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

Social contact patterns and their impact on the transmission of respiratory pathogens in rural China

Introduction

Social contact patterns significantly influence the transmission dynamics of respiratory pathogens. Previous surveys have quantified human social contact patterns, yielding heterogeneous results across different locations. However, significant gaps remain in understanding social contact patterns in rural areas of China.

Methods

We conducted a pioneering study to quantify social contact patterns in Anhua County, Hunan Province, China, from June to October 2021, when there were minimal coronavirus disease-related restrictions in the area. Additionally, we simulated the epidemics under different assumptions regarding the relative transmission risks of various contact types (e.g., indoor versus outdoor, and physical versus non-physical).

Results

Participants reported an average of 12.0 contacts per day (95% confidence interval: 11.3-12.6), with a significantly higher number of indoor contacts compared to outdoor contacts. The number of contacts was associated with various socio-demographic characteristics, including age, education level, income, household size, and travel patterns. Contact patterns were assortative by age and varied based on the type of contact (e.g., physical versus non-physical). The reproduction number, daily incidence, and infection attack rate of simulated epidemics were remarkably stable.

Discussion

We found many intergenerational households and contacts that pose challenges in preventing and controlling infections among the elderly in rural China. Our study also underscores the importance of integrating various types of contact pattern data into epidemiological models and provides guidance to public health authorities and other major stakeholders in preparing and responding to infectious disease threats in rural China.

Impact of tiered restrictions in December 2020 on COVID-19 hospitalisations in England: a synthetic control study

OBJECTIVES

To evaluate the effectiveness of localised Tier 3 restrictions, implemented in England in December 2020, on reducing COVID-19 hospitalisations compared with less stringent Tier 2 measures and the variations by neighbourhood deprivation and the prevalence of Alpha (B.1.1.7) variant, the primary variant of concern then, to measure hospital services' burden and inequalities across different communities.

DESIGN

Observational study using a synthetic control method, comparing weekly hospitalisation rates in Tier 3 areas to a synthetic control from Tier 2 areas.

SETTING

England between 4 October 2020 and 21 February 2021.

PARTICIPANTS

23 million people under Tier 3 restrictions, compared with a synthetic control group derived from 29 million people under Tier 2 restrictions.

INTERVENTIONS

Tier 3 restrictions in designated areas were implemented from 7 December 2020, imposing stricter limits on gatherings and hospitality than Tier 2, followed by a national lockdown on 6 January 2021.

PRIMARY AND SECONDARY OUTCOME MEASURES

Weekly COVID-19-related hospitalisations for neighbourhoods in England over 11 weeks following the interventions.

RESULTS

Implementing Tier 3 restrictions were associated with a 17% average reduction in hospitalisations compared with Tier 2 areas (95% CI 13% to 21%; 8158 (6286 to 9981) in total). The effects were similar across different levels of neighbourhood deprivation and prevalence of the Alpha variant.

CONCLUSIONS

Regionally targeted Tier 3 restrictions in England had a moderate but significant effect on reducing hospitalisations. The impact did not exacerbate socioeconomic inequalities during the pandemic. Our findings suggest that regionally targeted restrictions can be effective in managing infectious diseases.

Burden of Acute Respiratory Infections Caused by Influenza Virus, Respiratory Syncytial Virus, and SARS-CoV-2 with Consideration of Older Adults: A Narrative Review

Influenza virus, respiratory syncytial virus (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are acute respiratory infections (ARIs) that can cause substantial morbidity and mortality among at-risk individuals, including older adults. In this narrative review, we summarize themes identified in the literature regarding the epidemiology, seasonality, immunity after infection, clinical presentation, and transmission for these ARIs, along with the impact of the COVID-19 pandemic on seasonal patterns of influenza and RSV infections, with consideration of data specific to older adults when available. As the older adult population increases globally, it is of paramount importance to fully characterize the true disease burden of ARIs in order to develop appropriate mitigation strategies to minimize their impact in vulnerable populations. Challenges associated with characterizing the burden of these diseases include the shared symptomology and clinical presentation of influenza virus, RSV, and SARS-CoV-2, which complicate accurate diagnosis and highlight the need for improved testing and surveillance practices. To this end, multiple regional, national, and global virologic and disease surveillance systems have been established to provide accurate knowledge of viral epidemiology, support appropriate preparedness and response to potential outbreaks, and help inform prevention strategies to reduce disease severity and transmission. Beyond the burden of acute illness, long-term health consequences can also result from influenza virus, RSV, and SARS-CoV-2 infection. These include cardiovascular and pulmonary complications, worsening of existing chronic conditions, increased frailty, and reduced life expectancy. ARIs among older adults can also place a substantial financial burden on society and healthcare systems. Collectively, the existing data indicate that influenza virus, RSV, and SARS-CoV-2 infections in older adults present a substantial global health challenge, underscoring the need for interventions to improve health outcomes and reduce the disease burden of respiratory illnesses.Graphical abstract and video abstract available for this article.

Wastewater-based effective reproduction number and prediction under the absence of shedding information

Estimating effective reproduction number (Re) and predicting disease incidences are essential to formulate effective strategies for disease control. Although recent studies developed models for inferring Re from wastewater-based data, they require information on shedding dynamics. Here, we proposed a framework of Re estimation and prediction without shedding information. The framework consists of a space-state model for smoothing wastewater-based data and a renewal equation modified for wastewater-based data. The applicability of the framework was tested with simulated data and real-world data on Influenza A virus (IAV) and SARS-CoV-2 concentration in wastewater in 2022/2023 season in the USA. We confirmed the state-space model effectively fits various simulated epidemic curves and real-world data. In simulations, we found wastewater-based Re (Reww) closely aligns with instantaneous clinical Re when shedding dynamics are rapid. For more prolonged shedding, Reww approximates a smoothed Re over time. We also observed the necessary sampling frequency to trace dynamics of wastewater concentration and Reww accurately in the framework varies depending on the precision of detection methods, the epidemic status, the transmissibility of infectious diseases, and shedding dynamics. By applying our framework to real-world data, we found Reww for SARS-CoV-2 showed similar trend and values to clinically-based Re. Reww for IAV ranged from 0.66 to 1.52 with a clear peak in the winter season, which agrees with previously reported Re. We also succeeded in predicting wastewater concentration in a few weeks from available wastewater-based data. These results indicate that our framework potentially enables near real-time monitoring of approximated Re and prediction of infectious disease dynamics through wastewater surveillance, which limits the delay between infection and reporting. Our framework is useful especially for regions where reliable clinical surveillance is not available and notifiable surveillance is abolished, and can be expanded to multiple infectious diseases that have been detected from wastewater.

Epidemiological impact of revoking mask-wearing recommendation on COVID-19 transmission in Tokyo, Japan

Despite the global implementation of COVID-19 mitigation measures, the disease continues to maintain transmission. Although mask wearing became one of the key measures for preventing the transmission of COVID-19 early in the pandemic period, many countries have relaxed the mandatory or recommended wearing of masks. The objective of the present study was to estimate the epidemiological impact of removing the mask-wearing recommendation in Japan. We developed a model to assess the consequences of declining mask-wearing coverage after the government revoked its recommendation in February 2023. The declining mask-wearing coverage was estimated using serial cross-sectional data, and a mathematical model was devised to determine the age-specific incidence of COVID-19 using the observed case count in Tokyo from week of October 3, 2022 to October 30, 2023. We explored model-based counterfactual scenarios to measure hypothetical situations in which the mask-wearing coverage decreases or increases relative to the observed coverage. The results show that mask-wearing coverage declined from 97% to 69% by the week of October 30, 2023, and that if the mask-wearing recommendation had continued, 427 lives could have been saved in Tokyo. If the mask-wearing coverage had declined to 25% of the observed level, the model suggests there might have been 1587 additional deaths. Thus, revoking the mask-wearing recommendation had a substantial epidemiological impact. In future pandemics, our proposed approach could provide a real-time quantification of the effects of relaxing countermeasures.

Spatiotemporal Cluster Detection for COVID-19 Outbreak Surveillance: Descriptive Analysis Study

BACKGROUND

During the peak of the winter 2020-2021 surge, the number of weekly reported COVID-19 outbreaks in Washington State was 231; the majority occurred in high-priority settings such as workplaces, community settings, and schools. The Washington State Department of Health used automated address matching to identify clusters at health care facilities. No other systematic, statewide outbreak detection methods were in place. This was a gap given the high volume of cases, which delayed investigations and decreased data completeness, potentially leading to undetected outbreaks. We initiated statewide cluster detection using SaTScan, implementing a space-time permutation model to identify COVID-19 clusters for investigation.

OBJECTIVE

To improve outbreak detection, the Washington State Department of Health initiated a systematic cluster detection model to identify timely and actionable COVID-19 clusters for local health jurisdiction (LHJ) investigation and resource prioritization. This report details the model's implementation and the assessment of the tool's effectiveness.

METHODS

In total, 6 LHJs participated in a pilot to test model parameters including analysis type, geographic aggregation, cluster radius, and data lag. Parameters were determined through heuristic criteria to detect clusters early when they are smaller, making interventions more feasible. This study reviews all clusters detected after statewide implementation from July 17 to December 17, 2021. The clusters were analyzed by LHJ population and disease incidence. Clusters were compared with reported outbreaks.

RESULTS

A weekly, LHJ-specific retrospective space-time permutation model identified 2874 new clusters during this period. While the weekly analysis included case data from the prior 3 weeks, 58.25% (n=1674) of all clusters identified were timely-having occurred within 1 week of the analysis and early enough for intervention to prevent further transmission. There were 2874 reported outbreaks during this same period. Of those, 363 (12.63%) matched to at least one SaTScan cluster. The most frequent settings among reported and matched outbreaks were schools and youth programs (n=825, 28.71% and n=108, 29.8%), workplaces (n=617, 21.46% and n=56, 15%), and long-term care facilities (n=541, 18.82% and n=99, 27.3%). Settings with the highest percentage of clusters that matched outbreaks were community settings (16/72, 22%) and congregate housing (44/212, 20.8%). The model identified approximately one-third (119/363, 32.8%) of matched outbreaks before cases were associated with the outbreak event in our surveillance system.

CONCLUSIONS

Our goal was to routinely and systematically identify timely and actionable COVID-19 clusters statewide. Regardless of population or incidence, the model identified reasonably sized, timely clusters statewide, meeting the objective. Among some high-priority settings subject to public health interventions throughout the pandemic, such as schools and community settings, the model identified clusters that were matched to reported outbreaks. In workplaces, another high-priority setting, results suggest the model might be able to identify outbreaks sooner than existing outbreak detection methods.

Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics

Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method.

A follow up report validating long term predictions of the COVID-19 epidemic in the UK using a dynamic causal model

Background

This paper asks whether Dynamic Causal modelling (DCM) can predict the long-term clinical impact of the COVID-19 epidemic. DCMs are designed to continually assimilate data and modify model parameters, such as transmissibility of the virus, changes in social distancing and vaccine coverage-to accommodate changes in population dynamics and virus behavior. But as a novel way to model epidemics do they produce valid predictions? We presented DCM predictions 12 months ago, which suggested an increase in viral transmission was accompanied by a reduction in pathogenicity. These changes provided plausible reasons why the model underestimated deaths, hospital admissions and acute-post COVID-19 syndrome by 20%. A further 12-month validation exercise could help to assess how useful such predictions are.

Methods

we compared DCM predictions-made in October 2022-with actual outcomes over the 12-months to October 2023. The model was then used to identify changes in COVID-19 transmissibility and the sociobehavioral responses that may explain discrepancies between predictions and outcomes over this period. The model was then used to predict future trends in infections, long-COVID, hospital admissions and deaths over 12-months to October 2024, as a prelude to future tests of predictive validity.

Findings

Unlike the previous predictions-which were an underestimate-the predictions made in October 2022 overestimated incidence, death and admission rates. This overestimation appears to have been caused by reduced infectivity of new variants, less movement of people and a higher persistence of immunity following natural infection and vaccination.

Interpretation

despite an expressive (generative) model, with time-dependent epidemiological and sociobehavioral parameters, the model overestimated morbidity and mortality. Effectively, the model failed to accommodate the "law of declining virulence" over a timescale of years. This speaks to a fundamental issue in long-term forecasting: how to model decreases in virulence over a timescale of years? A potential answer may be available in a year when the predictions for 2024-under a model with slowly accumulating T-cell like immunity-can be assessed against actual outcomes.

Proposed new definition for hospital-acquired SARS-CoV-2 infections: results of a confirmatory factor analysis

Objective

The present study aims to develop and discuss an extension of hospital-acquired severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections (HA-SIs) definition which goes beyond the use of time parameters alone.

Design

A confirmatory factor analysis was carried out to test a suitable definition for HA-SI.

Setting and Patients

A two-center cohort study was carried out at two tertiary public hospitals in the German state of lower Saxony. The study involved a population of 366 laboratory-confirmed SARS-CoV-2-infected inpatients enrolled between March 2020 and August 2023.

Results

The proposed model shows adequate fit indices (CFI.scaled = 0.959, RMSEA = 0.049). A descriptive comparison with existing classifications revealed strong features of our model, particularly its adaptability to specific regional outbreaks.

Conclusion

The use of the regional incidence as a proxy variable to better define HA-SI cases represents a pragmatic and novel approach. The model aligns well with the latest scientific results in the literature. This work successfully unifies, within a single model, variables which the recent literature described as significant for the onset of HA-SI. Further potential improvements and adaptations of the model and its applications, such as automating the categorization process (in terms of hospital acquisition) or employing a comparable model for hospital-acquired influenza classification, are subjects open for discussion.

A retrospective assessment of forecasting the peak of the SARS-CoV-2 Omicron BA.1 wave in England

We discuss the invasion of the Omicron BA.1 variant into England as a paradigm for real-time model fitting and projection. Here we use a mixture of simple SIR-type models, analysis of the early data and a more complex age-structure model fit to the outbreak to understand the dynamics. In particular, we highlight that early data shows that the invading Omicron variant had a substantial growth advantage over the resident Delta variant. However, early data does not allow us to reliably infer other key epidemiological parameters-such as generation time and severity-which influence the expected peak hospital numbers. With more complete epidemic data from January 2022 are we able to capture the true scale of the epidemic in terms of both infections and hospital admissions, driven by different infection characteristics of Omicron compared to Delta and a substantial shift in estimated precautionary behaviour during December. This work highlights the challenges of real time forecasting, in a rapidly changing environment with limited information on the variant's epidemiological characteristics.

Impact of COVID-19 on adverse reactions to subcutaneous specific immunotherapy in children:a retrospective cohort study

BACKGROUND

COVID-19 is a new infectious disease. To investigate whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection increases the adverse reactions of subcutaneous specific immunotherapy (SCIT) in children.

METHODS

This study was conducted by collecting relevant data from children who underwent house dust mite SCIT from April 3, 2021, to March 18, 2023, including information on the time of COVID-19 infection, symptoms, and adverse reactions after each allergen injection. A mixed effects model was used to analyze the changes in adverse reactions before and after the COVID-19 infection.

RESULTS

Among the records of adverse reactions from 2658 injections in 123 children who underwent SCIT, the overall adverse reaction rate before COVID-19 infection was 39.8% and 30.0% after COVID-19 infection. Compared with pre-infection with COVID-19, the risks of overall adverse reactions, local adverse reactions, and systemic adverse reactions of immunotherapy after COVID-19 infection were reduced (odds ratio [OR] = 0.24, 0.31, and 0.28, all P < 0.05). Among the local adverse reactions, the incidence of the unvaccinated group was the highest (15.3% vs. 7.1%). The incidence of overall and local adverse reactions to SCIT decreased in 2-vaccinated COVID-19 recipients (OR = 0.29-0.31, P < 0.05).

CONCLUSIONS

In children, SARS-CoV-2 infection does not increase the incidence of adverse reactions to SCIT. This finding can provide a basis for the implementation of allergen-specific immunotherapy (AIT) during the COVID-19 pandemic.

rtestim: Time-varying reproduction number estimation with trend filtering

To understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates of the instantaneous reproduction number. While many estimation approaches exist, their utility may be limited. Challenges of surveillance data collection, model assumptions that are unverifiable with data alone, and computationally inefficient frameworks are critical limitations for many existing approaches. We propose a discrete spline-based approach that solves a convex optimization problem-Poisson trend filtering-using the proximal Newton method. It produces a locally adaptive estimator for instantaneous reproduction number estimation with heterogeneous smoothness. Our methodology remains accurate even under some process misspecifications and is computationally efficient, even for large-scale data. The implementation is easily accessible in a lightweight R package rtestim.

Impact of COVID-19 on healthcare-associated infections and antimicrobial use in Italy, 2022

BACKGROUND

It is unknown whether COVID-19 patients are at higher risk due to demographic and clinical characteristics associated with higher COVID-19 infection risk and severity of infection, or due to the disease and its management.

AIM

To assess the impact of COVID-19 on healthcare-associated infection (HAI) transmission and antimicrobial use (AMU) prevalence during the later stages of the pandemic.

METHODS

A point-prevalence survey (PPS) was conducted among 325 acute care hospitals of 19 out of 21 Regions of Italy, during November 2022. Non-COVID-19 patients were matched to COVID-19 patients according to age, sex, and severity of underlying conditions. HAI and AMU prevalence were calculated as the percentage of patients with at least one HAI or prescribed at least one antimicrobial over all included patients, respectively.

FINDINGS

In total, 60,403 patients were included, 1897 (3.14%) of which were classified as COVID-19 patients. Crude HAI prevalence was significantly higher among COVID-19 patients compared to non-COVID-19 patients (9.54% vs 8.01%; prevalence rate ratio (PRR): 1.19; 95% confidence interval (CI): 1.04-1.38; P < 0.05), and remained higher in the matched sample; however, statistical significance was not maintained (odds ratio (OR): 1.25; 95% CI: 0.99-1.59; P = 0.067). AMU prevalence was significantly higher among COVID-19 patients prior to matching (46.39% vs 41.52%; PRR: 1.21; 95% CI: 1.11-1.32; P < 0.001), and significantly lower after matching (OR: 0.77; 95% CI: 0.66-0.89; P < 0.001).

CONCLUSION

COVID-19 patients could be at higher HAI risk due to underlying clinical conditions and the intensity of healthcare needs. Further efforts should be dedicated to antimicrobial stewardship among COVID-19 patients.

A T-Cell-Derived 3-Gene Signature Distinguishes SARS-CoV-2 from Common Respiratory Viruses

Research on the host responses to respiratory viruses could help develop effective interventions and therapies against the current and future pandemics from the host perspective. To explore the pathogenesis that distinguishes SARS-CoV-2 infections from other respiratory viruses, we performed a multi-cohort analysis with integrated bioinformatics and machine learning. We collected 3730 blood samples from both asymptomatic and symptomatic individuals infected with SARS-CoV-2, seasonal human coronavirus (sHCoVs), influenza virus (IFV), respiratory syncytial virus (RSV), or human rhinovirus (HRV) across 15 cohorts. First, we identified an enhanced cellular immune response but limited interferon activities in SARS-CoV-2 infection, especially in asymptomatic cases. Second, we identified a SARS-CoV-2-specific 3-gene signature (CLSPN, RBBP6, CCDC91) that was predominantly expressed by T cells, could distinguish SARS-CoV-2 infection, including Omicron, from other common respiratory viruses regardless of symptoms, and was predictive of SARS-CoV-2 infection before detectable viral RNA on RT-PCR testing in a longitude follow-up study. Thereafter, a user-friendly online tool, based on datasets collected here, was developed for querying a gene of interest across multiple viral infections. Our results not only identify a unique host response to the viral pathogenesis in SARS-CoV-2 but also provide insights into developing effective tools against viral pandemics from the host perspective.

Parametric analysis of the transmission dynamics during indigenous aggregated outbreaks caused by five SARS-CoV-2 strains in Nanjing, China

Background

SARS-CoV-2 strains have been of great concern due to their high infectivity and antibody evasion.

Methods

In this study, data were collected on indigenous aggregated outbreaks in Nanjing from January 2020 to December 2022, caused by five strains including the original strain, the Delta variant, and the Omicron variant (BA.2, BA.5.2, and BF.7). The basic epidemiological characteristics of infected individuals were described and then parametric analysis of transmission dynamics was performed, including the calculation of incubation period, serial interval (SI), the basic reproductive number (R0), and the household secondary attack rate (HSAR). Finally, we compared the trends of transmission dynamic parameters of different strains.

Results

The incubation period for the original strain, the Delta variant, Omicron BA.2, Omicron BA.5.2, and Omicron BF.7 were 6 d (95% CI: 3.5-7.5 d), 5 d (95% CI: 4.0-6.0 d), 3 d (95% CI: 3.0-4.0 d), 3 d (95% CI: 3.0-3.0 d), and 2 d (95% CI: 2.0-3.0 d), respectively; Also, the SI of the five strains were 5.69 d, 4.79 d, 2.7 d, 2.12 d, and 2.43 d, respectively. Notably, the incubation period and SI of the five had both a progressive shortening trend (p < 0.001); Moreover, R0 of the five were 2.39 (95% CI: 1.30-4.29), 3.73 (95% CI: 2.66-5.15), 5.28 (95% CI: 3.52-8.10), 5.54 (95% CI: 2.69-11.17), 7.39 (95% CI: 2.97-18.76), with an increasing trend gradually (p < 0.01); HSAR of the five were 25.5% (95% CI: 20.1-31.7%), 27.4% (95% CI: 22.0-33.4%), 42.9% (95% CI: 34.3-51.8%), 53.1% (95% CI: 45.0-60.9%), 41.4% (95% CI, 25.5-59.3%), also with an increasing trend (p < 0.001).

Conclusion

Compared to the original strain, the incubation period and SI decreased while R0 and HSAR increased, suggesting that transmission in the population was faster and the scope of the population was wider. Overall, it's crucial to keep implementing comprehensive measures like monitoring and alert systems, herd immunization plans, and outbreak control.

Latent and incubation periods of Delta, BA.1, and BA.2 variant cases and associated factors: a cross-sectional study in China

BACKGROUND

The latent and incubation periods characterize the transmission of infectious viruses and are the basis for the development of outbreak prevention and control strategies. However, systematic studies on the latent period and associated factors with the incubation period for SAS-CoV-2 variants are still lacking. We inferred the two durations of Delta, BA.1, and BA.2 cases and analyzed the associated factors.

METHODS

The Delta, BA.1, and BA.2 (and its lineages BA.2.2 and BA.2.76) cases with clear transmission chains and infectors from 10 local SAS-CoV-2 epidemics in China were enrolled. The latent and incubation periods were fitted by the Gamma distribution, and associated factors were analyzed using the accelerated failure time model.

RESULTS

The mean latent period for 672 Delta, 208 BA.1, and 677 BA.2 cases was 4.40 (95%CI: 4.24 ~ 4.63), 2.50 (95%CI: 2.27 ~ 2.76), and 2.58 (95%CI: 2.48 ~ 2.69) days, respectively, with 85.65% (95%CI: 83.40 ~ 87.77%), 97.80% (95%CI: 96.35 ~ 98.89%), and 98.87% (95%CI: 98.40 ~ 99.27%) of them starting to shed viruses within 7 days after exposure. In 405 Delta, 75 BA.1, and 345 BA.2 symptomatic cases, the mean latent period was 0.76, 1.07, and 0.79 days shorter than the mean incubation period [5.04 (95%CI: 4.83 ~ 5.33), 3.42 (95%CI: 3.00 ~ 3.89), and 3.39 (95%CI: 3.24 ~ 3.55) days], respectively. No significant difference was observed in the two durations between BA.1 and BA.2 cases. After controlling for the sex, clinical severity, vaccination history, number of infectors, the length of exposure window and shedding window, the latent period [Delta: exp(β) = 0.81, 95%CI: 0.66 ~ 0.98, p = 0.034; Omicron: exp(β) = 0.82, 95%CI: 0.71 ~ 0.94, p = 0.004] and incubation period [Delta: exp(β) = 0.69, 95%CI: 0.55 ~ 0.86, p < 0.001; Omicron: exp(β) = 0.83, 95%CI: 0.72 ~ 0.96, p = 0.013] were significantly shorter in 18 ~ 49 years but did not change significantly in ≥ 50 years compared with 0 ~ 17 years.

CONCLUSION

Pre-symptomatic transmission can occur in Delta, BA.1, and BA.2 cases. The latent and incubation periods between BA.1 and BA.2 were similar but shorter compared with Delta. Age may be associated with the latent and incubation periods of SARS-CoV-2.

The effect of perceived social support and health literacy on parental COVID-19 vaccine hesitation in preschool children: a cross-sectional study

Children are generally susceptible to COVID-19, and infection with COVID-19 may cause serious harm to children. COVID-19 vaccination is an effective way to prevent infection at present, and many factors affect children's COVID-19 vaccination. This study aimed to explore the effects of perceived social support and health literacy on hesitancy towards first and second vaccine dose. This cross-sectional study was conducted in the Minhang District of Shanghai, China, in October 2022. A total of 1150 parents of preschool children from 10 kindergartens participated. The survey encompassed four sections, capturing data on sociodemographic attributes, health literacy, perceived social support, and parental COVID-19 vaccine hesitancy. Health literacy was measured using a self-designed questionnaire consisting of four dimensions. Perceived social support was assessed using the MSPSS questionnaire. Hierarchical multiple logistic regression was used to examine the relationship between the independent variables and parental hesitancy towards the first and second doses of COVID-19 vaccine. Parental hesitancy rate for the first dose of the COVID-19 vaccine was 69.6%, and for the second dose, it was 33.1%. The final integrated model showed that parental hesitancy towards the first and the second dose of COVID-19 vaccine was associated with parental educational level, allergy in children, information decision-making and information comprehension ability, perceived social support from family and friends. Health literacy and perceived social support are influence factors for parental hesitancy towards COVID-19 vaccine for preschool children. The findings will provide insights for future intervention studies on COVID-19 vaccine hesitancy and inform the development of vaccination policies.

The durability of vaccine-induced protection: an overview

INTRODUCTION

Current vaccines vary widely in both their efficacy against infection and disease, and the durability of the efficacy. Some vaccines provide practically lifelong protection with a single dose, while others provide only limited protection following annual boosters. What variables make vaccine-induced immune responses last? Can breakthroughs in these factors and technologies help us produce vaccines with better protection and fewer doses? The durability of vaccine-induced protection is now a hot area in vaccinology research, especially after COVID-19 vaccines lost their luster. It has fueled discussion on the eventual utility of existing vaccines to society and bolstered the anti-vaxxer camp. To sustain public trust in vaccines, lasting vaccines must be developed.

AREAS COVERED

This review summarizes licensed vaccines' protection. It analyses immunological principles and vaccine and vaccinee parameters that determine longevity of antibodies. The review concludes with challenges and the way forward to improve vaccine durability.

EXPERT OPINION

Despite enormous advances, we still lack essential markers and reliable correlates of lasting protection. Most research has focused on humoral immune responses, but we must also focus on innate, mucosal, and cellular responses - their assessment, correlates, determinants, and novel adjuvants. Suitable vaccine designs and platforms for durable immunity must be found.