Laura L. E. Cowen

We describe a new R package for extended batch‐mark methods. Extended batch marking is a valuable and efficient technique for estimating population sizes and survival probabilities of wild animals. It assigns the same identifier to a group of animals simultaneously, allowing researchers to track their survival over time. By analysing the proportions of batch‐marked individuals in subsequent samples, population size and survival probabilities, whether constant or time‐varying, can be estimated accurately. Despite its advantages, extended batch marking is not widely supported in R statistical software. To address this gap, we present extBatchMarking , an R package for fitting extended batch‐mark models using Hidden Markov Models and integrated population frameworks. We demonstrate the package's functionality through applications using datasets and simulation studies that show how the choice of package arguments can affect the parameter estimates.

Accurate estimation of the initial growth rate of an epidemic is critical for assessing transmissibility and guiding early interventions. Standard regression-based methods, such as negative binomial regression, often rely on independence assumptions that may underestimate uncertainty. We propose a hidden Markov model (HMM) framework that explicitly accounts for the unobserved infectious population. Using data generated from a stochastic linear SEIR model, we compare the performance of different methods for estimating the exponential growth rate. Our results show that HMMs yield more robust and reliable estimates than negative binomial regression, with improved coverage probabilities of the 95 % credible intervals. In particular, modelling the infectious population with a negative binomial distribution (with a constant probability parameter) or a binomial distribution provides more accurate inference than the other methods considered. Moreover, this framework is extended beyond the exponential phase by combining it with a logistic model to account for the slowing down of the exponential growth due to the depleting of the susceptible population. We apply this extended method to data on the early stage of COVID-19 pandemic in Africa and Ontario, Canada to estimate the initial growth rate, and we demonstrate that the HMM-logistic framework improves stability and reliability of estimates.

The accurate quantification of the impact of COVID-19 pandemic on both public health and the economy is essential for informed policy-making. However, the true scope of the pandemic remains challenging to ascertain due to undetected cases, particularly when relying on reported cases, which rely heavily on test availability and strategies. To accurately quantify COVID-19 cases in British Columbia (BC), we develop a Susceptible-Infectious-Recovered multi-event capture-recapture (SIRMECR) model to capture the dynamics of COVID-19. Specifically, we present a time-varying Markov model to estimate the number of undetected COVID-19 cases in five Health Authority Regions in BC, Canada, during the year 2020. We utilize individual-level information available from Population Data BC database to estimate the case detection probability, infection probability, survival probability, and recovery probability by incorporating testing volumes as covariates that improve the estimate of our parameters. We develop a Markov chain Monte Carlo (MCMC) algorithm to estimate SIRMECR model parameters. However, analyzing this big COVID-19 data set prompts a discussion on the computational challenges encountered. Therefore, we developed divide-and-conquer strategies to address the challenges. Our application provides an estimate of the total COVID-19 burden in year 2020 and found the percentage of undetected varying from 77.4 % to 84.0 %. More specifically, we validate our results through a simulation study and N-mixture model for Northern Health Authority Region of BC.

Contact tracing is an important public health measure to control disease transmission. However, it is difficult to assess contact tracing during the exponential stage of an epidemic with multiple control measures, because the exponential growth rate is influenced by all measures. We present new SEIR and SEAIR contact tracing models that track contacts in randomly mixed populations, and calibrate them to simulated epidemic curves to determine the data that allow us to assess the effect of contact tracing. We find that new-case counts, counts of cases identified by contact tracing (or voluntary tests), and counts of symptomatic onset are necessary to identify model parameters and evaluate the effect of contact tracing. We fit our contact tracing models to COVID-19 pandemic data in Ontario, Canada, during March 16–May 1, 2020. Our results show that approximately 29% of cases were identified via contact tracing of close contacts. Contact tracing moderately reduces the control reproduction number by about 25%, but significantly reduces the prevalence by more than half. Ignoring asymptomatic transmissions gives similar estimates for the effect of contact tracing, but significantly underestimates the prevalence.

Two-sample capture-recapture studies are commonly used in the epidemiologic and ecological literature. Most of these studies have been limited to analysis using the Lincoln-Petersen estimator, especially in epidemiologic studies. We examine the use of the Lincoln-Petersen estimator and 2 alternative closed-population methods: Huggins’ conditional likelihood method and Pledger’s likelihood method with mixtures. We assessed the ability of Akaike information criterion (AIC) to perform model selection when time, behavioral effects, or heterogeneity are present in capture probabilities. In addition, we examined the effects of model misspecification on estimates of population size. An extensive simulation study found that these models are not robust to misspecification and that AIC was not capable of selecting the correct model with 2 capture occasions.

Understanding the interrelationship between the survival and reproductive states of salmonids is important for both management and conservation purposes; however, their complex life history introduces challenges. The survival probabilities of anadromous Dolly Varden ( Salvelinus malma) are influenced by their reproductive cycle, while predicting their reproductive state in successive years is difficult as it can vary based on an individual’s sex. We developed a Bayesian multi-state capture–recapture model that estimates reproductive state transition probabilities, survival probabilities, and the effect of sex on state transitions using the Cormack–Jolly–Seber model. We applied the model to Dolly Varden data collected from five river systems in the western Canadian Arctic. We demonstrate sex-specific state transition probability differences, with females exhibiting higher transition rates into reproductive states. Moreover, survival probabilities were influenced by both sex and reproductive status. Across all rivers, survival probabilities for both sexes decreased by approximately 50% while spawning compared to non-spawning. This study provides important insights into how reproduction and sex affect survival among stocks, which improves their assessment to ensure management and conservation goals.

Wolverines (Gulo gulo) are a circumboreal species that has experienced substantial range reduction worldwide. In Canada, the wolverine has been extirpated entirely from the east, and from prairie regions in the west. The province of Alberta holds the south‐central portion of wolverines' Canadian range, and there they have been designated as Data Deficient since 2001 due to a historical lack of information. Our aim was to provide a first approximation of a wolverine abundance estimate at the provincial scale to inform science‐based management as well as status designation. We synthesised existing density estimates and wolverine–habitat relationships to create a province‐wide density estimate for wolverines. Densities were derived from five landscapes, spanning protected National Parks in the Rocky Mountains, the highly developed Foothills and the northcentral and northwestern boreal forests. Densities were estimated using spatially explicit capture–recapture (SECR) models. Densities ranged from 6.74 wolverines/1000 km² in the northwest boreal to 0.71 wolverines/1000 km² in the foothills. The proportion of adults was based on a study from the northwest, which estimated 57% adults to 43% subadults. Extrapolating densities across natural subregions (bioclimatic ecoregions), based on known habitat relationships, it was estimated that there were 955 wolverines in the province, of which 544 were adults. This number falls well below an IUCN threshold for a legally listed species; we suggest a reassessment of the wolverine status in Alberta and considering commensurate conservation actions.

Even with daily case counts, the true scope of the COVID-19 pandemic in Canada is unknown due to undetected cases. We develop a novel multisite disease analytics model which estimates undetected cases using discrete-valued multivariate time series in the framework of Bayesian hidden Markov modelling techniques. We apply our multisite model to estimate the pandemic scope using publicly available disease count data including detected cases, recoveries among detected cases, and total deaths. These counts are used to estimate the case detection probability, the infection fatality rate through time, the probability of recovery, and several important population parameters including the rate of spread and importation of external cases. We estimate the total number of active COVID-19 cases per region of Canada for each reporting interval. We applied this multisite model Canada-wide to all provinces and territories, providing an estimate of the total COVID-19 burden for the 90 weeks from 23 April 2020 to 10 February 2022. We also applied this model to the five health authority regions of British Columbia, Canada, describing the pandemic in B.C. over the 31 weeks from 2 April 2020 to 30 October 2020.

Humpback whales ( Megaptera novaeangliae) use southern British Columbia waters to feed, but the type and quantity of prey in many areas used for feeding is unknown. We conducted active acoustic prey mapping in 55 small grid-surveys in two regions off Vancouver Island. We quantitatively compared fish and zooplankton-dominated biomass in known feeding areas with and without foraging humpback whales, and qualitatively described the prey characteristics of the foraged areas. Surveys of the water column suggest that, on average, humpback whale foraging was associated more with increased zooplankton than fish biomass. Prey characteristics varied between the two regions (∼500 km apart), but there was no significant difference in mean backscatter strength in the actively foraged areas between the two regions. Frequency differencing discriminated between the dominant taxa in the water column, but potential epipelagic prey (<10 m) would have been omitted from analysis. However, average depth at the maximum acoustic prey detections was significantly deeper when whales were present (84 m) versus absent (60 m), suggesting predominantly subsurface foraging opportunities suitable to prey mapping.