Asymptomatic Transmission Research Articles

A cluster-control approach to a coronavirus disease 2019 (COVID-19) outbreak on a stroke ward with infection control considerations for dementia and vascular units.

We sought to contain a healthcare-associated coronavirus disease 2019 (COVID-19) outbreak, to evaluate contributory factors, and to prevent future outbreaks. Quasi-experimental cluster-control outbreak evaluation. All patients and staff on the outbreak ward (case cluster), and randomly selected patients and staff on COVID-19 wards (positive control cluster) and a non-COVID-19 wards (negative control cluster) underwent reverse-transcriptase polymerase chain reaction (RT-PCR) testing. Hand hygiene and personal protective equipment (PPE) compliance, detection of environmental SARS-COV-2 RNA, patient behavior, and SARS-CoV-2 IgG antibody prevalence were assessed. In total, 145 staff and 26 patients were exposed, resulting in 24 secondary cases. Also, 4 of 14 (29%) staff and 7 of 10 (70%) patients were asymptomatic or presymptomatic. There was no difference in mean cycle threshold between asymptomatic or presymptomatic versus symptomatic individuals. None of 32 randomly selected staff from the control wards tested positive. Environmental RNA detection levels were higher on the COVID-19 ward than on the negative control ward (OR, 19.98; 95% CI, 2.63-906.38; P < .001). RNA levels on the COVID-19 ward (where there were no outbreaks) and the outbreak ward were similar (OR, 2.38; P = .18). Mean monthly hand hygiene compliance, based on 20,146 observations (over preceding year), was lower on the outbreak ward (P < .006). Compared to both control wards, the proportion of staff with detectable antibodies was higher on the outbreak ward (OR, 3.78; 95% CI, 1.01-14.25; P = .008). Staff seroconversion was more likely during a short-term outbreak than from sustained duty on a COVID-19 ward. Environmental contamination and PPE use were similar on the outbreak and control wards. Patient noncompliance, decreased hand hygiene, and asymptomatic or presymptomatic transmission were more frequent on the outbreak ward.

The role of children in the transmission of SARS-CoV2: updated rapid review

BackgroundUnderstanding carriage and transmission potential of SARS-CoV-2 in children is of paramount importance to understand the spread of virus in school and community settings.MethodsWe performed an updated rapid review to investigate the role of children in the transmission of SARS-CoV-2. We synthesized evidence for five categories and results are reported narratively.ResultsA total of 33 new studies were included for this review. We did not identify additional studies that reported documented cases of SARS-CoV-2 transmission by children. We identified 15 new studies that demonstrate children’s susceptibility and transmission risk of SARS-CoV-2 with evidence provided on the chance of being index or secondary cases, the potential of faecal-oral transmission, and the possibility of asymptomatic transmission. There is little data on the transmission of SARS-CoV-2 in schools. There were three studies reporting COVID-19 school outbreaks in France (Oise), Australia (New South Wales) and Israel. The remaining four studies found that all reported cases did not infect any other pupils or staff. With data from seven studies and governmental websites, the proportion of children among all confirmed COVID-19 patients was estimated for 29 countries, varying from 0.3% (lowest in Spain) up to 13.8% (highest in Argentina). Lastly, we identified seven studies reporting on PIMS-TS linked to COVID-19 among paediatric patients.ConclusionsThere is somewhat limited evidence available for quantifying the extent to which children may contribute to overall transmission, but the balance of evidence so far suggests that children and schools play only a limited role in overall transmission.

SARS-CoV-2 Transmission From People Without COVID-19 Symptoms

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiology of coronavirus disease 2019 (COVID-19), is readily transmitted person to person. Optimal control of COVID-19 depends on directing resources and health messaging to mitigation efforts that are most likely to prevent transmission, but the relative importance of such measures has been disputed. To assess the proportion of SARS-CoV-2 transmissions in the community that likely occur from persons without symptoms. This decision analytical model assessed the relative amount of transmission from presymptomatic, never symptomatic, and symptomatic individuals across a range of scenarios in which the proportion of transmission from people who never develop symptoms (ie, remain asymptomatic) and the infectious period were varied according to published best estimates. For all estimates, data from a meta-analysis was used to set the incubation period at a median of 5 days. The infectious period duration was maintained at 10 days, and peak infectiousness was varied between 3 and 7 days (-2 and +2 days relative to the median incubation period). The overall proportion of SARS-CoV-2 was varied between 0% and 70% to assess a wide range of possible proportions. Level of transmission of SARS-CoV-2 from presymptomatic, never symptomatic, and symptomatic individuals. The baseline assumptions for the model were that peak infectiousness occurred at the median of symptom onset and that 30% of individuals with infection never develop symptoms and are 75% as infectious as those who do develop symptoms. Combined, these baseline assumptions imply that persons with infection who never develop symptoms may account for approximately 24% of all transmission. In this base case, 59% of all transmission came from asymptomatic transmission, comprising 35% from presymptomatic individuals and 24% from individuals who never develop symptoms. Under a broad range of values for each of these assumptions, at least 50% of new SARS-CoV-2 infections was estimated to have originated from exposure to individuals with infection but without symptoms. In this decision analytical model of multiple scenarios of proportions of asymptomatic individuals with COVID-19 and infectious periods, transmission from asymptomatic individuals was estimated to account for more than half of all transmissions. In addition to identification and isolation of persons with symptomatic COVID-19, effective control of spread will require reducing the risk of transmission from people with infection who do not have symptoms. These findings suggest that measures such as wearing masks, hand hygiene, social distancing, and strategic testing of people who are not ill will be foundational to slowing the spread of COVID-19 until safe and effective vaccines are available and widely used.

Is SARS-CoV-2 transmitted by asymptomatic and pre-symptomatic infected individuals?

KEY FINDINGSAsymptomatic and pre-symptomatic transmission of SARS-CoV-2 may occur.• Manifestations of COVID-19 are highly varied and may include asymptomatic cases, who do not manifest with anysigns and symptoms despite testing positive for COVID-19 by viral nucleic acid tests. Pre-symptomatic cases areinfected individuals who are still in their incubation period, hence do not exhibit any symptoms yet but eventuallydevelop symptoms.• As of June 2020, only 586 (2.8%) of the 20,990 active cases in the Philippines were classified as asymptomatic,but it is unclear whether cases are pre-symptomatic or carriers (true asymptomatic).• Based on 36 observational studies (case reports, case series, cross-sectional and cohort studies) and 9 statisticalmodeling analysis, asymptomatic and pre-symptomatic transmission of SARS-CoV-2 may occur. However, 3studies reported no transmission from pre-symptomatic and asymptomatic cases.• Studies on viral load comparing symptomatic cases with pre-symptomatic and asymptomatic cases reportedcontradicting results. The duration of viral shedding was significantly longer for symptomatic patients comparedto asymptomatic patients but similar for asymptomatic and pre-symptomatic patients.• Therewas no difference in the transmission rates of symptomatic and asymptomatic cases. However,the estimatedinfectivity and probability of transmission was higherfor symptomatic cases compared to asymptomatic cases, butresults were imprecise due to a wide confidence interval.• The World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) recognize thepossibility of pre-symptomatic and asymptomatic transmission. According to WHO, current evidence suggestsasymptomatic cases are less likely to transmit the virus than symptomatic cases.

Coronavirus and Influenza Viruses: An Overview of Their Differences and Similarities

SARS-CoV-2 is a new virus that is the cause of the current COVID-19 pandemic. We currently do not have a cure and immunity against this pathogen. Influenza viruses, on the other hand, are constantly evolving and undergo various levels of antigenic drift and shift that will become less recognizable by our immune system. This makes it difficult to develop a widespread effective influenza vaccine and also poses a risk of pandemics by leading to the emergence of new strains of zoonotic Influenza. Both Coronaviruses and Influenza viruses are enveloped RNA viruses and one of the primary pathogens affecting human respiration. COVID-19 and Influenza infections have similar transmission routes and symptoms. The reviewed literature indicates that there are important structural differences between COVID-19 and Influenza. These include differences in genome structures, surface proteins, number of strain and subtypes. In addition, incubation times, risk groups, asymptomatic transmission and transmission rate are important difference between the two viruses. However, unlike Influenza, the lack of vaccines and treatments for COVID-19 poses serious difficulties in controlling the spread of the disease. As a result, Coronavirus is spreading rapidly and due to the risk of possible co-infection with Influenza virus, it is extremely important to evaluate COVID-19 and Influenza infection together and developing public health measures accordingly.

Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19

The United States has the highest numbers of confirmed cases of COVID-19 in the world. The early hot spot states were New York, New Jersey, and Connecticut. The workforce in these states was required to work from home except for essential services. It was necessary to evaluate an appropriate date for resumption of business since the premature reopening of the economy would lead to a broader spread of COVID-19, while the opposite situation would cause greater loss of economy. To reflect the real-time risk of the spread of COVID-19, it was crucial to evaluate the population of infected individuals before or never being confirmed due to the pre-symptomatic and asymptomatic transmissions of COVID-19. To this end, we proposed an epidemic model and applied it to evaluate the real-time risk of epidemic for the states of New York, New Jersey, and Connecticut. We used California as the benchmark state because California began a phased reopening on May 8, 2020. The dates on which the estimated numbers of unidentified infectious individuals per 100,000 for states of New York, New Jersey, and Connecticut were close to those in California on May 8, 2020, were June 1, 22, and 22, 2020, respectively. By the practice in California, New York, New Jersey, and Connecticut might consider reopening their business. Meanwhile, according to our simulation models, to prevent resurgence of infections after reopening the economy, it would be crucial to maintain sufficient measures to limit the social distance after the resumption of businesses. This precaution turned out to be critical as the situation in California quickly deteriorated after our analysis was completed and its interventions after the reopening of business were not as effective as those in New York, New Jersey, and Connecticut.

Behavioral dynamics of COVID-19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations.

Effective responses to the COVID‐19 pandemic require integrating behavioral factors such as risk‐driven contact reduction, improved treatment, and adherence fatigue with asymptomatic transmission, disease acuity, and hospital capacity. We build one such model and estimate it for all 92 nations with reliable testing data. Cumulative cases and deaths through 22 December 2020 are estimated to be 7.03 and 1.44 times official reports, yielding an infection fatality rate (IFR) of 0.51 percent, which has been declining over time. Absent adherence fatigue, cumulative cases would have been 47 percent lower. Scenarios through June 2021 show that modest improvement in responsiveness could reduce cases and deaths by about 14 percent, more than the impact of vaccinating half of the population by that date. Variations in responsiveness to risk explain two orders of magnitude difference in per‐capita deaths despite reproduction numbers fluctuating around one across nations. A public online simulator facilitates scenario analysis over the coming months. © 2021 System Dynamics Society.

Transmission Characteristics of SARS-CoV-2 That Hinder Effective Control.

The most important characteristics of coronavirus disease 2019 (COVID-19) transmission that makes it difficult to control are 1) asymptomatic and presymptomatic transmission, 2) low incidence or lack of dominant systemic symptoms such as fever, 3) airborne transmission that may need a high infectious dose, and 4) super-spread events (SSEs). Patients with COVID-19 have high viral loads at symptom onset or even a few days prior to symptom onset, and most patients with COVID-19 have only mild respiratory symptoms or merely pauci-/null-symptoms. These characteristics of the virus enable it to easily spread to the community because most patients are unaware of their potential infectivity, and symptom-based control measures cannot prevent this type of transmission. Furthermore, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is also capable of airborne transmission in conditions such as aerosol-generating procedures, under-ventilated indoor spaces, and over-crowded areas. In this context, universal mask-wearing is important to prevent both outward and inward transmission until an adequate degree of herd immunity is achieved through vaccination. Lastly, the SSEs of SARS-CoV-2 transmission emphasize the importance of reducing contacts by limiting social gatherings. The above-mentioned transmission characteristics of SARS-CoV-2 have culminated in the failure of long-lasting quarantine measures, and indicate that only highly effective vaccines can keep the communities safe from this deadly, multifaceted virus.

Cryptosporidium and waterborne outbreaks - A mini review.

Cryptosporidium spp. is one of the prime agents of infectious diarrhea. Cryptosporidium spp. has been gaining awareness as a pathogen of public health importance in India and other developing countries. Owing to the nature of multiple transmission routes such as person-to-person, animal-to-person, waterborne and foodborne, the epidemiology of cryptosporidiosis in humans is not well known. A deeper understanding of the pathogenesis may lead to better diagnosis and better treatment of the condition. Asymptomatic human and animal transmission illustrates that the spread of infection through the environment is a more plausible explanation, waterborne transmission in particular. The disease burden is underestimated and its global impact is yet to be quantified due to the lack of country-specific estimates. Assessment of the disease itself has been crucial since the morphological indistinguishability, differences in distribution and transmission, and variations in the genotypes.

Mechanism of Optimal Time-Course COVID-19 Vaccine Prioritization Based on Non-Markovian Steady-State Prediction

Vaccination is essential for controlling the coronavirus disease (COVID-19) pandemic. An effective time-course strategy for the allocation of COVID-19 vaccines is crucial given that the global vaccine supply will still be limited in some countries/regions in the near future and that mutant strains have emerged and will continue to spread worldwide. Both asymptomatic and symptomatic transmission have played major roles in the COVID-19 pandemic, which can only be properly described as a typical non-Markovian process. However, the prioritization of vaccines in the non-Markovian framework still lacks sufficient research, and the underlying mechanism of the time-course vaccine allocation optimization has not yet been uncovered. In this paper, based on an age-stratified compartmental model calibrated through clinical and epidemiological data, we propose optimal vaccination strategies (OVS) through steady-state prediction in the non-Markovian framework. This OVS outperforms other empirical vaccine prioritization approaches in minimizing cumulative infections, cumulative deaths, or years of life lost caused by the pandemic. We found that there exists a fast decline in the prevention efficiency of vaccination if vaccines are solely administered to a selected age group, which indicates that the widely adopted strategy to continuously vaccinate high-risk group is not optimal. Through mathematical analysis of the model, we reveal that dynamic vaccine allocations to combinations of different age groups is necessary to achieve optimal vaccine prioritization. Our work not only provides meaningful references for vaccination in countries currently lacking vaccines and for vaccine allocation strategies to prevent mutant strains in the future, but also reveals the mechanism of dynamic vaccine allocation optimization, forming a theoretical and modelling framework empirically applicable to the optimal time-course prioritization.Funding: This work was supported by the Hong Kong Baptist University (HKBU) Strategic Development Fund. This research was conducted using the resources of the High- Performance Computing Cluster Centre at HKBU, which receives funding from the Hong Kong Research Grant Council and the HKBU. Declaration of Interests: The authors declare that they have no competing interests.

Discussion of “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”

Publisher: School of Statistics, Renmin University of China, Journal: Journal of Data Science, Title: Discussion of “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”, Authors: Corbin Quick, Xihong Lin

Discussion of “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”

Publisher: School of Statistics, Renmin University of China, Journal: Journal of Data Science, Title: Discussion of “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”, Authors: Shan Yu, Guannan Wang, Li Wang

Rejoinder: “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”

Publisher: School of Statistics, Renmin University of China, Journal: Journal of Data Science, Title: Rejoinder - “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”, Authors: Ting Tian, Jianbin Tan, Yukang Jiang

Discussion of “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”

Publisher: School of Statistics, Renmin University of China, Journal: Journal of Data Science, Title: Discussion of “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”, Authors: Yishu Xue, Hou-Cheng Yang, Yuqing Pan

Discussion of “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”

Publisher: School of Statistics, Renmin University of China, Journal: Journal of Data Science, Title: Discussion of “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”, Authors: Jeffrey S. Morris, Jing Huang

An Improved Agent-Based Model Using Discrete Event Simulation for Nonpharmaceutical Interventions

The traditional agent-based model requires high computing power of the central processing unit. Thus, an improved agent-based model combined with the discrete event simulation method is proposed. The result of the equation-based Susceptible-Exposed-Infective-Asymptomatic-Recovered (SEIAR) model with the same parameter combination, which has been demonstrated to be effective, is used to verify the validity of this improved agent-based model. Additionally, an analysis based on simulation results of the Contact Tracing Measure (CTM), Location-Based Checking-Testing Measure (LCTM), Lockdown Measure (LM), Mobile Cabin Isolation and Hospital Measure (MCHM) is presented. The simulation results show that implementing long-term lockdown measures has the best effect on epidemic control. Moreover, according to the simulation results, we inferred that using only nonpharmaceutical epidemic prevention measures may result in a second outbreak of COVID-19 owing to the risk of asymptomatic transmission.

COVID-19 Seroprevalence Study in Asymptomatic Healthcare Workers at a Tertiary Healthcare Centre, India

Introduction: The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection has evolved into a pandemic disease. The present knowledge is mainly based on available numerator data of confirmed positive cases only. The asymptomatic and mildly symptomatic cases are not brought into picture for testing at all, which is a major contributor to the pandemicity and hence creating bias in the documentation and understanding of the disease. The magnitude of the exposure of Healthcare Workers (HCW) and their potential for asymptomatic transmission makes it critical to know the incidence of infection in the healthcare population. Aim: To evaluate the seroprevalence of Immunoglobulin G (IgG) SARS-CoV-2 among the asymptomatic HCW. Materials and Methods: This was a cross-sectional study conducted between January 2021 to February 2021 in SRM Medical College Hospital and Research Centre a tertiary care hospital in Potheri, Chengalpattu district, Tamil Nadu, India. The HCW were asked to complete the standardised questionnaire including the basic information, symptoms of COVID-19 illness and utility of Personal Protective Equipment (PPE) based on World Health Organization (WHO) risk assessment and management of exposure of HCW in the context of COVID-19. They were divided into two groups, the staff who had direct patient exposure as group 1 with 82 participants and staff without direct patient exposure as group 2 with 46 participants. The serodetection of IgG SARS-CoV-2 antibodies was done using the Chemiluminescence Immunoassay (CLIA). The obtained results were statistically analysed with Statistical Package for the Social Sciences (SPSS) version 20.0. A Chi-square test (χ2) was performed and a p-value less than 0.05 was considered statistically significant. Results: A total of 128 HCW were studied. In group 1, there were 64.1% (n=82) of HCWs and in group 2 there were 35.9% (n=46) of HCWs. There were total of 74 (57.8%) males and 54 (42.2%) females. No gender-specific differences were observed. The mean age in group 1 was 28.93 years and group 2 was 32.2 years. The staffs older than 40 years were more commonly affected. Adherence to strict PPE protocol was observed in 92.6% (76/82) in group 1 and 82.6% (38/46) in group 2. The difference between the groups were statistically significant (p=0.025). In this study, though the seroprevalence of COVID-19 infection was 9.8% (n=8) in group 1 and 13% (n=6) in group 2, it was statistically not significant. Conclusion: SARS-CoV-2 Serology study helps to identify the asymptomatic (unestimated) cases. The results of the seroprevalence suggest that the strict adherence to PPE protocol helps to prevent COVID-19.

Discussion of “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”

Publisher: School of Statistics, Renmin University of China, Journal: Journal of Data Science, Title: Discussion of “Evaluate the Risk of Resumption of Business for the States of New York, New Jersey and Connecticut via a Pre-Symptomatic and Asymptomatic Transmission Model of COVID-19”, Authors: Chuanrong Zhang, Xinba Li

Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: Systematic review and meta-analysis.

Knowing the prevalence of true asymptomatic coronavirus disease 2019 (COVID-19) cases is critical for designing mitigation measures against the pandemic. We aimed to synthesize all available research on asymptomatic cases and transmission rates. We searched PubMed, Embase, Cochrane COVID-19 trials, and Europe PMC for primary studies on asymptomatic prevalence in which (1) the sample frame includes at-risk populations and (2) follow-up was sufficient to identify pre-symptomatic cases. Meta-analysis used fixed-effects and random-effects models. We assessed risk of bias by combination of questions adapted from risk of bias tools for prevalence and diagnostic accuracy studies. We screened 2,454 articles and included 13 low risk-of-bias studies from seven countries that tested 21,708 at-risk people, of which 663 were positive and 111 asymptomatic. Diagnosis in all studies was confirmed using a real-time reverse transcriptase-polymerase chain reaction test. The asymptomatic proportion ranged from 4% to 41%. Meta-analysis (fixed effects) found that the proportion of asymptomatic cases was 17% (95% CI 14% to 20%) overall and higher in aged care (20%; 95% CI 14% to 27%) than in non-aged care (16%; 95% CI 13% to 20%). The relative risk (RR) of asymptomatic transmission was 42% lower than that for symptomatic transmission (combined RR 0.58; 95% CI 0.34 to 0.99, p = 0.047). Our one-in-six estimate of the prevalence of asymptomatic COVID-19 cases and asymptomatic transmission rates is lower than those of many highly publicized studies but still sufficient to warrant policy attention. Further robust epidemiological evidence is urgently needed, including in subpopulations such as children, to better understand how asymptomatic cases contribute to the pandemic.

A Human-Pathogen Model for COVID-19 Outbreak: Flattening Epidemic Curve in Kenya

This work introduces a deterministic model of COVID-19 spread aimed at analyzing non-pharmaceutical interventions in Kenya. The model accounts for symptomatic, asymptomatic and environmental transmission. Using the SEIR (Susceptible-Exposed- Infected-Recovered) compartmental model with additional component of the pathogen, the dynamics of COVID-19 outbreak is simulated while focussing on the impact of different control measures in the reduction of the basic reproduction number.