Clinical characteristics and infectivity of asymptomatic carriers of SARS‑CoV‑2 (Review)

Zhang, Wanying; Wu, Xuejie; Zhou, Hui; Xu, Feng

doi:10.3892/etm.2020.9547

Cited by 9 publications

(5 citation statements)

References 47 publications

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“…Adding to our understanding of asymptomatic carriers, our findings indicated that the asymptomatic symptomology group had a low percentage of hospitalization; a high percentage of young non-Hispanic men with high income; and a low percentage of people with chronic conditions, smoking, and influenza vaccination. These characteristics add to those described in a review study of asymptomatic COVID-19 carriers' characteristics that found young age alone to be a significant factor for having no symptoms [46][47][48]. Another study of Mexican outpatients found a lower frequency of smokers and influenza vaccination among asymptomatic responders [43].…”

Section: Renderxsupporting

confidence: 66%

Assessing Associations Between COVID-19 Symptomology and Adverse Outcomes After Piloting Crowdsourced Data Collection: Cross-sectional Survey Study

et al. 2022

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Background Crowdsourcing is a useful way to rapidly collect information on COVID-19 symptoms. However, there are potential biases and data quality issues given the population that chooses to participate in crowdsourcing activities and the common strategies used to screen participants based on their previous experience. Objective The study aimed to (1) build a pipeline to enable data quality and population representation checks in a pilot setting prior to deploying a final survey to a crowdsourcing platform, (2) assess COVID-19 symptomology among survey respondents who report a previous positive COVID-19 result, and (3) assess associations of symptomology groups and underlying chronic conditions with adverse outcomes due to COVID-19. Methods We developed a web-based survey and hosted it on the Amazon Mechanical Turk (MTurk) crowdsourcing platform. We conducted a pilot study from August 5, 2020, to August 14, 2020, to refine the filtering criteria according to our needs before finalizing the pipeline. The final survey was posted from late August to December 31, 2020. Hierarchical cluster analyses were performed to identify COVID-19 symptomology groups, and logistic regression analyses were performed for hospitalization and mechanical ventilation outcomes. Finally, we performed a validation of study outcomes by comparing our findings to those reported in previous systematic reviews. Results The crowdsourcing pipeline facilitated piloting our survey study and revising the filtering criteria to target specific MTurk experience levels and to include a second attention check. We collected data from 1254 COVID-19–positive survey participants and identified the following 6 symptomology groups: abdominal and bladder pain (Group 1); flu-like symptoms (loss of smell/taste/appetite; Group 2); hoarseness and sputum production (Group 3); joint aches and stomach cramps (Group 4); eye or skin dryness and vomiting (Group 5); and no symptoms (Group 6). The risk factors for adverse COVID-19 outcomes differed for different symptomology groups. The only risk factor that remained significant across 4 symptomology groups was influenza vaccine in the previous year (Group 1: odds ratio [OR] 6.22, 95% CI 2.32-17.92; Group 2: OR 2.35, 95% CI 1.74-3.18; Group 3: OR 3.7, 95% CI 1.32-10.98; Group 4: OR 4.44, 95% CI 1.53-14.49). Our findings regarding the symptoms of abdominal pain, cough, fever, fatigue, shortness of breath, and vomiting as risk factors for COVID-19 adverse outcomes were concordant with the findings of other researchers. Some high-risk symptoms found in our study, including bladder pain, dry eyes or skin, and loss of appetite, were reported less frequently by other researchers and were not considered previously in relation to COVID-19 adverse outcomes. Conclusions We demonstrated that a crowdsourced approach was effective for collecting data to assess symptomology associated with COVID-19. Such a strategy may facilitate efficient assessments in a dynamic intersection between emerging infectious diseases, and societal and environmental changes.

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Section: Renderxsupporting

confidence: 66%

Assessing Associations Between COVID-19 Symptomology and Adverse Outcomes After Piloting Crowdsourced Data Collection: Cross-sectional Survey Study

et al. 2022

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“…The SARS-CoV-2-related morbidity and mortality are considerably lower among young children and adolescents, and children may be less vulnerable to infection ( 31 ). Although asymptomatic infections have not been systematically studied, some studies estimate that approximately 20%−50% of infections are asymptomatic, with significantly higher rates of asymptomatic infections among children ( 32 ).…”

Section: Scenarios For Covid-19 Epidemiological Variables—intrinsic O...mentioning

confidence: 99%

Epidemiological contemplation for a currently pragmatic COVID-19 health passport: a perspective

Ambalavanan,

Snead,

Marczika

et al. 2024

Front. Public Health

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The coronavirus disease 2019 (COVID-19) has caused a global pandemic that has wreaked havoc on the lives of millions of people around the world. Confinement measures aim to reduce the epidemic's spread and minimize the burden of morbidity and mortality. In response to the challenges caused by the pandemic, digital health passports have been developed exponentially. We highlight the latent epidemiological barriers to health passports to achieve standardized digital care platforms. This review paper not only highlights the epidemiological barriers but also articulates the possible infrastructure required to make the International Standard for a multi-factor authenticated and validated health passport.

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“…Therefore, some untested patients with COVID-19 showed mild or even no symptoms, resulting in spreading the virus in communities and hospitals unknowingly. [ 45 , 46 ] Furthermore, the ex vivo lung culture model will also help understand the earliest stages of SARS-CoV-2 infection in the human respiratory system and reduce the late-phase morbidity and mortality of COVID-19. It was found that lung cells presented a limited innate immune response to SARS-CoV-2, with an obvious lack of type I and III IFNs, contrasting with the strong innate immune response to the influenza virus.…”

Section: Model Applications In Lung Infectionmentioning

confidence: 99%

Short-term ex vivo tissue culture models help study human lung infectionsA review

Xia

Zeng

et al. 2023

Medicine

Self Cite

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Most studies on human lung infection have been performed using animal models, formalin or other fixed tissues, and in vitro cultures of established cell lines. However, the experimental data and results obtained from these studies may not completely represent the complicated molecular events that take place in intact human lung tissue in vivo. The newly developed ex vivo short-term tissue culture model can mimic the in vivo microenvironment of humans and allow investigations of different cell types that closely interact with each other in intact human lung tissues. Therefore, this kind of model may be a promising tool for future studies of different human lung infections, owing to its special advantages in providing more realistic events that occur in vivo. In this review, we have summarized the preliminary applications of this novel short-term ex vivo tissue culture model, with a particular emphasis on its applications in some common human lung infections. Abbreviations: AEC = alveolar epithelial cell, AM = alveolar macrophage, C. pneumoniae = Chlamydia pneumoniae, CD163 = cluster of differentiation 163, COPD = chronic obstructive pulmonary disease, COVID-19 = coronavirus disease 2019, CXCL = C-X-C motif chemokine ligand, EVLP = ex vivo lung perfusions, H. influenzae = Haemophilus influenzae, IFNs = interferons, IL = interleukin, L. pneumophila = Legionella pneumophila, MAPK = mitogen-activated protein kinase, NTHi = nontypeable Haemophilus influenzae, pHp = pulmonary haptoglobin, S. pneumoniae = Streptococcus pneumoniae, SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2, TLR2 = Toll-like receptor 2, TLR4 = Toll-like receptor 4, TNF-α = tumor necrosis factor-α.

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Clinical characteristics and infectivity of asymptomatic carriers of SARS‑CoV‑2 (Review)

Cited by 9 publications

References 47 publications

Assessing Associations Between COVID-19 Symptomology and Adverse Outcomes After Piloting Crowdsourced Data Collection: Cross-sectional Survey Study

Assessing Associations Between COVID-19 Symptomology and Adverse Outcomes After Piloting Crowdsourced Data Collection: Cross-sectional Survey Study

Epidemiological contemplation for a currently pragmatic COVID-19 health passport: a perspective

Short-term ex vivo tissue culture models help study human lung infectionsA review

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