Pandemic modelling for regions implementing an elimination strategy.

During the COVID-19 pandemic, some countries, such as Australia, China, Iceland, New Zealand, Thailand, and Vietnam successfully implemented an elimination strategy, enacting strict border control and periods of lockdowns to end community transmission. Atlantic Canada and Canada's territories implemented similar policies, and reported long periods with no community cases. In Newfoundland and Labrador (NL), Nova Scotia, and Prince Edward Island a median of 80% or more of daily reported cases were travel-related from July 1, 2020 to May 31, 2021. With increasing vaccination coverage, it may be appropriate to exit an elimination strategy, but most existing epidemiological frameworks are applicable only to situations where most cases occur in the community, and are not appropriate for regions that have implemented an elimination strategy. To inform the pandemic response in regions that are implementing an elimination strategy, we extend importation modelling to consider post-arrival travel restrictions, and pharmaceutical and non-pharmaceutical interventions in the local community. We find that shortly after the Omicron variant had begun spreading in Canada, the expected daily number of spillovers, infections spread to NL community members from travellers and their close contacts, was higher than any time previously in the pandemic. By December 24, 2021, the expected number of spillovers was 44% higher than the previous high, which occurred in late July 2021 shortly after travel restrictions were first relaxed. We develop a method to assess the characteristics of potential future community outbreaks in regions that are implementing an elimination strategy. We apply this method to predict the effect of variant and vaccination coverage on the size of hypothetical community outbreaks in Mount Pearl, a suburb of the St. John's metropolitan area in NL. Our methodology can be used to evaluate alternative plans to relax public health restrictions when vaccine coverage is high in regions that have implemented an elimination strategy. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Parasite-induced shifts in host movement may explain the transient coexistence of high- and low-pathogenic disease strains.

Many parasites induce decreased host movement, known as lethargy, which can impact disease spread and the evolution of virulence. Mathematical models have investigated virulence evolution when parasites cause host death, but disease-induced decreased host movement has received relatively less attention. Here, we consider a model where, due to the within-host parasite replication rate, an infected host can become lethargic and shift from a moving to a resting state, where it can die. We find that when the lethargy and disease-induced mortality costs to the parasites are not high, then evolutionary bistability can arise, and either moderate or high virulence can evolve depending on the initial virulence and the magnitude of mutation. These results suggest, firstly, the coexistence of strains with different virulence, which may explain the transient coexistence of low- and high-pathogenic strains of avian influenza viruses, and secondly, that medical interventions to treat the symptoms of lethargy or prevent disease-induced host deaths can result in a large jump in virulence and the rapid evolution of high virulence. In complement to existing results that show bistability when hosts are heterogeneous at the population level, we show that evolutionary bistability may arise due to transmission heterogeneity at the individual host level.

Regional climate affects salmon lice dynamics, stage structure and management.

Regional variation in climate can generate differences in population dynamics and stage structure. Where regional differences exist, the best approach to pest management may be region-specific. Salmon lice are a stage-structured marine copepod that parasitizes salmonids at aquaculture sites worldwide, and have fecundity, development and mortality rates that depend on temperature and salinity. We show that in Atlantic Canada and Norway, where the oceans are relatively cold, salmon lice abundance decreases during the winter months, but ultimately increases from year to year, while in Ireland and Chile, where the oceans are warmer, the population size grows monotonically without any seasonal declines. In colder regions, during the winter the stage structure is dominated by the adult stage, which is in contrast to warmer regions where all stages are abundant year round. These differences translate into region-specific recommendations for management: regions with slower population growth have lower critical stocking densities, and regions with cold winters have a seasonal dependence in the timing of follow-up chemotherapeutic treatments. Predictions of our salmon lice model agree with empirical data, and our approach provides a method to understand the effects of regional differences in climate on salmon lice dynamics and management.

Skewed temperature dependence affects range and abundance in a warming world.

Population growth metrics such as R0 are usually asymmetric functions of temperature, with cold-skewed curves arising when the positive effects of a temperature increase outweigh the negative effects, and warm-skewed curves arising in the opposite case. Classically, cold-skewed curves are interpreted as more beneficial to a species under climate warming, because cold-skewness implies increased population growth over a larger proportion of the species's fundamental thermal niche than warm-skewness. However, inference based on the shape of the fitness curve alone, and without considering the synergistic effects of net reproduction, density and dispersal, may yield an incomplete understanding of climate change impacts. We formulate a moving-habitat integrodifference equation model to evaluate how fitness curve skewness affects species' range size and abundance during climate warming. In contrast to classic interpretations, we find that climate warming adversely affects populations with cold-skewed fitness curves, positively affects populations with warm-skewed curves and has relatively little or mixed effects on populations with symmetric curves. Our results highlight the synergistic effects of fitness curve skewness, spatially heterogeneous densities and dispersal in climate change impact analyses, and that the common approach of mapping changes only in R0 may be misleading.

Mechanistic models for the spatial spread of species under climate change.

Global climate change is a major threat to biodiversity. The most common methods for predicting the response of biodiversity to changing climate do not explicitly incorporate fundamental evolutionary and ecological processes that determine species responses to changing climate, such as reproduction, dispersal, and adaptation. We provide an overview of an emerging mechanistic spatial theory of species range shifts under climate change. This theoretical framework explicitly defines the ecological processes that contribute to species range shifts via biologically meaningful dispersal, reproductive, and climate envelope parameters. We present methods for estimating the parameters of the model with widely available species occurrence and abundance data and then apply these methods to empirical data for 12 North American butterfly species to illustrate the potential use of the theory for global change biology. The model predicts species persistence in light of current climate change and habitat loss. On average, we estimate that the climate envelopes of our study species are shifting north at a rate of 3.25 +/- 1.36 km/yr (mean +/- SD) and that our study species produce 3.46 +/- 1.39 (mean +/- SD) viable offspring per individual per year. Based on our parameter estimates, we are able to predict the relative risk of our 12 study species for lagging behind changing climate. This theoretical framework improves predictions of global change outcomes by facilitating the development and testing of hypotheses, providing mechanistic predictions of current and future range dynamics, and encouraging the adaptive integration of theory and data. The theory is ripe for future developments such as the incorporation of biotic interactions and evolution of adaptations to novel climatic conditions, and it has the potential to be a catalyst for the development of more effective conservation strategies to mitigate losses of biodiversity from global climate change.

Resource and seasonality drive interspecific variability in simulations from a dynamic energy budget model.

Animals show a vast array of phenotypic traits in time and space. Such variation patterns have traditionally been described as ecogeographical rules; for example, the tendency of size and clutch size to increase with latitude (Bergmann's and Lack's rules, respectively). Despite considerable research into these variation patterns and their consequences for biodiversity and conservation, the processes behind trait variation remain controversial. Here, we show how food variability, largely set by climate and weather, can drive interspecific trait variation by determining individual energy input and allocation trade-offs. Using a dynamic energy budget (DEB) model, we simulated different food environments, as well as interspecific variability in the parameters for energy assimilation, mobilization and allocation to soma. We found that interspecific variability is greater when the resource is non-limiting in both constant and seasonal environments. Our findings further show that individuals can reach larger biomass and greater reproductive output in a seasonal environment than in a constant environment of equal average resource due to the peaks of food surplus. Our results agree with the classical patterns of interspecific trait variation and provide a mechanistic understanding that supports recent hypotheses which explain them: the resource and the eNPP (net primary production during the growing season) rules. Due to the current alterations to ecosystems and communities, disentangling the mechanisms of trait variation is increasingly important to understand and predict biodiversity dynamics under climate change, as well as to improve conservation strategies.

Extensive SARS-CoV-2 testing reveals BA.1/BA.2 asymptomatic rates and underreporting in school children.

Background: Case underreporting during the coronavirus disease 2019 (COVID-19) pandemic has been a major challenge to the planning and evaluation of public health responses. School children were often considered a less vulnerable population and underreporting rates may have been particularly high. In January 2022, the Canadian province of Newfoundland and Labrador (NL) was experiencing an Omicron variant outbreak (BA.1/BA.2 subvariants) and public health officials recommended that all returning students complete two rapid antigen tests (RATs) to be performed three days apart. Methods: To estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we asked parents and guardians to report the results of the RATs completed by K-12 students (approximately 59,000 students) using an online survey. Results: When comparing the survey responses with the number of cases and tests reported by the NL testing system, we found that one out of every 4.3 (95% CI, 3.1-5.3) positive households were captured by provincial case count, with 5.1% positivity estimated from the RAT results and 1.2% positivity reported by the provincial testing system. Of positive test results, 62.9% (95% CI, 44.3-83.0) were reported for elementary school students, and the remaining 37.1% (95% CI, 22.7-52.9) were reported for junior high and high school students. Asymptomatic infections were 59.8% of the positive cases. Given the low survey participation rate (3.5%), our results may suffer from sample selection biases and should be interpreted with caution. Conclusion: The underreporting ratio is consistent with ratios calculated from serology data and provides insights into infection prevalence and asymptomatic infections in school children; a currently understudied population.

Downsizing of COVID-19 contact tracing in highly immune populations.

Contact tracing is a key component of successful management of COVID-19. Contacts of infected individuals are asked to quarantine, which can significantly slow down (or prevent) community spread. Contact tracing is particularly effective when infections are detected quickly, when contacts are traced with high probability, when the initial number of cases is low, and when social distancing and border restrictions are in place. However, the magnitude of the individual contribution of these factors in reducing epidemic spread and the impact of population immunity (due to either previous infection or vaccination), in determining contact tracing outputs is not fully understood. We present a delayed differential equation model to investigate how the immunity status and the relaxation of social distancing requirements affect contact tracing practices. We investigate how the minimal contact tracing efficiency required to keep an outbreak under control depends on the contact rate and on the proportion of immune individuals. Additionally, we consider how delays in outbreak detection and increased case importation rates affect the number of contacts to be traced daily. We show that in communities that have reached a certain immunity status, a lower contact tracing efficiency is required to avoid a major outbreak, and delayed outbreak detection and relaxation of border restrictions do not lead to a significantly higher risk of overwhelming contact tracing. We find that investing in testing programs, rather than increasing the contact tracing capacity, has a larger impact in determining whether an outbreak will be controllable. This is because early detection activates contact tracing, which will slow, and eventually reverse exponential growth, while the contact tracing capacity is a threshold that will easily become overwhelmed if exponential growth is not curbed. Finally, we evaluate quarantine effectiveness in relation to the immunity status of the population and for different viral variants. We show that quarantine effectiveness decreases with increasing proportion of immune individuals, and increases in the presence of more transmissible variants. These results suggest that a cost-effective approach is to establish different quarantine rules for immune and nonimmune individuals, where rules should depend on viral transmissibility after vaccination or infection. Altogether, our study provides quantitative information for contact tracing downsizing in vaccinated populations or in populations that have already experienced large community outbreaks, to guide COVID-19 exit strategies.

Modelling the impact of travel restrictions on COVID-19 cases in Newfoundland and Labrador.

In many jurisdictions, public health authorities have implemented travel restrictions to reduce coronavirus disease 2019 (COVID-19) spread. Policies that restrict travel within countries have been implemented, but the impact of these restrictions is not well known. On 4 May 2020, Newfoundland and Labrador (NL) implemented travel restrictions such that non-residents required exemptions to enter the province. We fit a stochastic epidemic model to data describing the number of active COVID-19 cases in NL from 14 March to 26 June. We predicted possible outbreaks over nine weeks, with and without the travel restrictions, and for contact rates 40-70% of pre-pandemic levels. Our results suggest that the travel restrictions reduced the mean number of clinical COVID-19 cases in NL by 92%. Furthermore, without the travel restrictions there is a substantial risk of very large outbreaks. Using epidemic modelling, we show how the NL COVID-19 outbreak could have unfolded had the travel restrictions not been implemented. Both physical distancing and travel restrictions affect the local dynamics of the epidemic. Our modelling shows that the travel restrictions are a plausible reason for the few reported COVID-19 cases in NL after 4 May.

Mechanistic movement models to understand epidemic spread.

An overlooked aspect of disease ecology is considering how and why animals come into contact with one and other resulting in disease transmission. Mathematical models of disease spread frequently assume mass-action transmission, justified by stating that susceptible and infectious hosts mix readily, and foregoing any detailed description of host movement. Numerous recent studies have recorded, analysed and modelled animal movement. These movement models describe how animals move with respect to resources, conspecifics and previous movement directions and have been used to understand the conditions for the occurrence and the spread of infectious diseases when hosts perform a type of movement. Here, we summarize the effect of the different types of movement on the threshold conditions for disease spread. We identify gaps in the literature and suggest several promising directions for future research. The mechanistic inclusion of movement in epidemic models may be beneficial for the following two reasons. Firstly, the estimation of the transmission coefficient in an epidemic model is possible because animal movement data can be used to estimate the rate of contacts between conspecifics. Secondly, unsuccessful transmission events, where a susceptible host contacts an infectious host but does not become infected can be quantified. Following an outbreak, this enables disease ecologists to identify 'near misses' and to explore possible alternative epidemic outcomes given shifts in ecological or immunological parameters.This article is part of the themed issue 'Opening the black box: re-examining the ecology and evolution of parasite transmission'.

Benefits and unintended consequences of antimicrobial de-escalation: Implications for stewardship programs.

Sequential antimicrobial de-escalation aims to minimize resistance to high-value broad-spectrum empiric antimicrobials by switching to alternative drugs when testing confirms susceptibility. Though widely practiced, the effects de-escalation are not well understood. Definitions of interventions and outcomes differ among studies. We use mathematical models of the transmission and evolution of Pseudomonas aeruginosa in an intensive care unit to assess the effect of de-escalation on a broad range of outcomes, and clarify expectations. In these models, de-escalation reduces the use of high-value drugs and preserves the effectiveness of empiric therapy, while also selecting for multidrug-resistant strains and leaving patients vulnerable to colonization and superinfection. The net effect of de-escalation in our models is to increase infection prevalence while also increasing the probability of effective treatment. Changes in mortality are small, and can be either positive or negative. The clinical significance of small changes in outcomes such as infection prevalence and death may exceed more easily detectable changes in drug use and resistance. Integrating harms and benefits into ranked outcomes for each patient may provide a way forward in the analysis of these tradeoffs. Our models provide a conceptual framework for the collection and interpretation of evidence needed to inform antimicrobial stewardship.

Host allometry influences the evolution of parasite host-generalism: theory and meta-analysis.

Parasites vary widely in the diversity of hosts they infect: some parasite species are specialists-infecting just a single host species, while others are generalists, capable of infecting many. Understanding the factors that drive parasite host-generalism is of basic biological interest, but also directly relevant to predicting disease emergence in new host species, identifying parasites that are likely to have unidentified additional hosts, and assessing transmission risk. Here, we use mathematical models to investigate how variation in host body size and environmental temperature affect the evolution of parasite host-generalism. We predict that parasites are more likely to evolve a generalist strategy when hosts are large-bodied, when variation in host body size is large, and in cooler environments. We then explore these predictions using a newly updated database of over 20 000 fish-macroparasite associations. Within the database we see some evidence supporting these predictions, but also highlight mismatches between theory and data. By combining these two approaches, we establish a theoretical basis for interpreting empirical data on parasites' host specificity and identify key areas for future work that will help untangle the drivers of parasite host-generalism.This article is part of the themed issue 'Opening the black box: re-examining the ecology and evolution of parasite transmission'.

A model for sea lice (Lepeophtheirus salmonis) dynamics in a seasonally changing environment.

Sea lice (Lepeophtheirus salmonis) are a significant source of monetary losses on salmon farms. Sea lice exhibit temperature-dependent development rates and salinity-dependent mortality, but to date no deterministic models have incorporated these seasonally varying factors. To understand how environmental variation and life history characteristics affect sea lice abundance, we derive a delay differential equation model and parameterize the model with environmental data from British Columbia and southern Newfoundland. We calculate the lifetime reproductive output for female sea lice maturing to adulthood at different times of the year and find differences in the timing of peak reproduction between the two regions. Using a sensitivity analysis, we find that sea lice abundance is more sensitive to variation in mean annual water temperature and mean annual salinity than to variation in life history parameters. Our results suggest that effective sea lice management requires consideration of site-specific temperature and salinity patterns and, in particular, that the optimal timing of production cycles and sea lice treatments might vary between regions.

How to measure the impacts of antibiotic resistance and antibiotic development on empiric therapy: new composite indices.

OBJECTIVES: We aimed to construct widely useable summary measures of the net impact of antibiotic resistance on empiric therapy. Summary measures are needed to communicate the importance of resistance, plan and evaluate interventions, and direct policy and investment. DESIGN, SETTING AND PARTICIPANTS: As an example, we retrospectively summarised the 2011 cumulative antibiogram from a Toronto academic intensive care unit. OUTCOME MEASURES: We developed two complementary indices to summarise the clinical impact of antibiotic resistance and drug availability on empiric therapy. The Empiric Coverage Index (ECI) measures susceptibility of common bacterial infections to available empiric antibiotics as a percentage. The Empiric Options Index (EOI) varies from 0 to 'the number of treatment options available', and measures the empiric value of the current stock of antibiotics as a depletable resource. The indices account for drug availability and the relative clinical importance of pathogens. We demonstrate meaning and use by examining the potential impact of new drugs and threatening bacterial strains. CONCLUSIONS: In our intensive care unit coverage of device-associated infections measured by the ECI remains high (98%), but 37-44% of treatment potential measured by the EOI has been lost. Without reserved drugs, the ECI is 86-88%. New cephalosporin/ß-lactamase inhibitor combinations could increase the EOI, but no single drug can compensate for losses. Increasing methicillin-resistant Staphylococcus aureus (MRSA) prevalence would have little overall impact (ECI=98%, EOI=4.8-5.2) because many Gram-positives are already resistant to ß-lactams. Aminoglycoside resistance, however, could have substantial clinical impact because they are among the few drugs that provide coverage of Gram-negative infections (ECI=97%, EOI=3.8-4.5). Our proposed indices summarise the local impact of antibiotic resistance on empiric coverage (ECI) and available empiric treatment options (EOI) using readily available data. Policymakers and drug developers can use the indices to help evaluate and prioritise initiatives in the effort against antimicrobial resistance.

Determinants of the Final Size and Case Rate of Nosocomial Outbreaks.

Different nosocomial pathogen species have varying infectivity and durations of infectiousness, while the transmission route determines the contact rate between pathogens and susceptible patients. To determine if the pathogen species and transmission route affects the size and spread of outbreaks, we perform a meta-analysis that examines data from 933 outbreaks of hospital-acquired infection representing 14 pathogen species and 8 transmission routes. We find that the mean number of cases in an outbreak is best predicted by the pathogen species and the mean number of cases per day is best predicted by the species-transmission route combination. Our fitted model predicts the largest mean number of cases for Salmonella outbreaks (22.3) and the smallest mean number of cases for Streptococci outbreaks (8.5). The largest mean number of cases per day occurs during Salmonella outbreaks spread via the environment (0.33) and the smallest occurs for Legionella outbreaks spread by multiple transmission routes (0.005). When combined with information on the frequency of outbreaks these findings could inform the design of infection control policies in hospitals.

Immune evasion and the evolution of molecular mimicry in parasites.

Parasites that are molecular mimics express proteins which resemble host proteins. This resemblance facilitates immune evasion because the immune molecules with the specificity to react with the parasite also cross-react with the host's own proteins, and these lymphocytes are rare. Given this advantage, why are not most parasites molecular mimics? Here we explore potential factors that can select against molecular mimicry in parasites and thereby limit its occurrence. We consider two hypotheses: (1) molecular mimics are more likely to induce autoimmunity in their hosts, and hosts with autoimmunity generate fewer new infections (the "costly autoimmunity hypothesis"); and (2) molecular mimicry compromises protein functioning, lowering the within-host replication rate and leading to fewer new infections (the "mimicry trade-off hypothesis"). Our analysis shows that although both hypotheses may select against molecular mimicry in parasites, unique hallmarks of protein expression identify whether selection is due to the costly autoimmunity hypothesis or the mimicry trade-off hypothesis. We show that understanding the relevant selective forces is necessary to predict how different medical interventions will affect the proportion of hosts that experience the different infection types, and that if parasite evolution is ignored, interventions aimed at reducing infection-induced autoimmunity may ultimately fail.

Linking antimicrobial prescribing to antimicrobial resistance in the ICU: before and after an antimicrobial stewardship program.

Antimicrobials are an effective treatment for many types of infections, but their overuse promotes the spread of resistant microorganisms that defy conventional treatments and complicate patient care. In 2009, an antimicrobial stewardship program was implemented at Mount Sinai Hospital (MSH, Toronto, Canada). Components of this program were to alter the fraction of patients prescribed antimicrobials, to shorten the average duration of treatment, and to alter the types of antimicrobials prescribed. These components were incorporated into a mathematical model that was compared to data reporting the number of patients colonized with Pseudomonas aeruginosa and the number of patients colonized with antimicrobial-resistant P. aeruginosa first isolates before and after the antimicrobial stewardship program. Our analysis shows that the reported decrease in the number of patients colonized was due to treating fewer patients, while the reported decrease in the number of patients colonized with resistant P. aeruginosa was due to the combined effect of treating fewer patients and altering the types of antimicrobials prescribed. We also find that shortening the average duration of treatment was unlikely to have produced any noticeable effects and that further reducing the fraction of patients prescribed antimicrobials would most substantially reduce P. aeruginosa antimicrobial resistance in the future. The analytical framework that we derive considers the effect of colonization pressure on infection spread and can be used to interpret clinical antimicrobial resistance data to assess different aspects of antimicrobial stewardship within the ecological context of the intensive care unit.

Next-generation tools for evolutionary invasion analyses.

Evolutionary invasion analysis is a powerful technique for modelling in evolutionary biology. The general approach is to derive an expression for the growth rate of a mutant allele encoding some novel phenotype, and then to use this expression to predict long-term evolutionary outcomes. Mathematically, such 'invasion fitness' expressions are most often derived using standard linear stability analyses from dynamical systems theory. Interestingly, there is a mathematically equivalent approach to such stability analyses that is often employed in mathematical epidemiology, and that is based on so-called 'next-generation' matrices. Although this next-generation matrix approach has sometimes also been used in evolutionary invasion analyses, it is not yet common in this area despite the fact that it can sometimes greatly simplify calculations. The aim of this article is to bring the approach to a wider evolutionary audience in two ways. First, we review the next-generation matrix approach and provide a novel, and easily intuited, interpretation of how this approach relates to more standard techniques. Second, we illustrate next-generation methods in evolutionary invasion analysis through a series of informative examples. Although focusing primarily on evolutionary invasion analysis, we provide several insights that apply to biological modelling in general.

GPS measurement error gives rise to spurious 180 degree turning angles and strong directional biases in animal movement data.

BACKGROUND: Movement data are frequently collected using Global Positioning System (GPS) receivers, but recorded GPS locations are subject to errors. While past studies have suggested methods to improve location accuracy, mechanistic movement models utilize distributions of turning angles and directional biases and these data present a new challenge in recognizing and reducing the effect of measurement error. METHODS: I collected locations from a stationary GPS collar, analyzed a probabilistic model and used Monte Carlo simulations to understand how measurement error affects measured turning angles and directional biases. RESULTS: Results from each of the three methods were in complete agreement: measurement error gives rise to a systematic bias where a stationary animal is most likely to be measured as turning 180 degrees or moving towards a fixed point in space. These spurious effects occur in GPS data when the measured distance between locations is <20 meters. CONCLUSIONS: Measurement error must be considered as a possible cause of 180 degree turning angles in GPS data. Consequences of failing to account for measurement error are predicting overly tortuous movement, numerous returns to previously visited locations, inaccurately predicting species range, core areas, and the frequency of crossing linear features. By understanding the effect of GPS measurement error, ecologists are able to disregard false signals to more accurately design conservation plans for endangered wildlife.

A spatially explicit model for an Allee effect: why wolves recolonize so slowly in Greater Yellowstone.

A reduced probability of finding mates at low densities is a frequently hypothesized mechanism for a component Allee effect. At low densities dispersers are less likely to find mates and establish new breeding units. However, many mathematical models for an Allee effect do not make a distinction between breeding group establishment and subsequent population growth. Our objective is to derive a spatially explicit mathematical model, where dispersers have a reduced probability of finding mates at low densities, and parameterize the model for wolf recolonization in the Greater Yellowstone Ecosystem (GYE). In this model, only the probability of establishing new breeding units is influenced by the reduced probability of finding mates at low densities. We analytically and numerically solve the model to determine the effect of a decreased probability in finding mates at low densities on population spread rate and density. Our results suggest that a reduced probability of finding mates at low densities may slow recolonization rate.