Economic recovery forecasts under impacts of COVID-19.

This paper proposes a joint model by combining the time-varying coefficient susceptible-infected-removal model with the hierarchical Bayesian vector autoregression model. This model establishes the relationship between several critical macroeconomic variables and pandemic transmission states and performs economic predictions under two predefined pandemic scenarios. The empirical part of the model predicts the economic recovery of several countries severely affected by COVID-19 (e.g., the United States and India, among others). Under the proposed pandemic scenarios, economies tend to recover rather than fall into prolonged recessions. The economy recovers faster in the scenario where the COVID-19 pandemic is controlled.

Predicting COVID-19 epidemiological trend by applying population mobility data in two-stage modeling.

:To predict the epidemiological trend of coronavirus disease 2019 (COVID-19) by mathematical modeling based on the population mobility and the epidemic prevention and control measures. : As of February 8，2020，the information of 151 confirmed cases in Yueqing，Zhejiang province were obtained，including patients' infection process，population mobility between Yueqing and Wuhan，etc. To simulate and predict the development trend of COVID-19 in Yueqing, the study established two-stage mathematical models，integrating the population mobility data with the date of symptom appearance of confirmed cases and the transmission dynamics of imported and local cases. : It was found that in the early stage of the pandemic，the number of daily imported cases from Wuhan (using the date of symptom appearance) was positively associated with the number of population travelling from Wuhan to Yueqing on the same day and 6 and 9 days before that. The study predicted that the final outbreak size in Yueqing would be 170 according to the number of imported cases estimated by consulting the population number travelling from Wuhan to Yueqing and the susceptible-exposed-infectious-recovered (SEIR) model; while the number would be 165 if using the reported daily number of imported cases. These estimates were close to the 170，the actual monitoring number of cases in Yueqing as of April 27，2020. : The two-stage modeling approach used in this study can accurately predict COVID-19 epidemiological trend.

Impact of socioeconomic status，population mobility and control measures on COVID-10 development in major cities of China.

:To evaluate the impact of socioeconomic status，population mobility，prevention and control measures on the early-stage coronavirus disease 2019 (COVID-19) development in major cities of China. : The rate of daily new confirmed COVID-19 cases in the 51 cities with the largest number of cumulative confirmed cases as of February 19，2020 (except those in Hubei province) were collected and analyzed using the time series cluster analysis. It was then assessed according to three aspects，that is, socioeconomic status，population mobility，and control measures for the pandemic. : According to the analysis on the 51 cities，4 development patterns of COVID-19 were obtained，including a high-incidence pattern (in Xinyu)，a late high-incidence pattern (in Ganzi)，a moderate incidence pattern (in Wenzhou and other 12 cities)，and a low and stable incidence pattern (in Hangzhou and other 35 cities). Cities with different types and within the same type both had different scores on the three aspects. : There were relatively large difference on the COVID-19 development among different cities in China，possibly affected by socioeconomic status，population mobility and prevention and control measures that were taken. Therefore，a timely public health emergency response and travel restriction measures inside the city can interfere the development of the pandemic. Population flow from high risk area can largely affect the number of cumulative confirmed cases.

Impact of Wuhan lockdown on the spread of COVID-19 in China: a study based on the data of population mobility.

This study aimed to quantitatively assess the effectiveness of the Wuhan lockdown measure on controlling the spread of coronavirus diesase 2019 (COVID-19). : Firstly，estimate the daily new infection rate in Wuhan before January 23，2020 when the city went into lockdown by consulting the data of Wuhan population mobility and the number of cases imported from Wuhan in 217 cities of Mainland China. Then estimate what the daily new infection rate would have been in Wuhan from January 24 to January 30th if the lockdown measure had been delayed for 7 days，assuming that the daily new infection in Wuhan after January 23 increased in a high，moderate and low trend respectively (using exponential, linear and logarithm growth models). Based on that，calculate the number of infection cases imported from Wuhan during this period. Finally，predict the possible impact of 7-day delayed lockdown in Wuhan on the epidemic situation in China using the susceptible-exposed-infectious-removed (SEIR) model. : The daily new infection rate in Wuhan was estimated to be 0.021%，0.026%，0.029%，0.033% and 0.070% respectively from January 19 to January 23. And there were at least 20 066 infection cases in Wuhan by January 23，2020. If Wuhan lockdown measure had been delayed for 7 days，the daily new infection rate on January 30 would have been 0.335% in the exponential growth model，0.129% in the linear growth model，and 0.070% in the logarithm growth model. Correspondingly，there would have been 32 075，24 819 and 20 334 infection cases travelling from Wuhan to other areas of Mainland China，and the number of cumulative confirmed cases as of March 19 in Mainland China would have been 3.3-3.9 times of the officially reported number. Conclusions: Timely taking city-level lockdown measure in Wuhan in the early stage of COVID-19 outbreak is essential in containing the spread of the disease in China.

The value of lymphocyte count in determining the severity of COVID-19 and estimating the time for nucleic acid test results to turn negative.

Peripheral blood lymphocyte count is shown to be decreased in patients with COVID-19 in the early stage of the disease. The degree of lymphocyte count reduction is related to COVID-19 severity and could be used as an indicator to reflect the disease severity. Our aim was to investigate the value of lymphocyte count in determining COVID-19 severity and estimating the time for SARS-CoV-2 nucleic acid test results to turn negative. We retrospectively analyzed clinical data of 201 patients with severe and critical COVID-19. The patients were admitted to the West Campus of Union Hospital of Tongji Medical College of Huazhong University of Science and Technology. The data included age, gender, chronic disease, lymphocyte count, and SARS-CoV-2 nucleic acid test results. The age of patients in critically ill group was higher than in severely ill group (p = 0.019). The lymphocyte count of critically ill patients was lower than of severely ill patients. The cutoff value of lymphocyte count to distinguish between the critically ill and the severely ill was 0.735 × 109/L (p = 0.001). The cutoff value of lymphocyte count for SARS-CoV-2 nucleic acid test results turning negative in severely and critically ill patients with chronic diseases (hypertension, diabetes, and coronary heart disease) was 0.835 × 109/L (p = 0.017). The cutoff value of lymphocyte count for SARS-CoV-2 nucleic acid test results turning negative in severely and critically ill male patients was 0.835 × 109/L (p < 0.0001). Lymphocyte count could be an effective indicator to predict COVID-19 severity. It may also be useful in determining the time for nucleic acid test results to turn negative in COVID-19 patients with underlying chronic diseases or male COVID-19 patients with severe and critical conditions.

Clinical characteristics of patients with COVID-19 presenting with gastrointestinal symptoms as initial symptoms: Retrospective case series.

BACKGROUND: A large number of pneumonia cases due to coronavirus disease 2019 (COVID-19) have been first reported in China. Meanwhile, the virus is sweeping all around the world and has infected millions of people. Fever and pulmonary symptoms have been noticed as major and early signs of infection, whereas gastrointestinal symptoms were also observed in a significant portion of patients. The clinical investigation of disease onset was underestimated, especially due to the neglection of cases presenting with gastrointestinal symptoms. AIM: To characterize the clinical features of coronavirus-infected patients with gastrointestinal symptoms as initial symptoms. METHODS: This is a retrospective, single-center case series of the general consecutive hospitalized patients with confirmed COVID-19 at Wuhan Union Hospital from February 2, 2020 to February 13, 2020. According to their initial symptoms, these patients were classified into two groups. Patients in group one presented with pulmonary symptoms (PS) as initial symptoms, and group two presented with gastrointestinal symptoms (GS). Epidemiological, demographic, clinical, laboratory, and treatment data were collected for analysis. RESULTS: Among the 50 patients recruited, no patient has been admitted to intensive care units, and no patient died during the study. The duration of hospitalization was longer in the GS group than in the PS group (12.13 ± 2.44 vs 10.00 ± 2.13, P < 0.01). All of the 50 patients exhibited decreased lymphocytes. However, lymphocytes in the GS group were significantly lower compared to those in the PS group (0.94 ± 0.06 vs 1.04 ± 0.15, P < 0.01). Procalcitonin and hs-CRP were both significantly higher in the GS group than in the PS group. Accordingly, the duration of viral shedding was significantly longer in the GS group compared to the PS group (10.22 ± 1.93 vs 8.15 ± 1.87, P < 0.01). CONCLUSION: COVID-19 patients presenting with gastrointestinal symptoms as initial symptoms need more days of viral shedding and hospitalization than the patients presenting with pulmonary symptoms.