Susceptibility and infectiousness of SARS-CoV-2 in children versus adults, by variant (wild-type, Alpha, Delta): a systematic review and meta-analysis of household contact studies

ImportanceUnderstanding the susceptibility and infectiousness of children and adolescents in comparison to adults is important to appreciate their role in the COVID-19 pandemic. ObjectiveTo 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 SourcesWe searched EMBASE, PubMed and medRxiv up to January 2022. Additional studies were identified through contacting subject experts. Study SelectionTwo 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 SynthesisTwo 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 MeasuresOdds 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. ResultsTwenty-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 RelevanceWhen 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. KEY POINTSO_ST_ABSQuestionC_ST_ABSWhat is the evidence on the susceptibility and infectiousness of wild-type, Alpha and Delta variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among children and adolescents compared with adults in the household setting? FindingsIn this systematic review and meta-analysis of 28 studies that included 308,857 household contacts, children and adolescents were less susceptible to the wild-type and Delta variant and likely equally susceptible to the Alpha variant of SARS-Cov-2. Children aged 0-9 years old infected with the Alpha variant may be more infectious than adults, but for adolescents, Alpha infectiousness is unclear. The overall secondary attack rate (SAR) rose substantially from wild-type to Alpha and dropped somewhat from Alpha to Delta. MeaningThe epidemiological role of children and adolescents towards SARS-CoV-2 may be influenced by susceptibility, infectiousness, variant, age group and overall (relative) contagiousness.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support

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.

Investigating the effects of absolute humidity and human encounters on transmission of COVID-19 in the United States

BackgroundMounting 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. MethodsWe 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. ResultsThe 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. ConclusionsWe 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.

Modeling COVID-19 Transmission in Africa: Country-wise Projections of Total and Severe Infections Under Different Lockdown Scenarios

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

Modeling Hospital Energy and Economic Costs for COVID-19 Infection Control Interventions

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.

Explaining the Bomb-Like Dynamics of COVID-19 with Modeling and the Implications for Policy

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.