Real-time assessment of COVID-19 epidemic in Guangdong Province, China using mathematical models.

China government has relaxed the response measures of COVID-19 in early December 2022. In this report, we assessed the number of infections, the number of severe cases based on the current epidemic trend (October 22, 2022 to November 30, 2022) using a transmission dynamics model, called modified susceptible-exposed-infectious-removed (SEIR) to provide valuable information to ensure the medical operation of the healthcare system under the new situation. Our model showed that the present outbreak in Guangdong Province peaked during December 21, 2022 to December 25, 2022 with about 14.98 million new infections (95% CI: 14.23-15.73 million). The cumulative number of infections will reach about 70% of the province's population from December 24, 2022 to December 26, 2022. The number of existing severe cases is expected to peak during January 1, 2023 to January 5, 2023 with a peak number of approximately 101.45 thousand (95% CI: 96.38-106.52 thousand). In addition, the epidemic in Guangzhou which is the capital city of Guangdong Province is expected to have peaked around December 22, 2022 to December 23, 2022 with the number of new infections at the peak being about 2.45 million (95% CI: 2.33-2.57 million). The cumulative number of infected people will reach about 70% of the city's population from December 24, 2022 to December 25, 2022 and the number of existing severe cases is expected to peak around January 4, 2023 to January 6, 2023 with the number of existing severe cases at the peak being about 6.32 thousand (95% CI: 6.00-6.64 thousand). Predicted results enable the government to prepare medically and plan for potential risks in advance.

Clinical Application of Antibody Immunity Against SARS-CoV-2: Comprehensive Review on Immunoassay and Immunotherapy.

The current COVID-19 global pandemic poses immense challenges to global health, largely due to the difficulty to detect infection in the early stages of the disease, as well as the current lack of effective antiviral therapy. Research and understanding of the human immune system can provide important theoretical and technical support for the clinical diagnosis and treatment of COVID-19, the clinical implementations of which include immunoassays and immunotherapy, which play a crucial role in the fight against the pandemic. This review consolidates the current scientific evidence for immunoassay, which includes multiple methods of detecting antigen and antibody against SARS-CoV-2. We compared the characteristics, advantages and disadvantages, and clinical applications of these three detection techniques. In addition to detecting viral infections, knowledge on the body's immunity against the virus is desirable; thus, the immunotherapy-based neutralizing antibody (nAb) detection methods were discussed. We also gave a brief introduction to the new immunoassay technology such as biosensing. This was followed by an in-depth and extensive review on a variety of immunotherapy methods. It includes convalescent plasma therapy, neutralizing antibody-based treatments targeting different regions of SARS-CoV-2, immunotherapy targeted on the host cell including inhibiting the host cell receptor and cytokine storm, as well as cocktail antibodies, cross-neutralizing antibodies, and immunotherapy based on cross-reactivity between viral epitopes and autoepitopes and autoantibody. Despite the development of various immunological testing methods and antibody therapies, the current global situation of COVID-19 is still tense. We need more efficient detection methods and more reliable antibody therapies. The up-to-date knowledge on therapeutic strategies will likely help clinicians worldwide to protect patients from life-threatening viral infections.

Containment of SARS-CoV-2 Delta strain in Guangzhou, China by quarantine and social distancing: a modelling study.

China detected the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection with Delta variant in May 2021. We assessed control strategies against this variant of concern. We constructed a robust transmission model to assess the effectiveness of interventions against the Delta variant in Guangzhou with initial quarantine/isolation, followed by social distancing. We also assessed the effectiveness of alternative strategies and that against potentially more infectious variants. The effective reproduction number (Rt) fell below 1 when the average daily number of close contacts was reduced to ≤ 7 and quarantine/isolation was implemented on average at the same day of symptom onset in Guangzhou. Simulations showed that the outbreak could still be contained when quarantine is implemented on average 1 day after symptom onset while the average daily number of close contacts was reduced to ≤ 9 per person one week after the outbreak's beginning. Early quarantine and reduction of close contacts were found to be important for containment of the outbreaks. Early implementation of quarantine/isolation along with social distancing measures could effectively suppress spread of the Delta and more infectious variants.

The Short- and Long-Term Clinical, Radiological and Functional Consequences of COVID-19.

As with the rapid increase of the number of patients who have recovered from COVID-19 globally, there needs to be a major shift of the focus from rapid pathogen detection, treatment and prevention to the promotion of better recovery. Notwithstanding the scarcity of our understandings, recent studies have unraveled a plethora of pulmonary and systemic consequences which require medical attention. These consequences remained as of the end of follow-up which ranged from 1 month to 1 year. Here, we review the consequences of COVID-19 in terms of the residual symptoms, radiological and functional manifestations, and identify the potential risk factors that contribute to a prolonged recovery. We also summarize the benefits of clinical interventions (particularly the pulmonary rehabilitation program), and address several undetermined concerns and key future research directions.

Como consecuencia del rápido aumento del número de pacientes que se han recuperado de la COVID-19 en todo el mundo, es necesario cambiar el enfoque de la detección rápida del patógeno, el tratamiento y la prevención para promover una mejor recuperación. A pesar de la escasez de nuestros conocimientos, estudios recientes han desvelado una plétora de consecuencias pulmonares y sistémicas que requieren atención médica. Estas consecuencias se mantienen al final del seguimiento, que oscila entre 1 mes y 1 año. Aquí se hace una revisión de las consecuencias de la COVID-19 en términos de síntomas residuales y manifestaciones radiológicas y funcionales y se identifican los posibles factores de riesgo que contribuyen a una recuperación demorada. También se resumen los beneficios de las intervenciones clínicas (en particular el programa de rehabilitación pulmonar) y se abordan varias preocupaciones no resueltas y direcciones clave de investigación futura.

Ultrapotent neutralizing antibodies against SARS-CoV-2 with a high degree of mutation resistance.

Many SARS-CoV-2 neutralizing antibodies (nAbs) lose potency against variants of concern. In this study, we developed 2 strategies to produce mutation-resistant antibodies. First, a yeast library expressing mutant receptor binding domains (RBDs) of the spike protein was utilized to screen for potent nAbs that are least susceptible to viral escape. Among the candidate antibodies, P5-22 displayed ultrahigh potency for virus neutralization as well as an outstanding mutation resistance profile. Additionally, P14-44 and P15-16 were recognized as mutation-resistant antibodies with broad betacoronavirus neutralization properties. P15-16 has only 1 binding hotspot, which is K378 in the RBD of SARS-CoV-2. The crystal structure of the P5-22, P14-44, and RBD ternary complex clarified the unique mechanisms that underlie the excellent mutation resistance profiles of these antibodies. Secondly, polymeric IgG enhanced antibody avidity by eliminating P5-22's only hotspot, residue F486 in the RBD, thereby potently blocking cell entry by mutant viruses. Structural and functional analyses of antibodies screened using both potency assays and the yeast RBD library revealed rare, ultrapotent, mutation-resistant nAbs against SARS-CoV-2.

Different Circulation Pattern of Multiple Respiratory Viruses in Southern China During the COVID-19 Pandemic.

China implemented stringent non-pharmaceutical interventions (NPIs) in spring 2020, which has effectively suppressed SARS-CoV-2. In this study, we utilized data from routine respiratory virus testing requests from physicians and examined circulation of 11 other respiratory viruses in Southern China, from January 1, 2018 to December 31, 2020. A total of 58,169 throat swabs from patients with acute respiratory tract infections (ARTIs) were collected and tested. We found that while the overall activity of respiratory viruses was lower during the period with stringent NPIs, virus activity rebounded shortly after the NPIs were relaxed and social activities resumed. Only influenza was effectively suppressed with very low circulation which extended to the end of 2020. Circulation of other respiratory viruses in the community was maintained even during the period of stringent interventions, especially for rhinovirus. Our study shows that NPIs against COVID-19 have different impacts on respiratory viruses.

Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.

BACKGROUND: Main challenges for COVID-19 include the lack of a rapid diagnostic test, a suitable tool to monitor and predict a patient's clinical course and an efficient way for data sharing among multicenters. We thus developed a novel artificial intelligence system based on deep learning (DL) and federated learning (FL) for the diagnosis, monitoring, and prediction of a patient's clinical course. METHODS: CT imaging derived from 6 different multicenter cohorts were used for stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. Patients with more than 3 consecutive CT images were trained for the monitoring algorithm. FL has been applied for decentralized refinement of independently built DL models. RESULTS: A total of 1,552,988 CT slices from 4804 patients were used. The model can diagnose COVID-19 based on CT alone with the AUC being 0.98 (95% CI 0.97-0.99), and outperforms the radiologist's assessment. We have also successfully tested the incorporation of the DL diagnostic model with the FL framework. Its auto-segmentation analyses co-related well with those by radiologists and achieved a high Dice's coefficient of 0.77. It can produce a predictive curve of a patient's clinical course if serial CT assessments are available. INTERPRETATION: The system has high consistency in diagnosing COVID-19 based on CT, with or without clinical data. Alternatively, it can be implemented on a FL platform, which would potentially encourage the data sharing in the future. It also can produce an objective predictive curve of a patient's clinical course for visualization. KEY POINTS: • CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.

Quantifying the Effect of Public Activity Intervention Policies on COVID-19 Pandemic Containment Using Epidemiologic Data From 145 Countries.

OBJECTIVES: Most countries have adopted public activity intervention policies to control the coronavirus disease 2019 (COVID-19) pandemic. Nevertheless, empirical evidence of the effectiveness of different interventions on the containment of the epidemic was inconsistent. METHODS: We retrieved time-series intervention policy data for 145 countries from the Oxford COVID-19 Government Response Tracker from December 31, 2019, to July 1, 2020, which included 8 containment and closure policies. We investigated the association of timeliness, stringency, and duration of intervention with cumulative infections per million population on July 1, 2020. We introduced a novel counterfactual estimator to estimate the effects of these interventions on COVID-19 time-varying reproduction number (Rt). RESULTS: There is some evidence that earlier implementation, longer durations, and more strictness of intervention policies at the early but not middle stage were associated with reduced infections of COVID-19. The counterfactual model proved to have controlled for unobserved time-varying confounders and established a valid causal relationship between policy intervention and Rt reduction. The average intervention effect revealed that all interventions significantly decrease Rt after their implementation. Rt decreased by 30% (22%-41%) in 25 to 32 days after policy intervention. Among the 8 interventions, school closing, workplace closing, and public events cancellation demonstrated the strongest and most consistent evidence of associations. CONCLUSIONS: Our study provides more reliable evidence of the quantitative effects of policy interventions on the COVID-19 epidemic and suggested that stricter public activity interventions should be implemented at the early stage of the epidemic for improved containment.

Clinical Characteristics of Coronavirus Disease 2019 in China.

BACKGROUND: Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. METHODS: We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. RESULTS: The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. CONCLUSIONS: During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.).

Earlier diagnosis improves COVID-19 prognosis: a nationwide retrospective cohort analysis.

BACKGROUND: Risk of adverse outcomes in COVID-19 patients by stratifying by the time from symptom onset to confirmed diagnosis status is still uncertain. METHODS: We included 1,590 hospitalized COVID-19 patients confirmed by real-time RT-PCR assay or high-throughput sequencing of pharyngeal and nasal swab specimens from 575 hospitals across China between 11 December 2019 and 31 January 2020. Times from symptom onset to confirmed diagnosis, from symptom onset to first medical visit and from first medical visit to confirmed diagnosis were described and turned into binary variables by the maximally selected rank statistics method. Then, survival analysis, including a log-rank test, Cox regression, and conditional inference tree (CTREE) was conducted, regarding whether patients progressed to a severe disease level during the observational period (assessed as severe pneumonia according to the Chinese Expert Consensus on Clinical Practice for Emergency Severe Pneumonia, admission to an intensive care unit, administration of invasive ventilation, or death) as the prognosis outcome, the dependent variable. Independent factors included whether the time from symptom onset to confirmed diagnosis was longer than 5 days (the exposure) and other demographic and clinical factors as multivariate adjustments. The clinical characteristics of the patients with different times from symptom onset to confirmed diagnosis were also compared. RESULTS: The medians of the times from symptom onset to confirmed diagnosis, from symptom onset to first medical visit, and from first medical visit to confirmed diagnosis were 6, 3, and 2 days. After adjusting for age, sex, smoking status, and comorbidity status, age [hazard ratio (HR): 1.03; 95% CI: 1.01-1.04], comorbidity (HR: 1.84; 95% CI: 1.23-2.73), and a duration from symptom onset to confirmed diagnosis of >5 days (HR: 1.69; 95% CI: 1.10-2.60) were independent predictors of COVID-19 prognosis, which echoed the CTREE models, with significant nodes such as time from symptom onset to confirmed diagnosis, age, and comorbidities. Males, older patients with symptoms such as dry cough/productive cough/shortness of breath, and prior COPD were observed more often in the patients who procrastinated before initiating the first medical consultation. CONCLUSIONS: A longer time from symptom onset to confirmed diagnosis yielded a worse COVID-19 prognosis.

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.

Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.

Thoracic surgery and COVID-19: changes and managements during the pandemic.

BACKGROUND: Several articles have been published about the reorganization of surgical activity during the coronavirus disease 2019 (COVID-19) pandemic but little is known about the operative volume, distribution of cases, or capacity of The Department of Thoracic Surgery to deliver surgical services in the time of COVID-19. METHODS: A retrospective operative logbook review was completed in department of thoracic in a designated COVID-19 hospital. We reviewed and analyzed the operative logbook and discussed our countermeasures during the outbreak. A prediction model was established to discuss the time consuming about delayed surgeries during the pandemic. RESULTS: One thousand two hundred and seventy-five operation records were collected. The thoracic surgeries of this year has decreased (43.4%) during the Wuhan lockdown. From Jan 23rd to Apr 8th in 2020, there were 461 surgeries performed in The Department of Thoracic in our hospital with 0 cases of nosocomial COVID-19 infection. Prediction model showed that it will take 6 weeks to solve the backlog if department can reach the 85% of maximum of operations per week. CONCLUSIONS: An understanding of operative case volume and distribution is essential in facilitating targeted interventions to strengthen surgical capacity in the time of COVID-19. A proper guideline is imperative to ensure access to safe, timely surgical care. By developing a scientific and effective management of hospital, it is possible to ensure optimal surgical safety during this crisis. Regular updates and a further study include multicenter is required. CLINICAL TRIAL REGISTRY NUMBER: ChiCTR2000034346.

A Highly Automated Mobile Laboratory for On-site Molecular Diagnostics in the COVID-19 Pandemic.

BACKGROUND: Infectious disease outbreaks such as the COVID-19 (coronavirus disease 2019) pandemic call for rapid response and complete screening of the suspected community population to identify potential carriers of pathogens. Central laboratories rely on time-consuming sample collection methods that are rarely available in resource-limited settings. METHODS: We present a highly automated and fully integrated mobile laboratory for fast deployment in response to infectious disease outbreaks. The mobile laboratory was equipped with a 6-axis robot arm for automated oropharyngeal swab specimen collection; virus in the collected specimen was inactivated rapidly using an infrared heating module. Nucleic acid extraction and nested isothermal amplification were performed by a "sample in, answer out" laboratory-on-a-chip system, and the result was automatically reported by the onboard information platform. Each module was evaluated using pseudovirus or clinical samples. RESULTS: The mobile laboratory was stand-alone and self-sustaining and capable of on-site specimen collection, inactivation, analysis, and reporting. The automated sampling robot arm achieved sampling efficiency comparable to manual collection. The collected samples were inactivated in as short as 12 min with efficiency comparable to a water bath without damage to nucleic acid integrity. The limit of detection of the integrated microfluidic nucleic acid analyzer reached 150 copies/mL within 45 min. Clinical evaluation of the onboard microfluidic nucleic acid analyzer demonstrated good consistency with reverse transcription quantitative PCR with a κ coefficient of 0.979. CONCLUSIONS: The mobile laboratory provides a promising solution for fast deployment of medical diagnostic resources at critical junctions of infectious disease outbreaks and facilitates local containment of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) transmission.