The incubation period distribution of coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis

Contextual Hospital Conditions and the Risk of Nosocomial SARS-CoV-2 Infection: A Matched Case-Control Study with Density Sampling in a Large Portuguese Hospital

Objective: Knowledge of the role of hospital conditions in SARS-CoV-2 transmission should inform strategies for the prevention of nosocomial spread of this pathogen and of similarly transmitted viruses. This study aimed to identify risk factors for nosocomial acquisition of SARS-CoV-2. Methods: We ran a nested case-control study with incidence density sampling among adult patients hospitalized for >7 days (August-December 2020). Patients testing positive for SARS-CoV-2 after the 7th day of hospitalization were defined as cases and matched with controls (1:4) by date of admission, hospitalization duration until index date, and type of department. Individual and contextual characteristics were gathered, including admission characteristics and exposures during the risk period. Conditional logistic regression was used to estimate the odds ratios (ORs) with respective 95% confidence intervals (CI) separately for probable (diagnosed on day 8-13) and definitive (diagnosed after day 14) nosocomial sets. Results: We identified 65 cases (31 probable; 34 definitive) and 219 controls. No individual characteristic was related to nosocomial acquisition of SARS-CoV-2. Contextual risk factors for nosocomial acquisition were staying in a non-refurbished room (probable nosocomial: OR = 3.6, 1.18-10.87), contact with roommates with newly diagnosed SARS-CoV-2 (probable nosocomial: OR = 9.9, 2.11-46.55; definitive nosocomial: OR = 3.4, 1.09-10.30), and contact with roommates with a first positive test 21-90 days before the beginning of contact (probable nosocomial: OR = 10.7, 1.97-57.7). Conclusions: Hospital conditions and contact with recently infected patients modulated nosocomial SARS-CoV-2 transmission. These results alert us to the importance of the physical context and of agile screening procedures to shorten contact with patients with recent infection.

Unravelling the effect of New Year's Eve celebrations on SARS-CoV-2 transmission

Public holidays have been associated with SARS-CoV-2 incidence surges, although a firm link remains to be established. This association is sometimes attributed to events where transmissions occur at a disproportionately high rate, known as superspreading events. Here, we describe a sudden surge in new cases with the Omicron BA.1 strain amongst higher education students in Belgium. Contact tracers classed most of these cases as likely or possibly infected on New Year's Eve, indicating a direct trigger by New Year celebrations. Using a combination of contact tracing and phylogenetic data, we show the limited role of superspreading events in this surge. Finally, the numerous simultaneous transmissions allowed a unique opportunity to determine the distribution of incubation periods of the Omicron strain. Overall, our results indicate that, even under social restrictions, a surge in transmissibility of SARS-CoV-2 can occur when holiday celebrations result in small social gatherings attended simultaneously and communitywide.

Assessing changes in incubation period, serial interval, and generation time of SARS-CoV-2 variants of concern: a systematic review and meta-analysis

BACKGROUND

After the first COVID-19 wave caused by the ancestral lineage, the pandemic has been fueled from the continuous emergence of new SARS-CoV-2 variants. Understanding key time-to-event periods for each emerging variant of concern is critical as it can provide insights into the future trajectory of the virus and help inform outbreak preparedness and response planning. Here, we aim to examine how the incubation period, serial interval, and generation time have changed from the ancestral SARS-CoV-2 lineage to different variants of concern.

METHODS

We conducted a systematic review and meta-analysis that synthesized the estimates of incubation period, serial interval, and generation time (both realized and intrinsic) for the ancestral lineage, Alpha, Beta, and Omicron variants of SARS-CoV-2.

RESULTS

Our study included 280 records obtained from 147 household studies, contact tracing studies, or studies where epidemiological links were known. With each emerging variant, we found a progressive shortening of each of the analyzed key time-to-event periods, although we did not find statistically significant differences between the Omicron subvariants. We found that Omicron BA.1 had the shortest pooled estimates for the incubation period (3.49 days, 95% CI: 3.13-4.86 days), Omicron BA.5 for the serial interval (2.37 days, 95% CI: 1.71-3.04 days), and Omicron BA.1 for the realized generation time (2.99 days, 95% CI: 2.48-3.49 days). Only one estimate for the intrinsic generation time was available for Omicron subvariants: 6.84 days (95% CrI: 5.72-8.60 days) for Omicron BA.1. The ancestral lineage had the highest pooled estimates for each investigated key time-to-event period. We also observed shorter pooled estimates for the serial interval compared to the incubation period across the virus lineages. When pooling the estimates across different virus lineages, we found considerable heterogeneities (I2 > 80%; I2 refers to the percentage of total variation across studies that is due to heterogeneity rather than chance), possibly resulting from heterogeneities between the different study populations (e.g., deployed interventions, social behavior, demographic characteristics).

CONCLUSIONS

Our study supports the importance of conducting contact tracing and epidemiological investigations to monitor changes in SARS-CoV-2 transmission patterns. Our findings highlight a progressive shortening of the incubation period, serial interval, and generation time, which can lead to epidemics that spread faster, with larger peak incidence, and harder to control. We also consistently found a shorter serial interval than incubation period, suggesting that a key feature of SARS-CoV-2 is the potential for pre-symptomatic transmission. These observations are instrumental to plan for future COVID-19 waves.

Evaluation of the EsteR Toolkit for COVID-19 Decision Support: Sensitivity Analysis and Usability Study

BACKGROUND

During the COVID-19 pandemic, local health authorities were responsible for managing and reporting current cases in Germany. Since March 2020, employees had to contain the spread of COVID-19 by monitoring and contacting infected persons as well as tracing their contacts. In the EsteR project, we implemented existing and newly developed statistical models as decision support tools to assist in the work of the local health authorities.

OBJECTIVE

The main goal of this study was to validate the EsteR toolkit in two complementary ways: first, investigating the stability of the answers provided by our statistical tools regarding model parameters in the back end and, second, evaluating the usability and applicability of our web application in the front end by test users.

METHODS

For model stability assessment, a sensitivity analysis was carried out for all 5 developed statistical models. The default parameters of our models as well as the test ranges of the model parameters were based on a previous literature review on COVID-19 properties. The obtained answers resulting from different parameters were compared using dissimilarity metrics and visualized using contour plots. In addition, the parameter ranges of general model stability were identified. For the usability evaluation of the web application, cognitive walk-throughs and focus group interviews were conducted with 6 containment scouts located at 2 different local health authorities. They were first asked to complete small tasks with the tools and then express their general impressions of the web application.

RESULTS

The simulation results showed that some statistical models were more sensitive to changes in their parameters than others. For each of the single-person use cases, we determined an area where the respective model could be rated as stable. In contrast, the results of the group use cases highly depended on the user inputs, and thus, no area of parameters with general model stability could be identified. We have also provided a detailed simulation report of the sensitivity analysis. In the user evaluation, the cognitive walk-throughs and focus group interviews revealed that the user interface needed to be simplified and more information was necessary as guidance. In general, the testers rated the web application as helpful, especially for new employees.

CONCLUSIONS

This evaluation study allowed us to refine the EsteR toolkit. Using the sensitivity analysis, we identified suitable model parameters and analyzed how stable the statistical models were in terms of changes in their parameters. Furthermore, the front end of the web application was improved with the results of the conducted cognitive walk-throughs and focus group interviews regarding its user-friendliness.

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19

The severe shortfall in testing supplies during the initial COVID-19 outbreak and ensuing struggle to manage the pandemic have affirmed the critical importance of optimal supply-constrained resource allocation strategies for controlling novel disease epidemics. To address the challenge of constrained resource optimization for managing diseases with complications like pre- and asymptomatic transmission, we develop an integro partial differential equation compartmental disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals. Our model overcomes the limitations of typical ordinary differential equation compartmental models by decoupling symptom status from model compartments to allow a more realistic representation of symptom onset and presymptomatic transmission. To analyze the influence of these realistic features on disease controllability, we find optimal strategies for reducing total infection sizes that allocate limited testing resources between 'clinical' testing, which targets symptomatic individuals, and 'non-clinical' testing, which targets non-symptomatic individuals. We apply our model not only to the original, delta, and omicron COVID-19 variants, but also to generically parameterized disease systems with varying mismatches between latent and incubation period distributions, which permit varying degrees of presymptomatic transmission or symptom onset before infectiousness. We find that factors that decrease controllability generally call for reduced levels of non-clinical testing in optimal strategies, while the relationship between incubation-latent mismatch, controllability, and optimal strategies is complicated. In particular, though greater degrees of presymptomatic transmission reduce disease controllability, they may increase or decrease the role of non-clinical testing in optimal strategies depending on other disease factors like transmissibility and latent period length. Importantly, our model allows a spectrum of diseases to be compared within a consistent framework such that lessons learned from COVID-19 can be transferred to resource constrained scenarios in future emerging epidemics and analyzed for optimality.

Nonpharmaceutical interventions for managing SARS-CoV-2

PURPOSE OF REVIEW

Initial response strategies to the COVID-19 pandemic were heavily reliant on nonpharmaceutical interventions (NPIs), a set of measures implemented to slow or even stop the spread of infection. Here, we reviewed key measures used during the COVID-19 pandemic.

RECENT FINDINGS

Some NPIs were successful in reducing the transmission of SARS-CoV-2. Personal protective measures such as face masks were widely used, and likely had some effect on transmission. The development and production of rapid antigen tests allowed self-diagnosis in the community, informing isolation and quarantine measures. Community-wide measures such as school closures, workplace closures and complete stay-at-home orders were able to reduce contacts and prevent transmission. They were widely used in the pandemic and contributed to reduce transmission in the community; however, there were also negative unintended consequences in the society and economy.

SUMMARY

NPIs slowed the spread of SARS-CoV-2 and are essential for pandemic preparedness and response. Understanding which measures are more effective at reducing transmission with lower costs is imperative.

Towards robust and accurate estimates of the incubation time distribution, with focus on upper tail probabilities and SARS-CoV-2 infection

Quarantine length for individuals who have been at risk for infection with SARS-CoV-2 has been based on estimates of the incubation time distribution. The time of infection is often not known exactly, yielding data with an interval censored time origin. We give a detailed account of the data structure, likelihood formulation and assumptions usually made in the literature: (i) the risk of infection is assumed constant on the exposure window and (ii) the incubation time follows a specific parametric distribution. The impact of these assumptions remains unclear, especially for the right tail of the distribution which informs quarantine policy. We quantified bias in percentiles by means of simulation studies that mimic reality as close as possible. If assumption (i) is not correct, then median and upper percentiles are affected similarly, whereas misspecification of the parametric approach (ii) mainly affects upper percentiles. The latter may yield considerable bias. We suggest a semiparametric method that provides more robust estimates without the need of a parametric choice. Additionally, we used a simulation study to evaluate a method that has been suggested if all infection times are left censored. It assumes that the width of the interval from infection to latest possible exposure follows a uniform distribution. This assumption gave biased results in the exponential phase of an outbreak. Our application to open source data suggests that focus should be on the level of information in the observations, as expressed by the width of exposure windows, rather than the number of observations.

SARS-CoV-2 incubation period across variants of concern, individual factors, and circumstances of infection in France: a case series analysis from the ComCor study

BACKGROUND

The incubation period of SARS-CoV-2 has been estimated for the known variants of concern. However, differences in study designs and settings make comparing variants difficult. We aimed to estimate the incubation period for each variant of concern compared with the historical strain within a unique and large study to identify individual factors and circumstances associated with its duration.

METHODS

In this case series analysis, we included participants (aged ≥18 years) of the ComCor case-control study in France who had a SARS-CoV-2 diagnosis between Oct 27, 2020, and Feb 4, 2022. Eligible participants were those who had the historical strain or a variant of concern during a single encounter with a known index case who was symptomatic and for whom the incubation period could be established, those who reported doing a reverse-transcription-PCR (RT-PCR) test, and those who were symptomatic by study completion. Sociodemographic and clinical characteristics, exposure information, circumstances of infection, and COVID-19 vaccination details were obtained via an online questionnaire, and variants were established through variant typing after RT-PCR testing or by matching the time that a positive test was reported with the predominance of a specific variant. We used multivariable linear regression to identify factors associated with the duration of the incubation period (defined as the number of days from contact with the index case to symptom onset).

FINDINGS

20 413 participants were eligible for inclusion in this study. Mean incubation period varied across variants: 4·96 days (95% CI 4·90-5·02) for alpha (B.1.1.7), 5·18 days (4·93-5·43) for beta (B.1.351) and gamma (P.1), 4·43 days (4·36-4·49) for delta (B.1.617.2), and 3·61 days (3·55-3·68) for omicron (B.1.1.529) compared with 4·61 days (4·56-4·66) for the historical strain. Participants with omicron had a shorter incubation period than participants with the historical strain (-0·9 days, 95% CI -1·0 to -0·7). The incubation period increased with age (participants aged ≥70 years had an incubation period 0·4 days [0·2 to 0·6] longer than participants aged 18-29 years), in female participants (by 0·1 days, 0·0 to 0·2), and in those who wore a mask during contact with the index case (by 0·2 days, 0·1 to 0·4), and was reduced in those for whom the index case was symptomatic (-0·1 days, -0·2 to -0·1). These data were robust to sensitivity analyses correcting for an over-reporting of incubation periods of 7 days.

INTERPRETATION

SARS-CoV-2 incubation period is notably reduced in omicron cases compared with all other variants of concern, in young people, after transmission from a symptomatic index case, after transmission to a maskless secondary case, and (to a lesser extent) in men. These findings can inform future COVID-19 contact-tracing strategies and modelling.

FUNDING

Institut Pasteur, the French National Agency for AIDS Research-Emerging Infectious Diseases, Fondation de France, the INCEPTION project, and the Integrative Biology of Emerging Infectious Diseases project.

Integrating tuberculosis and COVID-19 molecular testing in Lima, Peru: a cross-sectional, diagnostic accuracy study

BACKGROUND

Integrated molecular testing could be an opportunity to detect and provide care for both tuberculosis and COVID-19. Many high tuberculosis burden countries, such as Peru, have existing GeneXpert systems for tuberculosis testing with GeneXpert Xpert MTB/RIF Ultra (Xpert Ultra), and a GeneXpert SARS-CoV-2 assay, GeneXpert Xpert Xpress SARS-CoV-2 (Xpert Xpress), is also available. We aimed to assess the feasibility of integrating tuberculosis and COVID-19 testing using one sputum specimen with Xpert Ultra and Xpert Xpress in Lima, Peru.

METHODS

In this cross-sectional, diagnostic accuracy study, we recruited adults presenting with clinical symptoms or suggestive history of tuberculosis or COVID-19, or both. Participants were recruited from a total of 35 primary health facilities in Lima, Peru. Participants provided one nasopharyngeal swab and one sputum sample. For COVID-19, we tested nasopharyngeal swabs and sputum using Xpert Xpress; for tuberculosis, we tested sputum using culture and Xpert Ultra. We compared diagnostic accuracy of sputum testing using Xpert Xpress with nasopharyngeal swab testing using Xpert Xpress. Individuals with positive Xpert Xpress nasopharyngeal swab results were considered COVID-19 positive, and a positive culture indicated tuberculosis. To assess testing integration, the proportion of cases identified in sputum by Xpert Xpress was compared with Xpert Xpress on nasopharyngeal swabs, and sputum by Xpert Ultra was compared with culture.

FINDINGS

Between Jan 11, 2021, and April 26, 2022, we recruited 600 participants (312 [52%] women and 288 [48%] men). In-study prevalence of tuberculosis was 13% (80 participants, 95% CI 11-16) and of SARS-CoV-2 was 35% (212 participants, 32-39). Among tuberculosis cases, 13 (2·2%, 1·2-3·7) participants were concurrently positive for SARS-CoV-2. Regarding the diagnostic yield of integrated testing, Xpert Ultra detected 96% (89-99) of culture-confirmed tuberculosis cases (n=77), and Xpert Xpress-sputum detected 67% (60-73) of COVID-19 cases (n=134). All five study staff reported that integrated molecular testing was easy and acceptable.

INTERPRETATION

The diagnostic yield of Xpert Xpress on sputum was moderate, but integrated testing for tuberculosis and COVID-19 with GeneXpert was feasible. However, systematic testing for both diseases might not be the ideal approach for everyone presenting with presumptive tuberculosis or COVID-19, as concurrent positive cases were rare during the study period. Further research might help to identify when integrated testing is most worthwhile and its optimal implementation.

FUNDING

Canadian Institutes of Health Research and International Development Research Centre.

TRANSLATION

For the Spanish translation of the abstract see Supplementary Materials section.

Do pathogens always evolve to be less virulent? The virulence–transmission trade-off in light of the COVID-19 pandemic

The direction the evolution of virulence takes in connection with any pathogen is a long-standing question. Formerly, it was theorized that pathogens should always evolve to be less virulent. As observations were not in line with this theoretical outcome, new theories emerged, chief among them the transmission–virulence trade-off hypotheses, which predicts an intermediate level of virulence as the endpoint of evolution. At the moment, we are very much interested in the future evolution of COVID-19’s virulence. Here, we show that the disease does not fulfill all the assumptions of the hypothesis. In the case of COVID-19, a higher viral load does not mean a higher risk of death; immunity is not long-lasting; other hosts can act as reservoirs for the virus; and death as a consequence of viral infection does not shorten the infectious period. Consequently, we cannot predict the short- or long-term evolution of the virulence of COVID-19.

SARS-CoV-2 viral load and shedding kinetics

SARS-CoV-2 viral load and detection of infectious virus in the respiratory tract are the two key parameters for estimating infectiousness. As shedding of infectious virus is required for onward transmission, understanding shedding characteristics is relevant for public health interventions. Viral shedding is influenced by biological characteristics of the virus, host factors and pre-existing immunity (previous infection or vaccination) of the infected individual. Although the process of human-to-human transmission is multifactorial, viral load substantially contributed to human-to-human transmission, with higher viral load posing a greater risk for onward transmission. Emerging SARS-CoV-2 variants of concern have further complicated the picture of virus shedding. As underlying immunity in the population through previous infection, vaccination or a combination of both has rapidly increased on a global scale after almost 3 years of the pandemic, viral shedding patterns have become more distinct from those of ancestral SARS-CoV-2. Understanding the factors and mechanisms that influence infectious virus shedding and the period during which individuals infected with SARS-CoV-2 are contagious is crucial to guide public health measures and limit transmission. Furthermore, diagnostic tools to demonstrate the presence of infectious virus from routine diagnostic specimens are needed.

Ascertainment rate of SARS-CoV-2 infections from healthcare and community testing in the UK

The proportion of SARS-CoV-2 infections ascertained through healthcare and community testing is generally unknown and expected to vary depending on natural factors and changes in test-seeking behaviour. Here we use population surveillance data and reported daily case numbers in the United Kingdom to estimate the rate of case ascertainment. We mathematically describe the relationship between the ascertainment rate, the daily number of reported cases, population prevalence, and the sensitivity of PCR and Lateral Flow tests as a function time since exposure. Applying this model to the data, we estimate that 20%-40% of SARS-CoV-2 infections in the UK were ascertained with a positive test with results varying by time and region. Cases of the Alpha variant were ascertained at a higher rate than the wild type variants circulating in the early pandemic, and higher again for the Delta variant and Omicron BA.1 sub-lineage, but lower for the BA.2 sub-lineage. Case ascertainment was higher in adults than in children. We further estimate the daily number of infections and compare this to mortality data to estimate that the infection fatality rate increased by a factor of 3 during the period dominated by the Alpha variant, and declined in line with the distribution of vaccines. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Household transmission of SARS-CoV-2 during the Omicron wave in Shanghai, China: A case-ascertained study

OBJECTIVES

We used a case-ascertained study to determine the features of household transmission of SARS-CoV-2 Omicron variant in Shanghai, China.

METHODS

In April 2022, we carried out a household transmission study from 309 households of 335 SARS-CoV-2 pediatric cases referred to a designated tertiary Children's Hospital. The detailed information can be collected from the 297 households for estimating the transmission parameters. The 236 households were qualified for estimating the secondary infection attack rates (SARI ) and secondary clinical attack rates (SARC ) among adult household contacts, characterizing the transmission heterogeneities in infectivity and susceptibility, and assessing the vaccine effectiveness.

RESULTS

We estimated the mean incubation period and serial interval of Omicron variant to be 4.6 ± 2.1 and 3.9 ± 3.7 days, respectively, with 57.2% of the transmission events occurring at the presymptomatic phase. The overall SARI and SARC among adult household contacts were 77.11% (95% confidence interval [CI]: 73.58%-80.63%) and 67.03% (63.09%-70.98%). We found higher household susceptibility in females. Infectivity was not significantly different between children and adults and symptomatic and asymptomatic cases. Two-dose and booster-dose of inactivated COVID-19 vaccination were 14.8% (5.8%-22.9%) and 18.9% (9.0%-27.7%) effective against Omicron infection and 21.5% (10.4%-31.2%) and 24.3% (12.3%-34.7%) effective against the symptomatic disease.

CONCLUSIONS

We found high household transmission during the Omicron wave in Shanghai due to presymptomatic and asymptomatic transmission despite implementation of strict interventions, indicating the importance of early detection and timely isolation of SARS-CoV-2 infections. Marginal effectiveness of inactivated vaccines against Omicron infection poses a great challenge for outbreak containment.

Pathophysiology of SARS-CoV-2 Infection of Nasal Respiratory and Olfactory Epithelia and Its Clinical Impact

PURPOSE OF REVIEW

While the predominant cause for morbidity and mortality with SARS-CoV-2 infection is the lower respiratory tract manifestations of the disease, the effects of SARS-CoV-2 infection on the sinonasal tract have also come to the forefront especially with the increased recognition of olfactory symptom. This review presents a comprehensive summary of the mechanisms of action of the SARS-CoV-2 virus, sinonasal pathophysiology of COVID-19, and the correlation with the clinical and epidemiological impact on olfactory dysfunction.

RECENT FINDINGS

ACE2 and TMPRSS2 receptors are key players in the mechanism of infection of SARS-CoV-2. They are present within both the nasal respiratory as well as olfactory epithelia. There are however differences in susceptibility between different groups of individuals, as well as between the different SARS-CoV-2 variants. The sinonasal cavity is an important route for SARS-CoV-2 infection. While the mechanism of infection of SARS-CoV-2 in nasal respiratory and olfactory epithelia is similar, there exist small but significant differences in the susceptibility of these epithelia and consequently clinical manifestations of the disease. Understanding the differences and nuances in sinonasal pathophysiology in COVID-19 would allow the clinician to predict and counsel patients suffering from COVID-19. Future research into molecular pathways and cytokine responses at different stages of infection and different variants of SARS-CoV-2 would evaluate the individual clinical phenotype, prognosis, and possibly response to vaccines and therapeutics.

COVID-19 in one region of New Zealand: a descriptive epidemiological study

OBJECTIVE

To describe the epidemiology of COVID-19 in one region of New Zealand in the context of the national lockdown and provide a reference for comparing infection dynamics and control measures between SARS-Cov-2 strains.  Methods: Epidemiological linking and analysis of COVID-19 cases and their close contacts residing in the geographical area served by the Southern District Health Board (SDHB).  Results: From 13 March to 5 April 5 2020, 186 cases were laboratory-confirmed with wild-type Sars-Cov-2 in SDHB. Overall, 35·1% of cases were attributable to household transmission, 27·0% to non-household, 25·4% to overseas travel and 12·4% had no known epidemiological links. The highest secondary attack rate was observed in households during lockdown (15·3%, 95%CI 10·4-21·5). The mean serial interval in 50 exclusive infector-infectee pairs was 4·0 days (95%CI 3·2-4·7days), and the mean incubation period was 3.4 days (95%CI 2·7-4·2).

CONCLUSIONS

The SARS-CoV-2 incubation period may be shorter than early estimates that were limited by uncertainties in exposure history or small sample sizes.

IMPLICATIONS FOR PUBLIC HEALTH

The continuation of household transmission during lockdown highlights the need for effective home-based quarantine guidance. Our findings of a short incubation period highlight the need to contact trace and isolate as rapidly as possible.

The Association Between Clinical Severity and Incubation Period of SARS-CoV-2 Delta Variants: Retrospective Observational Study

BACKGROUND

As of August 25, 2021, Jiangsu province experienced the largest COVID-19 outbreak in eastern China that was seeded by SARS-CoV-2 Delta variants. As one of the key epidemiological parameters characterizing the transmission dynamics of COVID-19, the incubation period plays an essential role in informing public health measures for epidemic control. The incubation period of COVID-19 could vary by different age, sex, disease severity, and study settings. However, the impacts of these factors on the incubation period of Delta variants remains uninvestigated.

OBJECTIVE

The objective of this study is to characterize the incubation period of the Delta variant using detailed contact tracing data. The effects of age, sex, and disease severity on the incubation period were investigated by multivariate regression analysis and subgroup analysis.

METHODS

We extracted contact tracing data of 353 laboratory-confirmed cases of SARS-CoV-2 Delta variants' infection in Jiangsu province, China, from July to August 2021. The distribution of incubation period of Delta variants was estimated by using likelihood-based approach with adjustment for interval-censored observations. The effects of age, sex, and disease severity on the incubation period were expiated by using multivariate logistic regression model with interval censoring.

RESULTS

The mean incubation period of the Delta variant was estimated at 6.64 days (95% credible interval: 6.27-7.00). We found that female cases and cases with severe symptoms had relatively longer mean incubation periods than male cases and those with nonsevere symptoms, respectively. One-day increase in the incubation period of Delta variants was associated with a weak decrease in the probability of having severe illness with an adjusted odds ratio of 0.88 (95% credible interval: 0.71-1.07).

CONCLUSIONS

In this study, the incubation period was found to vary across different levels of sex, age, and disease severity of COVID-19. These findings provide additional information on the incubation period of Delta variants and highlight the importance of continuing surveillance and monitoring of the epidemiological characteristics of emerging SARS-CoV-2 variants as they evolve.

First Detection of the SARS-CoV-2 Omicron BA.5/22B in Monaco

The Omicron BA.5/22B variant has been designated as a "variant of concern" by the World Health Organization. We describe, here, the first evidence in Monaco of infection with an Omicron BA.5/22B variant, probably imported from the Republic of Seychelles, harboring a rare combination of non-BA.5/22B signature amino acid changes. SARS-CoV-2 neutralizing antibodies were measured with a surrogate virus neutralization test. SARS-CoV-2 genotype screening was performed on nasopharyngeal samples with a multiplex qPCR assay. The SARS-CoV-2 genome was obtained by next-generation sequencing with the Illumina COVID-seq protocol, then assembly using bioinformatics pipelines and software was performed. The BA.5/22B spike protein structure was obtained by molecular modeling. Two spouses were SARS-CoV-2-diagnosed the day they returned from a one-week trip in the Republic of Seychelles. SARS-CoV-2 qPCR screening for variant-specific mutations identified an Omicron variant BA.1/21K, BA.4/22A, or BA.5/22B. A SARS-Co-2 BA.5/22B variant genome was recovered from one of the spouses. Aside from BA.5/22B-defining amino acid substitutions, four other amino acid changes were encoded including Q556K in ORF1a, K2557R in ORF1b, and A67V and A829T in spike; only 13 genomes in sequence databases harbored these four mutations concurrently. Structural analysis of this BA.5/22B variant predicted that A829T in spike may result in a compaction that may affect conformational plasticity. Overall, our findings warrant performing genome-based genotypic surveillance to survey accurately the emergence and circulation of SARS-CoV-2 variants worldwide and point out that their first occurrence in a country is often through international travel despite implemented countermeasures.

Shorter serial intervals and incubation periods in SARS-CoV-2 variants than the SARS-CoV-2 ancestral strain

The Delta and Omicron variants have the pooled estimates of serial interval as 3.4 days (95% CI: 3.0, 3.7) and 3.1 days (95% CI: 2.9, 3.2), respectively; incubation periods as 4.8 days (95% CI: 3.9, 5.6) and 3.6 days (95% CI: 2.3, 4.9), respectively.

The unmitigated profile of COVID-19 infectiousness

Quantifying the temporal dynamics of infectiousness of individuals infected with SARS-CoV-2 is crucial for understanding the spread of COVID-19 and for evaluating the effectiveness of mitigation strategies. Many studies have estimated the infectiousness profile using observed serial intervals. However, statistical and epidemiological biases could lead to underestimation of the duration of infectiousness. We correct for these biases by curating data from the initial outbreak of the pandemic in China (when mitigation was minimal), and find that the infectiousness profile of the original strain is longer than previously thought. Sensitivity analysis shows our results are robust to model structure, assumed growth rate and potential observational biases. Although unmitigated transmission data is lacking for variants of concern (VOCs), previous analyses suggest that the alpha and delta variants have faster within-host kinetics, which we extrapolate to crude estimates of variant-specific unmitigated generation intervals. Knowing the unmitigated infectiousness profile of infected individuals can inform estimates of the effectiveness of isolation and quarantine measures. The framework presented here can help design better quarantine policies in early stages of future epidemics.

Estimating the incubation period of SARS-CoV-2 Omicron BA.1 variant in comparison with that during the Delta variant dominance in South Korea

Based on exposure history and symptom onset of 22 Omicron BA.1 cases in South Korea from November to December 2021, we estimated mean incubation period of 3.5 days (95% CI: 2.5, 3.8), and then compared to that of 6.5 days (95% CI: 5.3, 7.7) for 64 cases during Delta variants' dominance in June 2021. For Omicron BA.1 variants, we found that 95% of symptomatic cases developed clinical conditions within 6.0 days (95% CI: 4.3, 6.6) after exposure. Thus, a shorter quarantine period may be considered based on symptoms, or similarly laboratory testing, when Omicron BA.1 variants are circulating.

Incubation period of wild type of SARS-CoV-2 infections by age, gender, and epidemic periods

Background

The incubation period of the coronavirus disease 2019 (COVID-19) is estimated to vary by demographic factors and the COVID-19 epidemic periods.

Objective

This study examined the incubation period of the wild type of SARS-CoV-2 infections by the different age groups, gender, and epidemic periods in South Korea.

Methods

We collected COVID-19 patient data from the Korean public health authorities and estimated the incubation period by fitting three different distributions, including log-normal, gamma, and Weibull distributions, after stratification by gender and age groups. To identify any temporal impact on the incubation period, we divided the study period into two different epidemic periods (Period-1: 19 January−19 April 2020 and Period-2: 20 April−16 October 2020), and assessed for any differences.

Results

We identified the log-normal as the best-fit model. The estimated median incubation period was 4.6 (95% CI: 3.9–4.9) days, and the 95th percentile was 11.7 (95% CI: 10.2–12.2) days. We found that the incubation period did not differ significantly between males and females (p = 0.42), age groups (p = 0.60), and the two different epidemic periods (p = 0.77).

Conclusions

The incubation period of wild type of SARS-CoV-2 infection during the COVID-19 pandemic 2020, in South Korea, does not likely differ by age group, gender and epidemic period.

Transmission Potential of the Omicron Variant of Severe Acute Respiratory Syndrome Coronavirus 2 in South Korea, 25 November 2021-8 January 2022

Our study indicates sustained transmission (effective reproduction number, 1.3; serial interval, 4.2 days; regional doubling times, 3.3-11.4 days) of the severe acute respiratory syndrome coronavirus 2 Omicron (B.1.1.529) variant (N = 2351) in South Korea (25 November 2021-8 January 2022), implicating insufficient protection through vaccination and supporting nonpharmaceutical control measures.

COVID-19 in Japan, January-March 2020: insights from the first three months of the epidemic

BACKGROUND

Understanding the characteristics and natural history of novel pathogens is crucial to inform successful control measures. Japan was one of the first affected countries in the COVID-19 pandemic reporting their first case on 14 January 2020. Interventions including airport screening, contact tracing, and cluster investigations were quickly implemented. Here we present insights from the first 3 months of the epidemic in Japan based on detailed case data.

METHODS

We conducted descriptive analyses based on information systematically extracted from individual case reports from 13 January to 31 March 2020 including patient demographics, date of report and symptom onset, symptom progression, travel history, and contact type. We analysed symptom progression and estimated the time-varying reproduction number, Rt, correcting for epidemic growth using an established Bayesian framework. Key delays and the age-specific probability of transmission were estimated using data on exposures and transmission pairs.

RESULTS

The corrected fitted mean onset-to-reporting delay after the peak was 4 days (standard deviation: ± 2 days). Early transmission was driven primarily by returning travellers with Rt peaking at 2.4 (95% CrI: 1.6, 3.3) nationally. In the final week of the trusted period (16-23 March 2020), Rt accounting for importations diverged from overall Rt at 1.1 (95% CrI: 1.0, 1.2) compared to 1.5 (95% CrI: 1.3, 1.6), respectively. Household (39.0%) and workplace (11.6%) exposures were the most frequently reported potential source of infection. The estimated probability of transmission was assortative by age with individuals more likely to infect, and be infected by, contacts in a similar age group to them. Across all age groups, cases most frequently onset with cough, fever, and fatigue. There were no reported cases of patients < 20 years old developing pneumonia or severe respiratory symptoms.

CONCLUSIONS

Information collected in the early phases of an outbreak are important in characterising any novel pathogen. The availability of timely and detailed data and appropriate analyses is critical to estimate and understand a pathogen's transmissibility, high-risk settings for transmission, and key symptoms. These insights can help to inform urgent response strategies.

Shorter Incubation Period among COVID-19 Cases with the BA.1 Omicron Variant

We aimed to elucidate the range of the incubation period in patients infected with the SARS-CoV-2 Omicron variant in comparison with the Alpha variant. Contact tracing data from three Japanese public health centers (total residents, 1.06 million) collected following the guidelines of the Infectious Diseases Control Law were reviewed for 1589 PCR-confirmed COVID-19 cases diagnosed in January 2022. We identified 77 eligible symptomatic patients for whom the date and setting of transmission were known, in the absence of any other probable routes of transmission. The observed incubation period was 3.03 ± 1.35 days (mean ± SDM). In the log-normal distribution, 5th, 50th and 95th percentile values were 1.3 days (95% CI: 1.0−1.6), 2.8 days (2.5−3.1) and 5.8 days (4.8−7.5), significantly shorter than among the 51 patients with the Alpha variant diagnosed in April and May in 2021 (4.94 days ± 2.19, 2.1 days (1.5−2.7), 4.5 days (4.0−5.1) and 9.6 days (7.4−13.0), p < 0.001). As this incubation period, mainly of sublineage BA.1, is even shorter than that in the Delta variant, it is thought to partially explain the variant replacement occurring in late 2021 to early 2022 in many countries.

Passive Immunotherapy Against SARS-CoV-2: From Plasma-Based Therapy to Single Potent Antibodies in the Race to Stay Ahead of the Variants

The COVID-19 pandemic is now approaching 2 years old, with more than 440 million people infected and nearly six million dead worldwide, making it the most significant pandemic since the 1918 influenza pandemic. The severity and significance of SARS-CoV-2 was recognized immediately upon discovery, leading to innumerable companies and institutes designing and generating vaccines and therapeutic antibodies literally as soon as recombinant SARS-CoV-2 spike protein sequence was available. Within months of the pandemic start, several antibodies had been generated, tested, and moved into clinical trials, including Eli Lilly's bamlanivimab and etesevimab, Regeneron's mixture of imdevimab and casirivimab, Vir's sotrovimab, Celltrion's regdanvimab, and Lilly's bebtelovimab. These antibodies all have now received at least Emergency Use Authorizations (EUAs) and some have received full approval in select countries. To date, more than three dozen antibodies or antibody combinations have been forwarded into clinical trials. These antibodies to SARS-CoV-2 all target the receptor-binding domain (RBD), with some blocking the ability of the RBD to bind human ACE2, while others bind core regions of the RBD to modulate spike stability or ability to fuse to host cell membranes. While these antibodies were being discovered and developed, new variants of SARS-CoV-2 have cropped up in real time, altering the antibody landscape on a moving basis. Over the past year, the search has widened to find antibodies capable of neutralizing the wide array of variants that have arisen, including Alpha, Beta, Gamma, Delta, and Omicron. The recent rise and dominance of the Omicron family of variants, including the rather disparate BA.1 and BA.2 variants, demonstrate the need to continue to find new approaches to neutralize the rapidly evolving SARS-CoV-2 virus. This review highlights both convalescent plasma- and polyclonal antibody-based approaches as well as the top approximately 50 antibodies to SARS-CoV-2, their epitopes, their ability to bind to SARS-CoV-2 variants, and how they are delivered. New approaches to antibody constructs, including single domain antibodies, bispecific antibodies, IgA- and IgM-based antibodies, and modified ACE2-Fc fusion proteins, are also described. Finally, antibodies being developed for palliative care of COVID-19 disease, including the ramifications of cytokine release syndrome (CRS) and acute respiratory distress syndrome (ARDS), are described.

Reassessing Reported Deaths and Estimated Infection Attack Rate during the First 6 Months of the COVID-19 Epidemic, Delhi, India

India reported >10 million coronavirus disease (COVID-19) cases and 149,000 deaths in 2020. To reassess reported deaths and estimate incidence rates during the first 6 months of the epidemic, we used a severe acute respiratory syndrome coronavirus 2 transmission model fit to data from 3 serosurveys in Delhi and time-series documentation of reported deaths. We estimated 48.7% (95% credible interval 22.1%-76.8%) cumulative infection in the population through the end of September 2020. Using an age-adjusted overall infection fatality ratio based on age-specific estimates from mostly high-income countries, we estimated that just 15.0% (95% credible interval 9.3%-34.0%) of COVID-19 deaths had been reported, indicating either substantial underreporting or lower age-specific infection-fatality ratios in India than in high-income countries. Despite the estimated high attack rate, additional epidemic waves occurred in late 2020 and April-May 2021. Future dynamics will depend on the duration of natural and vaccine-induced immunity and their effectiveness against new variants.

Reassessing Reported Deaths and Estimated Infection Attack Rate during the First 6 Months of the COVID-19 Epidemic, Delhi, India

India reported >10 million coronavirus disease (COVID-19) cases and 149,000 deaths in 2020. To reassess reported deaths and estimate incidence rates during the first 6 months of the epidemic, we used a severe acute respiratory syndrome coronavirus 2 transmission model fit to data from 3 serosurveys in Delhi and time-series documentation of reported deaths. We estimated 48.7% (95% credible interval 22.1%-76.8%) cumulative infection in the population through the end of September 2020. Using an age-adjusted overall infection fatality ratio based on age-specific estimates from mostly high-income countries, we estimated that just 15.0% (95% credible interval 9.3%-34.0%) of COVID-19 deaths had been reported, indicating either substantial underreporting or lower age-specific infection-fatality ratios in India than in high-income countries. Despite the estimated high attack rate, additional epidemic waves occurred in late 2020 and April-May 2021. Future dynamics will depend on the duration of natural and vaccine-induced immunity and their effectiveness against new variants.

Transmission dynamics and epidemiological characteristics of SARS-CoV-2 Delta variant infections in Guangdong, China, May to June 2021

Background

The Delta variant of SARS-CoV-2 had become predominant globally by November 2021.

Aim

We evaluated transmission dynamics and epidemiological characteristics of the Delta variant in an outbreak in southern China.

Methods

Data on confirmed COVID-19 cases and their close contacts were retrospectively collected from the outbreak that occurred in Guangdong, China in May and June 2021. Key epidemiological parameters, temporal trend of viral loads and secondary attack rates were estimated. We also evaluated the association of vaccination with viral load and transmission.

Results

We identified 167 patients infected with the Delta variant in the Guangdong outbreak. Mean estimates of latent and incubation period were 3.9 days and 5.8 days, respectively. Relatively higher viral load was observed in infections with Delta than in infections with wild-type SARS-CoV-2. Secondary attack rate among close contacts of cases with Delta was 1.4%, and 73.1% (95% credible interval (CrI): 32.9–91.4) of the transmissions occurred before onset. Index cases without vaccination (adjusted odds ratio (aOR): 2.84; 95% CI: 1.19–8.45) or with an incomplete vaccination series (aOR: 6.02; 95% CI: 2.45–18.16) were more likely to transmit infection to their contacts than those who had received the complete primary vaccination series.

Discussion

Patients infected with the Delta variant had more rapid symptom onset compared with the wild type. The time-varying serial interval should be accounted for in estimation of reproduction numbers. The higher viral load and higher risk of pre-symptomatic transmission indicated the challenges in control of infections with the Delta variant.

Waning of SARS-CoV-2 booster viral-load reduction effectiveness

The BNT162b2 COVID-19 vaccine has been shown to reduce viral load of breakthrough infections (BTIs), an important factor affecting infectiousness. This viral-load protective effect has been waning with time post the second vaccine and later restored with a booster shot. It is currently unclear though for how long this regained effectiveness lasts. Analyzing Ct values of SARS-CoV-2 qRT-PCR tests of over 22,000 infections during a Delta-variant-dominant period in Israel, we find that this viral-load reduction effectiveness significantly declines within months post the booster dose. Adjusting for age, sex and calendric date, Ct values of RdRp gene initially increases by 2.7 [CI: 2.3-3.0] relative to unvaccinated in the first month post the booster dose, yet then decays to a difference of 1.3 [CI: 0.7-1.9] in the second month and becomes small and insignificant in the third to fourth months. The rate and magnitude of this post-booster decline in viral-load reduction effectiveness mirror those observed post the second vaccine. These results suggest rapid waning of the booster’s effectiveness in reducing infectiousness, possibly affecting community-level spread of the virus.

The BNT162b2 COVID-19 vaccine has been shown to reduce viral load of breakthrough infections (BTIs). Here, analyzing viral loads of BTIs post third vaccine shot, Levine-Tiefenbrun et al. show waning of the booster’s effectiveness in reducing infectiousness within months, mirroring the rate and magnitude of decline observed post the second shot.

Shorter Incubation Period among Unvaccinated Delta Variant Coronavirus Disease 2019 Patients in Japan

Few studies have assessed incubation periods of the severe acute respiratory syndrome coronavirus 2 Delta variant. This study aimed to elucidate the transmission dynamics, especially the incubation period, for the Delta variant compared with non-Delta strains. We studied unvaccinated coronavirus disease 2019 patients with definite single exposure date from August 2020 to September 2021 in Japan. The incubation periods were calculated and compared by Mann-Whitney U test for Delta (with L452R mutation) and non-Delta cases. We estimated mean and percentiles of incubation period by fitting parametric distribution to data in the Bayesian statistical framework. We enrolled 214 patients (121 Delta and 103 non-Delta cases) with one specific date of exposure to the virus. The mean incubation period was 3.7 days and 4.9 days for Delta and non-Delta cases, respectively (p-value = 0.000). When lognormal distributions were fitted, the estimated mean incubation periods were 3.7 (95% credible interval (CI) 3.4-4.0) and 5.0 (95% CI 4.5-5.6) days for Delta and non-Delta cases, respectively. The estimated 97.5th percentile of incubation period was 6.9 (95% CI 5.9-8.0) days and 10.4 (95% CI 8.6-12.7) days for Delta and non-Delta cases, respectively. Unvaccinated Delta variant cases had shorter incubation periods than non-Delta variant cases.

Transmission and containment of the SARS-CoV-2 Delta variant of concern in Guangzhou, China: A population-based study

BACKGROUND

The first community transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant of concern (VOC) in Guangzhou, China occurred between May and June 2021. Herein, we describe the epidemiological characteristics of this outbreak and evaluate the implemented containment measures against this outbreak.

METHODOLOGY/PRINCIPAL FINDINGS

Guangzhou Center for Disease Control and Prevention provided the data on SARS-CoV-2 infections reported between 21 May and 24 June 2021. We estimated the incubation period distribution by fitting a gamma distribution to the data, while the serial interval distribution was estimated by fitting a normal distribution. The instantaneous effective reproductive number (Rt) was estimated to reflect the transmissibility of SARS-CoV-2. Clinical severity was compared for cases with different vaccination statuses using an ordinal regression model after controlling for age. Of the reported local cases, 7/153 (4.6%) were asymptomatic. The median incubation period was 6.02 (95% confidence interval [CI]: 5.42-6.71) days and the means of serial intervals decreased from 5.19 (95% CI: 4.29-6.11) to 3.78 (95% CI: 2.74-4.81) days. The incubation period increased with age (P<0.001). A hierarchical prevention and control strategy against COVID-19 was implemented in Guangzhou, with Rt decreasing from 6.83 (95% credible interval [CrI]: 3.98-10.44) for the 7-day time window ending on 27 May 2021 to below 1 for the time window ending on 8 June and thereafter. Individuals with partial or full vaccination schedules with BBIBP-CorV or CoronaVac accounted for 15.3% of the COVID-19 cases. Clinical symptoms were milder in partially or fully vaccinated cases than in unvaccinated cases (odds ratio [OR] = 0.26 [95% CI: 0.07-0.94]).

CONCLUSIONS/SIGNIFICANCE

The hierarchical prevention and control strategy against COVID-19 in Guangzhou was timely and effective. Authorised inactivated vaccines are likely to contribute to reducing the probability of developing severe disease. Our findings have important implications for the containment of COVID-19.

Air travel-related outbreak of multiple SARS-CoV-2 variants

BACKGROUND

A large cluster of 59 cases were linked to a single flight with 146 passengers from New Delhi to Hong Kong in April 2021. This outbreak coincided with early reports of exponential pandemic growth in New Delhi, which reached a peak of > 400 000 newly confirmed cases on 7 May 2021.

METHODS

Epidemiological information including date of symptom onset, date of positive-sample detection and travel and contact history for individual cases from this flight were collected. Whole genome sequencing was performed, and sequences were classified based on the dynamic Pango nomenclature system. Maximum-likelihood phylogenetic analysis compared sequences from this flight alongside other cases imported from India to Hong Kong on 26 flights between June 2020 and April 2021, as well as sequences from India or associated with India-related travel from February to April 2021 and 1217 reference sequences.

RESULTS

Sequence analysis identified six lineages of SARS-CoV-2 belonging to two variants of concern (Alpha and Delta) and one variant of public health interest (Kappa) involved in this outbreak. Phylogenetic analysis confirmed at least three independent sub-lineages of Alpha with limited onward transmission, a superspreading event comprising 37 cases of Kappa and transmission of Delta to only one passenger. Additional analysis of another 26 flights from India to Hong Kong confirmed widespread circulation of all three variants in India since early March 2021.

CONCLUSIONS

The broad spectrum of disease severity and long incubation period of SARS-CoV-2 pose a challenge for surveillance and control. As illustrated by this particular outbreak, opportunistic infections of SARS-CoV-2 can occur irrespective of variant lineage, and requiring a nucleic acid test within 72 hours of departure may be insufficient to prevent importation or in-flight transmission.

Viral loads of Delta-variant SARS-CoV-2 breakthrough infections after vaccination and booster with BNT162b2

The effectiveness of the coronavirus disease 2019 (COVID-19) BNT162b2 vaccine in preventing disease and reducing viral loads of breakthrough infections (BTIs) has been decreasing, concomitantly with the rise of the Delta variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, it is unclear whether the observed decreased effectiveness of the vaccine in reducing viral loads is inherent to the Delta variant or is dependent on time from immunization. By analyzing viral loads of over 16,000 infections during the current, Delta-variant-dominated pandemic wave in Israel, we found that BTIs in recently fully vaccinated individuals have lower viral loads than infections in unvaccinated individuals. However, this effect starts to decline 2 months after vaccination and ultimately vanishes 6 months or longer after vaccination. Notably, we found that the effect of BNT162b2 on reducing BTI viral loads is restored after a booster dose. These results suggest that BNT162b2 might decrease the infectiousness of BTIs even with the Delta variant, and that, although this protective effect declines with time, it can be restored, at least temporarily, with a third, booster, vaccine dose.

Efficacy of a Nasal Spray Containing Iota-Carrageenan in the Postexposure Prophylaxis of COVID-19 in Hospital Personnel Dedicated to Patients Care with COVID-19 Disease

BACKGROUND

Iota-Carrageenan (I-C) is a sulfate polysaccharide synthesized by red algae, with demonstrated antiviral activity and clinical efficacy as nasal spray in the treatment of common cold. In vitro, I-C inhibits SARS-CoV-2 infection in cell culture.

RESEARCH QUESTION

Can a nasal spray with Iota-Carrageenan be useful in the prophylaxis of COVID-19 in health care workers managing patients with COVID-19 disease?

STUDY DESIGN AND METHODS

This is a pilot pragmatic multicenter, randomized, double-blind, placebo-controlled study assessing the use of a nasal spray containing I-C in the prophylaxis of COVID-19 in hospital personnel dedicated to care of COVID-19 patients. Clinically healthy physicians, nurses, kinesiologists and other health care providers managing patients hospitalized for COVID-19 were assigned in a 1:1 ratio to receive four daily doses of I-C spray or placebo for 21 days. The primary end point was clinical COVID-19, as confirmed by reverse transcriptase polymerase chain reaction testing, over a period of 21 days. The trial is registered at ClinicalTrials.gov (NCT04521322).

RESULTS

A total of 394 individuals were randomly assigned to receive I-C or placebo. Both treatment groups had similar baseline characteristics. The incidence of COVID-19 differs significantly between subjects receiving the nasal spray with I-C (2 of 196 [1.0%]) and those receiving placebo (10 of 198 [5.0%]). Relative risk reduction: 79.8% (95% CI 5.3 to 95.4; p=0.03). Absolute risk reduction: 4% (95% CI 0.6 to 7.4).

INTERPRETATION

In this pilot study a nasal spray with I-C showed significant efficacy in preventing COVID-19 in health care workers managing patients with COVID-19 disease.

CLINICAL TRIALS REGISTRATION

NCT04521322.

High-resolution within-sewer SARS-CoV-2 surveillance facilitates informed intervention

To assist in the COVID-19 public health guidance on a college campus, daily composite wastewater samples were withdrawn at 20 manhole locations across the University of Colorado Boulder campus. Low-cost autosamplers were fabricated in-house to enable an economical approach to this distributed study. These sample stations operated from August 25th until November 23rd during the fall 2020 semester, with 1512 samples collected. The concentration of SARS-CoV-2 in each sample was quantified through two comparative reverse transcription quantitative polymerase chain reactions (RT-qPCRs). These methods were distinct in the utilization of technical replicates and normalization to an endogenous control. (1) Higher temporal resolution compensates for supply chain or other constraints that prevent technical or biological replicates. (2) The data normalized by an endogenous control agreed with the raw concentration data, minimizing the utility of normalization. The raw wastewater concentration values reflected SARS-CoV-2 prevalence on campus as detected by clinical services. Overall, combining the low-cost composite sampler with a method that quantifies the SARS-CoV-2 signal within six hours enabled actionable and time-responsive data delivered to key stakeholders. With daily reporting of the findings, wastewater surveillance assisted in decision making during critical phases of the pandemic on campus, from detecting individual cases within populations ranging from 109 to 2048 individuals to monitoring the success of on-campus interventions.

Estimating the latent period of coronavirus disease 2019 (COVID-19)

Using detailed exposure information on COVID-19 cases, we estimated the mean latent period to be 5.5 days (95% confidence interval: 5.1-5.9 days), shorter than the mean incubation period (6.9 days). Laboratory testing may allow shorter quarantines since 95% of COVID-19 cases shed virus within 10.6 days (95%CI: 9.6-11.6) of infection.