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(ß) = 0.81, 95%CI: 0.66 ~ 0.98, p = 0.034; Omicron: exp(ß) = 0.82, 95%CI: 0.71 ~ 0.94, p = 0.004] and incubation period [Delta: exp(ß) = 0.69, 95%CI: 0.55 ~ 0.86, p < 0.001; Omicron: exp(ß) = 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.

Impact of climate change on temperature variations and extrinsic incubation period of malaria parasites in Chennai, India: implications for its disease transmission potential.

BACKGROUND: The global temperature has significantly risen in the past century. Studies have indicated that higher temperature intensifies malaria transmission in tropical and temperate countries. Temperature fluctuations will have a potential impact on parasite development in the vector Anopheles mosquito. METHODS: Year-long microclimate temperatures were recorded from a malaria-endemic area, Chennai, India, from September 2021 to August 2022. HOBO data loggers were placed in different vector resting sites including indoor and outdoor roof types. Downloaded temperatures were categorised by season, and the mean temperature was compared with data from the same study area recorded from November 2012 to October 2013. The extrinsic incubation period for Plasmodium falciparum and P. vivax was calculated from longitudinal temperatures recorded during both periods. Vector surveillance was also carried out in the area during the summer season. RESULTS: In general, temperature and daily temperature range (DTR) have increased significantly compared to the 2012-2013 data, especially the DTR of indoor asbestos structures, from 4.30 â„ƒ to 12.62 â„ƒ in 2021-2022, unlike the marginal increase observed in thatched and concrete structures. Likewise, the average DTR of outdoor asbestos structures increased from 5.02 â„ƒ (2012-2013) to 8.76 â„ƒ (2021-2022) although the increase was marginal in thatched structures and, surprisingly, showed no such changes in concrete structures. The key finding of the extrinsic incubation period (EIP) is that a decreasing trend was observed in 2021-2022 compared to 2012-2013, mainly in indoor asbestos structures from 7.01 to 6.35 days, which negatively correlated with the current observation of an increase in temperature. Vector surveillance undertaken in the summer season revealed the presence of Anopheles breeding in various habitats. Anopheles stephensi could be collected using CDC light traps along with other mosquito species. CONCLUSION: The microclimate temperature has increased significantly over the years, and mosquitoes are gradually adapting to this rising temperature. Temperature negatively correlates with the extrinsic incubation period of the parasite. As the temperature increases, the development of the parasite in An. stephensi will be faster because of a decrease in EIP, thus requiring relatively fewer days, posing a risk for disease transmission and a hindrance to malaria elimination efforts.

Half a Century in Hiding: A Unique Case of Tuberculoid Leprosy with an Unprecedented Incubation Period.

BACKGROUND Leprosy, also known as Hansen's disease, is a neglected tropical disease with low prevalence in the United States. The disease's long incubation period can cause delayed presentation, and most affected individuals have a history of travel or work in leprosy-endemic regions. The immune response to Mycobacterium leprae determines the clinical characteristics of leprosy, with tuberculoid leprosy being characterized by well-defined granulomas and involvement of peripheral nerves. The recommended treatment is a combination of dapsone and rifampin for 12 months. CASE REPORT A 78-year-old man with a history of extensive travel to Africa and Asia 50 years ago, presented with a non-tender, non-pruritic, and hypopigmented skin lesion on his left knee. Biopsy results confirmed granulomatous inflammation and the presence of Mycobacterium leprae, leading to a diagnosis of tuberculoid/paucibacillary leprosy. The patient received dapsone and rifampin treatment, which resulted in symptom improvement. CONCLUSIONS The patient's long incubation period of 50 years between exposure and symptom onset is remarkable and possibly one of the longest reported for tuberculoid leprosy. It emphasizes the importance of considering leprosy in cases with an extensive travel history and long incubation periods. Our patient's case presented contradictory staining results, suggesting potential sampling variation or a rare mixed leprosy form. Based on his clinical findings, he was diagnosed with tuberculoid leprosy. Early diagnosis and treatment are crucial to prevent irreversible nerve damage and improve patient outcomes. Healthcare providers should be vigilant in acquiring a detailed travel history to facilitate early diagnosis and appropriate management of leprosy cases.

Estimation of Incubation Period of Mpox during 2022 Outbreak in Pereira, Colombia.

We estimated the incubation period for mpox during an outbreak in Pereira, Colombia, using data from 11 confirmed cases. Mean incubation period was 7.1 (95% CI 4.9-9.9) days, consistent with previous outbreaks. Accurately estimating the incubation period provides insights into transmission dynamics, informing public health interventions and surveillance strategies.

[Comparison of incubation periods of infections of Omicron variants BA.2 and BF.7 in Beijing].

Objective: To understand the incubation periods of infections of Omicron variants BA.2 and BF.7 in two COVID-19 epidemics and related factors in Beijing and provide basic parameters for the establishment of 2019-nCoV dynamic transmission model. Methods: The COVID-19 cases with specific exposure time and onset time in the Omicron variant BA.2 infection epidemic in April 2022 and in the Omicron variant BF.7 infection epidemic in October 2022 in Beijing were included in the analysis. The rank-sum test was conducted to estimate the differences in the incubation period between two types of infections. The incubation period distribution of the Omicron variant infection was fitted by using Weibull, Gamma and lognormal distributions. Multivariate analysis of variance was conducted to assess the effects of age, sex, variant type and vaccination status on the incubation periods. Results: A total of 64 cases of variant BA.2 infection and 58 cases of variant BF.7 infection were included. The M(Q1,Q3) of the incubation period was 3.00 (3.00, 4.00) days for BA.2 infection and 3.00 (2.00, 3.25) days for BF.7 infection. The lognormal distribution was the best fit. Multivariate analysis of variance showed that there were some differences in the incubation periods between two types of infections of Omicron variants, and the incubation period of variant BF.7 infection was shorter than that of variant BA.2 infection. Conclusion: Omicron variant BF.7 infection had shorter incubation period compared with Omicron variant BA.2 infection.

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.

Estimated incubation period distributions of mpox using cases from two international European festivals and outbreaks in a club in Berlin, May to June 2022.

BackgroundSince May 2022, an mpox outbreak affecting primarily men who have sex with men (MSM) has occurred in numerous non-endemic countries worldwide. As MSM frequently reported multiple sexual encounters in this outbreak, reliably determining the time of infection is difficult; consequently, estimation of the incubation period is challenging.AimWe aimed to provide valid and precise estimates of the incubation period distribution of mpox by using cases associated with early outbreak settings where infection likely occurred.MethodsColleagues in European countries were invited to provide information on exposure intervals and date of symptom onset for mpox cases who attended a fetish festival in Antwerp, Belgium, a gay pride festival in Gran Canaria, Spain or a particular club in Berlin, Germany, where early mpox outbreaks occurred. Cases of these outbreaks were pooled; doubly censored models using the log-normal, Weibull and Gamma distributions were fitted to estimate the incubation period distribution.ResultsWe included data on 122 laboratory-confirmed cases from 10 European countries. Depending on the distribution used, the median incubation period ranged between 8 and 9 days, with 5th and 95th percentiles ranging from 2 to 3 and from 20 to 23 days, respectively. The shortest interval that included 50% of incubation periods spanned 8 days (4-11 days).ConclusionCurrent public health management of close contacts should consider that in approximately 5% of cases, the incubation period exceeds the commonly used monitoring period of 21 days.

Updated estimation of cutaneous leishmaniasis incubation period in French Guiana.

BACKGROUND: The cutaneous leishmaniasis (CL) incubation period (IP) is defined as the time between parasite inoculation by sandfly bite and the onset of the first CL lesion. IP distribution is difficult to assess for CL because the date of exposure to an infectious bite cannot be accurately determined in endemic areas. IP current estimates for CL range from 14 days to several months with a median around 30-60 days, as established by a few previous studies in both New and Old Worlds. METHODOLOGY: We estimated CL incubation period distribution using time-to-event models adapted to interval-censored data based on declared date of travels from symptomatic military personnel living in non-endemic areas that were exposed during their short stays in French Guiana (FG) between January 2001 and December 2021. PRINCIPAL FINDINGS: A total of 180 patients were included, of which 176 were men (97.8%), with a median age of 26 years. When recorded, the parasite species was always Leishmania guyanensis (31/180, 17.2%). The main periods of CL diagnosis spread from November to January (84/180, 46.7%) and over March-April (54/180, 30.0%). The median IP was estimated at 26.2 days (95% Credible Level, 23.8-28.7 days) using a Bayesian accelerated failure-time regression model. Estimated IP did not exceed 62.1 days (95% CI, 56-69.8 days) in 95% of cases (95th percentile). Age, gender, lesion number, lesion evolution and infection date did not significantly modify the IP. However, disseminated CL was significantly associated with a 2.8-fold shortening of IP. CONCLUSIONS: This work suggests that the CL IP distribution in French Guiana is shorter and more restricted than anticipated. As the incidence of CL in FG usually peaks in January and March, these findings suggest that patients are contaminated at the start of the rainy season.

How valid is the 2- to 10-day incubation period for cases of Legionnaires' disease?: A reappraisal in the context of the German LeTriWa study; Berlin, 2016-2020.

The evidence for the incubation period of Legionnaires' disease is based on data from a small number of outbreaks. An incubation period of 2-10 days is commonly used for the definition and investigation of cases. In the German LeTriWa study, we collaborated with public health departments to identify evidence-based sources of exposure among cases of Legionnaires' disease within 1-14 days before symptom onset. For each individual, we assigned weights to the numbered days of exposure before symptom onset, giving the highest weight to exposure days of cases with only one possible day of exposure. We then calculated an incubation period distribution where the median was 5 days and the mode was 6 days. The cumulative distribution reached 89% by the 10th day before symptom onset. One case-patient with immunosuppression had a single day of exposure to the likely infection source only 1 day before symptom onset. Overall, our results support the 2- to 10-day incubation period used in case definition, investigation, and surveillance of cases with Legionnaires' disease.

Intervention reducing malaria parasite load in vector mosquitoes: No impact on Plasmodium falciparum extrinsic incubation period and the survival of Anopheles gambiae.

In the fight against malaria, transmission blocking interventions (TBIs) such as transmission blocking vaccines or drugs, are promising approaches to complement conventional tools. They aim to prevent the infection of vectors and thereby reduce the subsequent exposure of a human population to infectious mosquitoes. The effectiveness of these approaches has been shown to depend on the initial intensity of infection in mosquitoes, often measured as the mean number of oocysts resulting from an infectious blood meal in absence of intervention. In mosquitoes exposed to a high intensity of infection, current TBI candidates are expected to be ineffective at completely blocking infection but will decrease parasite load and therefore, potentially also affect key parameters of vector transmission. The present study investigated the consequences of changes in oocyst intensity on subsequent parasite development and mosquito survival. To address this, we experimentally produced different intensities of infection for Anopheles gambiae females from Burkina Faso by diluting gametocytes from three natural Plasmodium falciparum local isolates and used a newly developed non-destructive method based on the exploitation of mosquito sugar feeding to track parasite and mosquito life history traits throughout sporogonic development. Our results indicate the extrinsic incubation period (EIP) of P. falciparum and mosquito survival did not vary with parasite density but differed significantly between parasite isolates with estimated EIP50 of 16 (95% CI: 15-18), 14 (95% CI: 12-16) and 12 (95% CI: 12-13) days and median longevity of 25 (95% CI: 22-29), 15 (95% CI: 13-15) and 18 (95% CI: 17-19) days for the three isolates respectively. Our results here do not identify unintended consequences of the decrease of parasite loads in mosquitoes on the parasite incubation period or on mosquito survival, two key parameters of vectorial capacity, and hence support the use of transmission blocking strategies to control malaria.

Estimate the incubation period of coronavirus 2019 (COVID-19).

COVID-19 is an infectious disease that presents unprecedented challenges to society. Accurately estimating the incubation period of the coronavirus is critical for effective prevention and control. However, the exact incubation period remains unclear, as COVID-19 symptoms can appear in as little as 2 days or as long as 14 days or more after exposure. Accurate estimation requires original chain-of-infection data, which may not be fully available from the original outbreak in Wuhan, China. In this study, we estimated the incubation period of COVID-19 by leveraging well-documented and epidemiologically informative chain-of-infection data collected from 10 regions outside the original Wuhan areas prior to February 10, 2020. We employed a proposed Monte Carlo simulation approach and nonparametric methods to estimate the incubation period of COVID-19. We also utilized manifold learning and related statistical analysis to uncover incubation relationships between different age and gender groups. Our findings revealed that the incubation period of COVID-19 did not follow general distributions such as lognormal, Weibull, or Gamma. Using proposed Monte Carlo simulations and nonparametric bootstrap methods, we estimated the mean and median incubation periods as 5.84 (95% CI, 5.42-6.25 days) and 5.01 days (95% CI 4.00-6.00 days), respectively. We also found that the incubation periods of groups with ages greater than or equal to 40 years and less than 40 years demonstrated a statistically significant difference. The former group had a longer incubation period and a larger variance than the latter, suggesting the need for different quarantine times or medical intervention strategies. Our machine-learning results further demonstrated that the two age groups were linearly separable, consistent with previous statistical analyses. Additionally, our results indicated that the incubation period difference between males and females was not statistically significant.

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.

Differences in Longevity and Temperature-Driven Extrinsic Incubation Period Correlate with Varying Dengue Risk in the Arizona-Sonora Desert Region.

Dengue transmission is determined by a complex set of interactions between the environment, Aedes aegypti mosquitoes, dengue viruses, and humans. Emergence in new geographic areas can be unpredictable, with some regions having established mosquito populations for decades without locally acquired transmission. Key factors such as mosquito longevity, temperature-driven extrinsic incubation period (EIP), and vector-human contact can strongly influence the potential for disease transmission. To assess how these factors interact at the edge of the geographical range of dengue virus transmission, we conducted mosquito sampling in multiple urban areas located throughout the Arizona-Sonora desert region during the summer rainy seasons from 2013 to 2015. Mosquito population age structure, reflecting mosquito survivorship, was measured using a combination of parity analysis and relative gene expression of an age-related gene, SCP-1. Bloodmeal analysis was conducted on field collected blood-fed mosquitoes. Site-specific temperature was used to estimate the EIP, and this predicted EIP combined with mosquito age were combined to estimate the abundance of "potential" vectors (i.e., mosquitoes old enough to survive the EIP). Comparisons were made across cities by month and year. The dengue endemic cities Hermosillo and Ciudad Obregon, both in the state of Sonora, Mexico, had higher abundance of potential vectors than non-endemic Nogales, Sonora, Mexico. Interestingly, Tucson, Arizona consistently had a higher estimated abundance of potential vectors than dengue endemic regions of Sonora, Mexico. There were no observed city-level differences in species composition of blood meals. Combined, these data offer insights into the critical factors required for dengue transmission at the ecological edge of the mosquito's range. However, further research is needed to integrate an understanding of how social and additional environmental factors constrain and enhance dengue transmission in emerging regions.

[Study of incubation period of infection with 2019-nCoV Omicron variant BA.5.1.3].

Objective: To study the incubation period of the infection with 2019-nCoV Omicron variant BA.5.1.3. Methods: Based on the epidemiological survey data of 315 COVID-19 cases and the characteristics of interval censored data structure, log-normal distribution and Gamma distribution were used to estimate the incubation. Bayes estimation was performed for the parameters of each distribution function using discrete time Markov chain Monte Carlo algorithm. Results: The mean age of the 315 COVID-19 cases was (42.01±16.54) years, and men accounted for 30.16%. A total of 156 cases with mean age of (41.65±16.32) years reported the times when symptoms occurred. The log-normal distribution and Gamma distribution indicated that the M (Q1, Q3) of the incubation period from exposure to symptom onset was 2.53 (1.86, 3.44) days and 2.64 (1.91, 3.52) days, respectively, and the M (Q1, Q3) of the incubation period from exposure to the first positive nucleic acid detection was 2.45 (1.76, 3.40) days and 2.57 (1.81, 3.52) days, respectively. Conclusions: The incubation period by Bayes estimation based on log-normal distribution and Gamma distribution, respectively, was similar to each other, and the best distribution of incubation period was Gamma distribution, the difference between the incubation period from exposure to the first positive nucleic acid detection and the incubation period from exposure to symptom onset was small. The median of incubation period of infection caused by Omicron variant BA.5.1.3 was shorter than those of previous Omicron variants.

[Latent period and incubation period with associated factors of COVID-19 caused by Omicron variant].

Objective: To estimate the latent period and incubation period of Omicron variant infections and analyze associated factors. Methods: From January 1 to June 30, 2022, 467 infections and 335 symptomatic infections in five local Omicron variant outbreaks in China were selected as the study subjects. The latent period and incubation period were estimated by using log-normal distribution and gamma distribution models, and the associated factors were analyzed by using the accelerated failure time model (AFT). Results: The median (Q1, Q3) age of 467 Omicron infections including 253 males (54.18%) was 26 (20, 39) years old. There were 132 asymptomatic infections (28.27%) and 335 (71.73%) symptomatic infections. The mean latent period of 467 Omicron infections was 2.65 (95%CI: 2.53-2.78) days, and 98% of infections were positive for nucleic acid test within 6.37 (95%CI: 5.86-6.82) days after infection. The mean incubation period of 335 symptomatic infections was 3.40 (95%CI: 3.25-3.57) days, and 97% of them developed clinical symptoms within 6.80 (95%CI: 6.34-7.22) days after infection. The results of the AFT model analysis showed that compared with the group aged 18-49 years old, the latent period [exp(ß)=1.36 (95%CI: 1.16-1.60), P<0.001] and incubation period [exp(ß)=1.24 (95%CI: 1.07-1.45), P=0.006] of infections aged 0-17 years old were prolonged. The latent period [exp(ß)=1.38 (95%CI: 1.17-1.63), P<0.001] and the incubation period [exp(ß)=1.26 (95%CI: 1.06-1.48), P=0.007] of infections aged 50 years old and above were also prolonged. Conclusion: The latent period and incubation period of most Omicron infections are within 7 days, and age may be a influencing factor of the latent period and incubation period.

Serial Interval and Incubation Period Estimates of Monkeypox Virus Infection in 12 Jurisdictions, United States, May-August 2022.

Using data from 12 US health departments, we estimated mean serial interval for monkeypox virus infection to be 8.5 (95% credible interval 7.3-9.9) days for symptom onset, based on 57 case pairs. Mean estimated incubation period was 5.6 (95% credible interval 4.3-7.8) days for symptom onset, based on 35 case pairs.

Comparing the incubation period, serial interval, and infectiousness profile between SARS-CoV-2 Omicron and Delta variants.

In January 2022, the SARS-CoV-2 Omicron variants initiated major outbreaks and dominated the transmissions in Hong Kong, displacing an earlier outbreak seeded by the Delta variants. To provide insight into the transmission potential of the emerging variants, we aimed to compare the epidemiological characteristics of the Omicron and Delta variants. We analyzed the line-list clinical and contact tracing data of the SARS-CoV-2 confirmed cases in Hong Kong. Transmission pairs were constructed based on the individual contact history. We fitted bias-controlled models to the data to estimate the serial interval, incubation period and infectiousness profile of the two variants. Viral load data were extracted and fitted to the random effect models to investigate the potential risk modifiers for the clinical viral shedding course. Totally 14 401 confirmed cases were reported between January 1 and February 15, 2022. The estimated mean serial interval (4.4 days vs. 5.8 days) and incubation period (3.4 days vs. 3.8 days) were shorter for the Omicron than the Delta variants. A larger proportion of presymptomatic transmission was observed for the Omicron (62%) compared to the Delta variants (48%). The Omicron cases had higher mean viral load over an infection course than the Delta cases, with the elder cases appearing more infectious than the younger cases for both variants. The epidemiological features of Omicron variants were likely an obstacle to contact tracing measures, imposed as a major intervention in settings like Hong Kong. Continuously monitoring the epidemiological feature for any emerging SARS-CoV-2 variants in the future is needed to assist officials in planning measures for COVID-19 control.

Serial Intervals and Incubation Periods of SARS-CoV-2 Omicron and Delta Variants, Singapore.

We compared serial intervals and incubation periods for SARS-CoV-2 Omicron BA.1 and BA.2 subvariants and Delta variants in Singapore. Median incubation period was 3 days for BA.1 versus 4 days for Delta. Serial interval was 2 days for BA.1 and 3 days for BA.2 but 4 days for Delta.