ABSTRACT
Importance: Understanding the susceptibility and infectiousness of children and adolescents in comparison to adults is important to appreciate their role in the COVID-19 pandemic. Objective: To determine SARS-CoV-2 susceptibility and infectiousness of children and adolescents with adults as comparator for three variants (wild-type, Alpha, Delta) in the household setting. We aimed to identify the effects independent of vaccination. Data Sources: We searched EMBASE, PubMed and medRxiv up to January 2022. Additional studies were identified through contacting subject experts. Study Selection: Two reviewers independently identified studies providing secondary attack rates (SAR) for SARS-CoV-2 infection in children (0-9 years), adolescents (10-19 years) or both compared with adults (20 years and older) derived from household data. Data Extraction and Synthesis: Two reviewers independently performed data extraction. We assessed risk of bias of included studies using a critical appraisal checklist and a random-effects meta-analysis model to pool association estimates. Main Outcomes and Measures: Odds ratio (OR) for SARS-CoV-2 infection comparing children and adolescents with adults stratified by wild-type, Alpha, and Delta variant, respectively. Susceptibility was defined as the secondary attack rate (SAR) among susceptible household contacts irrespective of the age of the index case. Infectiousness was defined as the SAR irrespective of the age of household contacts when children/adolescents/adults were the index case. Results: Twenty-eight studies (308,857 contacts) were included in the susceptibility analysis, for Delta only one (large) study was available. Compared to adults children and adolescents were less susceptible to the wild-type and Delta variant, but equally susceptible to the Alpha variant. In the infectiousness analysis, 21 studies (201,199 index cases) were included. Compared to adults, children and adolescents were less infectious when infected with the wild-type and Delta variant. Alpha variant-related infectiousness remained unclear, 0-9 year old children were at least as infectious as adults. SAR among household contacts was highest during circulation of the Alpha variant, lowest during wild-type circulation and intermediate during Delta circulation. Conclusions and Relevance: When considering the potential role of children and adolescents, for each variant susceptibility, infectiousness, age group and overall transmissibility need to be assessed to guide public health policy.
Subject(s)
COVID-19ABSTRACT
Background: A first generation COVID-19 vaccine is expected to be approved and made available by the end of 2020. While questions of vaccine allocation strategies have received significant attention among scientific and professional healthcare communities, important questions remain regarding the potential impact of the vaccine given uncertainties regarding efficacy against transmission, availability, timing, and durability.Methods: We adapted a susceptible-exposed-infectious-recovered (SEIR) model to examine the potential impact on hospitalization and mortality assuming increasing rates of vaccine efficacy. Specifically, though vaccine efficacy is typically evaluated as reduction in disease, we also evaluated efficacy against infectiousness given the uncertainty of the vaccine to prevent infectiousness as well as potential effects of waning immunity. Furthermore, we evaluated these potential vaccine properties in three US states at different stages of the epidemic to assess the impact on outcomes from distribution timing.Findings: Increased vaccine efficacy against disease reduces hospitalizations and deaths from COVID-19; however, the relative benefit of transmission blocking varied depending on the timing of vaccine distribution. Early in an outbreak, a vaccine that reduces transmission will be relatively more effective than one introduced later in the outbreak. Results from analysis of selected US states further suggest that earlier implementation of a less effective vaccine is more impactful than later implementation of a more effective vaccine. These findings are magnified when considering the durability of the vaccine. Vaccination in the spring will be less impactful when immunity is less durable. Interpretation: Policy choices regarding non-pharmaceutical interventions, such as social distancing and face mask use, will need to remain in place longer if the vaccine is less effective at reducing transmission. In addition, the stage of the local outbreak greatly impacts the overall effectiveness of the vaccine in a region and should be considered when allocating vaccines.Funding: Centers for Disease Control and Prevention (CDC) MInD-Healthcare Program (U01CK000589, 1U01CK000536), James S. McDonnell Foundation 21st Century Science Initiative Collaborative Award in Understanding Dynamic and Multiscale Systems, National Science Foundation (CNS-2027908), National Science Foundation Expeditions (CCF1917819), C3.ai Digital Transformation Institute (AWD1006615), and Google, LLC.Declaration of Interests: The authors declare no competing interests.
Subject(s)
COVID-19ABSTRACT
Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.
Subject(s)
COVID-19 , Cognition DisordersABSTRACT
The widespread occurrence of SARS-CoV-2 has had a profound effect on society and a vaccine is currently being developed. Angiotensin-converting enzyme 2 (ACE2) is the primary host cell receptor that interacts with the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Although pneumonia is the main symptom in severe cases of SARS-CoV-2 infection, the expression levels of ACE2 in the lung is low, suggesting the presence of another receptor for the spike protein. In order to identify the additional receptors for the spike protein, we screened a receptor for the SARS-CoV-2 spike protein from the lung cDNA library. We cloned L-SIGN as a specific receptor for the N-terminal domain (NTD) of the SARS-CoV-2 spike protein. The RBD of the spike protein did not bind to L-SIGN. In addition, not only L-SIGN but also DC-SIGN, a closely related C-type lectin receptor to L-SIGN, bound to the NTD of the SARS-CoV-2 spike protein. Importantly, cells expressing L-SIGN and DC-SIGN were both infected by SARS-CoV-2. Furthermore, L-SIGN and DC-SIGN induced membrane fusion by associating with the SARS-CoV-2 spike protein. Serum antibodies from infected patients and a patient-derived monoclonal antibody against NTD inhibited SARS-CoV-2 infection of L-SIGN or DC-SIGN expressing cells. Our results highlight the important role of NTD in SARS-CoV-2 dissemination through L-SIGN and DC-SIGN and the significance of having anti-NTD neutralizing antibodies in antibody-based therapeutics.
Subject(s)
Pneumonia , Severe Acute Respiratory Syndrome , COVID-19ABSTRACT
SARS-CoV-2 is a coronavirus that sparked the current COVID-19 pandemic. To stop the shattering effect of COVID-19, effective and safe vaccines, and antiviral therapies are urgently needed. To facilitate the preclinical evaluation of intervention approaches, relevant animal models need to be developed and validated. Rhesus macaques (Macaca mulatta) and cynomolgus macaques (Macaca fascicularis) are widely used in biomedical research and serve as models for SARS-CoV-2 infection. However, differences in study design make it difficult to compare and understand potential species-related differences. Here, we directly compared the course of SARS-CoV-2 infection in the two genetically closely-related macaque species. After inoculation with a low passage SARS-CoV-2 isolate, clinical, virological, and immunological characteristics were monitored. Both species showed slightly elevated body temperatures in the first days after exposure while a decrease in physical activity was only observed in the rhesus macaques and not in cynomolgus macaques. The virus was quantified in tracheal, nasal, and anal swabs, and in blood samples by qRT-PCR, and showed high similarity between the two species. Immunoglobulins were detected by various enzyme-linked immunosorbent assays (ELISAs) and showed seroconversion in all animals by day 10 post-infection. The cytokine responses were highly comparable between species and computed tomography (CT) imaging revealed pulmonary lesions in all animals. Consequently, we concluded that both rhesus and cynomolgus macaques represent valid models for evaluation of COVID-19 vaccine and antiviral candidates in a preclinical setting.
Subject(s)
COVID-19 , Lung DiseasesABSTRACT
Motivation: In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Results: Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19. Availability: https://neo4covid19.ncats.io . Keywords: SARS-CoV-2, COVID-19, network pharmacology, graph database, Neo4j, data integration, drug repositioning
Subject(s)
COVID-19ABSTRACT
Background: Mounting evidence suggests that the primary mode of transmission of SARS-CoV-2 is aerosolized transmission from close contact with infected individuals. Even though transmission is a direct result of human encounters, environmental conditions, such as lower humidity, may enhance aerosolized transmission risks similar to other respiratory viruses such as influenza. Methods: We utilized dynamic time warping to cluster all 3,137 counties in the United States based on temporal data on absolute humidity from March 10 to September 29, 2020. We then used a multivariate generalized additive model (GAM) combining data on human mobility derived from mobile phone data with humidity data to identify the potential effect of absolute humidity and mobility on new daily cases of COVID-19 while considering the temporal differences between seasons. Results: The clustering analysis found ten groups of counties with similar humidity levels. We found a significant negative effect between increasing humidity and new cases of COVID-19 in most regions, particularly in the period from March to July. The effect was greater in regions with generally lower humidity in the Western, Midwest, and Northeast regions of the US. In the two regions with the largest effect, a 1 g/m3 increase of absolute humidity resulted in a 0.21 and 0.15 decrease in cases. The effect of mobility on cases was positive and significant across all regions in the July-Sept time period, though the relationship in some regions was more mixed in the March to June period. Conclusions: We found that increasing humidity played an important role in falling cases in the spring, while increasing mobility in the summer contributed more significantly to increases in the summer. Our findings suggest that, similar to other respiratory viruses, the decreasing humidity in the winter is likely to lead to an increase in COVID-19 cases. Furthermore, the fact that mobility data were positively correlated suggests that efforts to counteract the rise in cases due to falling humidity can be effective in limiting the burden of the pandemic.
Subject(s)
COVID-19ABSTRACT
ObjectivesAs of August 24th 2020, there have been 1,084,904 confirmed cases of SARS-CoV-2 and 24,683 deaths across the African continent. Despite relatively lower numbers of cases initially, many African countries are now experiencing an exponential increase in case numbers. Estimates of the progression of disease and potential impact of different interventions are needed to inform policy making decisions. Herein, we model the possible trajectory of SARS-CoV-2 in 52 African countries under different intervention scenarios. DesignWe developed a compartmental model of SARS-CoV-2 transmission to estimate the COVID-19 case burden for all African countries while considering four scenarios: no intervention, moderate lockdown, hard lockdown, and hard lockdown with continued restrictions once lockdown is lifted. We further analyzed the potential impact of COVID-19 on vulnerable populations affected by HIV/AIDS and TB. ResultsIn the absence of an intervention, the most populous countries had the highest peaks in active projected number of infections with Nigeria having an estimated 645,081 severe infections. The scenario with a hard lockdown and continued post-lockdown interventions to reduce transmission was the most efficacious strategy for delaying the time to the peak and reducing the number of cases. In South Africa projected peak severe infections increase from 162,977 to 203,261, when vulnerable populations with HIV/AIDS and TB are included in the analysis. ConclusionThe COVID-19 pandemic is rapidly spreading across the African continent. Estimates of the potential impact of interventions and burden of disease are essential for policy makers to make evidence-based decisions on the distribution of limited resources and to balance the economic costs of interventions with the potential for saving lives. ARTICLE SUMMARY Strengths and limitations of this studyO_LIThough the rapid spread of SARS-CoV-2 through China, Europe and the United States has been well-studied, leading to a detailed understanding of its biology and epidemiology, the population and resources for combatting the spread of the disease in Africa greatly differ to those areas and require models specific to this context. C_LIO_LIFew models that provide estimates for policymakers, donors, and aid organizations focused on Africa to plan an effective response to the pandemic threat that optimizes the use of limited resources. C_LIO_LIThis is a compartmental model and as such has inherent weaknesses; including the possible overestimation of the number of infections as it is assumed people are well mixed, despite many social, physical and geographical barriers to mixing within countries. C_LIO_LIPeaks in transmission are likely to occur at different times in different regions, with multiple epicenters. C_LIO_LIThis model is not stochastic and case data are modeled from the first twenty or more cases, each behaving as an average case; in reality, there are no average cases; some individuals are likely to have many contacts, causing multiple infections, and others to have very few. C_LI
Subject(s)
COVID-19ABSTRACT
The objective of this study was to assess the energy demand and economic cost of two hospital-based COVID-19 infection control interventions. The intervention control measures evaluated include use of negative pressure (NP) treatment rooms and xenon pulsed ultraviolet (XP-UV) infection control equipment. After projecting COVID-19 hospitalizations, a Hospital Energy Model and Infection De-escalation Models are applied to quantify increases in energy demand and reductions in secondary infections. The scope of the interventions consisted of implementing NP in 11, 22, and 44 rooms (at small, medium, and large hospitals) while the XP-UV equipment was used eight, nine, and ten hours a day, respectively. The annum kilowatt-hours (kWh) for NP (and costs were at $0.1015 per kWh) were 116,700 ($11,845), 332,530 ($33,752), 795,675 ($80,761) for small, medium, and large hospitals ($1,077, $1,534, $1,836 added annum energy cost per NP room). For XP-UV, the annum kilowatt-hours and costs were 438 ($45), 493 ($50), 548 ($56) for small, medium, and large hospitals. There are other initial costs associated with the purchase and installation of the equipment, with XP-UV having a higher initial cost. XP-UV had a greater reduction in secondary COVID-19 infections in large and medium hospitals. NP rooms had a greater reduction in secondary SARS-CoV-2 transmission in small hospitals. Early implementation of interventions can result in realized cost savings through reduced hospital-acquired infections.
Subject(s)
COVID-19 , Disease Models, AnimalABSTRACT
Using a Bayesian approach to epidemiological compartmental modeling, we demonstrate the bomb-like behavior of exponential growth in COVID-19 cases can be explained by transmission of asymptomatic and mild cases that are typically unreported at the beginning of pandemic events due to lower prevalence of testing. We studied the exponential phase of the pandemic in Italy, Spain, and South Korea, and found the R0 to be 2.56 (95% CrI, 2.41-2.71), 3.23 (95% CrI, 3.06-3.4), and 2.36 (95% CrI, 2.22-2.5) if we use Bayesian priors that assume a large portion of cases are not detected. Weaker priors regarding the detection rate resulted in R0 values of 9.22 (95% CrI, 9.01-9.43), 9.14 (95% CrI, 8.99-9.29), and 8.06 (95% CrI, 7.82-8.3) and assumes nearly 90% of infected patients are identified. Given the mounting evidence that potentially large fractions of the population are asymptomatic, the weaker priors that generate the high R0 values to fit the data required assumptions about the epidemiology of COVID-19 that do not fit with the biology, particularly regarding the timeframe that people remain infectious. Our results suggest that models of transmission assuming a relatively lower R0 value that do not consider a large number of asymptomatic cases can result in misunderstanding of the underlying dynamics, leading to poor policy decisions and outcomes.