldwide epidemiological event with a high infectivity and mortality. However, the predicting biomarkers and their potential mechanism in the progression of COVID-19 are not well known. OBJECTIVE: The aim of this study is to identify the candidate predictors of COVID-19 and investigate their underlying mechanism. METHODS: The retrospective study was conducted to identify the potential laboratory indicators with prognostic values of COVID-19 disease. Then, the prognostic nomogram was constructed to predict the overall survival of COVID-19 patients. Additionally, the scRNA-seq data of BALF and PBMCs from COVID-19 patients were downloaded to investigate the underlying mechanism of the most important prognostic indicators in lungs and peripherals, respectively. RESULTS: In total, 304 hospitalized adult COVID-19 patients in Wuhan Jinyintan Hospital were included in the retrospective study. CEA was the only laboratory indicator with significant difference in the univariate (P < 0.001) and multivariate analysis (P = 0.020). The scRNA-seq data of BALF and PBMCs from COVID-19 patients were downloaded to investigate the underlying mechanism of CEA in lungs and peripherals, respectively. The results revealed the potential roles of CEA were significantly distributed in type II pneumocytes of BALF and developing neutrophils of PBMCs, participating in the progression of COVID-19 by regulating the cell-cell communication. CONCLUSION: This study identifies the prognostic roles of CEA in COVID-19 patients and implies the potential roles of CEACAM8-CEACAM6 in the progression of COVID-19 by regulating the cell-cell communication of developing neutrophils and type II pneumocyte.

Construction and validation of a machine learning-based nomogram: A tool to predict the risk of getting severe coronavirus disease 2019 (COVID-19).

BACKGROUND: Identifying patients who may develop severe coronavirus disease 2019 (COVID-19) will facilitate personalized treatment and optimize the distribution of medical resources. METHODS: In this study, 590 COVID-19 patients during hospitalization were enrolled (Training set: n = 285; Internal validation set: n = 127; Prospective set: n = 178). After filtered by two machine learning methods in the training set, 5 out of 31 clinical features were selected into the model building to predict the risk of developing severe COVID-19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in two different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance. RESULTS: From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C-reactive protein (CRP), lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS), and erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID-19. In the training cohort, the area under curves (AUCs) were 0.822 (95% CI, 0.765-0.875) and the internal validation cohort was 0.762 (95% CI, 0.768-0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI, 0.627-0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and the actual situation. And DCA analysis also conferred high clinical net benefit. CONCLUSION: In this study, our predicting model based on five clinical characteristics of COVID-19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management.

Diagnosis and treatment of 471 patients with 2019 novel coronavirus disease (COVID-19).

BACKGROUND: While the 2019 novel coronavirus disease (COVID-19) outbreak has been largely kept under control in China, it remains a global pandemic, and the source, transmission route, and treatments of SARS-COV-2 are still being investigated. Here, we summarized the clinical features, diagnosis, treatment, and prognosis of COVID-19 patients based on our clinical practice. METHODS: The clinical and imaging findings, treatments, and follow-up data of 471 patients with COVID-19 who were discharged from the Wuhan Jinyintan Hospital prior to February 6, 2020, were retrospectively analyzed. RESULTS: Among these patients, there were 2 mild cases, 282 moderate cases, 181 severe cases, and 6 critical cases. There were 250 males and 221 females aged 17 to 90 years. The median age was 54 years in the severe/critical group, which was significantly older than in the mild/moderate group (P<0.05). 44.59% of them had one or more underlying diseases. The most common symptoms were fever, cough, expectoration, and dyspnea. The median body temperature in the severe/critical group was 39°C, which was significantly higher than in the mild/moderate group (P<0.05). The incidences of lymphopenia and CD4+ T lymphocytopenia were 53.5% and 41.86%, respectively. Ground-glass opacity and small patchy shadows were the most common findings on chest computed tomography (CT). Compared with the mild/moderate group, the severe/critical group showed higher proportions of severe lymphocytopenia and CD4+ T lymphocytopenia, along with more ground-glass shadows and large-scale consolidation. After anti-infection, oxygen therapy, and symptomatic support, lymphocytes and CD4+ T lymphocytes were markedly increased, all patients were discharged. The median time of nucleic acid conversion and hospital stay were 9 and 12 days, respectively, which were significantly longer in the severe/critical group than in the mild/moderate group. Of the 390 cases followed, only 19 were hospitalized again due to other diseases. All patients recovered well from COVID-19, with negative nucleic acid test results. CONCLUSIONS: Lymphocytopenia and CD4+ T lymphocytopenia were found to be associated with COVID-19 and thus may be important indicators in evaluating the severity and prognosis. Multidisciplinary management including antiviral treatment, immune regulation, and symptomatic support is effective, and yields a low recurrence rate.

Corticosteroid therapy for coronavirus disease 2019-related acute respiratory distress syndrome: a cohort study with propensity score analysis.

BACKGROUND: The impact of corticosteroid therapy on outcomes of patients with coronavirus disease 2019 (COVID-19) is highly controversial. We aimed to compare the risk of death between COVID-19-related ARDS patients with corticosteroid treatment and those without. METHODS: In this single-center retrospective observational study, patients with ARDS caused by COVID-19 between January 20, 2020, and February 24, 2020, were enrolled. The primary outcome was 60-day in-hospital death. The exposure was prescribed systemic corticosteroids or not. Time-dependent Cox regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for 60-day in-hospital mortality. RESULTS: A total of 382 patients [60.7 ± 14.1 years old (mean ± SD), 61.3% males] were analyzed. The median of sequential organ failure assessment (SOFA) score was 2.0 (IQR 2.0-3.0). Of these cases, 94 (24.6%) patients had invasive mechanical ventilation. The number of patients received systemic corticosteroids was 226 (59.2%), and 156 (40.8%) received standard treatment. The maximum dose of corticosteroids was 80.0 (IQR 40.0-80.0) mg equivalent methylprednisolone per day, and duration of corticosteroid treatment was 7.0 (4.0-12.0) days in total. In Cox regression analysis using corticosteroid treatment as a time-varying variable, corticosteroid treatment was associated with a significant reduction in risk of in-hospital death within 60 days after adjusting for age, sex, SOFA score at hospital admission, propensity score of corticosteroid treatment, comorbidities, antiviral treatment, and respiratory supports (HR 0.42; 95% CI 0.21, 0.85; p = 0.0160). Corticosteroids were not associated with delayed viral RNA clearance in our cohort. CONCLUSION: In this clinical practice setting, low-dose corticosteroid treatment was associated with reduced risk of in-hospital death within 60 days in COVID-19 patients who developed ARDS.

The prediction for development of COVID-19 in global major epidemic areas through empirical trends in China by utilizing state transition matrix model.

BACKGROUND: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. METHODS: Data from reports released by the National Health Commission of the People's Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. RESULTS: The optimistic scenario (non-Hubei model, daily increment rate of - 3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of - 2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of - 1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20 k in South Korea, 209 k in Italy, and 226 k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. CONCLUSION: The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.