Does incubation period of COVID-19 vary with age? A study of epidemiologically linked cases in Singapore

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.

How much natural ventilation rate can suppress COVID-19 transmission in occupancy zones?

Background

Previous research has emphasized the importance of efficient ventilation in suppressing COVID-19 transmission in indoor spaces, yet suitable ventilation rates have not been suggested.

Materials and Methods

This study investigated the impacts of mechanical, natural, single-sided, cross-ventilation, and three mask types (homemade, surgical, N95) on COVID-19 spread across eight common indoor settings. Viral exposure was quantified using a mass balance calculation of inhaled viral particles, accounting for initial viral load, removal via ventilation, and mask filtration efficiency.

Results

Results demonstrated that natural cross-ventilation significantly reduced viral load, decreasing from 10,000 to 0 viruses over 15 minutes in a 100 m2 space by providing ~1325 m3/h of outdoor air via two 0.6 m2 openings at 1.5 m/s wind speed. In contrast, single-sided ventilation only halved viral load at best.

Conclusion

Natural cross-ventilation with masks effectively suppressed airborne viruses, lowering potential infections and disease transmission. The study recommends suitable ventilation rates to reduce COVID-19 infection risks in indoor spaces.

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.

Adjuvant Pomegranate Juice Intake Improves the Inflammatory Status of Hospitalized COVID-19 Patients: A Randomized and Placebo-Controlled Trial

BACKGROUND

This study aimed to evaluate the effect of pomegranate juice intake on the inflammatory status and complete blood count in hospitalized Covid-19 patients.

METHODS

This randomized, double-blinded placebo-controlled trial included 48 patients with two parallel arms. In addition to the standard care provided at the hospital, the patients consumed 500mL of whole pomegranate juice (PJ) daily or a placebo for 14 days. Inflammatory markers (C-reactive protein (CRP), interleukin-6 (IL-6), erythrocyte sedimentation rate (ESR)) and complete blood count were determined at baseline and after the 14 days of intervention.

RESULTS

At the end of the intervention, a significant decreased was observed in primary outcomes [mean difference (95%CI)] including IL-6 [5.24(0.87 to 9.61)], CRP [23.19(11.93 to 34.44)] and ESR [10.52(1.54 to 19.50)] in the PJ group vs. before the intervention. In addition, significant changes were also observed in the some of the secondary outcomes, including neutrophils, lymphocytes, platelets, platelets-to-lymphocyte(PLR) and neutrophils-to-lymphocyte (NLR) ratios (p<0.05) in the PJ group compared to before the intervention. At the end of the intervention period, the mean change of IL-6 [-7.09(-12.21 to -1.96)], white blood cells [-3.09(-6.14 to -0.05)], neutrophils [-9.12(-18.08 to -0.15)], lymphocyte [7.05(0.17 to 13.92)], platelets [-94.54(-139.33 to -49.75)], PLR [-15.99(-29.31 to -2.67)], blood oxygen saturation [1.75(0.13 to 3.37)] and MCV [0.31(-0.25 to 0.88)] levels were significantly different between groups while no difference was observed between the two groups in other blood indices.

CONCLUSION

Our results suggest that pomegranate juice intake might slightly improve the inflammatory status and CBC outcomes of COVID-19 patients and it may be beneficial.

AVAILABILITY OF DATA AND MATERIALS

The final dataset of trial will be available upon request from the primary investigator via e-mail at panahande.b@gmail.com, after obtaining permission from Regional Ethics Committee.

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.

COVID-19 endgame: From pandemic to endemic? Vaccination, reopening and evolution in low- and high-vaccinated populations

COVID-19 remains a major public health concern, with large resurgences even where there has been widespread uptake of vaccines. Waning immunity and the emergence of new variants will shape the long-term burden and dynamics of COVID-19. We explore the transition to the endemic state, and the endemic incidence in British Columbia (BC), Canada and South Africa (SA), to compare low and high vaccination coverage settings with differing public health policies, using a combination of modelling approaches. We compare reopening (relaxation of public health measures) gradually and rapidly as well as at different vaccination levels. We examine how the eventual endemic state depends on the duration of immunity, the rate of importations, the efficacy of vaccines and the transmissibility. These depend on the evolution of the virus, which continues to undergo selection. Slower reopening leads to a lower peak level of incidence and fewer overall infections in the wave following reopening: as much as a 60% lower peak and a 10% lower total in some illustrative simulations; under realistic parameters, reopening when 70% of the population is vaccinated leads to a large resurgence in cases. The long-term endemic behaviour may stabilize as late as January 2023, with further waves of high incidence occurring depending on the transmissibility of the prevalent variant, duration of immunity, and antigenic drift. We find that long term endemic levels are not necessarily lower than current pandemic levels: in a population of 100,000 with representative parameter settings (Reproduction number 5, 1-year duration of immunity, vaccine efficacy at 80% and importations at 3 cases per 100K per day) there are over 100 daily incident cases in the model. Predicted prevalence at endemicity has increased more than twofold after the emergence and spread of Omicron. The consequent burden on health care systems depends on the severity of infection in immunized or previously infected individuals.

Semiparametric regression analysis of doubly-censored data with applications to incubation period estimation

The incubation period is a key characteristic of an infectious disease. In the outbreak of a novel infectious disease, accurate evaluation of the incubation period distribution is critical for designing effective prevention and control measures . Estimation of the incubation period distribution based on limited information from retrospective inspection of infected cases is highly challenging due to censoring and truncation. In this paper, we consider a semiparametric regression model for the incubation period and propose a sieve maximum likelihood approach for estimation based on the symptom onset time, travel history, and basic demographics of reported cases. The approach properly accounts for the pandemic growth and selection bias in data collection. We also develop an efficient computation method and establish the asymptotic properties of the proposed estimators. We demonstrate the feasibility and advantages of the proposed methods through extensive simulation studies and provide an application to a dataset on the outbreak of COVID-19.

Estimation of the incubation period and generation time of SARS-CoV-2 Alpha and Delta variants from contact tracing data

Quantitative information on epidemiological quantities such as the incubation period and generation time of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants is scarce. We analysed a dataset collected during contact tracing activities in the province of Reggio Emilia, Italy, throughout 2021. We determined the distributions of the incubation period for the Alpha and Delta variants using information on negative polymerase chain reaction tests and the date of last exposure from 282 symptomatic cases. We estimated the distributions of the intrinsic generation time using a Bayesian inference approach applied to 9724 SARS-CoV-2 cases clustered in 3545 households where at least one secondary case was recorded. We estimated a mean incubation period of 4.9 days (95% credible intervals, CrI, 4.4-5.4) for Alpha and 4.5 days (95% CrI 4.0-5.0) for Delta. The intrinsic generation time was estimated to have a mean of 7.12 days (95% CrI 6.27-8.44) for Alpha and of 6.52 days (95% CrI 5.54-8.43) for Delta. The household serial interval was 2.43 days (95% CrI 2.29-2.58) for Alpha and 2.74 days (95% CrI 2.62-2.88) for Delta, and the estimated proportion of pre-symptomatic transmission was 48-51% for both variants. These results indicate limited differences in the incubation period and intrinsic generation time of SARS-CoV-2 variants Alpha and Delta compared to ancestral lineages.

SARS-Cov-2 incubation period according to vaccination status during the fifth COVID-19 wave in a tertiary-care center in Spain: a cohort study

Background

The incubation period of an infectious disease is defined as the elapsed time between the exposure to the pathogen and the onset of symptoms. Although both the mRNA-based and the adenoviral vector-based vaccines have shown to be effective, there have been raising concerns regarding possible decreases in vaccine effectiveness for new variants and variations in the incubation period.

Methods

We conducted a unicentric observational study at the Hospital Universitari de Bellvitge, Barcelona, using a structured telephone survey performed by trained interviewers to estimate the incubation period of the SARS-CoV-2 Delta variant in a cohort of Spanish hospitalized patients. The distribution of the incubation period was estimated using the generalized odds-rate class of regression models.

Results

From 406 surveyed patients, 242 provided adequate information to be included in the analysis. The median incubation period was 2.8 days (95%CI: 2.5–3.1) and no differences between vaccinated and unvaccinated patients were found. Sex and age are neither shown not to be significantly related to the COVID-19 incubation time.

Conclusions

Knowing the incubation period is crucial for controlling the spread of an infectious disease: decisions on the duration of the quarantine or on the periods of active monitoring of people who have been at high risk of exposure depend on the length of the incubation period. Furthermore, its probability distribution is a key element for predicting the prevalence and the incidence of the disease.

Incubation Period of COVID-19 Caused by Unique SARS-CoV-2 Strains: A Systematic Review and Meta-analysis

IMPORTANCE

Several studies were conducted to estimate the average incubation period of COVID-19; however, the incubation period of COVID-19 caused by different SARS-CoV-2 variants is not well described.

OBJECTIVE

To systematically assess the incubation period of COVID-19 and the incubation periods of COVID-19 caused by different SARS-CoV-2 variants in published studies.

DATA SOURCES

PubMed, EMBASE, and ScienceDirect were searched between December 1, 2019, and February 10, 2022.

STUDY SELECTION

Original studies of the incubation period of COVID-19, defined as the time from infection to the onset of signs and symptoms.

DATA EXTRACTION AND SYNTHESIS

Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline, 3 reviewers independently extracted the data from the eligible studies in March 2022. The parameters, or sufficient information to facilitate calculation of those values, were derived from random-effects meta-analysis.

MAIN OUTCOMES AND MEASURES

The mean estimate of the incubation period and different SARS-CoV-2 strains.

RESULTS

A total of 142 studies with 8112 patients were included. The pooled incubation period was 6.57 days (95% CI, 6.26-6.88) and ranged from 1.80 to 18.87 days. The incubation period of COVID-19 caused by the Alpha, Beta, Delta, and Omicron variants were reported in 1 study (with 6374 patients), 1 study (10 patients), 6 studies (2368 patients) and 5 studies (829 patients), respectively. The mean incubation period of COVID-19 was 5.00 days (95% CI, 4.94-5.06 days) for cases caused by the Alpha variant, 4.50 days (95% CI, 1.83-7.17 days) for the Beta variant, 4.41 days (95% CI, 3.76-5.05 days) for the Delta variant, and 3.42 days (95% CI, 2.88-3.96 days) for the Omicron variant. The mean incubation was 7.43 days (95% CI, 5.75-9.11 days) among older patients (ie, aged over 60 years old), 8.82 days (95% CI, 8.19-9.45 days) among infected children (ages 18 years or younger), 6.99 days (95% CI, 6.07-7.92 days) among patients with nonsevere illness, and 6.69 days (95% CI, 4.53-8.85 days) among patients with severe illness.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that SARS-CoV-2 has evolved and mutated continuously throughout the COVID-19 pandemic, producing variants with different enhanced transmission and virulence. Identifying the incubation period of different variants is a key factor in determining the isolation period.

Clinical Profile of Indoor Covid-19 Patients at a Medical Institute In India: A Retrospective analysis

Background: COVID-19 has caused a very high burden of morbidity and mortality across the world, India being  also badly affected. The disease has a wide spectrum ranging from asymptomatic to severe illness and death. Research work on the epidemiological and clinical profile of patients is scarce in India. Objectives: This study was conducted to know the clinico-epidemiological profile of indoor hospitalized Covid 19 patients. Methods: a retrospective analysis (record based) conducted at a designated COVID tertiary care center. Study period-1st August 2020 to 31st October 2020. Study Unit- Laboratory confirmed COVID 19 patients admitted in the hospital. Sample size- All the 894 patients with complete record were included in the study. Results: The mean age was 47.68± 17.62 years. Around 14.1% of the population was asymptomatic. 75.7%  were classified as having a mild disease. The cure rate was 82.9% and deaths were reported in 9.4%. Conclusions: Our findings were similar to findings observed in other studies. A positive significant correlation was noticed between age, duration, clinical severity and outcome of patient. The clinical severity was also found to be associated with age, duration of symptoms and outcome of patient 

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.

Epidemiological features of COVID-19 patients with prolonged incubation period and its implications for controlling the epidemics in China

BACKGROUND

COVID-19 patients with long incubation period were reported in clinical practice and tracing of close contacts, but their epidemiological or clinical features remained vague.

METHODS

We analyzed 11,425 COVID-19 cases reported between January-August, 2020 in China. The accelerated failure time model, Logistic and modified Poisson regression models were used to investigate the determinants of prolonged incubation period, as well as their association with clinical severity and transmissibility, respectively.

RESULT

Among local cases, 268 (10.2%) had a prolonged incubation period of > 14 days, which was more frequently seen among elderly patients, those residing in South China, with disease onset after Level I response measures administration, or being exposed in public places. Patients with prolonged incubation period had lower risk of severe illness (OR_adjusted = 0.386, 95% CI: 0.203-0.677). A reduced transmissibility was observed for the primary patients with prolonged incubation period (50.4, 95% CI: 32.3-78.6%) than those with an incubation period of ≤14 days.

CONCLUSIONS

The study provides evidence supporting a prolonged incubation period that exceeded 2 weeks in over 10% for COVID-19. Longer monitoring periods than 14 days for quarantine or persons potentially exposed to SARS-CoV-2 should be justified in extreme cases, especially for those elderly.

Estimation of COVID-19 recovery and decease periods in Canada using delay model

We derive a novel model escorted by large scale compartments, based on a set of coupled delay differential equations with extensive delays, in order to estimate the incubation, recovery and decease periods of COVID-19, and more generally any infectious disease. This is possible thanks to some optimization algorithms applied to publicly available database of confirmed corona cases, recovered cases and death toll. In this purpose, we separate (1) the total cases into 14 groups corresponding to 14 incubation periods, (2) the recovered cases into 406 groups corresponding to a combination of incubation and recovery periods, and (3) the death toll into 406 groups corresponding to a combination of incubation and decease periods. In this paper, we focus on recovery and decease periods and their correlation with the incubation period. The estimated mean recovery period we obtain is 22.14 days (95% Confidence Interval (CI) 22.00–22.27), and the 90th percentile is 28.91 days (95% CI 28.71–29.13), which is in agreement with statistical supported studies. The bimodal gamma distribution reveals that there are two groups of recovered individuals with a short recovery period, mean 21.02 days (95% CI 20.92–21.12), and a long recovery period, mean 38.88 days (95% CI 38.61–39.15). Our study shows that the characteristic of the decease period and the recovery period are alike. From the bivariate analysis, we observe a high probability domain for recovered individuals with respect to incubation and recovery periods. A similar domain is obtained for deaths analyzing bivariate distribution of incubation and decease periods.

COVID-19 patients with increasing age experience differential time to initial medical care and severity of symptoms

We conducted a retrospective observational study in patients with laboratory-confirmed coronavirus disease (COVID-19) who received medical care in 688 COVID-19 ambulatory units and hospitals in Mexico City between 24 February 2020 and 24 December 2020, to study if the elderly seek medical care later than younger patients and their severity of symptoms at initial medical evaluation. Patients were categorised into eight groups (<20, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79 and ≥80 years). Symptoms at initial evaluation were classified according to a previously validated classification into respiratory and non-respiratory symptoms. Comparisons between time from symptom onset to medical care for every age category were performed through variance analyses. Logistic regression models were applied to determine the risk of presenting symptoms of severity according to age, and mortality risk according to delays in medical care. In total, 286 020 patients were included (mean age: 42.8, s.d.: 16.8 years; 50.4% were women). Mean time from symptom onset to medical care was 4.04 (s.d.: 3.6) days and increased with older age categories (P < 0.0001). Mortality risk increased by 6.4% for each day of delay in medical care from symptom onset. The risk of presenting with the symptoms of severity was greater with increasing age categories. In conclusion, COVID-19 patients with increasing ages tend to seek medical care later, with higher rates of symptoms of severity at initial presentation in both ambulatory units and hospitals.

Epidemiology of COVID-19: An updated review

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a zoonotic infection, is responsible for COVID-19 pandemic and also is known as a public health concern. However, so far, the origin of the causative virus and its intermediate hosts is yet to be fully determined. SARS-CoV-2 contains nearly 30,000 letters of RNA that allows the virus to infect cells and hijack them to make new viruses. On the other hand, among 14 detected mutations in the SARS-CoV-2 S protein that provide advantages to virus for transmission and evasion form treatment, the D614G mutation (substitution of aspartic acid [D] with glycine [G] in codon 614 was particular which could provide the facilitation of the transmission of the virus and virulence. To date, in contrary to the global effort to come up with various aspects of SARS-CoV-2, there are still great pitfalls in the knowledge of this disease and many angles remain unclear. That's why, the monitoring and periodical investigation of this emerging infection in an epidemiological study seems to be essential. The present study characterizes the current epidemiological status (i.e., possible transmission route, mortality and morbidity risk, emerging SARS-CoV-2 variants, and clinical feature) of the SARS-CoV-2 in the world during these pandemic.

Modeling the effect of age on quantiles of the incubation period distribution of COVID-19

BACKGROUND

The novel coronavirus SARS-CoV-2 (coronavirus disease 2019, COVID-19) has caused serious consequences on many aspects of social life throughout the world since the first case of pneumonia with unknown etiology was identified in Wuhan, Hubei province in China in December 2019. Note that the incubation period distribution is key to the prevention and control efforts of COVID-19. This study aimed to investigate the conditional distribution of the incubation period of COVID-19 given the age of infected cases and estimate its corresponding quantiles from the information of 2172 confirmed cases from 29 provinces outside Hubei in China.

METHODS

We collected data on the infection dates, onset dates, and ages of the confirmed cases through February 16th, 2020. All the data were downloaded from the official websites of the health commission. As the epidemic was still ongoing at the time we collected data, the observations subject to biased sampling. To address this issue, we developed a new maximum likelihood method, which enables us to comprehensively study the effect of age on the incubation period.

RESULTS

Based on the collected data, we found that the conditional quantiles of the incubation period distribution of COVID-19 vary by age. In detail, the high conditional quantiles of people in the middle age group are shorter than those of others while the low quantiles did not show the same differences. We estimated that the 0.95-th quantile related to people in the age group 23 ∼55 is less than 15 days.

CONCLUSIONS

Observing that the conditional quantiles vary across age, we may take more precise measures for people of different ages. For example, we may consider carrying out an age-dependent quarantine duration in practice, rather than a uniform 14-days quarantine period. Remarkably, we may need to extend the current quarantine duration for people aged 0 ∼22 and over 55 because the related 0.95-th quantiles are much greater than 14 days.

The incubation period of COVID-19: a global meta-analysis of 53 studies and a Chinese observation study of 11 545 patients

BACKGROUND

The incubation period is a crucial index of epidemiology in understanding the spread of the emerging Coronavirus disease 2019 (COVID-19). In this study, we aimed to describe the incubation period of COVID-19 globally and in the mainland of China.

METHODS

The searched studies were published from December 1, 2019 to May 26, 2021 in CNKI, Wanfang, PubMed, and Embase databases. A random-effect model was used to pool the mean incubation period. Meta-regression was used to explore the sources of heterogeneity. Meanwhile, we collected 11 545 patients in the mainland of China outside Hubei from January 19, 2020 to September 21, 2020. The incubation period fitted with the Log-normal model by the coarseDataTools package.

RESULTS

A total of 3235 articles were searched, 53 of which were included in the meta-analysis. The pooled mean incubation period of COVID-19 was 6.0 days (95% confidence interval [CI] 5.6-6.5) globally, 6.5 days (95% CI 6.1-6.9) in the mainland of China, and 4.6 days (95% CI 4.1-5.1) outside the mainland of China (P = 0.006). The incubation period varied with age (P = 0.005). Meanwhile, in 11 545 patients, the mean incubation period was 7.1 days (95% CI 7.0-7.2), which was similar to the finding in our meta-analysis.

CONCLUSIONS

For COVID-19, the mean incubation period was 6.0 days globally but near 7.0 days in the mainland of China, which will help identify the time of infection and make disease control decisions. Furthermore, attention should also be paid to the region- or age-specific incubation period.

COVID-19 Outbreak in a Military Unit in Korea

OBJECTIVES

This study presents the response of a military unit to the COVID-19 outbreak in Gyeonggi Province. As soon as two soldiers were identified as index cases, the infectious disease investigators of the Gyeonggi Provincial Government, Korea Disease Control and Prevention Agency and the Armed Forces Epidemiologic Investigation Center, discussed the investigation and response plan for an imminent massive outbreak.

METHODS

The joint immediate response team (IRT) conducted interviews with confirmed patients with COVID-19, reviewed medical records, performed contact tracing using global positioning system (GPS), and undertook a field investigation. For risk assessment, the joint IRT visited all eight sites of the military units and the army chaplain's church to evaluate the transmission risk of each site. The evaluation items included the size of the site, the use of air conditioning, whether windows were opened, and whether masks were worn. A pooled testing was used for a low-risk population to quickly detect the spread of COVID-19 in the military base.

RESULTS

A day before the symptom onset of the index case, the lecturer and >50% of the attendees were infected with COVID-19 while attending a lecture that lasted 2 h and 30 min. Attendees were not wearing masks and were in a poorly ventilated room.

CONCLUSION

Since the disease can be spread before symptom onset, contact tracing must be performed to investigate potential exposures prior to symptom onset and manage any exposed persons.

Targeted pandemic containment through identifying local contact network bottlenecks

Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts–between individuals or between population centres–are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods.

During the COVID-19 pandemic decision makers frequently face questions like where to impose a lockdown, which traffic to close, and whom to quarantine, all required to be carried out at minimal costs. Establishing cost-effective pandemic control policies requires identifying good targets. New computational models from network theory and epidemic simulations over real contact networks provide a valuable tool for finding the right bottlenecks to target upon. Here we study a computationally efficient network centrality measure that enables us to detect local transmission bottlenecks, i.e., contact edges that are especially important for the spread of disease among small communities or local network structures inside large networks. We find that pandemic intervention strategies that target at local network structures significantly outperform interventions that solely focus on the entire network structure as a whole, which are traditionally believed to be the most effective.

One Year on: An Overview of Singapore's Response to COVID-19-What We Did, How We Fared, How We Can Move Forward

Background—One year has passed since the first COVID-19 case in Singapore. This scoping review commemorates Singaporean researchers that have expanded the knowledge on this novel virus. We aim to provide an overview of healthcare-related articles published in peer-reviewed journals, authored by the Singapore research community about COVID-19 during the first year of the pandemic. Methods—This was reported using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) protocol. It included healthcare-related articles about COVID-19 published between 23 January 2020 and 22 January 2021 with a Singapore-affiliated author. MEDLINE, Embase, Scopus, Web of Science, CINAHL, PsycINFO, Google Scholar, and local journals were searched. The articles were screened independently by two reviewers. Results—The review included 504 articles. Most of the articles narrated the changes to hospital practice (210), while articles on COVID-19 pathology (94) formed most of the non-narrative papers. Publications on public health (61) and the indirect impacts to clinical outcomes (45) were other major themes explored by the research community. The remaining articles detailed the psychological impact of the pandemic (35), adaptations of medical education (30), and narratives of events (14). Conclusion—Amidst a resurgence of community cases involving variant COVID-19 strains, the resources from the research community will provide valuable guidance to navigate these uncertain times.

Patterns of Virus Exposure and Presumed Household Transmission among Persons with Coronavirus Disease, United States, January-April 2020

We characterized common exposures reported by a convenience sample of 202 US patients with coronavirus disease during January–April 2020 and identified factors associated with presumed household transmission. The most commonly reported settings of known exposure were households and healthcare facilities; among case-patients who had known contact with a confirmed case-patient compared with those who did not, healthcare occupations were more common. Among case-patients without known contact, use of public transportation was more common. Within the household, presumed transmission was highest from older (>65 years) index case-patients and from children to parents, independent of index case-patient age. These findings may inform guidance for limiting transmission and emphasize the value of testing to identify community-acquired infections.

Distribution of incubation periods of COVID-19 in the Canadian context

We propose a novel model based on a set of coupled delay differential equations with fourteen delays in order to accurately estimate the incubation period of COVID-19, employing publicly available data of confirmed corona cases. In this goal, we separate the total cases into fourteen groups for the corresponding fourteen incubation periods. The estimated mean incubation period we obtain is 6.74 days (95% Confidence Interval(CI): 6.35 to 7.13), and the 90th percentile is 11.64 days (95% CI: 11.22 to 12.17), corresponding to a good agreement with statistical supported studies. This model provides an almost zero-cost computational complexity to estimate the incubation period.

No Patients Having Elective Outpatient Orthopaedic Surgery Performed in an Ambulatory Surgery Center Using Preoperative Screening Protocols During the Coronavirus Pandemic Developed COVID-19

Purpose

The purpose of this study is to determine the safety of elective, outpatient orthopaedic sports procedures during the Coronavirus (COVID-19) pandemic at a high-volume orthopaedic practice.

Methods

All patients who were scheduled for elective, outpatient orthopaedic sports medicine procedures at 1 of 2 of outpatient surgical centers between July 1, 2020, and December 31, 2020, were asked to complete a custom survey during a postoperative clinic visit or phone call at a minimum of 2 weeks or were subject to a routine screening questionnaire and temperature screening at the time of the first postoperative follow-up visit. The survey questionnaire assessed for any COVID-19-related symptoms. Surgical case logs were retrieved to review for any cancelled surgeries due to a positive preoperative COVID-19 test.

Results

In total, 3.5% of patients (n = 39/1119) scheduled for surgery were diagnosed with COVID-19 during preoperative testing, resulting in surgical cancellation. Patients with a positive preoperative COVID-19 test result were found to be significantly younger (46 ± 20 years) when compared to all other patients with a negative test (51 ± 21 years; P = .002). No patient was diagnosed with COVID-19, reported symptoms concerning for COVID infection, underwent additional testing, or reported close contact with another individual with a positive test or possessed symptoms concerning for COVID-19 at a minimum of 2 weeks after surgery.

Conclusion

This study found that 3.5% of patients tested positive for COVID-19 and were significantly younger when compared with patients testing negative. No patient investigated for potential infection after surgery reported the development of any symptoms related to COVID-19 or reported a positive test result after surgery. Using current protocols for preoperative screening, elective outpatient orthopaedic surgery performed in an Ambulatory Surgical Center is safe with no documented cases of COVID-19 transmission in this cohort.

Level of Evidence

Level IV, prognostic case series.

Increased complications in patients who test COVID-19 positive after elective surgery and implications for pre and postoperative screening

Background

The COVID-19 pandemic has necessitated the adoption of protocols to minimize risk of periprocedural complications associated with SARS-CoV-2 infection. This typically involves a preoperative symptom screen and nasal swab RT-PCR test for viral RNA. Asymptomatic patients with a negative COVID-19 test are cleared for surgery. However, little is known about the rate of postoperative COVID-19 positivity among elective surgical patients, risk factors for this group and rate of complications.

Methods

This prospective multicenter study included all patients undergoing elective surgery at 170 Veterans Health Administration (VA) hospitals across the United States. Patients were divided into groups based on first positive COVID-19 test within 30 days after surgery (COVID[-/+]), before surgery (COVID[+/−]) or negative throughout (COVID[−/−]). The cumulative incidence, risk factors for and complications of COVID[-/+], were estimated using univariate analysis, exact matching, and multivariable regression.

Results

Between March 1 and December 1, 2020 90,093 patients underwent elective surgery. Of these, 60,853 met inclusion criteria, of which 310 (0.5%) were in the COVID[-/+] group. Adjusted multivariable logistic regression identified female sex, end stage renal disease, chronic obstructive pulmonary disease, congestive heart failure, cancer, cirrhosis, and undergoing neurosurgical procedures as risk factors for being in the COVID[-/+] group. After matching on current procedural terminology code and month of procedure, multivariable Poisson regression estimated the complication rate ratio for the COVID[-/+] group vs. COVID[−/−] to be 8.4 (C.I. 4.9–14.4) for pulmonary complications, 3.0 (2.2, 4.1) for major complications, and 2.6 (1.9, 3.4) for any complication.

Discussion

Despite preoperative COVID-19 screening, there remains a risk of COVID infection within 30 days after elective surgery. This risk is increased for patients with a high comorbidity burden and those undergoing neurosurgical procedures. Higher intensity preoperative screening and closer postoperative monitoring is warranted in such patients because they have a significantly elevated risk of postoperative complications.

The incubation period during the pandemic of COVID-19: a systematic review and meta-analysis

BACKGROUND

The aim of our study was to determine through a systematic review and meta-analysis the incubation period of COVID-19. It was conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA). Criteria for eligibility were all published population-based primary literature in PubMed interface and the Science Direct, dealing with incubation period of COVID-19, written in English, since December 2019 to December 2020. We estimated the mean of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables.

RESULTS

This review included 42 studies done predominantly in China. The mean and median incubation period were of maximum 8 days and 12 days respectively. In various parametric models, the 95th percentiles were in the range 10.3-16 days. The highest 99th percentile would be as long as 20.4 days. Out of the 10 included studies in the meta-analysis, 8 were conducted in China, 1 in Singapore, and 1 in Argentina. The pooled mean incubation period was 6.2 (95% CI 5.4, 7.0) days. The heterogeneity (I² 77.1%; p < 0.001) was decreased when we included the study quality and the method of calculation used as moderator variables (I² 0%). The mean incubation period ranged from 5.2 (95% CI 4.4 to 5.9) to 6.65 days (95% CI 6.0 to 7.2).

CONCLUSIONS

This work provides additional evidence of incubation period for COVID-19 and showed that it is prudent not to dismiss the possibility of incubation periods up to 14 days at this stage of the epidemic.

Transmission of SARS-CoV-2 before and after symptom onset: impact of nonpharmaceutical interventions in China

Nonpharmaceutical interventions, such as contact tracing and quarantine, have been the primary means of controlling the spread of SARS-CoV-2; however, it remains uncertain which interventions are most effective at reducing transmission at the population level. Using serial interval data from before and after the rollout of nonpharmaceutical interventions in China, we estimate that the relative frequency of presymptomatic transmission increased from 34% before the rollout to 71% afterward. The shift toward earlier transmission indicates a disproportionate reduction in transmission post-symptom onset. We estimate that, following the rollout of nonpharmaceutical interventions, transmission post-symptom onset was reduced by 82% whereas presymptomatic transmission decreased by only 16%. The observation that only one-third of transmission was presymptomatic at baseline, combined with the finding that NPIs reduced presymptomatic transmission by less than 20%, suggests that the overall impact of NPIs was driven in large part by reductions in transmission following symptom onset. This implies that interventions which limit opportunities for transmission in the later stages of infection, such as contact tracing and isolation, are particularly important for control of SARS-CoV-2. Interventions which specifically reduce opportunities for presymptomatic transmission, such as quarantine of asymptomatic contacts, are likely to have smaller, but non-negligible, effects on overall transmission.

Epidemiology, clinical spectrum, viral kinetics and impact of COVID-19 in the Asia-Pacific region

COVID-19 has hit the world by surprise, causing substantial mortality and morbidity since 2020. This narrative review aims to provide an overview of the epidemiology, induced impact, viral kinetics and clinical spectrum of COVID-19 in the Asia-Pacific Region, focusing on regions previously exposed to outbreaks of coronavirus. COVID-19 progressed differently by regions, with some (such as China and Taiwan) featured by one to two epidemic waves and some (such as Hong Kong and South Korea) featured by multiple waves. There has been no consensus on the estimates of important epidemiological time intervals or proportions, such that using them for making inferences should be done with caution. Viral loads of patients with COVID-19 peak in the first week of illness around days 2 to 4 and hence there is very high transmission potential causing community outbreaks. Various strategies such as government-guided and suppress-and-lift strategies, trigger-based/suppression approaches and alert systems have been employed to guide the adoption and easing of control measures. Asymptomatic and pre-symptomatic transmission is a hallmark of COVID-19. Identification and isolation of symptomatic patients alone is not effective in controlling the ongoing outbreaks. However, early, prompt and coordinated enactment predisposed regions to successful disease containment. Mass COVID-19 vaccinations are likely to be the light at the end of the tunnel. There is a need to review what we have learnt in this pandemic and examine how to transfer and improve existing knowledge for ongoing and future epidemics.

In pursuit of the right tail for the COVID-19 incubation period

Definition of the incubation period for COVID-19 is critical for implementing quarantine and thus infection control. Whereas the classical definition relies on the time from exposure to time of first symptoms, a more practical working definition is the time from exposure to time of first live virus excretion. For COVID-19, average incubation period times commonly span 5–7 days which are generally longer than for most typical other respiratory viruses. There is considerable variability reported however for the late right-hand statistical distribution. A small but yet epidemiologically important subset of patients may have the late end of the incubation period extend beyond the 14 days that is frequently assumed. Conservative assumptions of the right tail end distribution favor safety, but pragmatic working modifications may be required to accommodate high rates of infection and/or healthcare worker exposures. Despite the advent of effective vaccines, further attention and study in these regards are warranted. It is predictable that vaccine application will be associated with continued confusion over protection and its longevity. Measures for the application of infectivity will continue to be extremely relevant.

Peptide substrate screening for the diagnosis of SARS-CoV-2 using fluorescence resonance energy transfer (FRET) assay

The novel corona (SARS-CoV-2) virus causes a global pandemic, which motivates researchers to develop reliable and effective methods for screening and detection of SARS-CoV-2. Though there are several methods available for the diagnosis of SARS-CoV-2 such as RT-PCR and ELSIA, nevertheless, these methods are time-consuming and may not apply at the point of care. In this study, we have developed a specific, sensitive, quantitative and fast detection method for SARS-CoV-2 by fluorescence resonance energy transfer (FRET) assay. The total extracellular protease proteolytic activity from the virus has been used as the biomarker. The specific peptide sequences from the library of 115 dipeptides were identified via changes in the fluorescence signal. The fluorogenic dipeptide substrates have the fluorophore and a quencher at the N- and the C- terminals, respectively. When the protease hydrolyzes the peptide bond between the two specific amino acids, it leads to a significant increase in the fluorescence signals. The specific fluorogenic peptide (H-d) produces a high fluorescence signal. A calibration plot was obtained from the changes in the fluorescence intensity against the different concentrations of the viral protease. The lowest limit of detection of this method was 9.7 ± 3 pfu/mL. The cross-reactivity of the SARS-CoV-2-specific peptide was tested against the MERS-CoV which does not affect the fluorescence signal. A significant change in the fluorescence signal with patient samples indicates that this FRET-based assay might be applied for the diagnosis of SARS-CoV-2 patients.

Analysis of the early COVID-19 epidemic curve in Germany by regression models with change points

We analysed the coronavirus disease 2019 epidemic curve from March to the end of April 2020 in Germany. We use statistical models to estimate the number of cases with disease onset on a given day and use back-projection techniques to obtain the number of new infections per day. The respective time series are analysed by a trend regression model with change points. The change points are estimated directly from the data. We carry out the analysis for the whole of Germany and the federal state of Bavaria, where we have more detailed data. Both analyses show a major change between 9 and 13 March for the time series of infections: from a strong increase to a decrease. Another change was found between 25 March and 29 March, where the decline intensified. Furthermore, we perform an analysis stratified by age. A main result is a delayed course of the pandemic for the age group 80 + resulting in a turning point at the end of March. Our results differ from those by other authors as we take into account the reporting delay, which turned out to be time dependent and therefore changes the structure of the epidemic curve compared to the curve of newly reported cases.

Characterizing COVID-19 and Influenza Illnesses in the Real World via Person-Generated Health Data

The fight against COVID-19 is hindered by similarly presenting viral infections that may confound detection and monitoring. We examined person-generated health data (PGHD), consisting of survey and commercial wearable data from individuals' everyday lives, for 230 people who reported a COVID-19 diagnosis between March 30, 2020, and April 27, 2020 (n = 41 with wearable data). Compared with self-reported diagnosed flu cases from the same time frame (n = 426, 85 with wearable data) or pre-pandemic (n = 6,270, 1,265 with wearable data), COVID-19 patients reported a distinct symptom constellation that lasted longer (median of 12 versus 9 and 7 days, respectively) and peaked later after illness onset. Wearable data showed significant changes in daily steps and prevalence of anomalous resting heart rate measurements, of similar magnitudes for both the flu and COVID-19 cohorts. Our findings highlight the need to include flu comparator arms when evaluating PGHD applications aimed to be highly specific for COVID-19.