COVID-19 epidemic in New York City: development of an age group-specific mathematical model to predict the outcome of various vaccination strategies.

BACKGROUND: Since December 14, 2020, New York City (NYC) has started the first batch of COVID-19 vaccines. However, the shortage of vaccines is currently an inevitable problem. Therefore, optimizing the age-specific COVID-19 vaccination is an important issue that needs to be addressed as a priority. OBJECTIVE: Combined with the reported COVID-19 data in NYC, this study aimed to construct a mathematical model with five age groups to estimate the impact of age-specific vaccination on reducing the prevalence of COVID-19. METHODS: We proposed an age-structured mathematical model and estimated the unknown parameters based on the method of Markov Chain Monte Carlo (MCMC). We also calibrated our model by using three different types of reported COVID-19 data in NYC. Moreover, we evaluated the reduced cumulative number of deaths and new infections with different vaccine allocation strategies. RESULTS: Compared with the current vaccination strategy in NYC, if we gradually increased the vaccination coverage rate for only one age groups from March 1, 2021 such that the vaccination coverage rate would reach to 40% by June 1, 2021, then as of June 1, 2021, the cumulative deaths in the 75-100 age group would be reduced the most, about 72 fewer deaths per increased 100,000 vaccinated individuals, and the cumulative new infections in the 0-17 age group would be reduced the most, about 21,591 fewer new infections per increased 100,000 vaccinated individuals. If we gradually increased the vaccination coverage rate for two age groups from March 1, 2021 such that the vaccination coverage rate would reach to 40% by June 1, 2021, then as of June 1, 2021, the cumulative deaths in the 65-100 age group would be reduced the most, about 36 fewer deaths per increased 100,000 vaccinated individuals, and the cumulative new infections in the 0-44 age group would be reduced the most, about 17,515 fewer new infections per increased 100,000 vaccinated individuals. In addition, if we had an additional 100,000 doses of vaccine for 0-17 and 75-100 age groups as of June 1, 2021, then the allocation of 80% to the 0-17 age group and 20% to the 75-100 age group would reduce the maximum numbers of new infections and deaths simultaneously in NYC. CONCLUSIONS: The COVID-19 burden including deaths and new infections would decrease with increasing vaccination coverage rate. Priority vaccination to the elderly and adolescents would minimize both deaths and new infections.

The Evolution and Disparities of Online Attitudes Toward COVID-19 Vaccines: Year-long Longitudinal and Cross-sectional Study.

BACKGROUND: Due to the urgency caused by the COVID-19 pandemic worldwide, vaccine manufacturers have to shorten and parallel the development steps to accelerate COVID-19 vaccine production. Although all usual safety and efficacy monitoring mechanisms remain in place, varied attitudes toward the new vaccines have arisen among different population groups. OBJECTIVE: This study aimed to discern the evolution and disparities of attitudes toward COVID-19 vaccines among various population groups through the study of large-scale tweets spanning over a whole year. METHODS: We collected over 1.4 billion tweets from June 2020 to July 2021, which cover some critical phases concerning the development and inoculation of COVID-19 vaccines worldwide. We first developed a data mining model that incorporates a series of deep learning algorithms for inferring a range of individual characteristics, both in reality and in cyberspace, as well as sentiments and emotions expressed in tweets. We further conducted an observational study, including an overall analysis, a longitudinal study, and a cross-sectional study, to collectively explore the attitudes of major population groups. RESULTS: Our study derived 3 main findings. First, the whole population's attentiveness toward vaccines was strongly correlated (Pearson r=0.9512) with official COVID-19 statistics, including confirmed cases and deaths. Such attentiveness was also noticeably influenced by major vaccine-related events. Second, after the beginning of large-scale vaccine inoculation, the sentiments of all population groups stabilized, followed by a considerably pessimistic trend after June 2021. Third, attitude disparities toward vaccines existed among population groups defined by 8 different demographic characteristics. By crossing the 2 dimensions of attitude, we found that among population groups carrying low sentiments, some had high attentiveness ratios, such as males and individuals aged ≥40 years, while some had low attentiveness ratios, such as individuals aged ≤18 years, those with occupations of the 3rd category, those with account age <5 years, and those with follower number <500. These findings can be used as a guide in deciding who should be given more attention and what kinds of help to give to alleviate the concerns about vaccines. CONCLUSIONS: This study tracked the year-long evolution of attitudes toward COVID-19 vaccines among various population groups defined by 8 demographic characteristics, through which significant disparities in attitudes along multiple dimensions were revealed. According to these findings, it is suggested that governments and public health organizations should provide targeted interventions to address different concerns, especially among males, older people, and other individuals with low levels of education, low awareness of news, low income, and light use of social media. Moreover, public health authorities may consider cooperating with Twitter users having high levels of social influence to promote the acceptance of COVID-19 vaccines among all population groups.

The dynamic changes of cellular immunity among frontline medical workers who supported Wuhan for fighting against the COVID-19.

The outbreak of novel coronavirus disease 2019 (COVID-19) poses a great stress to frontline medical workers. Our previous study indicated that immune cells in the peripheral blood of frontline medical workers changed significantly. However, the dynamic changes of immune cells of frontline medical workers remain unclear. Here, we reported the dynamic changes of lymphocyte subsets in the peripheral blood of 51 frontline medical worker. The frontline medical workers struggling with COVID-19 from February 8 to March 31, 2020. Demographic and clinical data, including routine blood test data were extracted from the electronic health examination record and retrospectively analyzed. The lymphocyte (LYM) count and LYM ratio increased while the monocyte (MONO) ratio, neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR) and neutrophil (NEUT) ratio in the peripheral blood of frontline medical workers decreased 10 days after struggling with COVID-19. Interestingly, the differences of LYM count, LYM ratio, MONO ratio, NLR, NEUT ratio were more significantly in nurse than doctor. The differences of LYM ratio, NLR and NEUT ratio were more significantly in female than male. However, the changes of LYM count, LYM ratio, MONO ratio, NLR, MLR, NEUT ratio returned to the baseline 10 months after struggling with COVID-19. Together, these data indicated that immune cells in the peripheral blood changed significantly 10 days after struggling with COVID-19, but returned to normal after 10 months. Those maybe caused by psychological stress and we recommend to pay more attention to mental health and immune response of frontline medical workers.

How to Reduce the Transmission Risk of COVID-19 More Effectively in New York City: An Age-Structured Model Study.

Background: In face of the continuing worldwide COVID-19 epidemic, how to reduce the transmission risk of COVID-19 more effectively is still a major public health challenge that needs to be addressed urgently. Objective: This study aimed to develop an age-structured compartment model to evaluate the impact of all diagnosed and all hospitalized on the epidemic trend of COVID-19, and explore innovative and effective releasing strategies for different age groups to prevent the second wave of COVID-19. Methods: Based on three types of COVID-19 data in New York City (NYC), we calibrated the model and estimated the unknown parameters using the Markov Chain Monte Carlo (MCMC) method. Results: Compared with the current practice in NYC, we estimated that if all infected people were diagnosed from March 26, April 5 to April 15, 2020, respectively, then the number of new infections on April 22 was reduced by 98.02, 93.88, and 74.08%. If all confirmed cases were hospitalized from March 26, April 5, and April 15, 2020, respectively, then as of June 7, 2020, the total number of deaths in NYC was reduced by 67.24, 63.43, and 51.79%. When only the 0-17 age group in NYC was released from June 8, if the contact rate in this age group remained below 61% of the pre-pandemic level, then a second wave of COVID-19 could be prevented in NYC. When both the 0-17 and 18-44 age groups in NYC were released from June 8, if the contact rates in these two age groups maintained below 36% of the pre-pandemic level, then a second wave of COVID-19 could be prevented in NYC. Conclusions: If all infected people were diagnosed in time, the daily number of new infections could be significantly reduced in NYC. If all confirmed cases were hospitalized in time, the total number of deaths could be significantly reduced in NYC. Keeping a social distance and relaxing lockdown restrictions for people between the ages of 0 and 44 could not lead to a second wave of COVID-19 in NYC.

Understanding Concerns, Sentiments, and Disparities Among Population Groups During the COVID-19 Pandemic Via Twitter Data Mining: Large-scale Cross-sectional Study.

BACKGROUND: Since the beginning of the COVID-19 pandemic in late 2019, its far-reaching impacts have been witnessed globally across all aspects of human life, such as health, economy, politics, and education. Such widely penetrating impacts cast significant and profound burdens on all population groups, incurring varied concerns and sentiments among them. OBJECTIVE: This study aims to identify the concerns, sentiments, and disparities of various population groups during the COVID-19 pandemic through a cross-sectional study conducted via large-scale Twitter data mining infoveillance. METHODS: This study consisted of three steps: first, tweets posted during the pandemic were collected and preprocessed on a large scale; second, the key population attributes, concerns, sentiments, and emotions were extracted via a collection of natural language processing procedures; third, multiple analyses were conducted to reveal concerns, sentiments, and disparities among population groups during the pandemic. Overall, this study implemented a quick, effective, and economical approach for analyzing population-level disparities during a public health event. The source code developed in this study was released for free public use at GitHub. RESULTS: A total of 1,015,655 original English tweets posted from August 7 to 12, 2020, were acquired and analyzed to obtain the following results. Organizations were significantly more concerned about COVID-19 (odds ratio [OR] 3.48, 95% CI 3.39-3.58) and expressed more fear and depression emotions than individuals. Females were less concerned about COVID-19 (OR 0.73, 95% CI 0.71-0.75) and expressed less fear and depression emotions than males. Among all age groups (ie, ≤18, 19-29, 30-39, and ≥40 years of age), the attention ORs of COVID-19 fear and depression increased significantly with age. It is worth noting that not all females paid less attention to COVID-19 than males. In the age group of 40 years or older, females were more concerned than males, especially regarding the economic and education topics. In addition, males 40 years or older and 18 years or younger were the least positive. Lastly, in all sentiment analyses, the sentiment polarities regarding political topics were always the lowest among the five topics of concern across all population groups. CONCLUSIONS: Through large-scale Twitter data mining, this study revealed that meaningful differences regarding concerns and sentiments about COVID-19-related topics existed among population groups during the study period. Therefore, specialized and varied attention and support are needed for different population groups. In addition, the efficient analysis method implemented by our publicly released code can be utilized to dynamically track the evolution of each population group during the pandemic or any other major event for better informed public health research and interventions.

Immune response of frontline medical workers providing medical support for Wuhan COVID-19 patients, China.

The outbreak of novel coronavirus disease 2019 (COVID-19) posed a great challenge and stress to frontline medical workers in China. Stress is closely related to immunity. However, the immune response of frontline medical workers providing medical support for COVID-19 patients is unclear. Here, we reported the immune response of 76 frontline medical workers and 152 controls from the Second Affiliated Hospital of Xi'an Jiaotong University. The frontline medical workers were involved in the care for Wuhan COVID-19 patients from February 8 to March 31, 2020 in Tongji Hospital of Huazhong University of Science and Technology. The controls were medical workers of our hospital who had not been in contact with COVID-19 patients during the same period. Demographic and clinical data, including routine blood test data were extracted from the electronic health examination record and retrospectively analyzed. The post-stress frontline medical workers had higher lymphocyte (LYM) count compared with controls or pre-stress. However, the post-stress frontline medical workers had lower monocyte (MONO) count, neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR) and neutrophil (NEUT) ratio than controls or pre-stress. Interestingly, we found the differences were more significantly in female subgroup and nurse subgroup. Together, these data indicated that changes of immune response were found in frontline medical workers providing medical support for Wuhan COVID-19 patients, especially in females and nurses. Those maybe caused by psychological stress and we recommend to pay more attention to mental health of frontline medical workers, and provide appropriate psychological interventions for them.

Transmission patterns of COVID-19 in the mainland of China and the efficacy of different control strategies: a data- and model-driven study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) outbreak has seriously endangered the health and lives of Chinese people. In this study, we predicted the COVID-19 epidemic trend and estimated the efficacy of several intervention strategies in the mainland of China. METHODS: According to the COVID-19 epidemic status, we constructed a compartmental model. Based on reported data from the National Health Commission of People's Republic of China during January 10-February 17, 2020, we estimated the model parameters. We then predicted the epidemic trend and transmission risk of COVID-19. Using a sensitivity analysis method, we estimated the efficacy of several intervention strategies. RESULTS: The cumulative number of confirmed cases in the mainland of China will be 86 763 (95% CI: 86 067-87 460) on May 2, 2020. Up until March 15, 2020, the case fatality rate increased to 6.42% (95% CI: 6.16-6.68%). On February 23, 2020, the existing confirmed cases reached its peak, with 60 890 cases (95% CI: 60 350-61 431). On January 23, 2020, the effective reproduction number was 2.620 (95% CI: 2.567-2.676) and had dropped below 1.0 since February 5, 2020. Due to governmental intervention, the total number of confirmed cases was reduced by 99.85% on May 2, 2020. Had the isolation been relaxed from February 24, 2020, there might have been a second peak of infection. However, relaxing the isolation after March 16, 2020 greatly reduced the number of existing confirmed cases and deaths. The total number of confirmed cases and deaths would increase by 8.72 and 9.44%, respectively, due to a 1-day delayed diagnosis in non-isolated infected patients. Moreover, if the coverage of close contact tracing was increased to 100%, the cumulative number of confirmed cases would be decreased by 88.26% on May 2, 2020. CONCLUSIONS: The quarantine measures adopted by the Chinese government since January 23, 2020 were necessary and effective. Postponing the relaxation of isolation, early diagnosis, patient isolation, broad close-contact tracing, and strict monitoring of infected persons could effectively control the COVID-19 epidemic. April 1, 2020 would be a reasonable date to lift quarantine in Hubei and Wuhan.

Clinical course and treatment efficacy of COVID-19 near Hubei Province, China: A multicentre, retrospective study.

Currently, COVID-19 has been reported in nearly all countries globally. To date, little is known about the viral shedding duration, clinical course and treatment efficacy of COVID-19 near Hubei Province, China. This multicentre, retrospective study was performed in 12 hospitals in Henan and Shaanxi Provinces from 20 January to 8 February 2020. Clinical outcomes were followed up until 26 March 2020. The viral shedding duration, full clinical course and treatment efficacy were analysed in different subgroups of patients. A total of 149 COVID-19 patients were enrolled. The median age was 42 years, and 61.1% (91) were males. Of them, 133 (89.3%) had fever, 131 of 144 (91%) had pneumonia, 27 (18.1%) required intensive care unit (ICU) management, 3 (2%) were pregnant, and 3 (2%) died. Two premature newborns were negative for SARS-CoV-2. In total, the median SARS-CoV-2 shedding period and clinical course were 12 (IQR: 9-17; mean: 13.4, 95% CI: 12.5, 14.2) and 20 (IQR: 16-24; mean: 21.2, 95% CI: 20.1, 22.3) days, respectively, and ICU patients had longer median viral shedding periods (21 [17-24] versus 11 [9-15]) and clinical courses (30 [22-33] vs. 19 [15.8-22]) than non-ICU patients (both p < .0001). SARS-CoV-2 clearances occurred at least 2 days before fatality in 3 non-survivors. Current treatment with any anti-viral agent or combination did not present the benefit of shortening viral shedding period and clinical course (all p > .05) in real-life settings. In conclusion, the viral shedding duration and clinical course in Henan and Shaanxi Provinces were shorter than those in Hubei Province, and current anti-viral therapies were ineffective for shortening viral shedding duration and clinical course in real-world settings. These findings expand our knowledge of the SARS-CoV-2 infection and may be helpful for management of the epidemic outbreak of COVID-19 worldwide. Further studies concerning effective anti-viral agents and vaccines are urgently needed.