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Maruotti et al. 2022 used a mark-recapture approach to estimate bounds on the true number of monkeypox infections in various countries. We show that such approaches are fundamentally flawed. We argue on conceptual grounds that it is impossible to estimate undercounting based solely on a single stream of reported cases, supporting our contention by applying the authors' method to simulated epidemics. The simulations show that the proposed methods estimate bounds on the ascertainment ratio of ≈0.2-0.5 roughly independently of the true ascertainment ratio. We conclude that the proposed mark-recapture approach should not be used to estimate undercounting or ascertainment ratios. This article is protected by copyright. All rights reserved. Public Health Agency of Canada, NSERC (Discovery grants)

BackgroundTaking a representative sample to determine prevalence of variables such as disease is difficult when little is known about the target population. Several methods have been proposed that apply cluster sampling techniques to Primary Sampling Units (PSUs). The PSUs are typically towns or census tracts. Some methods are based on random walks within towns, e.g., the original World Health Organization’s Extended Program on Immunization (‘EPI’) surveys and variants, including sampling from four quadrants of each town (‘Quad’). Several major international surveys take random samples from small areas (‘SA’) such as census tracts. Another method uses satellite images and Global Positioning Systems to randomly sample within PSUs from squares in a superimposed grid (‘Square’). We used computer simulations to compare these sampling methods and simple random sampling within towns (‘SRS’) in virtual populations. SRS was our standard, even though it is impractical in low-information settings.MethodsWe constructed 50 virtual populations with varying characteristics, each comprising about a million people spread over 300 towns. The risk of disease for each person varied within and between towns. We created a binary exposure variable and allocated disease statuses to individuals assuming four relative risks (RRs) from exposure. We added three populations with equal risk of disease for every person in the population.For each population, each of the sampling methods – EPI, Quad, SA, Square, and SRS - and each of three sample sizes per PSU (7, 15, and 30), we simulated 1000 samples. For each simulation we estimated the prevalence and RRs. We used the bias and variance of the estimates to calculate the Root Mean Squared Error (RMSE) of these estimates. We ranked the RMSEs of each method and computed the ratio of each method’s RMSE to that of SRS for each population. We computed the mean ranks and ratios across the 50 populations.ResultsApart from SRS, Square had the lowest mean rank of RMSEs for all samples sizes when estimating prevalence. When estimating RR, Square had the lowest mean ranks for samples sizes of 15 and 30 per PSU; for n=7 per PSU, the Quad mean rank was the lowest.The results for the mean ratios of RMSEs showed the same pattern; Square had the lowest values for all sample sizes when estimating prevalence and the lowest values for the two larger sample sizes when estimating RRs. Notably, when estimating prevalence, the ratios increased with sample size per PSU for SA, Quad, and EPI, suggesting those methods did not benefit as much from the larger sample sizes as would be expected from statistical theory.ConclusionsOf several methods that are practical in an imperfectly known population, the Square method was mostly the best, especially for the larger sample sizes. The methods that sample within small areas (Quad, SA, and EPI) do not gain as much statistical benefit as expected from larger sample sizes per PSU, because of some clustering within the areas.

The Cox proportional hazard model is commonly used in evaluating risk factors in cancer survival data. The model assumes an additive, linear relationship between the risk factors and the log hazard. However, this assumption may be too simplistic. Further, failure to take time-varying covariates into account, if present, may lower prediction accuracy. In this retrospective, population-based, prognostic study of data from patients diagnosed with cancer from 2008 to 2015 in Ontario, Canada, we applied machine learning-based time-to-event prediction methods and compared their predictive performance in two sets of analyses: 1) yearly-cohort-based time-invariant and 2) fully time-varying covariates analysis. Machine learning-based methods — gradient boosting model (gbm), random survival forest (rsf), elastic net (enet), lasso, ridge, and deepsurv neural network (nnet) — were compared to the traditional Cox proportional hazard (coxph) model and the prior study which used the yearly-cohort-based time-invariant analysis. Using Harrell's C index as our primary measure, we found that using both machine-learning techniques and incorporating time-dependent covariates can improve predictive performance. Gradient boosting machine showed the best performance on test data in both time-invariant and time-varying covariates analysis.

Inferring the relative strength (i.e. the ratio of reproduction numbers) and relative speed (i.e. the difference between growth rates) of new SARS-CoV-2 variants is critical to predicting and controlling the course of the current pandemic. Analyses of new variants have primarily focused on characterizing changes in the proportion of new variants, implicitly or explicitly assuming that the relative speed remains fixed over the course of an invasion. We use a generation-interval-based framework to challenge this assumption and illustrate how relative strength and speed change over time under two idealized interventions: a constant-strength intervention like idealized vaccination or social distancing, which reduces transmission rates by a constant proportion, and a constant-speed intervention like idealized contact tracing, which isolates infected individuals at a constant rate. In general, constant-strength interventions change the relative speed of a new variant, while constant-speed interventions change its relative strength. Differences in the generation-interval distributions between variants can exaggerate these changes and modify the effectiveness of interventions. Finally, neglecting differences in generation-interval distributions can bias estimates of relative strength.

Background Patient age is one of the most salient clinical indicators of risk from COVID-19. Age-specific distributions of known SARS-CoV-2 infections and COVID-19-related deaths are available for many regions. Less attention has been given to the age distributions of serious medical interventions administered to COVID-19 patients, which could reveal sources of potential pressure on the healthcare system should SARS-CoV-2 prevalence increase, and could inform mass vaccination strategies. The aim of this study is to quantify the relationship between COVID-19 patient age and serious outcomes of the disease, beyond fatalities alone. Methods We analysed 277,555 known SARS-CoV-2 infection records for Ontario, Canada, from 23 January 2020 to 16 February 2021 and estimated the age distributions of hospitalizations, Intensive Care Unit admissions, intubations, and ventilations. We quantified the probability of hospitalization given known SARS-CoV-2 infection, and of survival given COVID-19-related hospitalization. Results The distribution of hospitalizations peaks with a wide plateau covering ages 60–90, whereas deaths are concentrated in ages 80+. The estimated probability of hospitalization given known infection reaches a maximum of 27.8% at age 80 (95% CI 26.0%–29.7%). The probability of survival given hospitalization is nearly 100% for adults younger than 40, but declines substantially after this age; for example, a hospitalized 54-year-old patient has a 91.7% chance of surviving COVID-19 (95% CI 88.3%–94.4%). Conclusions Our study demonstrates a significant need for hospitalization in middle-aged individuals and young seniors. This need is not captured by the distribution of deaths, which is heavily concentrated in very old ages. The probability of survival given hospitalization for COVID-19 is lower than is generally perceived for patients over 40. If acute care capacity is exceeded due to an increase in COVID-19 prevalence, the distribution of deaths could expand toward younger ages. These results suggest that vaccine programs should aim to prevent infection not only in old seniors, but also in young seniors and middle-aged individuals, to protect them from serious illness and to limit stress on the healthcare system.

We conduct a sensitivity analysis of a new type of integrated climate-economic model recently proposed in the literature, where the core economic component is based on the Goodwin--Keen dynamics in...

29 and 2929=67 are both prime. We explore that curiosity and find connections to deep questions on the distribution of the primes: the prime number theorem, Dickson’s conjecture, and Zhang’s bounde...

A new SARS-CoV-2 variant of concern, Omicron (B.1.1.529), has been identified based on genomic sequencing and epidemiological data in South Africa. Presumptive Omicron cases in South Africa have grown extremely rapidly, despite high prior exposure and moderate vaccination coverage. The available evidence suggests that Omicron spread is at least in part due to evasion of this immune protection, though Omicron may also exhibit higher intrinsic transmissibility. Using detailed laboratory and epidemiological data from South Africa, we estimate the constraints on these two characteristics of the new variant and their relationship. Our estimates and associated uncertainties provide essential information to inform projection and scenario modeling analyses, which are crucial planning tools for governments around the world.One Sentence SummaryWe report a region of plausibility for the relative transmissibility and immune escape characteristics of the SARS-CoV-2 Omicron variant estimated by integrating laboratory and epidemiological data from South Africa.

Predicting the combined effects of predators on shared prey has long been a focus of community ecology, yet quantitative predictions often fail. Failure to account for nonlinearity is one reason for this. Moreover, prey depletion in multiple predator effects (MPE) studies generates biased predictions in applications of common experimental and quantitative frameworks. Here, we explore additional sources of bias stemming from nonlinearities in prey predation risk. We show that in order to avoid bias, predictions about the combined effects of independent predators must account for nonlinear size-dependent risk for prey as well as changes in prey risk driven by nonlinear predator functional responses and depletion. Historical failure to account for biases introduced by well-known nonlinear processes that affect predation risk suggest that we may need to reevaluate the general conclusions that have been drawn about the ubiquity of emergent MPEs over the past three decades.

Goldstein et al.’s (1) article, “Vaccinating the oldest against COVID-19…,” provides a useful and timely analysis of the value of protecting older people from infection. The authors show convincingly that the expected number of life years saved by focusing protection on old people is more than commonly supposed. Unfortunately, the article is framed, and titled, in a way that seems certain to lead to misunderstanding. The core analysis is about the effects of reducing mortality in different age groups, but is framed in the paper as an analysis of the effects of vaccination. Although superficially similar, these are very different questions, primarily … [↵][1]1To whom correspondence may be addressed. Email: dushoff{at}mcmaster.ca. [1]: #xref-corresp-1-1

Background:Taking a representative sample to determine prevalence of variables like disease is difficult when little is known about the target population. Several methods have been proposed, including a recent revision of the World Health Organization’s Extended Program on Immunization (EPI) surveys. The original EPI method samples towns as Primary Sampling Units (PSUs) with probability proportional to size and uses a nearest neighbour approach to sample households within PSUs. The new version samples from smaller PSUs and conducts a probability sample of households within those PSUs. Other techniques use satellite images and Global Positioning Systems to sample within towns from circles around randomly identified points (‘Circle’ method) or from randomly sampled squares in a superimposed grid (‘Square’ method). We compared these sampling methods in multiple virtual populations using computer simulation.Methods:We constructed 50 virtual populations with varying characteristics. Populations comprised about a million people across 300 towns. We created three populations with different prevalences of disease but with uniform characteristics across each population. We created a binary exposure variable and allocated disease statuses to individuals assuming different relative risks (RRs) of exposure. We simulated thirteen methods of sampling: simple random sampling; the original EPI method and variants; the Square and Circle methods; and the new EPI method. For each population, each sampling method, and each of three sample sizes per PSU (7, 15, and 30), we simulated 1,000 samples. For most sampling methods, the PSUs were towns. We conducted simulations using the same 30 PSUs and using a freshly-chosen set of PSUs. For each simulation we estimated prevalence and RRs and combined the bias and variance of the 1,000 samples to compute the Root Mean Squared Error (RMSE).Results: The Circle and Square methods produced almost identical results, so we report only the Square method results. Apart from simple random sampling, the RMSE for the Square method was almost universally best for estimating prevalence, and generally best when estimating relative risks. The revised EPI approach was worse, but generally better than the original EPI. Conclusions:The Square method is recommended as statistically optimal, unless practical considerations favour another approach.

Inferring the relative strength (i.e., the ratio of reproduction numbers, ℛvar/ℛwt) and relative speed (i.e., the difference between growth rates, rvar −rwt) of new SARS-CoV-2 variants compared to their wild types is critical to predicting and controlling the course of the current pandemic. Multiple studies have estimated the relative strength of new variants from the observed relative speed, but they typically neglect the possibility that the new variants have different generation intervals (i.e., time between infection and transmission), which determines the relationship between relative strength and speed. Notably, the increasingly predominant B.1.1.7 variant may have a longer infectious period (and therefore, a longer generation interval) than prior dominant lineages. Here, we explore how differences in generation intervals between a new variant and the wild type affect the relationship between relative strength and speed. We use simulations to show how neglecting these differences can lead to biases in estimates of relative strength in practice and to illustrate how such biases can be assessed. Finally, we discuss implications for control: if new variants have longer generation intervals then speed-like interventions such as contact tracing become more effective, whereas strength-like interventions such as social distancing become less effective.

Testing individuals for pathogens can affect the spread of epidemics. Understanding how individual-level processes of sampling and reporting test results can affect community- or population-level spread is a dynamical modeling question. The effect of testing processes on epidemic dynamics depends on factors underlying implementation, particularly testing intensity and on whom testing is focused. Here, we use a simple model to explore how the individual-level effects of testing might directly impact population-level spread. Our model development was motivated by the COVID-19 epidemic, but has generic epidemiological and testing structures. To the classic SIR framework we have added a per capita testing intensity, and compartment-specific testing weights, which can be adjusted to reflect different testing emphases—surveillance, diagnosis, or control. We derive an analytic expression for the relative reduction in the basic reproductive number due to testing, test-reporting and related isolation behaviours. Intensive testing and fast test reporting are expected to be beneficial at the community level because they can provide a rapid assessment of the situation, identify hot spots, and may enable rapid contact-tracing. Direct effects of fast testing at the individual level are less clear, and may depend on how individuals’ behaviour is affected by testing information. Our simple model shows that under some circumstances both increased testing intensity and faster test reporting can reduce the effectiveness of control, and allows us to explore the conditions under which this occurs. Conversely, we find that focusing testing on infected individuals always acts to increase effectiveness of control.

Significance The generation and serial interval distributions are key, but different, quantities in outbreak analyses. Recent studies suggest that the two distributions give different estimates of the reproduction number R as inferred from the observed growth rate r. Here, we show that estimating R based on r and the serial interval distribution, when defined from the correct reference cohort, gives the same estimate as using r and the generation interval distribution. We apply our framework to COVID-19 serial interval data from China, outside Hubei province (January 21 to February 8, 2020), revealing systematic biases in prior inference methods. Our study provides the theoretical basis for practical changes to the principled use of serial interval distributions in estimating R during epidemics., The reproduction number R and the growth rate r are critical epidemiological quantities. They are linked by generation intervals, the time between infection and onward transmission. Because generation intervals are difficult to observe, epidemiologists often substitute serial intervals, the time between symptom onset in successive links in a transmission chain. Recent studies suggest that such substitution biases estimates of R based on r. Here we explore how these intervals vary over the course of an epidemic, and the implications for R estimation. Forward-looking serial intervals, measuring time forward from symptom onset of an infector, correctly describe the renewal process of symptomatic cases and therefore reliably link R with r. In contrast, backward-looking intervals, which measure time backward, and intrinsic intervals, which neglect population-level dynamics, give incorrect R estimates. Forward-looking intervals are affected both by epidemic dynamics and by censoring, changing in complex ways over the course of an epidemic. We present a heuristic method for addressing biases that arise from neglecting changes in serial intervals. We apply the method to early (21 January to February 8, 2020) serial interval-based estimates of R for the COVID-19 outbreak in China outside Hubei province; using improperly defined serial intervals in this context biases estimates of initial R by up to a factor of 2.6. This study demonstrates the importance of early contact tracing efforts and provides a framework for reassessing generation intervals, serial intervals, and R estimates for COVID-19.

BackgroundPatient age is the most salient clinical indicator of risk from COVID-19. Age-specific distributions of known SARS-CoV-2 infections and COVID-19-related deaths are available for most countries. However, relatively little attention has been given to the age distributions of hospitalizations and serious healthcare interventions administered to COVID-19 patients. We examined these distributions in Ontario, Canada, in order to quantify the age-related impacts of COVID-19, and to identify potential risks should the healthcare system become overwhelmed with COVID-19 patients in the future.MethodsWe analysed known SARS-CoV-2 infection records from the integrated Public Health Information System (iPHIS) and the Toronto Public Health Coronavirus Rapid Entry System (CORES) between 23 January 2020 and 17 June 2020 (N = 30,546), and estimated the age distributions of hospitalizations, ICU admissions, intubations, and ventilations. We quantified the probability of hospitalization given known SARS-CoV-2 infection, and of survival given COVID-19-related hospitalization.ResultsThe distribution of COVID-19-related hospitalizations peaks with a wide plateau covering ages 54-90, whereas deaths are sharply concentrated in very old ages, with a maximum at age 90. The estimated probability of hospitalization given known SARS-CoV-2 infection reaches a maximum of 32.0% at age 75 (95% CI 27.5%-36.7%). The probability of survival given COVID-19-related hospitalization is uncertain for children (due to small sample size), and near 100% for adults younger than 40. After age 40, survival of hospitalized COVID-19 patients declines substantially; for example, a hospitalized 50-year-old patient has a 90.4% chance of surviving COVID-19 (95% CI 81.9%-95.7%).InterpretationConcerted efforts to control the spread of SARS-CoV-2 have kept prevalence of the virus low in the population of Ontario. The healthcare system has not been overstretched, yet the probability of survival given hospitalization for COVID-19 has been lower than is generally recognized for patients over 40. If prevalence of the virus were to increase and healthcare capacities were to be exceeded, survival of individuals in the broad age range requiring acute care would be expected to decrease, potentially expanding the distribution of COVID-19-related deaths toward younger ages.

A novel coronavirus (SARS-CoV-2) emerged as a global threat in December 2019. As the epidemic progresses, disease modellers continue to focus on estimating the basic reproductive numberR0—the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modelling approaches and resulting estimates ofR0during the beginning of the outbreak vary widely, despite relying on similar data sources. Here, we present a statistical framework for comparing and combining different estimates ofR0across a wide range of models by decomposing the basic reproductive number into three key quantities: the exponential growth rate, the mean generation interval and the generation-interval dispersion. We apply our framework to early estimates ofR0for the SARS-CoV-2 outbreak, showing that manyR0estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components ofR0, including the shape of the generation-interval distribution, in efforts to estimateR0at the outset of an epidemic.

Generation intervals and serial intervals are critical quantities for characterizing outbreak dynamics. Generation intervals characterize the time between infection and transmission, while serial intervals characterize the time between the onset of symptoms in a chain of transmission. They are often used interchangeably, leading to misunderstanding of how these intervals link the epidemic growth raterand the reproduction numberℛ. Generation intervals provide a mechanistic link betweenrandℛbut are harder to measure via contact tracing. While serial intervals are easier to measure from contact tracing, recent studies suggest that the two intervals give different estimates ofℛfromr. We present a general framework for characterizing epidemiological delays based on cohorts (i.e., a group of individuals that share the same event time, such as symptom onset) and show thatforward-lookingserial intervals, which correctly linkℛwithr, are not the same as “intrinsic” serial intervals, but instead change withr. We provide a heuristic method for addressing potential biases that can arise from not accounting for changes in serial intervals across cohorts and apply the method to estimatingℛfor the COVID-19 outbreak in China using serial-interval data — our analysis shows that using incorrectly defined serial intervals can severely bias estimates. This study demonstrates the importance of early epidemiological investigation through contact tracing and provides a rationale for reassessing generation intervals, serial intervals, andℛestimates, for COVID-19.Significance StatementThe generation- and serial-interval distributions are key, but different, quantities in outbreak analyses. Recent theoretical studies suggest that two distributions give different estimates of the reproduction numberℛfrom the exponential growth rater; however, both intervals, by definition, describe disease transmission at the individual level. Here, we show that the serial-interval distribution, defined from the correct reference time and cohort, gives the same estimate ofℛas the generation-interval distribution. We then apply our framework to serial-interval data from the COVID-19 outbreak in China. While our study supports the use of serial-interval distributions in estimatingℛ, it also reveals necessary changes to the current understanding and applications of serial-interval distribution.

Large trees in the tropics are reportedly more vulnerable to droughts than their smaller neighbours. This pattern is of interest due to what it portends for forest structure, timber production, carbon sequestration and multiple other values given that intensified El Niño Southern Oscillation (ENSO) events are expected to increase the frequency and intensity of droughts in the Amazon region. What remains unclear is what characteristics of large trees render them especially vulnerable to drought-induced mortality and how this vulnerability changes with forest degradation. Using a large-scale, long-term silvicultural experiment in a transitional Amazonian forest in Bolivia, we disentangle the effects of stem diameter, tree height, crown exposure and logging-induced degradation on risks of drought-induced mortality during the 2004/2005 ENSO event. Overall, tree mortality increased in response to drought in both logged and unlogged plots. Tree height was a much stronger predictor of mortality than stem diameter. In unlogged plots, tree height but not crown exposure was positively associated with drought-induced mortality, whereas in logged plots, neither tree height nor crown exposure was associated with drought-induced mortality. Our results suggest that, at the scale of a site, hydraulic factors related to tree height, not air humidity, are a cause of elevated drought-induced mortality of large trees in unlogged plots. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

Parents providing care must sometimes choose between rearing locations that are most favourable for offspring versus those that are most favourable for themselves. Here, we measured how both parental and offspring performance varied in nest sites distributed along an environmental gradient. The plainfin midshipman fish Porichthys notatus nests along a tidal gradient. When ascending from the subtidal to the high intertidal at low tide, both nest temperature and frequency of air exposure increase. We used one lab and two field experiments to investigate how parental nest site choices across tidal elevations are linked to the physiological costs incurred by parents and the developmental benefits accrued by offspring. Under warmer incubation conditions, simulating high intertidal nests, offspring developed faster but had higher mortality rates compared to those incubated in cooler conditions that mimicked subtidal nests. In the field, males in higher intertidal nests were more active caregivers, but their young still died at the fastest rates. Larger males claimed and retained low intertidal nests, where offspring survival and development rates were also highest. Our results suggest that males compete more intensively for nest sites in the low intertidal, where they can raise their young quickly and with lower per-offspring investments. Smaller, less-competitive males forced into higher intertidal sites nest earlier in the season and provide more active parental care, possibly to bolster brood survival under harsh environmental conditions.

We use two modelling approaches to forecast synthetic Ebola epidemics in the context of the RAPIDD Ebola Forecasting Challenge. The first approach is a standard stochastic compartmental model that aims to forecast incidence, hospitalization and deaths among both the general population and health care workers. The second is a model based on the renewal equation with latent variables that forecasts incidence in the whole population only. We describe fitting and forecasting procedures for each model and discuss their advantages and drawbacks. We did not find that one model was consistently better in forecasting than the other. Keywords: Forecasting, Epidemics, Renewal equation, Compartmental model

In nature, it can be challenging to figure out whether success at attracting mates and reproducing is due to something intrinsic about an individual or something about the quality of the resources they possess. In a series of lab and field studies using the plainfin midshipman fish, we show that both factors are complex and that resource quality can impose a limit on reproductive success regardless of the quality of the resource owner.

Popular songs are often said to be ‘contagious’, ‘infectious’ or ‘viral’. We find that download count time series for many popular songs resemble infectious disease epidemic curves. This paper suggests infectious disease transmission models could help clarify mechanisms that contribute to the ‘spread’ of song preferences and how these mechanisms underlie song popularity. We analysed data from MixRadio, comprising song downloads through Nokia cell phones in Great Britain from 2007 to 2014. We compared the ability of the standard susceptible–infectious–recovered (SIR) epidemic model and a phenomenological (spline) model to fit download time series of popular songs. We fitted these same models to simulated epidemic time series generated by the SIR model. Song downloads are captured better by the SIR model, to the same extent that actual SIR simulations are fitted better by the SIR model than by splines. This suggests that the social processes underlying song popularity are similar to those that drive infectious disease transmission. We draw conclusions about song popularity within specific genres based on estimated SIR parameters. In particular, we argue that faster spread of preferences for Electronica songs may reflect stronger connectivity of the ‘susceptible community’, compared with the larger and broader community that listens to more common genres.

A greater understanding of how environmental factors and anthropogenic landscape features influence animal movements can inform management and potentially aid in mitigating human–wildlife conflicts. We investigated the movement patterns of 16 Florida black bears (Ursus americanus floridanus; 6 females, 10 males) in north-central Florida at multiple temporal scales using GPS data collected from 2011 to 2014. We calculated bi-hourly step-lengths and directional persistence, as well as daily and weekly observed displacements and expected displacements. We used those movement metrics as response variables in linear mixed models and tested for effects of sex, season, and landscape features. We found that step-lengths of males were generally longer than step-lengths of females, and both sexes had the shortest step-lengths during the daytime. Bears moved more slowly (shorter step-lengths) and exhibited less directed movement when near creeks, in forested wetlands, and in marsh habitats, possibly indicating foraging behavior. In urban areas, bears moved more quickly (longer step-lengths) and along more directed paths. The results were similar across all temporal scales. Major roads tended to act as a semipermeable barrier to bear movement. Males crossed major roads more frequently than females but both sexes crossed major roads much less frequently than minor roads. Our findings regarding the influence of landscape and habitat features on movement patterns of Florida black bears could be useful for planning effective wildlife corridors and understanding how future residential or commercial development and road expansions may affect animal movement.

Background: Antidepressants (ADs) are commonly prescribed medications, but their long-term health effects are debated. ADs disrupt multiple adaptive processes regulated by evolutionarily ancient biochemicals, potentially increasing mortality. However, many ADs also have anticlotting properties that can be efficacious in treating cardiovascular disease. We conducted a meta-analysis assessing the effects of ADs on all-cause mortality and cardiovascular events in general-population and cardiovascular-patient samples. Methods: Two reviewers independently assessed articles from PubMed, EMBASE, and Google Scholar for AD-related mortality controlling for depression and other comorbidities. From these articles, we extracted information about cardiovascular events, cardiovascular risk status, and AD class. We conducted mixed-effect meta-analyses testing sample type and AD class as moderators of all-cause mortality and new cardiovascular events. Results: Seventeen studies met our search criteria. Sample type consistently moderated health risks. In general-population samples, AD use increased the risks of mortality (HR = 1.33, 95% CI: 1.14-1.55) and new cardiovascular events (HR = 1.14, 95% CI: 1.08-1.21). In cardiovascular patients, AD use did not significantly affect risks. AD class also moderated mortality, but the serotonin reuptake inhibitors were not significantly different from tricyclic ADs (TCAs) (HR = 1.10, 95% CI: 0.93-1.31, p = 0.27). Only “other ADs” were differentiable from TCAs (HR = 1.35, 95% CI: 1.08-1.69). Mortality risk estimates increased when we analyzed the subset of studies controlling for premedication depression, suggesting the absence of confounding by indication. Conclusions: The results support the hypothesis that ADs are harmful in the general population but less harmful in cardiovascular patients.

Background Mathematical and statistical models are used to project the future time course of infectious disease epidemics and the expected future burden on health care systems and economies. Influenza is a particularly important disease in this context because it causes annual epidemics and occasional pandemics. In order to forecast health care utilization during epidemics—and the effects of hospitalizations and deaths on the contact network and, in turn, on transmission dynamics—modellers must make assumptions about the lengths of time between infection, visiting a physician, being admitted to hospital, leaving hospital, and death. More reliable forecasts could be be made if the distributions of times between these types of events (“delay distributions”) were known. Methods We estimated delay distributions in the province of Ontario, Canada, between 2006 and 2010. To do so, we used encrypted health insurance numbers to link 1.34 billion health care billing records to 4.27 million hospital inpatient stays. Because the four year period we studied included three typical influenza seasons and the 2009 influenza pandemic, we were able to compare the delay distributions in non-pandemic and pandemic settings. We also estimated conditional probabilities such as the probability of hospitalization within the year if diagnosed with influenza. Results In non-pandemic [pandemic] years, delay distribution medians (inter-quartile ranges) were: Service to Admission 6.3 days (0.1–17.6 days) [2.4 days (-0.3–13.6 days)], Admission to Discharge 3 days (1.4–5.9 days) [2.6 days (1.2–5.1 days)], Admission to Death 5.3 days (2.1–11 days) [6 days (2.6–13.1 days)]. (Service date is defined as the date, within the year, of the first health care billing that included a diagnostic code for influenza-like-illness.) Among individuals diagnosed with either pneumonia or influenza in a given year, 19% [16%] were hospitalized within the year and 3% [2%] died in hospital. Among all individuals who were hospitalized, 10% [12%] were diagnosed with pneumonia or influenza during the year and 5% [5%] died in hospital. Conclusion Our empirical delay distributions and conditional probabilities should help facilitate more accurate forecasts in the future, including improved predictions of hospital bed demands during influenza outbreaks, and the expected effects of hospitalizations on epidemic dynamics. Electronic supplementary material The online version of this article (10.1186/s12889-019-7369-x) contains supplementary material, which is available to authorized users.

Background West Nile virus (WNV) is a mosquito-transmitted disease of birds that has caused bird population declines and can spill over into human populations. Previous research has identified bird species that infect a large fraction of the total pool of infected mosquitoes and correlate with human infection risk; however, these analyses cover small spatial regions and cannot be used to predict transmission in bird communities in which these species are rare or absent. Here we present a mechanistic model for WNV transmission that predicts WNV spread (R0) in any bird community in North America by scaling up from the physiological responses of individual birds to transmission at the level of the community. We predict unmeasured bird species’ responses to infection using phylogenetic imputation, based on these species’ phylogenetic relationships with bird species with measured responses. Results We focused our analysis on Texas, USA, because it is among the states with the highest total incidence of WNV in humans and is well sampled by birders in the eBird database. Spatio-temporal patterns: WNV transmission is primarily driven by temperature variation across time and space, and secondarily by bird community composition. In Texas, we predicted WNV R0 to be highest in the spring and fall when temperatures maximize the product of mosquito transmission and survival probabilities. In the most favorable months for WNV transmission (April, May, September and October), we predicted R0 to be highest in the “Piney Woods” and “Oak Woods & Prairies” ecoregions of Texas, and lowest in the “High Plains” and “South Texas Brush County” ecoregions. Dilution effect: More abundant bird species are more competent hosts for WNV, and predicted WNV R0 decreases with increasing species richness. Keystone species: We predicted that northern cardinals (Cardinalis cardinalis) are the most important hosts for amplifying WNV and that mourning doves (Zenaida macroura) are the most important sinks of infection across Texas. Conclusions Despite some data limitations, we demonstrate the power of phylogenetic imputation in predicting disease transmission in heterogeneous host communities. Our mechanistic modeling framework shows promise both for assisting future analyses on transmission and spillover in heterogeneous multispecies pathogen systems and for improving model transparency by clarifying assumptions, choices and shortcomings in complex ecological analyses. Electronic supplementary material The online version of this article (10.1186/s13071-019-3656-8) contains supplementary material, which is available to authorized users.

Sexual reproduction persists in nature despite its large cost. The Red Queen Hypothesis postulates that parasite pressure maintains sexual reproduction in the host population by selecting for the ability to produce rare genotypes that are resistant to infection. Mathematical models have been used to lay theoretical foundations for the hypothesis; empirical studies have confirmed these predictions. For example, Lively used a simple host-parasite model to predict that the frequency of sexual hosts should be positively correlated with the prevalence of infection. Lively et al. later confirmed the prediction through numerous field studies of snail-trematode systems in New Zealand. In this study, we fit a simple metapopulation host-parasite coevolution model to three data sets, each representing a different snail-trematode system, by matching the observed prevalence of sexual reproduction and trematode infection among hosts. Using the estimated parameters, we perform a power analysis to test the feasibility of observing the positive correlation predicted by Lively. We discuss anomalies in the data that are poorly explained by the model and provide practical guidance to both modelers and empiricists. Overall, our study suggests that a simple Red Queen model can only partially explain the observed relationships between parasite infection and the maintenance of sexual reproduction.

Many disease models focus on characterizing the underlying transmission mechanism but make simple, possibly naive assumptions about how infections are reported. In this note, we use a simple deterministic Susceptible-Infected-Removed (SIR) model to compare two common assumptions about disease incidence reports: individuals can report their infection as soon as they become infected or as soon as they recover. We show that incorrect assumptions about the underlying observation processes can bias estimates of the basic reproduction number and lead to overly narrow confidence intervals., Comment: 9 pages, 2 figures

Summary Integral projection models (IPMs) allow us to describe quantitatively the dynamics of a population structured by a continuous variable. They rely on information gathered at the individual level by recording survival, reproduction and changes in some structuring variable over time. This requires the ability to track individuals over the course of their entire life cycle. When this is not feasible, we would like to use alternative information to infer a population's dynamics. Time series of population-level data are an option. An inverse modelling approach allows inferring the vital rates of a population when only population-level data, in the form of a time series of the size of a population and the distribution of its individuals along a structuring variable, are available. The approach also allows incorporating estimates obtained through individual-level data. Here, we explore how inverse modelling performs with simulated data and a relatively simple demographic model. We explore scenarios of data availability in terms of time-series length, per-year sample size and availability of independent vital-rate estimates. We also test model performance in a real system using a 15-year long data set from a chamaephyte plant, Cryptantha flava. We show that an inverse model can provide accurate reconstructions of the vital rates in a scenario where no individual-level information is available. Better results can be obtained if independent estimates on any vital rate are provided, as was the case for C. flava where high interannual variation is present. Parameter estimation becomes more difficult with shorter time series, but per-year sample size can be greatly reduced without significantly affecting parameter accuracy. Inverse modelling of IPMs allow for the estimation of unobserved vital rates, which is important for systems where any or all of the vital rates are hard to quantify. It also helps to determine whether a forward IPM is capturing the population dynamics: if the inverse version produces incorrect reconstructions of the vital rates, the forward IPM can be considered as inadequately describing the system.

Context Animals' use of space and habitat selection emerges from their movement patterns, which are, in turn, determined by their behavioural or physiological states and extrinsic factors. Aim The aims of the present study were to investigate animal movement and incorporate the movement patterns into habitat selection analyses using Global Positioning System (GPS) location data from 16 black bears (Ursus americanus) in a fragmented area of Florida, USA. Methods Hidden Markov models (HMMs) were used to discern the movement patterns of the bears. These results were then used in step-selection functions (SSFs) to evaluate habitat selection patterns and the factors influencing these patterns. Key results HMMs revealed that black bear movement patterns are best described by three behavioural states: (1) resting (very short step-lengths and large turning angles); (2) encamped (moderate step-lengths and large turning angles); and (3) exploratory (long step-lengths and small turning angles). Bears selected for forested wetlands and marsh wetlands more than any other land cover type, and generally avoided urban areas in all seasons and when in encamped and exploratory behavioural states. Bears also chose to move to locations farther away from major roads. Conclusions Because habitat selection is influenced by how animals move within landscapes, it is essential to consider animals' movement patterns when making inferences about habitat selection. The present study achieves this goal by using HMMs to first discern black bear movement patterns and associated parameters, and by using these results in SSFs to investigate habitat selection patterns. Thus, the methodological framework developed in this study effectively incorporates state-specific movement patterns while making inferences regarding habitat selection. The unified methodological approach employed here will contribute to an improved understanding of animal ecology as well as informed management decisions. Implications Conservation plans focused on preserving forested wetlands would benefit bears by not only providing habitat for resting and foraging, but also by providing connectivity through fragmented landscapes. Additionally, the framework could be applied to species that follow annual cycles and may provide a tool for investigating how animals are using dispersal corridors.

Null hypothesis significance testing remains popular despite decades of concern about misuse and misinterpretation. We believe that much of the problem is due to language: significance testing has little to do with other meanings of the word "significance". Despite the limitations of null-hypothesis tests, we argue here that they remain useful in many contexts as a guide to whether a certain effect can be seen clearly in that context (e.g. whether we can clearly see that a correlation or between-group difference is positive or negative). We therefore suggest that researchers describe the conclusions of null-hypothesis tests in terms of statistical "clarity" rather than statistical "significance". This simple semantic change could substantially enhance clarity in statistical communication., 11 Pages, 1 Table, 0 Figures

Large trees in the tropics are reportedly more vulnerable to droughts than their smaller neighbours. This pattern is of interest due to what it portends for forest structure, timber production, carbon sequestration and multiple other values given that intensified El Niño Southern Oscillation (ENSO) events are expected to increase the frequency and intensity of droughts in the Amazon region. What remains unclear is what characteristics of large trees render them especially vulnerable to drought-induced mortality and how this vulnerability changes with forest degradation. Using a large-scale, long-term silvicultural experiment in a transitional Amazonian forest in Bolivia, we disentangle the effects of stem diameter, tree height, crown exposure and logging-induced degradation on risks of drought-induced mortality during the 2004/2005 ENSO event. Overall, tree mortality increased in response to drought in both logged and unlogged plots. Tree height was a much stronger predictor of mortality than stem diameter. In unlogged plots, tree height but not crown exposure was positively associated with drought-induced mortality, whereas in logged plots, neither tree height nor crown exposure was associated with drought-induced mortality. Our results suggest that, at the scale of a site, hydraulic factors related to tree height, not air humidity, are a cause of elevated drought-induced mortality of large trees in unlogged plots. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

Reproduction by individuals is typically recorded as count data (e.g. number of fledglings from a nest or inflorescences on a plant) and commonly modeled using Poisson or negative binomial distributions, which assume that variance is greater than or equal to the mean. However, distributions of reproductive effort are often underdispersed (i.e., variance

A plant lineage can compete for resources in a spatially variable environment by colonizing new areas, exploiting resources in those areas quickly before other plants arrive to compete with it, or tolerating competition once other plants do arrive. These specializations are ubiquitous in plant communities, but all three have never been derived from a spatial model of community dynamics-instead, the possibility of rapid exploitation has been either overlooked or confounded with colonization. We use moment equations, equations for the mean densities and spatial covariance of competing plant populations, to characterize these strategies in a fully spatial stochastic model. The moment equations predict endogenous spatial pattern formation and the efficacy of spatial strategies under different conditions. The model shows that specializations for colonization, exploitation, and tolerance are all possible, and these are the only possible spatial strategies; among them, they partition all of the endogenous spatial structure in the environment. The model predicts two distinct short-dispersal specializations where parents disperse their offspring locally, either to exploit empty patches quickly or to fill patches to exclude competitors.

Emerging infectious diseases pose an important threat to wildlife. While established protocols exist for combating outbreaks of human and agricultural pathogens, appropriate management actions before, during, and after the invasion of wildlife pathogens have not been developed. We describe stage-specific goals and management actions that minimize disease impacts on wildlife, and the research required to implement them. Before pathogen arrival, reducing the probability of introduction through quarantine and trade restrictions is key because prevention is more cost effective than subsequent responses. On the invasion front, the main goals are limiting pathogen spread and preventing establishment. In locations experiencing an epidemic, management should focus on reducing transmission and disease, and promoting the development of resistance or tolerance. Finally, if pathogen and host populations reach a stable stage, then recovery of host populations in the face of new threats is paramount. Successful management of wildlife disease requires risk-taking, rapid implementation, and an adaptive approach. "Funding was provided by the US National Science Foundation (grants EF-0914866, DGE-0741448, DEB-1115069, DEB-1336290) and the National Institutes of Health (grant 1R010AI090159)." https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/140241