Exact confidence intervals for population growth rate, longevity and generation time.

By quantifying key life history parameters in populations, such as growth rate, longevity, and generation time, researchers and administrators can obtain valuable insights into its dynamics. Although point estimates of demographic parameters have been available since the inception of demography as a scientific discipline, the construction of confidence intervals has typically relied on approximations through series expansions or computationally intensive techniques. This study introduces the first mathematical expression for calculating confidence intervals for the aforementioned life history traits when individuals are unidentifiable and data are presented as a life table. The key finding is the accurate estimation of the confidence interval for r, the instantaneous growth rate, which is tested using Monte Carlo simulations with four arbitrary discrete distributions. In comparison to the bootstrap method, the proposed interval construction method proves more efficient, particularly for experiments with a total offspring size below 400. We discuss handling cases where data are organized in extended life tables or as a matrix of vital rates. We have developed and provided accompanying code to facilitate these computations.

Measurement of Isfahan heavy water zero-power reactor kinetic parameters using advanced pulsed neutron source method in a near critical state.

The pulsed neutron source (PNS) technique was used to determine the prompt neutron decay constant for two different lattice pitches in the HWZPR heavy water zero power reactor. The results were compared to the variance-to-mean ratio (VTM) method. The neutron mean generation time was also calculated for both pitches, and the results were compared to previous Monte Carlo calculations. The findings of this research can be used as a benchmark nuclear codes to validate kinetic parameters.

Transmissibility of severe acute respiratory syndrome coronavirus 2 among household contacts of coronavirus disease 2019-positive patients: A community-based study in India.

BACKGROUND: This study identified the risk factors for severe acute respiratory syndrome coronavirus 2 infection among household contacts of index patients and determined the incubation period (IP), serial interval, and estimates of secondary infection rate in Kerala, India. METHODS: We conducted a cohort study in three districts of Kerala among the inhabitants of households of reverse transcriptase polymerase chain reaction-positive coronavirus disease 2019 patients between January and July 2021. About 147 index patients and 362 household contacts were followed up for 28 days to determine reverse transcriptase polymerase chain reaction positivity and the presence of total antibodies against SARS-CoV-2 on days 1, 7, 14, and 28. RESULTS: The mean IP, serial interval, and generation time were 1.6, 3, and 3.9 days, respectively. The secondary infection rate at 14 days was 43.0%. According to multivariable regression analysis persons who worked outside the home were protected (adjusted odds ratio [aOR], 0.45; 95% confidence interval [CI], 0.24-0.85), whereas those who had kissed the coronavirus disease 2019-positive patients during illness were more than twice at risk of infection (aOR, 2.23; 95% CI, 1.01-5.2) than those who had not kissed the patients. Sharing a toilet with the index patient increased the risk by more than twice (aOR, 2.5; 95% CI, 1.42-4.64) than not sharing a toilet. However, the contacts who reported using masks (aOR, 2.5; 95% CI, 1.4-4.4) were at a higher risk of infection in household settings. CONCLUSIONS: Household settings have a high secondary infection rate and the changing transmissibility dynamics such as IP, serial interval should be considered in the prevention and control of SARS-CoV-2.

The Influence of Chestnut Extract and Its Components on Antibacterial Activity against Staphylococcus aureus.

Increasing antimicrobial resistance has caused a great interest in natural products as alternatives or potentiators of antibiotics. The objective of this study was to isolate individual tannins from crude chestnut extract as well as to determine the influence of both crude extracts (tannic acid extract, chestnut extract) and individual pure tannins (gallic acid, vescalin, vescalagin, castalin, castalagin) on the growth of Gram-positive Staphylococcus aureus bacteria. Their antibacterial activity was monitored by measuring the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) as well as the duration of the lag phase, growth rate and generation time. The effect of growth medium strength on the MIC of different tannins was also investigated. Bacterial growth was followed spectrophotometrically, and MIC values were determined by the microdilution method. The MIC values of various isolated compounds allowed us to determine the bioactive compounds and their contribution to antimicrobial activity. It was found that MIC values increase with increasing growth medium strength and that the lag phase lengthens with increasing tannin concentrations, while the growth rates decrease. Comparing the results of the two studies, the antimicrobial activity of tannins against S. aureus was not as pronounced as in the case of E. coli, which may indicate that a different mechanism of action is responsible for the antimicrobial effects of tannins on Gram-positive than on Gram-negative bacteria, or that a different mechanism is more pronounced.

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.

Anchoring the mean generation time in the SEIR to mitigate biases in ℜ0 estimates due to uncertainty in the distribution of the epidemiological delays.

The basic reproduction number, ℜ0, is of paramount importance in the study of infectious disease dynamics. Primarily, ℜ0 serves as an indicator of the transmission potential of an emerging infectious disease and the effort required to control the invading pathogen. However, its estimates from compartmental models are strongly conditioned by assumptions in the model structure, such as the distributions of the latent and infectious periods (epidemiological delays). To further complicate matters, models with dissimilar delay structures produce equivalent incidence dynamics. Following a simulation study, we reveal that the nature of such equivalency stems from a linear relationship between ℜ0 and the mean generation time, along with adjustments to other parameters in the model. Leveraging this knowledge, we propose and successfully test an alternative parametrization of the SEIR model that produces accurate ℜ0 estimates regardless of the distribution of the epidemiological delays, at the expense of biases in other quantities deemed of lesser importance. We further explore this approach's robustness by testing various transmissibility levels, generation times and data fidelity (overdispersion). Finally, we apply the proposed approach to data from the 1918 influenza pandemic. We anticipate that this work will mitigate biases in estimating ℜ0.

Reconsidering Dogmas about the Growth of Bacterial Populations.

The growth of bacterial populations has been described as a dynamic process of continuous reproduction and cell death. However, this is far from the reality. In a well fed, growing bacterial population, the stationary phase inevitably occurs, and it is not due to accumulated toxins or cell death. A population spends the most time in the stationary phase, where the phenotype of the cells alters from the proliferating ones, and only the colony forming unit (CFU) decreases after a while, not the total cell concentration. A bacterial population can be considered as a virtual tissue as a result of a specific differentiation process, in which the exponential-phase cells develop to stationary-phase cells and eventually reach the unculturable form. The richness of the nutrient had no effect on growth rate or on stationary cell density. The generation time seems not to be a constant value, but it depended on the concentration of the starter cultures. Inoculations with serial dilutions of stationary populations reveal a so-called minimal stationary cell concentration (MSCC) point, up to which the cell concentrations remain constant upon dilutions; that seems to be universal among unicellular organisms.

Multiple Sources of Uncertainty Confound Inference of Historical Human Generation Times.

Wang et al. (2023) recently proposed an approach to infer the history of human generation intervals from changes in mutation profiles over time. As the relative proportions of different mutation types depend on the ages of parents, binning variants by the time they arose allows for the inference of changes in average paternal and maternal generation intervals. Applying this approach to published allele age estimates, Wang et al. (2023) inferred long-lasting sex differences in average generation times and surprisingly found that ancestral generation times of West African populations remained substantially higher than those of Eurasian populations extending tens of thousands of generations into the past. Here, we argue that the results and interpretations in Wang et al. (2023) are primarily driven by noise and biases in input data and a lack of validation using independent approaches for estimating allele ages. With the recent development of methods to reconstruct genome-wide gene genealogies, coalescence times, and allele ages, we caution that downstream analyses may be strongly influenced by uncharacterized biases in their output.

Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants.

BACKGROUND: The serial interval is the period of time between symptom onset in the primary case and symptom onset in the secondary case. Understanding the serial interval is important for determining transmission dynamics of infectious diseases like COVID-19, including the reproduction number and secondary attack rates, which could influence control measures. Early meta-analyses of COVID-19 reported serial intervals of 5.2 days (95% CI: 4.9-5.5) for the original wild-type variant and 5.2 days (95% CI: 4.87-5.47) for Alpha variant. The serial interval has been shown to decrease over the course of an epidemic for other respiratory diseases, which may be due to accumulating viral mutations and implementation of more effective nonpharmaceutical interventions. We therefore aggregated the literature to estimate serial intervals for Delta and Omicron variants. METHODS: This study followed Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A systematic literature search was conducted of PubMed, Scopus, Cochrane Library, ScienceDirect, and preprint server medRxiv for articles published from April 4, 2021, through May 23, 2023. Search terms were: ("serial interval" or "generation time"), ("Omicron" or "Delta"), and ("SARS-CoV-2" or "COVID-19"). Meta-analyses were done for Delta and Omicron variants using a restricted maximum-likelihood estimator model with a random effect for each study. Pooled average estimates and 95% confidence intervals (95% CI) are reported. RESULTS: There were 46,648 primary/secondary case pairs included for the meta-analysis of Delta and 18,324 for Omicron. Mean serial interval for included studies ranged from 2.3-5.8 days for Delta and 2.1-4.8 days for Omicron. The pooled mean serial interval for Delta was 3.9 days (95% CI: 3.4-4.3) (20 studies) and Omicron was 3.2 days (95% CI: 2.9-3.5) (20 studies). Mean estimated serial interval for BA.1 was 3.3 days (95% CI: 2.8-3.7) (11 studies), BA.2 was 2.9 days (95% CI: 2.7-3.1) (six studies), and BA.5 was 2.3 days (95% CI: 1.6-3.1) (three studies). CONCLUSIONS: Serial interval estimates for Delta and Omicron were shorter than ancestral SARS-CoV-2 variants. More recent Omicron subvariants had even shorter serial intervals suggesting serial intervals may be shortening over time. This suggests more rapid transmission from one generation of cases to the next, consistent with the observed faster growth dynamic of these variants compared to their ancestors. Additional changes to the serial interval may occur as SARS-CoV-2 continues to circulate and evolve. Changes to population immunity (due to infection and/or vaccination) may further modify it.

Quantifying the predictability of renewable energy data for improving power systems decision-making.

Decision-making in the power systems domain often relies on predictions of renewable generation. While sophisticated forecasting methods have been developed to improve the accuracy of such predictions, their accuracy is limited by the inherent predictability of the data used. However, the predictability of time series data cannot be measured by existing prediction techniques. This important measure has been overlooked by researchers and practitioners in the power systems domain. In this paper, we systematically assess the suitability of various predictability measures for renewable generation time series data, revealing the best method and providing instructions for tuning it. Using real-world examples, we then illustrate how predictability could save end users and investors millions of dollars in the electricity sector.

Analysis of a multi-type resurgence of Mycobacterium bovis in cattle and badgers in Southwest France, 2007-2019.

Although control measures to tackle bovine tuberculosis (bTB) in cattle have been successful in many parts of Europe, this disease has not been eradicated in areas where Mycobacterium bovis circulates in multi-host systems. Here we analyzed the resurgence of 11 M. bovis genotypes (defined based on spoligotyping and MIRU-VNTR) detected in 141 farms between 2007 and 2019, in an area of Southwestern France where wildlife infection was also detected from 2012 in 65 badgers. We used a spatially-explicit model to reconstruct the simultaneous diffusion of the 11 genotypes in cattle farms and badger populations. Effective reproduction number R was estimated to be 1.34 in 2007-2011 indicating a self-sustained M. bovis transmission by a maintenance community although within-species Rs were both < 1, indicating that neither cattle nor badger populations acted as separate reservoir hosts. From 2012, control measures were implemented, and we observed a decrease of R below 1. Spatial contrasts of the basic reproduction ratio suggested that local field conditions may favor (or penalize) local spread of bTB upon introduction into a new farm. Calculation of generation time distributions showed that the spread of M. bovis has been more rapid from cattle farms (0.5-0.7 year) than from badger groups (1.3-2.4 years). Although eradication of bTB appears possible in the study area (since R < 1), the model suggests it is a long-term prospect, because of the prolonged persistence of infection in badger groups (2.9-5.7 years). Supplementary tools and efforts to better control bTB infection in badgers (including vaccination for instance) appear necessary.

Establishment of Neural Series Transmission Keys (NSTKs) and Security in the Field of COVID-19 Telepediatric Oral Health.

In this exceptional COVID-19 crisis, telemedicine had arisen as a substitute technique for medicines. Even more unequivocally, pediatric children were at high risk to outside homes. The spread of COVID-19 has suddenly ascended. Because of lockdown conventions and isolation protocols, kids were confined to live inside their homes. Non-emergency youngsters ought to be managed remotely through the telepediatric health. An establishment of Neural Series Transmission Keys (NSTKs) has been created and security had been planned on the intraoral data. Oral cavity is a kind of dental disease occurring in children. It is for the most cases caused due to drawn out bacterial invasions. Bacterial attacks are more because of sticky chocolates, desserts, sugar, and so forth. Homeopathy medicines are the best prescribed to fix such dental diseases in this current unprecedented COVID-19. Since, it needs no dental medical procedure for the non-invasive kids, which is the reason that the homeopathy medicines are most appropriate in this COVID-19 lockdown stages. The doctor can gather symptoms of the kids from their parents through online interfaces. Some of the normal homeopathy drugs are: Kreasotum, Mercurius, Mezereum, etc. Moreover, in this pandemic situation online telepediatric homeopathy medicines were better alternatives to investigate from home disengages. Additionally it bears no voyaging consumptions and costs. Secure online transmission of clinical pediatric information has been the most challenging issue in COVID-19 telepediatric oral wellbeing. Data mystery factor is protected with tendency in this proposed cryptographic technique. Neural Series Transmission Keys (NSTKs) were established based on neural network based hamming codes. It has been diffused inside the intraoral pediatric data. The proposed key was so particularly amazing that it gives assorted blend after each bit of evolving. Beginning seeds were kept at the dentists and the patients, in order to go against external attacks inside the public channel, especially during this hyper digitized COVID-19 times. Standard graphs were drawn with accuracy using the proposed cryptographic method. The absolute cryptographic time in this strategy was 2.88 ms which was significantly important. By applying Chi Square test, we have noted χ 2 = 17.012, under 5% level of significance.

Limited role of generation time changes in driving the evolution of the mutation spectrum in humans.

Recent studies have suggested that the human germline mutation rate and spectrum evolve rapidly. Variation in generation time has been linked to these changes, though its contribution remains unclear. We develop a framework to characterize temporal changes in polymorphisms within and between populations, while controlling for the effects of natural selection and biased gene conversion. Application to the 1000 Genomes Project dataset reveals multiple independent changes that arose after the split of continental groups, including a previously reported, transient elevation in TCC>TTC mutations in Europeans and novel signals of divergence in C>Gand T>A mutation rates among population samples. We also find a significant difference between groups sampled in and outside of Africa in old T>C polymorphisms that predate the out-of-Africa migration. This surprising signal is driven by TpG>CpG mutations and stems in part from mis-polarized CpG transitions, which are more likely to undergo recurrent mutations. Finally, by relating the mutation spectrum of polymorphisms to parental age effects on de novo mutations, we show that plausible changes in the generation time cannot explain the patterns observed for different mutation types jointly. Thus, other factors - genetic modifiers or environmental exposures - must have had a non-negligible impact on the human mutation landscape.

Each human has 23 pairs of chromosomes, one set inherited from each parent. But the child's chromosomes are not an exact copy of their parents' chromosomes. Spontaneous changes or mutations in the DNA during the formation of the egg or sperm cells, or early development of the embryo, can change a small fraction of the nucleotides or 'letters' that make up the DNA. These modifications are an important source of genetic diversity in human populations and contribute to the evolution of new traits. Each genetic variant in present-day human populations represents a mutation in one of their ancestors. The types and frequencies of variants vary across human populations and have changed over time, suggesting that mutation patterns have evolved in the past. But the processes driving these population-level differences remain elusive. One possible factor may be changes in the average age of reproduction or the generation time in a population . For example, older parents contribute more ­ and also different types of ­ new mutations to their children than younger parents do. Populations, where it is customary to have children at older ages, may therefore have a different mutation landscape. To find out if this is indeed the case, Gao et al. used computer algorithms to analyze the genomes of hundreds of people living on three continents who participated in 'the 1,000 Genomes Project'. The analysis identified differences in mutation patterns across continental groups and estimated when these changes occurred. Further, they showed that although the age of reproduction had an impact on the mutation landscape, differences in generation time alone could not explain the observed changes in the human mutation spectrum. Factors other than generation time, such as environmental exposures, may have played a role in shifting these patterns. The study provides new insights into the changes in the mutation landscape over the course of human evolution. Mapping these patterns in humans worldwide may help scientists understand the causes underlying these changes. The techniques used by Gao et al. may also help analyze changes in mutation patterns in other organisms.

Amplified Cyclicality in Mast Seeding Dynamics Positively Influences the Dynamics of a Seed Consumer Species.

AbstractTemporal autocorrelation in environmental conditions influences population dynamics through its effects on vital rates. However, a comprehensive understanding of how and to what extent temporal autocorrelation shapes population dynamics is still lacking because most empirical studies have unrealistically assumed that environmental conditions are temporally independent. Mast seeding is a biological event characterized by highly fluctuating and synchronized seed production at the tree population scale as well as a marked negative temporal autocorrelation. In the current context of global change, mast seeding events are expected to become more frequent, leading to strengthened negative temporal autocorrelations and thereby amplified cyclicality in mast seeding dynamics. Theory predicts that population growth rates are maximized when the environmental cyclicality of consumer resources and their generation times are closely matched. To test this prediction, we took advantage of the long-term monitoring of a wild boar population, a widespread seed consumer species characterized by a short generation time (∼2 years). As expected, simulations indicated that its stochastic population growth rate increased as mast seeding dynamics became more negatively autocorrelated. Our findings demonstrate that accounting for temporal autocorrelations in environmental conditions relative to the generation time of the focal population is required, especially under conditions of global warming, where the cyclicality in resource dynamics is likely to change.

Uncertainty and error in SARS-CoV-2 epidemiological parameters inferred from population-level epidemic models.

During the SARS-CoV-2 pandemic, epidemic models have been central to policy-making. Public health responses have been shaped by model-based projections and inferences, especially related to the impact of various non-pharmaceutical interventions. Accompanying this has been increased scrutiny over model performance, model assumptions, and the way that uncertainty is incorporated and presented. Here we consider a population-level model, focusing on how distributions representing host infectiousness and the infection-to-death times are modelled, and particularly on the impact of inferred epidemic characteristics if these distributions are mis-specified. We introduce an SIR-type model with the infected population structured by 'infected age', i.e. the number of days since first being infected, a formulation that enables distributions to be incorporated that are consistent with clinical data. We show that inference based on simpler models without infected age, which implicitly mis-specify these distributions, leads to substantial errors in inferred quantities relevant to policy-making, such as the reproduction number and the impact of interventions. We consider uncertainty quantification via a Bayesian approach, implementing this for both synthetic and real data focusing on UK data in the period 15 Feb-14 Jul 2020, and emphasising circumstances where it is misleading to neglect uncertainty. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

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.

Olfactory metacognition and memory in individuals with different subjective odor imagery abilities.

Imagery vividness is one of the key indicators to evaluate the ability to generate mental images. There is large inter-individual variability in olfactory imagery (OI) abilities, however, little is known about the underlying factors for individual OI abilities. Using a word cueing imagery paradigm and the trial-by-trial imagery vividness rating method, participants with high or low OI abilities (differentiated by the Vividness of Olfactory Imagery Questionnaire) completed two OI tasks with either shorter (2 s) or longer (8 s) image generation time. Participants' olfactory function, olfactory-related working memory and episodic recognition memory were measured using validated methods. Moreover, olfactory metacognition was assessed using the Odor Awareness Scale (OAS) and the Importance of Olfaction Questionnaire (IOQ). Compared to participants with high OI abilities, those with low OI abilities reported less vivid odor images during OI tasks. For participants with low OI abilities, the imagery vividness significantly improved as the image generation time increased. There was no difference regarding olfactory perception or olfactory-related memory performances between the high and the low OI ability groups. However, participants with higher OI abilities had significant higher scores on the OAS and the IOQ, indicating a superior olfactory-related metacognition. These results provide evidences supporting the underlying factors that related to variances of subjective ability of generating vivid odor mental images.

Reproductive dispersion and damping time scale with life-history speed.

Iteroparous species may reproduce at many different ages, resulting in a reproductive dispersion that affects the damping of population perturbations, and varies among life histories. Since generation time ( T c ) is known to capture aspects of life-history variation, such as life-history speed, does T c also determine reproductive dispersion ( S ) or damping time ( τ )? Using phylogenetically corrected analyses on 633 species of animals and plants, we find, firstly, that reproductive dispersion S scales isometrically with T c . Secondly, and unexpectedly, we find that the damping time ( τ ) does not scale isometrically with generation time, but instead changes only as T c b with b < 1 (also, there is a similar scaling with S ). This non-isometric scaling implies a novel demographic contrast: increasing generation times correspond to a proportional increase in reproductive dispersion, but only to a slower increase in the damping time. Thus, damping times are partly decoupled from the slow-fast continuum, and are determined by factors other than allometric constraints.

Early Estimates of Monkeypox Incubation Period, Generation Time, and Reproduction Number, Italy, May-June 2022.

We analyzed the first 255 PCR-confirmed cases of monkeypox in Italy in 2022. Preliminary estimates indicate mean incubation period of 9.1 (95% CI 6.5-10.9) days, mean generation time of 12.5 (95% CI 7.5-17.3) days, and reproduction number among men who have sex with men of 2.43 (95% CI 1.82-3.26).

Estimating relative generation times and reproduction numbers of Omicron BA.1 and BA.2 with respect to Delta variant in Denmark.

The Omicron variant spreads fastest as ever among the severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) we had so far. The BA.1 and BA.2 sublineages of Omicron are circulating worldwide and it is urgent to evaluate the transmission advantages of these sublineages. Using a mathematical model describing trajectories of variant frequencies that assumes a constant ratio in mean generation times and a constant ratio in effective reproduction numbers among variants, trajectories of variant frequencies in Denmark from November 22, 2021 to February 26, 2022 were analyzed. We found that the mean generation time of Omicron BA.1 is 0.44-0.46 times that of Delta and the effective reproduction number of Omicron BA.1 is 1.88-2.19 times larger than Delta under the epidemiological conditions at the time. We also found that the mean generation time of Omicron BA.2 is 0.76-0.80 times that of BA.1 and the effective reproduction number of Omicron BA.2 is 1.25-1.27 times larger than Omicron BA.1. These estimates on the ratio of mean generation times and the ratio of effective reproduction numbers have epidemiologically important implications. The contact tracing for Omicron BA.2 infections must be done more quickly than that for BA.1 to stop further infections by quarantine. In the Danish population, the control measures against Omicron BA.2 need to reduce 20-21% of additional contacts compared to that against BA.1.