Nowcasting the prevalence of asymptomatic, preclinical, and clinical COVID-19 infections.
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ABSTRACT:
SUBMITTER: Su SY
PROVIDER: S-EPMC8011633 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Recently, considerable efforts have been focused on intensifying the screening process for asymptomatic COVID-19 cases in the Chinese Mainland, especially for up to 10 million citizens living in Wuhan City by nucleic acid testing. However, a high percentage of domestic asymptomatic cases did not develop into symptomatic ones, which is abnormal and has drawn considerable public attention. Here, we aimed to investigate the prevalence of COVID-19 infections in the Chinese Mainland from a statistical perspective, as it is of referential significance for other regions. By conservatively assuming a development time lag from pre-symptomatic (i.e., referring to the infected cases that were screened before the COVID-19 symptom onset) to symptomatic as an incubation time of 5.2 days, our results indicated that 92.5% of those tested in Wuhan City, China, and 95.1% of those tested in the Chinese Mainland should have COVID-19 syndrome onset, which was extremely higher than their corresponding practical percentages of 0.8% and 3.3%, respectively. We propose that a certain false positive rate may exist if large-scale nucleic acid screening tests for asymptomatic cases are conducted in common communities with a low incidence rate. Despite adopting relatively high-sensitivity, high-specificity detection kits, we estimated a very low prevalence of COVID-19 infections, ranging from 10-6 to 10-4 in both Wuhan City and the Chinese Mainland. Thus, the prevalence rate of asymptomatic infections in China had been at a very low level. Furthermore, given the lower prevalence of the infection, close examination of the data for false positive results is necessary to minimize social and economic impacts.
| S-EPMC7399709 | biostudies-literature
Project description:To assess the current dynamics of an epidemic, it is central to collect information on the daily number of newly diseased cases. This is especially important in real-time surveillance, where the aim is to gain situational awareness, for example, if cases are currently increasing or decreasing. Reporting delays between disease onset and case reporting hamper our ability to understand the dynamics of an epidemic close to now when looking at the number of daily reported cases only. Nowcasting can be used to adjust daily case counts for occurred-but-not-yet-reported events. Here, we present a novel application of nowcasting to data on the current COVID-19 pandemic in Bavaria. It is based on a hierarchical Bayesian model that considers changes in the reporting delay distribution over time and associated with the weekday of reporting. Furthermore, we present a way to estimate the effective time-varying case reproduction number Re(t) based on predictions of the nowcast. The approaches are based on previously published work, that we considerably extended and adapted to the current task of nowcasting COVID-19 cases. We provide methodological details of the developed approach, illustrate results based on data of the current pandemic, and evaluate the model based on synthetic and retrospective data on COVID-19 in Bavaria. Results of our nowcasting are reported to the Bavarian health authority and published on a webpage on a daily basis (https://corona.stat.uni-muenchen.de/). Code and synthetic data for the analysis are available from https://github.com/FelixGuenther/nc_covid19_bavaria and can be used for adaption of our approach to different data.
| S-EPMC7753318 | biostudies-literature
Project description:Previous studies have showed clinical characteristics of patients with the 2019 novel coronavirus disease (COVID-19) and the evidence of person-to-person transmission. Limited data are available for asymptomatic infections. This study aims to present the clinical characteristics of 24 cases with asymptomatic infection screened from close contacts and to show the transmission potential of asymptomatic COVID-19 virus carriers. Epidemiological investigations were conducted among all close contacts of COVID-19 patients (or suspected patients) in Nanjing, Jiangsu Province, China, from Jan 28 to Feb 9, 2020, both in clinic and in community. Asymptomatic carriers were laboratory-confirmed positive for the COVID-19 virus by testing the nucleic acid of the pharyngeal swab samples. Their clinical records, laboratory assessments, and chest CT scans were reviewed. As a result, none of the 24 asymptomatic cases presented any obvious symptoms while nucleic acid screening. Five cases (20.8%) developed symptoms (fever, cough, fatigue, etc.) during hospitalization. Twelve (50.0%) cases showed typical CT images of ground-glass chest and 5 (20.8%) presented stripe shadowing in the lungs. The remaining 7 (29.2%) cases showed normal CT image and had no symptoms during hospitalization. These 7 cases were younger (median age: 14.0 years; P=0.012) than the rest. None of the 24 cases developed severe COVID-19 pneumonia or died. The median communicable period, defined as the interval from the first day of positive nucleic acid tests to the first day of continuous negative tests, was 9.5 days (up to 21 days among the 24 asymptomatic cases). Through epidemiological investigation, we observed a typical asymptomatic transmission to the cohabiting family members, which even caused severe COVID-19 pneumonia. Overall, the asymptomatic carriers identified from close contacts were prone to be mildly ill during hospitalization. However, the communicable period could be up to three weeks and the communicated patients could develop severe illness. These results highlighted the importance of close contact tracing and longitudinally surveillance via virus nucleic acid tests. Further isolation recommendation and continuous nucleic acid tests may also be recommended to the patients discharged.
| S-EPMC7088568 | biostudies-literature
Project description:BackgroundThe missing asymptomatic COVID-19 infections have been overlooked because of the imperfect sensitivity of the nucleic acid testing (NAT). Globally understanding the humoral immunity in asymptomatic carriers will provide scientific knowledge for developing serological tests, improving early identification, and implementing more rational control strategies against the pandemic.MeasureUtilizing both NAT and commercial kits for serum IgM and IgG antibodies, we extensively screened 11 766 epidemiologically suspected individuals on enrollment and 63 asymptomatic individuals were detected and recruited. Sixty-three healthy individuals and 51 mild patients without any preexisting conditions were set as controls. Serum IgM and IgG profiles were further probed using a SARS-CoV-2 proteome microarray, and neutralizing antibody was detected by a pseudotyped virus neutralization assay system. The dynamics of antibodies were analyzed with exposure time or symptoms onset.ResultsA combination test of NAT and serological testing for IgM antibody discovered 55.5% of the total of 63 asymptomatic infections, which significantly raises the detection sensitivity when compared with the NAT alone (19%). Serum proteome microarray analysis demonstrated that asymptomatics mainly produced IgM and IgG antibodies against S1 and N proteins out of 20 proteins of SARS-CoV-2. Different from strong and persistent N-specific antibodies, S1-specific IgM responses, which evolved in asymptomatic individuals as early as the seventh day after exposure, peaked on days from 17 days to 25 days, and then disappeared in two months, might be used as an early diagnostic biomarker. 11.8% (6/51) mild patients and 38.1% (24/63) asymptomatic individuals did not produce neutralizing antibody. In particular, neutralizing antibody in asymptomatics gradually vanished in two months.ConclusionOur findings might have important implications for the definition of asymptomatic COVID-19 infections, diagnosis, serological survey, public health, and immunization strategies.
| S-EPMC7675426 | biostudies-literature
Project description:Purpose: To investigate molecular mechanisms of SARS-CoV-2-induced mucin expression and synthesis and test candidate countermeasures. Methods: Bulk RNA-seq was performed on well-differentiated human bronchial epithelial (HBE) cell culture lysates with/without SARS-CoV-2 inoculation. Results: SARS-CoV-2-infected HBE cultures exhibited peak titers 3 days post inoculation, whereas induction of MUC5B/MUC5AC peaked 7-14 days post inoculation. Conclusions: SARS-CoV-2-infection to HBE culture causes mucus goblet cell metaplasia and increased expression of MUC5B-dominated mucin overproduction.
2022-08-03 | GSE210223 | GEO
Project description:The coronavirus disease 2019 (COVID-19) pandemic has created unprecedented challenges for solid organ transplant programs. While transplant activity has largely recovered, appropriate management of deceased donor candidates who are asymptomatic but have positive nucleic acid testing (NAT) for SARS-CoV-2 is unclear, as this result may reflect active infection or prolonged viral shedding. Furthermore, candidates who are unvaccinated or partially vaccinated continue to receive donor offers. In the absence of robust outcomes data, transplant professionals at US adult kidney transplant centers were surveyed (February 13, 2021 to April 29, 2021) to determine community practice (N: 92 centers, capturing 41% of centers and 57% of transplants performed). The majority (97%) of responding centers declined organs for asymptomatic NAT+ patients without documented prior infection. However, 32% of centers proceed with kidney transplant in NAT+ patients who were at least 30 days from initial diagnosis with negative chest imaging. Less than 7% of programs reported inactivating patients who were unvaccinated or partially vaccinated. In conclusion, despite national recommendations to wait for negative testing, many centers are proceeding with kidney transplant in patients with positive SARS-CoV-2 NAT results due to presumed viral shedding. Furthermore, few centers are requiring COVID-19 vaccination prior to transplantation at this time.
| S-EPMC8420523 | biostudies-literature
Project description:Understanding the trajectory of the daily number of COVID-19 deaths is essential to decisions on how to respond to the pandemic, but estimating this trajectory is complicated by the delay between deaths occurring and being reported. In England the delay is typically several days, but it can be weeks. This causes considerable uncertainty about how many deaths occurred in recent days. Here we estimate the deaths per day in five age strata within seven English regions, using a Bayesian model that accounts for reporting-day effects and longer-term changes in the delay distribution. We show how the model can be computationally efficiently fitted when the delay distribution is the same in multiple strata, for example, over a wide range of ages.
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Project description:Near-term forecasts, also called nowcasts, are most challenging but also most important when the economy experiences an abrupt change. In this paper, we explore the performance of models with different information sets and data structures in order to best nowcast US initial unemployment claims in spring of 2020 in the midst of the COVID-19 pandemic. We show that the best model, particularly near the structural break in claims, is a state-level panel model that includes dummy variables to capture the variation in timing of state-of-emergency declarations. Autoregressive models perform poorly at first but catch up relatively quickly. The state-level panel model, exploiting the variation in timing of state-of-emergency declarations, also performs better than models including Google Trends. Our results suggest that in times of structural change there is a bias-variance tradeoff. Early on, simple approaches to exploit relevant information in the cross sectional dimension improve forecasts, but in later periods the efficiency of autoregressive models dominates.
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Project description:A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.
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Project description:During the COVID-19 pandemic, economists have struggled to obtain reliable economic predictions, with standard models becoming outdated and their forecasting performance deteriorating rapidly. This paper presents two novelties that could be adopted by forecasting institutions in unconventional times. The first innovation is the construction of an extensive data set for macroeconomic forecasting in Europe. We collect more than a thousand time series from conventional and unconventional sources, complementing traditional macroeconomic variables with timely big data indicators and assessing their added value at nowcasting. The second novelty consists of a methodology to merge an enormous amount of non-encompassing data with a large battery of classical and more sophisticated forecasting methods in a seamlessly dynamic Bayesian framework. Specifically, we introduce an innovative “selection prior” that is used not as a way to influence model outcomes, but as a selecting device among competing models. By applying this methodology to the COVID-19 crisis, we show which variables are good predictors for nowcasting Gross Domestic Product and draw lessons for dealing with possible future crises.
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