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.

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.

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.

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.

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".

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.

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.

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).

Generating odour imagery enhances brain activity in individuals with low subjective olfactory imagery ability.

Olfactory imagery (OI) is defined as the generation of odour images in the mind. There are large inter-individual differences regarding OI abilities. However, the neural representations of OI among individuals with high or low OI abilities are less understood. Participants with high or low OI abilities evaluated using the Vividness of Olfactory Imagery Questionnaire were recruited in this study. Brain activations were measured during a word cueing OI and visual imagery (VI) tasks using functional magnetic resonance imaging (fMRI). In addition, the OI task was divided into two parts. In one part, OI was performed for 8 s (long imagery generation time) and in the other part for 2 s (short imagery generation time). Ratings of the overall imagery vividness were collected at the end of each task. The vividness of OI during short OI was lower among participants with low OI abilities compared to participants with high OI abilities. Brain imaging results showed that participants with low OI ability had stronger brain activation of the supplementary motor area and the superior frontal cortex, compared to participants with higher OI abilities during the short versus long imagery generation time conditions. These results suggest that when generating odour images in a relatively short period of time (e.g., 2 s), people with either high or low OI abilities may have adopted different approaches, resulting in diverse brain activation.

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.

Impact of climate change on Helicoverpa armigera voltinism in different Agro-Climatic Zones of India.

The Gram pod borer, Helicoverpa armigera (Noctuidae: Lepidoptera) is a cosmopolitan agricultural insect pest that prefers to feed on plant's protein biomolecules. Out of different density-independent factors, surface air temperature majorly affects the incidence and damage of the H. armigera on the crops. Early prediction of H. armigera generations (voltinism) in future climate years perhaps prevent additional damage in various crops and improve the farmers preparedness. In this study, future climate data that is temperature obtained for eleven Agro-Climatic Zones (ACZs) of India under four Representative Concentration Pathways (RCPs) scenarios in different climate years (2010, 2030, 2050, 2070, 2090) using weather file generator MarkSim web application. The accumulation of Growing Degree-days (GDD) by H. armigera at eleven ACZs in each climate year under different RCP scenarios was estimated using temperature data. The mean surface air temperature is predicted to 0.51 °C, 1.03 °C, 1.57 °C and 2.1 °C in climate years 2030, 2050, 2070 and 2090, which escalated annual H. armigera Gen. to 12.88, 13.33, 13.79 and 14.23, respectively over the baseline climate year 2010. Likewise, under RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 scenarios H. armigera Gen. is predicted to 12.86, 13.29, 13.23 and 13.97 per annum with mean surface air temperatures 27.4 °C, 27.92 °C, 27.86 °C and 28.72 °C, respectively. The Eastern Coastal Plains and Hills Zone (ACZ 11) across climate years and RCPs has experienced a considerable increase in mean surface air temperature minimum (25.22 °C) and maximum (34.61 °C), which likely favor the GDD accumulation (6319.91) and the Genrations (14.97) in H. armigera. Therefore, the Eastern Coastal Plains and Hills Zone of India could be identified as H. armigera risk zone in near future. The present predictions in various ACZs of India may be significant in planning H. armigera management.

Joint Estimation of Generation Time and Incubation Period for Coronavirus Disease 2019.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has caused a heavy disease burden globally. The impact of process and timing of data collection on the accuracy of estimation of key epidemiological distributions are unclear. Because infection times are typically unobserved, there are relatively few estimates of generation time distribution. METHODS: We developed a statistical framework to jointly estimate generation time and incubation period from human-to-human transmission pairs, accounting for sampling biases. We applied the framework on 80 laboratory-confirmed human-to-human transmission pairs in China. We further inferred the infectiousness profile, serial interval distribution, proportions of presymptomatic transmission, and basic reproduction number (R0) for COVID-19. RESULTS: The estimated mean incubation period was 4.8 days (95% confidence interval [CI], 4.1-5.6), and mean generation time was 5.7 days (95% CI, 4.8-6.5). The estimated R0 based on the estimated generation time was 2.2 (95% CI, 1.9-2.4). A simulation study suggested that our approach could provide unbiased estimates, insensitive to the width of exposure windows. CONCLUSIONS: Properly accounting for the timing and process of data collection is critical to have correct estimates of generation time and incubation period. R0 can be biased when it is derived based on serial interval as the proxy of generation time.

Range of reproduction number estimates for COVID-19 spread.

To monitor local and global COVID-19 outbreaks, and to plan containment measures, accessible and comprehensible decision-making tools need to be based on the growth rates of new confirmed infections, hospitalization or case fatality rates. Growth rates of new cases form the empirical basis for estimates of a variety of reproduction numbers, dimensionless numbers whose value, when larger than unity, describes surging infections and generally worsening epidemiological conditions. Typically, these determinations rely on noisy or incomplete data gained over limited periods of time, and on many parameters to estimate. This paper examines how estimates from data and models of time-evolving reproduction numbers of national COVID-19 infection spread change by using different techniques and assumptions. Given the importance acquired by reproduction numbers as diagnostic tools, assessing their range of possible variations obtainable from the same epidemiological data is relevant. We compute control reproduction numbers from Swiss and Italian COVID-19 time series adopting both data convolution (renewal equation) and a SEIR-type model. Within these two paradigms we run a comparative analysis of the possible inferences obtained through approximations of the distributions typically used to describe serial intervals, generation, latency and incubation times, and the delays between onset of symptoms and notification. Our results suggest that estimates of reproduction numbers under these different assumptions may show significant temporal differences, while the actual variability range of computed values is rather small.

Bridging gaps in demographic analysis with phylogenetic imputation.

Phylogenetically informed imputation methods have rarely been applied to estimate missing values in demographic data but may be a powerful tool for reconstructing vital rates of survival, maturation, and fecundity for species of conservation concern. Imputed vital rates could be used to parameterize demographic models to explore how populations respond when vital rates are perturbed. We used standardized vital rate estimates for 50 bird species to assess the use of phylogenetic imputation to fill gaps in demographic data. We calculated imputation accuracy for vital rates of focal species excluded from the data set either singly or in combination and with and without phylogeny, body mass, and life-history trait data. We used imputed vital rates to calculate demographic metrics, including generation time, to validate the use of imputation in demographic analyses. Covariance among vital rates and other trait data provided a strong basis to guide imputation of missing vital rates in birds, even in the absence of phylogenetic information. Mean NRMSE for null and phylogenetic models differed by <0.01 except when no vital rates were available or for vital rates with high phylogenetic signal (Pagel's λ > 0.8). In these cases, including body mass and life-history trait data compensated for lack of phylogenetic information: mean normalized root mean square error (NRMSE) for null and phylogenetic models differed by <0.01 for adult survival and <0.04 for maturation rate. Estimates of demographic metrics were sensitive to the accuracy of imputed vital rates. For example, mean error in generation time doubled in response to inaccurate estimates of maturation time. Accurate demographic data and metrics, such as generation time, are needed to inform conservation planning processes, for example through International Union for Conservation of Nature Red List assessments and population viability analysis. Imputed vital rates could be useful in this context but, as for any estimated model parameters, awareness of the sensitivities of demographic model outputs to the imputed vital rates is essential.

Cerrando Brechas en los Análisis Demográficos con Imputación Filogenética Resumen Los métodos de imputación guiados filogenéticamente se han aplicado con poca frecuencia para estimar los valores faltantes en los datos demográficos, aunque pueden ser una herramienta poderosa para la reconstrucción de tasas vitales de supervivencia, maduración y fecundidad de especies de importancia para la conservación. Las tasas vitales imputadas podrían usarse para generar parámetros en los modelos demográficos para explorar cómo responden las poblaciones cuando se perturban las tasas vitales. Utilizamos estimaciones de tasas vitales estandarizadas para 50 especies de aves para analizar el uso de la imputación filogenética para llenar los vacíos en los datos demográficos. Calculamos la certeza de imputación para las tasas vitales de las especies focales excluidas del conjunto de datos por sí solas o en combinación y con y sin datos de filogenia, masa corporal y características de historia de vida. Usamos las tasas vitales imputadas para calcular las medidas demográficas, incluyendo el tiempo de generación, y así validar el uso de la imputación en los análisis demográficos. La covarianza entre las tasas vitales y otros datos de características proporcionó una base sólida para orientar la imputación de tasas vitales faltantes en las aves, incluso la ausencia de información filogenética. El NRMSE medio para los modelos nulo y filogenético difirió por <0.01 salvo cuando no hubo tasas vitales disponibles o para tasas vitales con una señal filogenética alta (λ de Pagel > 0.8). En estos casos, la inclusión de la masa corporal y las características de historia de vida compensó la falta de información filogenética: el error cuadrático medio de la raíz normalizada media (NRMSE) para los modelos nulo y filogenéticos difirió por <0.01 para la supervivencia adulta y <0.04 para la tasa de maduración. Las estimaciones de las medidas demográficas fueron sensibles a la certeza de las tasas vitales imputadas. Por ejemplo, el error medio en el tiempo generacional se duplicó en respuesta a las estimaciones imprecisas del tiempo de maduración. Las medidas y datos demográficos certeros, como el tiempo generacional, son necesarios para orientar los procesos de planeación de la conservación; por ejemplo, a través de las valoraciones de la Lista Roja de la Unión Internacional para la Conservación de la Naturaleza y los análisis de viabilidad poblacional. Las tasas vitales imputadas podrían ser útiles en este contexto, pero como para cualquier tipo de parámetro de modelo estimado, el conocimiento de las sensibilidades del rendimiento del modelo demográfico es esencial para las tasas vitales imputadas.

Re-estimation of basic reproduction number of COVID-19 based on the epidemic curve by symptom onset date.

Previous studies have reported the basic reproduction number (R0) of coronavirus disease from publicly reported data that lack information such as onset of symptoms, presence of importations or known super-spreading events. Using data from the Republic of Korea, we illustrated how estimates of R0 can be biased and provided improved estimates with more detailed data. We used COVID-19 contact trace system in Korea, which can provide symptom onset date and also serial intervals between contacted people. The total R0 was estimated as 2.10 (95% confidence interval (CI) 1.84-2.42). Also, early transmission of COVID-19 differed by regional or social behaviours of the population. Regions affected by a specific church cluster, which showed a rapid and silent transmission under non-official religious meetings, had a higher R0 of 2.40 (95% CI 2.08-2.77).

Measurability of the epidemic reproduction number in data-driven contact networks.

The basic reproduction number is one of the conceptual cornerstones of mathematical epidemiology. Its classical definition as the number of secondary cases generated by a typical infected individual in a fully susceptible population finds a clear analytical expression in homogeneous and stratified mixing models. Along with the generation time (the interval between primary and secondary cases), the reproduction number allows for the characterization of the dynamics of an epidemic. A clear-cut theoretical picture, however, is hardly found in real data. Here, we infer from highly detailed sociodemographic data two multiplex contact networks representative of a subset of the Italian and Dutch populations. We then simulate an infection transmission process on these networks accounting for the natural history of influenza and calibrated on empirical epidemiological data. We explicitly measure the reproduction number and generation time, recording all individual-level transmission events. We find that the classical concept of the basic reproduction number is untenable in realistic populations, and it does not provide any conceptual understanding of the epidemic evolution. This departure from the classical theoretical picture is not due to behavioral changes and other exogenous epidemiological determinants. Rather, it can be simply explained by the (clustered) contact structure of the population. Finally, we provide evidence that methodologies aimed at estimating the instantaneous reproduction number can operationally be used to characterize the correct epidemic dynamics from incidence data.

Resistance of a Halobacterium salinarum isolate from a solar saltern to cadmium, lead, nickel, zinc, and copper.

The current study focuses on the tolerance of a strain of Halobacterium salinarum isolated from Sfax solar saltern (Tunisia) towards cadmium (Cd), lead (Pb), nickel (Ni), zinc (Zn), and copper (Cu) by using agar dilution methods in complex and minimal media. The results showed the least inhibitory metals based on Minimum Inhibitory Concentrations (MICs) were lead (MIC = 4.5 mM), cadmium (MIC = 4 mM), and nickel (MIC = 2.5 mM) in complex medium. The MICs of these metals were more inhibitory (MIC < 2 mM) in the other tested media. The archaeal strain revealed a high sensitivity for copper and zinc, with MICs below 0.5 mM for both metals. Growth kinetics in complex and minimal media showed the strain to be more sensitive to the metals in liquid media than in solid media. The growth kinetic assays indicated the presence of selected heavy metals resulted in a lower growth rate and lower total cell mass relative to the control. Despite that cadmium and lead are nonessential and have no nutrient value, they were the most tolerated metals by H. salinarum strain. In addition, pigment intensity in the strain was inhibited by the presence of the heavy metals relative to the control.

Methyl-compound use and slow growth characterize microbial life in 2-km-deep subseafloor coal and shale beds.

The past decade of scientific ocean drilling has revealed seemingly ubiquitous, slow-growing microbial life within a range of deep biosphere habitats. Integrated Ocean Drilling Program Expedition 337 expanded these studies by successfully coring Miocene-aged coal beds 2 km below the seafloor hypothesized to be "hot spots" for microbial life. To characterize the activity of coal-associated microorganisms from this site, a series of stable isotope probing (SIP) experiments were conducted using intact pieces of coal and overlying shale incubated at in situ temperatures (45 °C). The 30-month SIP incubations were amended with deuterated water as a passive tracer for growth and different combinations of 13C- or 15N-labeled methanol, methylamine, and ammonium added at low (micromolar) concentrations to investigate methylotrophy in the deep subseafloor biosphere. Although the cell densities were low (50-2,000 cells per cubic centimeter), bulk geochemical measurements and single-cell-targeted nanometer-scale secondary ion mass spectrometry demonstrated active metabolism of methylated substrates by the thermally adapted microbial assemblage, with differing substrate utilization profiles between coal and shale incubations. The conversion of labeled methylamine and methanol was predominantly through heterotrophic processes, with only minor stimulation of methanogenesis. These findings were consistent with in situ and incubation 16S rRNA gene surveys. Microbial growth estimates in the incubations ranged from several months to over 100 y, representing some of the slowest direct measurements of environmental microbial biosynthesis rates. Collectively, these data highlight a small, but viable, deep coal bed biosphere characterized by extremely slow-growing heterotrophs that can utilize a diverse range of carbon and nitrogen substrates.