The Road to SARS-CoV-2 Seasonality: A Vision of the Post-Pandemic Era

The end of the pandemic could be marked, not by the total eradication of the virus but by a decrease in cases and seasonal peaks in the frequency of SARSCoV-2. Although this has already happened with the influenza A (H1N1) pdm09 virus responsible for the 2009 pandemic, unlike on that occasion, many of the countries that have widely covered their population with the vaccination scheme, still receive the onslaught of COVID-19 and have resumed containment measures due to the appearance, above all, of new variants. The latter suggests that the path to SARS-CoV-2 seasonality may not be as benevolent as the 2009 influenza virus was. Therefore, it is necessary to study the characteristics by which this new virus can acquire seasonality. to consider this scenario and take the necessary measures to face it from a different perspective. © 2022 Academia Nacional de Medicina. All rights reserved.

The Incubation Period of COVID-19 Caused by Different SARS-CoV-2 Variants

Since the beginning of the COVID-19 epidemic, the pathogen of COVID-19, SARS-CoV-2, has evolved and mutated continuously, producing variants with different enhanced transmission and virulence, such as Alpha (B.1.1.7), Beta(B.1.351), Gamma(P.1), Delta (B.1.617.2) and Omicron(B.1.1.529). An intensive study of the incubation period of COVID-19 caused by different SARS-CoV-2 variants will contribute to tracing the origin of COVID-19, determining the detention, quarantine and isolation time of close contacts, and timely improving measures for containing COVID-19. We reviewed the major studies on the incubation period of COVID-19 caused by wild-type strains and different variants of SARS-CoV-2, which estimated that the incubation period of COVID-19 caused by wild-type SARS-CoV-2 strains was 4-8(median 5.5) days. And that for COVID-19 caused by Beta or Gamma variant was generally similar to that by wild-type strains, about 5 days. The incubation period of COVID-19 caused by Alpha, Delta and Omicron variants was shorter than that of other strains, which was 4, 4 and 3 days, respectively. Copyright © 2022 by the Chinese General Practice.

A Novel Tool for Real-time Estimation of Epidemiological Parameters of Communicable Diseases Using Contact-Tracing Data: Development and Deployment.

BACKGROUND: The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in their epidemic response. It consists of the documentation, linkage, and follow-up of cases, contacts, and events. To allow SORMAS users to visualize data, compute essential surveillance indicators, and estimate epidemiological parameters from such network data in real-time, we developed the SORMAS Statistics (SORMAS-Stats) application. OBJECTIVE: This study aims to describe the essential visualizations, surveillance indicators, and epidemiological parameters implemented in the SORMAS-Stats application and illustrate the application of SORMAS-Stats in response to the COVID-19 outbreak. METHODS: Based on findings from a rapid review and SORMAS user requests, we included the following visualization and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number R(t), dispersion parameter k, and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptom onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. Furthermore, we applied the Markov Chain Monte Carlo approach and estimated R(t) using the incidence data and the observed SI computed from the transmission network data. RESULTS: Using COVID-19 contact-tracing data of confirmed cases reported between July 31 and October 29, 2021, in the Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63,570 nodes. The network comprises 1.75% (1115/63,570) events, 19.59% (12,452/63,570) case persons, and 78.66% (50,003/63,570) exposed persons, including 1238 infector-infectee pairs and 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with the best fit to the observed SI data was a lognormal distribution with a mean of 4.30 (95% CI 4.09-4.51) days. We estimated a dispersion parameter k of 21.11 (95% CI 7.57-34.66) and an effective reproduction number R of 0.9 (95% CI 0.58-0.60). The weekly estimated R(t) values ranged from 0.80 to 1.61. CONCLUSIONS: We provide an application for real-time estimation of epidemiological parameters, which is essential for informing outbreak response strategies. The estimates are commensurate with findings from previous studies. The SORMAS-Stats application could greatly assist public health authorities in the regions using SORMAS or similar tools by providing extensive visualizations and computation of surveillance indicators.

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.

Incubation period of COVID-19: A systematic review and meta-analysis.

BACKGROUND AND OBJECTIVE: The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19. METHODS: For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. RESULTS: We included seven studies (n=792) in the meta-analysis. The heterogeneity (I2 83.0%, p<0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I2 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2-6.0) to 6.7 days (95% CI: 6.0-7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years. CONCLUSION: Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns.

[Incubation period of COVID-19: A systematic review and meta-analysis]. / Período de incubación de la COVID-19: revisión sistemática y metaanálisis.

BACKGROUND AND OBJECTIVE: The incubation period of COVID-19 helps to determine the optimal duration of the quarantine and inform predictive models of incidence curves. Several emerging studies have produced varying results; this systematic review aims to provide a more accurate estimate of the incubation period of COVID-19. METHODS: For this systematic review, a literature search was conducted using Pubmed, Scopus/EMBASE, and the Cochrane Library databases, covering all observational and experimental studies reporting the incubation period and published from 1 January 2020 to 21 March 2020.We estimated the mean and 95th percentile of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. RESULTS: We included seven studies (n = 792) in the meta-analysis. The heterogeneity (I2 83.0%, p < 0.001) was significantly decreased when we included the study quality and the statistical model used as moderator variables (I2 15%). The mean incubation period ranged from 5.6 (95% CI: 5.2 to 6.0) to 6.7 days (95% CI: 6.0 to 7.4) according to the statistical model. The 95th percentile was 12.5 days when the mean age of patients was 60 years, increasing 1 day for every 10 years. CONCLUSION: Based on the published data reporting the incubation period of COVID-19, the mean time between exposure and onset of clinical symptoms depended on the statistical model used, and the 95th percentile depended on the mean age of the patients. It is advisable to record sex and age when collecting data in order to analyze possible differential patterns.

Transmission onset distribution of COVID-19.

OBJECTIVES: The distribution of the transmission onset of COVID-19 relative to the symptom onset is a key parameter for infection control. It is often not easy to study the transmission onset time, as it is difficult to know who infected whom exactly when. METHODS: We inferred transmission onset time from 72 infector-infectee pairs in South Korea, either with known or inferred contact dates, utilizing the incubation period. Combining this data with known information of the infector's symptom onset, we could generate the transmission onset distribution of COVID-19, using Bayesian methods. Serial interval distribution could be automatically estimated from our data. RESULTS: We estimated the median transmission onset to be 1.31 days (standard deviation, 2.64 days) after symptom onset with a peak at 0.72 days before symptom onset. The pre-symptomatic transmission proportion was 37% (95% credible interval [CI], 16-52%). The median incubation period was estimated to be 2.87 days (95% CI, 2.33-3.50 days), and the median serial interval to be 3.56 days (95% CI, 2.72-4.44 days). CONCLUSIONS: Considering that the transmission onset distribution peaked with the symptom onset and the pre-symptomatic transmission proportion is substantial, the usual preventive measures might be too late to prevent SARS-CoV-2 transmission.

A Chinese Case of COVID-19 Did Not Show Infectivity During the Incubation Period: Based on an Epidemiological Survey.

Controversy remains over whether the novel coronavirus 2019 (COVID-19) virus may have infectivity during the incubation period before the onset of symptoms. The author had the opportunity to examine the infectivity of COVID-19 during the incubation period by conducting an epidemiological survey on a confirmed patient who had visited Jeju Island during the incubation period. The epidemiological findings support the claim that the COVID-19 virus does not have infectivity during the incubation period.