Estimating the daily trend in the size of the COVID-19 infected population in Wuhan.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has become a pandemic causing global health problem. We provide estimates of the daily trend in the size of the epidemic in Wuhan based on detailed information of 10 940 confirmed cases outside Hubei province. METHODS: In this modelling study, we first estimate the epidemic size in Wuhan from 10 January to 5 April 2020 with a newly proposed model, based on the confirmed cases outside Hubei province that left Wuhan by 23 January 2020 retrieved from official websites of provincial and municipal health commissions. Since some confirmed cases have no information on whether they visited Wuhan before, we adjust for these missing values. We then calculate the reporting rate in Wuhan from 20 January to 5 April 2020. Finally, we estimate the date when the first infected case occurred in Wuhan. RESULTS: We estimate the number of cases that should be reported in Wuhan by 10 January 2020, as 3229 (95% confidence interval [CI]: 3139-3321) and 51 273 (95% CI: 49 844-52 734) by 5 April 2020. The reporting rate has grown rapidly from 1.5% (95% CI: 1.5-1.6%) on 20 January 2020, to 39.1% (95% CI: 38.0-40.2%) on 11 February 2020, and increased to 71.4% (95% CI: 69.4-73.4%) on 13 February 2020, and reaches 97.6% (95% CI: 94.8-100.3%) on 5 April 2020. The date of first infection is estimated as 30 November 2019. CONCLUSIONS: In the early stage of COVID-19 outbreak, the testing capacity of Wuhan was insufficient. Clinical diagnosis could be a good complement to the method of confirmation at that time. The reporting rate is very close to 100% now and there are very few cases since 17 March 2020, which might suggest that Wuhan is able to accommodate all patients and the epidemic has been controlled.

Identifying informative tweets during a pandemic via a topic-aware neural language model.

Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines.

Statewide evaluation of COVID-19 vaccine hesitancy in Rhode Island.

BACKGROUND: Vaccines are effective in preventing Coronavirus Disease 2019 (COVID-19). Vaccine hesitancy defined as delay of acceptance or refusal of the vaccine is a major barrier to effective implementation. METHODS: Participants were recruited statewide through an English and Spanish social media marketing campaign conducted by a local news station during a one-month period as vaccines were becoming available in Rhode Island (from December 21, 2020 to January 22, 2021). Participants completed an online survey about COVID-19 vaccines and vaccine hesitancy with constructs and items adopted from the Health Belief Model. RESULTS: A total of 2,007 individuals completed the survey. Eight percent (n = 161) reported vaccine hesitancy. The sample had a median age of 58 years (interquartile range [IQR]: 45, 67), were majority female (78%), White (96%), Non-Hispanic (94%), employed (58%), and reported an annual individual income of $50,000 (59%). COVID-19 vaccine hesitancy was associated with attitudes and behaviors related to COVID-19. A one unit increase in concern about COVID-19 was associated with a 69% (Adjusted Odds Ratio: 0.31, 95% CI: 0.26-0.37) decrease in vaccine hesitancy. A one-level increase in the likelihood of getting influenza vaccine was associated with a 55% (AOR: 0.45 95% CI: 0.41-0.50) decrease in vaccine hesitancy. CONCLUSIONS: COVID-19 vaccine hesitancy was relatively low in a state-wide survey in Rhode Island. Future research is needed to better understand and tailor messaging related to vaccine hesitancy.

The association of intensity and duration of non-pharmacological interventions and implementation of vaccination with COVID-19 infection, death, and excess mortality: Natural experiment in 22 European countries.

BACKGROUND: Critical questions remain regarding the need for intensity to continue NPIs as the public was vaccinated. We evaluated the association of intensity and duration of non-pharmaceutical interventions (NPIs) and vaccines with COVID-19 infection, death, and excess mortality in Europe. METHODS: Data comes from Our Word in Data. We included 22 European countries from January 20, 2020, to May 30, 2021. The time-varying constrained distribution lag model was used in each country to estimate the impact of different intensities and duration of NPIs on COVID-19 control, considering vaccination coverage. Country-specific effects were pooled through meta-analysis. RESULTS: This study found that high-intensity and long-duration of NPIs showed a positive main effect on reducing infection in the absence of vaccines, especially in the intensity above the 80th percentile and lasted for 7 days (RR = 0.93, 95% CI: 0.89-0.98). However, the adverse effect on excess mortality also increased with the duration and intensity. Specifically, it was associated with an increase of 44.16% (RR = 1.44, 95% CI: 1.27-1.64) in the excess mortality under the strict intervention (the intensity above the 80th percentile and lasted for 21 days). As the vaccine rollouts, the inhibition of the strict intervention on cases growth rate was increased (RR dropped from 0.95 to 0.87). Simultaneously, vaccination also alleviated the negative impact of the strict intervention on excess mortality (RR decreased from 1.44 to 1.25). Besides, maintaining the strict intervention appeared to more reduce the cases, as well as avoids more overall burden of death compared with weak intervention. CONCLUSIONS: Our study highlights the importance of continued high-intensity NPIs in low vaccine coverage. Lifting of NPIs in insufficient vaccination coverage may cause increased infections and death burden. Policymakers should coordinate the intensity and duration of NPIs and allocate medical resources reasonably with widespread vaccination.

Identifying informative tweets during a pandemic via a topic-aware neural language model

Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines.

Shumian capsule improves symptoms of sleep mood disorder in convalescent patients of Corona Virus Disease 2019.

OBJECTIVE: To evaluate the clinical effectiveness of Shumian capsule in improving the symptoms of insomnia, anxiety, depression, and other symptoms of convalescent patients of COVID-19. METHODS: Totally 200 patients were collected and randomly divided into experiment group (n = 100) and control group (n = 100). The control group was treated with Shumian capsule simulator, and the experiment group was treated with Shumian capsule. The improvement of TCM symptom score, the total effective rate and symptom disappearance rate of TCM symptoms in the two groups before and after treatment were observed, and the clinical effect was evaluated. RESULTS: One week after treatment, the scores of anxiety symptoms in the experiment group were significantly different from those in the control group (P < 0.05), but there was no significant difference in the scores of insomnia and depression between the experiment group and the control group (P > 0.05). There was no significant difference in the total effective rate and disappearance rate of TCM symptoms of insomnia, anxiety and depression between the experiment group and the control group (P > 0.05). After 2 weeks of treatment, the scores of insomnia, anxiety, depression and the total effective rate of TCM symptoms in the experiment group were significantly different from those in the control group (P < 0.05). There was no significant difference in the disappearance rate of insomnia, anxiety and depression between the experiment group and the control group (P > 0.05). There were no significant differences in heart rate, respiration, systolic blood pressure and diastolic blood pressure between the experiment group and the control group (P > 0.05). CONCLUSION: Shumian capsule can significantly improve the symptoms of insomnia, anxiety and depression in COVID-19's convalescent patients with sleep and mood disorders.

Impact of the COVID-19 pandemic on HIV testing rates across four geographically diverse urban centres in the United States: An observational study.

BACKGROUND: Non-emergent clinical services were limited or suspended during the early stages of the coronavirus disease 2019 (COVID-19) pandemic in the United States (U.S.). This could adversely impact epidemics of public health importance, such as HIV, and access to testing, which is a cornerstone of prevention efforts. METHODS: In this observational study, we collected HIV testing and positivity rate clinical data from four geographically diverse U.S. healthcare systems in New Orleans, Louisiana; Minneapolis, Minnesota; Providence, Rhode Island; and, Seattle, Washington. Data from 2019 to 2020 were examined to assess changes in HIV testing in community-based, emergency department, and outpatient settings. Poisson regression was used to explore trends in HIV testing through phases of the COVID-19 pandemic. FINDINGS: In outpatient settings, there was a 68-97% reduction in the number of HIV tests per week during each state's stay-at-home order period, compared to during the pre-stay-at-home order period in early 2020. HIV testing remained reduced 11-54% after states transitioned to advisory phases. The HIV positivity rate increased slightly at outpatient settings, except in New Orleans where it fell. INTERPRETATION: We found a concerning trend of substantially decreased HIV testing across four geographically diverse sites. These findings suggest that new HIV infections within the U.S. may be undiagnosed and not yet linked to clinical care and services, as a consequence of the COVID-19 pandemic. Thus, augmented efforts to identify patients and link them to HIV services will be needed as healthcare settings return to full operation. FUNDING: U.S. National Institute of Mental Health.

Evaluation of the Treatment Efficacy and Safety of Remdesivir for COVID-19: a Meta-analysis.

Remdesivir is one of few FDA-approved treatments for severe cases of Coronavirus Disease 2019 (COVID-19). To better assess its efficacy and safety, we conducted a meta-analysis to systematically identify and synthesize existing findings. We conducted a comprehensive literature search among six electronic databases and unpublished studies. Random-effects meta-analyses were performed to summarize the risk ratio (RR) and rate estimates from eligible studies. Funnel plots, the Egger test, and the trim and fill analysis were used to detect publication bias. Thirteen eligible studies were included in this meta-analysis, giving a pooled sample size of 10,002 COVID-19 hospitalized patients (5068 administered remdesivir; 4934 control). Among patients on remdesivir, we synthesized mortality (15%, 95% confidence interval [CI]: 9%, 22%), clinical improvement (64%, 95% CI: 51%, 78%), recovery (70%, 95% CI: 57%, 83%), hospital discharge (74%, 95% CI: 60%, 87%), serious adverse effect (SAE) (21%, 95% CI:13%, 29%), and Grade 3 or 4 adverse effect (AE) (30%, 95% CI: 12%, 48%). Patients on remdesivir were 17% (RR: 0.83, 95% CI: 0.65, 1.06) less likely to die than those within the control group. Additionally, remdesivir had favorable outcomes in terms of clinical improvement, recovery, and hospital discharge. Lastly, non-mechanically ventilated patients had better overall clinical outcomes than mechanically ventilated patients. Remdesivir shows a moderate-favorable treatment efficacy among hospitalized COVID-19 patients with disproportionate impact among non-mechanically ventilated patients; however, a substantial proportion of COVID-19 patients may suffer from SAE or Grade 3 or 4 AE during the treatment course. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42399-021-01014-y.

A Community-Adapted Approach to SARS-CoV-2 Testing for Medically Underserved Populations, Rhode Island, USA.

We developed a testing program for severe acute respiratory syndrome coronavirus 2 in an urban Latinx neighborhood in Providence, Rhode Island, USA. Approximately 11% of Latinx participants (n = 180) tested positive. Culturally tailored, community-based programs that reduce barriers to testing help identify persons at highest risk for coronavirus disease.

Characterizing the Impact of COVID-19 on Pre-Exposure Prophylaxis (PrEP) Care.

COVID-19 is a public health crisis that has fundamentally altered health care provision. The purpose of this study was to examine the impact of COVID-19 on pre-exposure prophylaxis (PrEP) care. We reviewed all patient records for those who presented for PrEP care at a PrEP program in Providence, Rhode Island from September 1st, 2019 to May 29th, 2020. The number of PrEP encounters decreased but was not significantly different over time (ps > .05). Patients were still able to access PrEP clinical services during the COVID-19 pandemic. Implementing flexible and timely PrEP delivery approaches in this setting likely minimized the disruption of PrEP care during COVID-19.

RESUMEN: COVID-19 es una crisis de salud pública que ha alterado fundamentalmente la prestación de servicios de salud. El propósito de este estudio fue examinar el impacto de COVID-19 en los servicios de la profilaxis preexposición (PrEP). Revisamos todos los registros de pacientes que se presentaron para recibir atención de PrEP en un programa de PrEP en Providence, Rhode Island desde el 1 de septiembre de 2019 hasta el 29 de mayo de 2020. El número de encuentros de PrEP disminuyó pero no fue significativamente diferente con el paso del tiempo (ps > .05). Los pacientes aún pudieron acceder a los servicios clínicos de PrEP durante la pandemia de COVID-19. La implementación de enfoques de entrega de PrEP que eran flexibles y oportunos en este entorno probablemente minimizó la interrupción de la atención médica de la PrEP durante COVID-19.

The Impact of COVID-19 on Sexual Behavior and Psychosocial Functioning in a Clinical Sample of Men who have Sex with Men Using HIV Pre-exposure Prophylaxis.

The purpose of this study was to evaluate the impact of the Coronavirus Disease 2019 (COVID-19) pandemic on sexual behavior, mental health, and substance use among men who have sex with men (MSM) engaged in pre-exposure prophylaxis (PrEP) care. Generalized linear mixed models and logistic mixed-effect models examined change over time for number of sexual partners, mood, and alcohol consumption. From February 29, 2020 to July 31, 2020, 177 MSM actively engaged in PrEP care were evaluated. The median age was 37 [interquartile range (IQR 30, 51]. Patients in the sample were largely representative of the clinic population and identified as White (73.0%), Black/African American (9.2%), and other race (17.2%), and 11.8% identified as Hispanic/Latino ethnicity. Men reported an average of 2.60 fewer sexual partners (95% CI -4.04, -1.40) during the pandemic compared to pre-COVID-19. Rates of depressive symptoms and alcohol use remained stable and few patients reported substance use. The reduced number of sexual partners may be explained by patients' efforts to reduce risk of exposure to COVID-19 and low rates of psychosocial symptoms may be indicative of only the highest functioning patients continuing to engage in care. Reductions in sexual partners may offset reduced engagement in care and help mitigate risk of HIV and other sexually transmitted infections (STIs).

RESUMEN: El propósito de este estudio fue evaluar el impacto que tuvo la pandemia causada por la enfermedad del coronavirus 2019 (COVID-19) en el comportamiento sexual, la salud mental y el uso de sustancias en hombres que tienen sexo con hombres (HSH) que reciben profilaxis previa a la exposición (PrEP). Los modelos lineales mixtos generalizados y los modelos logísticos de efectos mixtos examinaron el cambio a través del tiempo en el número de parejas sexuales, el estado de ánimo y el consumo de alcohol. Desde el 29 de febrero de 2020 hasta el 31 de julio de 2020, se evaluaron 177 HSH que recibían los servicios de la PrEP. La mediana de edad fue de 37 (rango intercuartílico (IQR: 30, 51). Los pacientes de la muestra eran en gran parte representativos de la población de la clínica y se identificaban como Blancos (73.0%), Negros/Afroamericanos (9.2%) y de Otra raza (17.2%) y el 11.8% se identificó de origen Hispano/Latino. Los hombres reportaron un promedio de 2.60 parejas sexuales menos (IC del 95%: -4.04, -1.40) durante la pandemia en comparación con antes de COVID-19. Las tasas de síntomas depresivos y el uso de alcohol se mantuvieron estables y pocos pacientes reportaron uso de sustancias. El número reducido de parejas sexuales puede explicarse por los esfuerzos de los pacientes para reducir su riesgo de exposición al COVID-19 y las bajas tasas de síntomas psicosociales pueden ser indicativos de que solo los pacientes con mayor funcionamiento continúan participando. La reducción en el número de parejas sexuales puede contrarrestar la baja participación en la atención medica de la PrEP y ayudar a mitigar el riesgo de contraer el VIH y otras infecciones de transmisión sexual (ITS).

High Infection Fatality Rate Among Elderly and Risk Factors Associated With Infection Fatality Rate and Asymptomatic Infections of COVID-19 Cases in Hong Kong.

Background: Since the emergence in December 2019, the COVID-19 pandemic has become one of the greatest global public health threats in history. However, asymptomatic infections have increased the challenges of providing accurate estimates for the infection fatality rate (IFR) of COVID-19. Methods: We calculated the asymptomatic case ratios based on the reported COVID-19 cases in Hong Kong where intensive testing has been conducted in close contacts and high-risk populations. We estimated the IFR using both symptomatic and asymptomatic cases as denominator. The boosted regression tree (BRT) and multivariable logistic regression models were used to identify relative contribution and effect size of the risk factors associated with the asymptomatic cases and IFRs. Results: The ratio of the asymptomatic patients in Hong Kong was higher than many other regions over the world. Imported cases had a higher asymptomatic proportion than local cases. Older age and male were associated with a higher IFR than younger age and females. Conclusion: Policymakers should consider the potential risk factors for the asymptomatic infections and IFRs by the Hong Kong surveillance data to mitigate the diseases and reduce the case mortality of COVID-19.

Reduction of air pollutants and associated mortality during and after the COVID-19 lockdown in China: Impacts and implications.

Although strict lockdown measurements implemented during the COVID-19 pandemic have dramatically reduced the anthropogenic-based emissions, changes in air quality and its health impacts remain unclear in China. We comprehensively described air pollution during and after the lockdown periods in 2020 compared with 2018-2019, and estimated the mortality burden indicated by the number of deaths and years of life lost (YLL) related to the air pollution changes. The mean air quality index (AQI), PM10, PM2.5, NO2, SO2 and CO concentrations during the lockdown across China declined by 18.2 (21.2%), 27.0 µg/m3 (28.9%), 10.5 µg/m3 (18.3%), 8.4 µg/m3 (44.2%), 13.1 µg/m3 (38.8%), and 0.3 mg/m3 (27.3%) respectively, when compared to the same periods during 2018-2019. We observed an increase in O3 concentration during the lockdown by 5.5 µg/m3 (10.4%), and a slight decrease after the lockdown by 3.4 µg/m3 (4.4%). As a result, there were 51.3 (95%CI: 32.2, 70.1) thousand fewer premature deaths (16.2 thousand during and 35.1 thousand after the lockdown), and 1066.8 (95%CI: 668.7, 1456.8) thousand fewer YLLs (343.3 thousand during and 723.5 thousand after the lockdown) than these in 2018-2019. Our findings suggest that the COVID-19 lockdown has caused substantial decreases in air pollutants except for O3, and that substantial human health benefits can be achieved when strict control measures for air pollution are taken to reduce emissions from vehicles and industries. Stricter tailored policy solutions of air pollution are urgently needed in China and other countries, especially in well-developed industrial regions, such as upgrading industry structure and promoting green transportation.

Modular composite building in urgent emergency engineering projects: A case study of accelerated design and construction of Wuhan Thunder God Mountain/Leishenshan hospital to COVID-19 pandemic.

Wuhan Leishenshan/Leishenshan ("Leishenshan" for short) hospital is a makeshift emergency hospital for treating patients diagnosed with the novel coronavirus-infected pneumonia (NCIP). Engineering construction uses modular composite building finished products to the greatest extent, which reduces the workload of field operations and saves a lot of time. The building information model (BIM) technology assists in design and construction work to meet rapid construction requirements. Besides, based on the unmanned aerial vehicles (UAVs) data analysis and application platform, digitization and intelligence in engineering construction are improved. Simultaneously, on-site construction and overall hoisting were carried out to achieve maximum efficiency. This article aims to take the construction of Leishenshan Hospital as an example to illustrate how to adopt BIM technology and other high-tech technology such as big data, artificial intelligence, drones, and 5G for the fast construction of the fabricated steel structure systems in emergency engineering projects.

Impact of the COVID-19 Pandemic on Sexually Transmitted Infection Clinic Visits.

Coronavirus disease 2019 is responsible for a global pandemic and has impacted health care accessibility and delivery. Clinic data were reviewed for an STI clinic from September 2019 to May 2020. A significant decrease in rates of STI visits and treatments during the coronavirus disease 2019 pandemic was observed.

Coronavirus Disease 2019 Epidemic Doubling Time in the United States Before and During Stay-at-Home Restrictions.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread rapidly in the United States since January 2020. METHODS: We estimated mean epidemic doubling time, an important measure of epidemic growth, nationally, by state, and in association with stay-at-home orders. RESULTS: The epidemic doubling time in the United States was 2.68 days (95% confidence interval [CI], 2.30-3.24 days) before widespread mitigation efforts, increasing by 460% to 15 days (12.89-17.94 days) during the mitigation phase. Among states without stay-at-home orders, the median increase in doubling time was 60% (95% CI, 9.2-223.3), compared with 269% (95% CI, 277.0-394.0) for states with stay-at-home orders. CONCLUSIONS: Statewide mitigation strategies were strongly associated with increased epidemic doubling time.

Influenza versus COVID-19 cases among influenza-like illness patients in travelers from Wuhan to Hong Kong in January 2020.

OBJECTIVE: The COVID-19 outbreak in Wuhan, Hubei, China, followed the seasonal influenza epidemic. Since some COVID-19 cases may have been misdiagnosed as seasonal influenza in January 2020, before testing capacity was adequate, it is relevant to study the proportions of influenza and COVID-19 cases among influenza-like-illness (ILI) patients and their temporal pattern. RESULTS: This study analyzed the record of the ILI patients with a recent travel history to Wuhan who arrived in Hong Kong between 31 December 2019 and 21 January 2020. We found that the proportion of COVID-19 cases among the total ILI patients is much smaller than a study among ILI in Wuhan. This difference in proportion could be due to the difference in sampling. We argue that it is essential to consider both samples when inferring the number of COVID-19 cases from ILI patients.

Further explanations for the eq. (3) in "Estimating the daily trend in the size of the COVID-19 infected population in Wuhan".

To avoid possible confusions to the readers, we provide further explanations for the eq. (3) in the research article "Estimating the daily trend in the size of the COVID-19 infected population in Wuhan" published in the Infectious Diseases of Poverty.