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Of the 3,500 species of mosquitoes worldwide, only a small portion carry and transmit the mosquito-borne diseases (MBDs) that cause approximately half a million deaths annually worldwide. The most common exotic MBDs, such as malaria and dengue, are not currently established in Canada, in part because of our relatively harsh climate; however, this situation could evolve with climate change. Mosquitoes native to Canada may become infected with new pathogens and move into new regions within Canada. In addition, new mosquito species may move into Canada from other countries, and these exotic species may bring exotic MBDs as well. With high levels of international travel, including to locations with exotic MBDs, there will be more travel-acquired cases of MBDs. With climate change, there is the potential for exotic mosquito populations to become established in Canada. There is already a small area of Canada where exotic Aedes mosquitoes have become established although, to date, there is no evidence that these carry any exotic (or already endemic) MBDs. The increased risks of spreading MBDs, or introducing exotic MBDs, will need a careful clinical and public health response. Clinicians will need to maintain a high level of awareness of current trends, to promote mosquito bite prevention strategies, and to know the laboratory tests needed for early detection and when to report laboratory results to public health. Public health efforts will need to focus on ongoing active surveillance, public and professional awareness and mosquito control. Canadians need to be aware of the risks of acquiring exotic MBDs while travelling abroad as well as the risk that they could serve as a potential route of introduction for exotic MBDs into Canada when they return home.

Public health management of Lyme disease (LD) is a dynamic challenge in Canada. Climate warming is driving the northward expansion of suitable habitat for the tick vector, Ixodes scapularis. Information about tick population establishment is used to inform the risk of LD but is challenged by sampling biases from surveillance data. Misclassifying areas as having no established tick population underestimates the LD risk classification. We used a logistic regression model at the municipal level to predict the probability of I. scapularis population establishment based on passive tick surveillance data during the period of 2010-2017 in southern Quebec. We tested for the effect of abiotic and biotic factors hypothesized to influence tick biology and ecology. Additional variables controlled for sampling biases in the passive surveillance data. In our final selected model, tick population establishment was positively associated with annual cumulative degree-days0°C, precipitation and deer density, and negatively associated with coniferous and mixed forest types. Sampling biases from passive tick surveillance were controlled for using municipal population size and public health instructions on tick submissions. The model performed well as indicated by an area under the curve (AUC) of 0.92, sensitivity of 86% and specificity of 81%. Our model enables prediction of I. scapularis population establishment in areas which lack data from passive tick surveillance and may improve the sensitivity of LD risk categorization in these areas. A more sensitive system of LD risk classification is important for increasing awareness and use of protective measures employed against ticks, and decreasing the morbidity associated with LD.

Background: To maintain control of the coronavirus disease 2019 (COVID-19) epidemic as lockdowns are lifted, it will be crucial to enhance alternative public health measures. For surveillance, it will be necessary to detect a high proportion of any new cases quickly so that they can be isolated, and people who have been exposed to them traced and quarantined. Here we introduce a mathematical approach that can be used to determine how many samples need to be collected per unit area and unit time to detect new clusters of COVID-19 cases at a stage early enough to control an outbreak. Methods: We present a sample size determination method that uses a relative weighted approach. Given the contribution of COVID-19 test results from sub-populations to detect the disease at a threshold prevalence level to control the outbreak to 1) determine if the expected number of weekly samples provided from current healthcare-based surveillance for respiratory virus infections may provide a sample size that is already adequate to detect new clusters of COVID-19 and, if not, 2) to determine how many additional weekly samples were needed from volunteer sampling. Results: In a demonstration of our method at the weekly and Canadian provincial and territorial (P/T) levels, we found that only the more populous P/T have sufficient testing numbers from healthcare visits for respiratory illness to detect COVID-19 at our target prevalence level—assumed to be high enough to identify and control new clusters. Furthermore, detection of COVID-19 is most efficient (fewer samples required) when surveillance focuses on healthcare symptomatic testing demand. In the volunteer populations: the higher the contact rates; the higher the expected prevalence level; and the fewer the samples were needed to detect COVID-19 at a predetermined threshold level. Conclusion: This study introduces a targeted surveillance strategy, combining both passive and active surveillance samples, to determine how many samples to collect per unit area and unit time to detect new clusters of COVID-19 cases. The goal of this strategy is to allow for early enough detection to control an outbreak.

Rabies occurs throughout the Arctic, representing an ongoing public health concern for residents of northern communities. The Arctic fox (Vulpes lagopus) is the main reservoir of the Arctic rabies virus variant, yet little is known about the epidemiology of Arctic rabies, such as the ecological mechanisms driving where and when epizootics in fox populations occur. In this study, we provide the first portrait of the spatio-temporal spread of rabies across northern Canada. We also explore the impact of seasonal and multiannual dynamics in Arctic fox populations and climatic factors on rabies transmission dynamics. We analysed data on rabies cases collected through passive surveillance systems in the Yukon, Northwest Territories, Nunavut, Nunavik and Labrador from 1953 to 2014. In addition, we analysed a large and unique database of trapped foxes tested for rabies in the Northwest Territories and Nunavut from 1974 to 1984 as part of active surveillance studies. Rabies cases occurred in all Arctic regions of Canada and were relatively synchronous among foxes and dogs (Canis familiaris). This study highlights the spread of Arctic rabies virus variant across northern Canada, with contrasting rabies dynamics between different yet connected areas. Population fluctuations of Arctic fox populations could drive rabies transmission dynamics in a complex way across northern Canada. Furthermore, this study suggests different impacts of climate and sea ice cover on the onset of rabies epizootics in northern Canada. These results lay the groundwork for the development of epidemiological models to better predict the spatio-temporal dynamics of rabies occurrence in both wild and domestic carnivores, leading to better estimates of human exposure and transmission risk.

Advancements in artificial intelligence (AI), more precisely the subfield of machine learning, and their applications to open-source internet data, such as social media, are growing faster than the management of ethical issues for use in society. An ethical framework helps scientists and policy makers consider ethics in their fields of practice, legitimize their work and protect members of the data-generating public. A central question for advancing the ethical framework is whether or not Tweets, Facebook posts and other open-source social media data generated by the public represent a human or not. The objective of this paper is to highlight ethical issues that the public health sector will be or is already confronting when using social media data in practice. The issues include informed consent, privacy, anonymization and balancing these issues with the benefits of using social media data for the common good. Current ethical frameworks need to provide guidance for addressing issues arising from the use of social media data in the public health sector. Discussions in this area should occur while the application of open-source data is still relatively new, and they should also keep pace as other problems arise from ongoing technological change.

Warming waters are changing marine pathogen dispersal patterns and infectivity worldwide. Coupled biological–physical modelling has been used in many systems to determine the connectivity of metapopulations via infectious disease particles. Here we model the connectivity of sea lice larvae (Lepeophtheirus salmonis) among salmon farms in the Broughton Archipelago, British Columbia, Canada, using a coupled biological–physical model. The physical model simulated pathogen dispersal, while the biological component influenced the survival and developmental rates of the sea lice. Model results predicted high temporal variability in connectivity strength among farms, an emergent effect from the interacting parts of the simulation (dispersion versus survival and development). Drivers of temporal variability were disentangled using generalized additive modeling, which revealed the variability was most strongly impacted by the spring freshet, which can act as a natural aid for sea lice control in the Broughton Archipelago. Our results suggest that farm management strategies can benefit by accounting for short-term spikes in regional pathogen connectivity among farms. Additionally, future scenarios of a warming climate with reduced snowpack can make sea lice control more challenging.

Italy is currently experiencing an epidemic of COVID-19 which emerged in the Lombardy region . During the interval between February 25-29, 2020, we identified 46 cases of COVID-19 reported in 21 countries in Europe, Africa, North America, and South America which were either in individuals with recent travel from Italy, or who had presumed infection by a traveler from Italy 2. In six cases, in four of the affected countries (Switzerland, France, Austria, Croatia), land travel was a likely route of introduction, or was documented to have been the route of introduction. We used air travel volume between Italian cities and cities in other countries as an index of connectedness, using data available from the International Air Transport Association (IATA) for February 2015 (2.61 million total departing international air passengers from Italy). We used the methods of Fraser et al. to estimate the size of the underlying epidemic in Italy necessary in order for these cases to be observed with a reasonable probability. To estimate the time at risk of COVID-19 exposure for travelers departing Italy, we obtained data from the United Nations World Tourism Organization (UNWTO) for the proportion of international travelers that are non-residents of Italy (63%) and the average length of stay of tourists to Italy (3.4 days), and assumed the Italian epidemic began one month prior to February 29, 2020. We also performed sensitivity analyses in which we included outbound travel to all countries regardless of reported case importations, inflated travel volumes by 35%, to account for the relative increase in flight numbers from 2015-2019, and excluded cases in bordering countries and which were documented to have been introduced by overland travel. When all cases were considered we estimated a true outbreak size of 3971 cases (95% CI 2907-5297), as compared to a reported case count of 1128 on February 29, 2020, suggesting non-identification of 72% (61-79%) of cases. In sensitivity analyses, outbreak sizes varied from 1552 to 4533 cases (implying non-identification of 27-75% of cases). We recently used similar methods to estimate a much larger epidemic size in Iran, with a far greater degree of under-reporting, based on many fewer exported cases. The reason for this difference relates to the relatively high volume of travel from Italy, relative to Iran. In summary, we suggest that the numerous COVID-19 case exportations from Italy in recent days suggest an epidemic that is larger than official case counts suggest, and which is approximately on a par with that currently occurring in South Korea, which reports 3526 cases (and fewer deaths) as of February 29, 2020.

Animal movement influences the spatial spread of wildlife infectious diseases through host-host contact structure and hence pathogen transmission. Wildlife disease hosts vary in characteristics related to pathogen transmission, which can increase the spread and intensity of disease outbreaks. The consequences of home range size variation on wildlife disease dynamics are poorly understood, but could help to predict disease spread and determine more effective disease management strategies.We developed a spatially-explicit individual-based model to examine the effect of variation in host home range size on the spatial spread rate, persistence, and incidence of rabies virus (RABV) in raccoons (Procyon lotor). We tested the hypothesis that host home range area variation decreases vaccination effectiveness in wildlife host populations following pathogen invasion into a vaccination zone.We simulated raccoon demography and RABV dynamics across a range of magnitudes and variances in weekly home range radius distributions for raccoons, and compared results to conditions that assumed a fixed host home range area. We examined how variable host home range radius distributions influenced the relative effectiveness of three components of orally-baited raccoon RABV vaccination (ORV) programs—timing and frequency of bait delivery, width of the zone where ORV baits were delivered, and proportion of hosts immunized.Variability in home range radius distributions increased RABV spread rates by 1.2 - 5.2-fold compared to simulations with fixed radii. More variable host home range radius distributions decreased relative vaccination effectiveness by 71% compared to less variable host home range radius distributions under conventional vaccination conditions. We found that vaccination timing was more influential for vaccination effectiveness than vaccination frequency or vaccination zone width.Our results suggest that variation in wildlife home range exploration increases the spatial spread and incidence of wildlife disease. Our vaccination results underscore the importance of prioritizing individual-level space use data collection to understand the dynamics of wildlife diseases and plan their effective control and elimination.

The number of human cases of several climate-related infectious diseases, including tick- and mosquito-borne diseases, has increased in Canada and other parts of the world since the end of the last century. Predicting and mapping the risks associated with these diseases using environmental and climatic determinants derived from satellite images is an emerging method that can support research, surveillance, prevention and control activities and help to better assess the impacts of climate change in Canada. Earth observation images can be used to systematically monitor changes in the Earth’s surface and atmosphere at different scales of time and space. These images can inform estimation and monitoring of environmental and climatic determinants, and thus disease prediction and risk mapping. The current array of Earth observation satellites provides access to a large quantity and variety of data. These data have different characteristics in terms of spatial, temporal and thematic precision and resolution. The objectives of this overview are to describe how Earth observation images may inform risk assessment and mapping of tick-borne and mosquito-borne diseases in Canada, their potential benefits and limitations, the implications and next steps.

The sea louse is considered an important ectoparasite that affects farmed salmonids around the world. Sea lice control relies heavily on pharmacological treatments in several salmon-producing countries, including Chile. Among options for drug administration, immersion treatments represent the majority of antiparasitic control strategies used in Chile. As a topical procedure, immersion treatments do not induce a long lasting effect; therefore, re-infestation from neighbouring farms may undermine their efficacy. Synchronization of treatments has been proposed as a strategy to improve immersion treatment performance, but it has not been evaluated so far. Using a repeated-measures linear mixed-effect model, we evaluated the impact of treatment synchronization of neighbouring farms (within 10km seaway distance) on the adult lice mean abundance from weeks 2 to 8 post-treatment on rainbow trout and Atlantic salmon farms in Chile, while controlling for external and internal sources of lice before the treatments, and also for environmental and fish-related variables. Results indicate that treatment synchronization was significantly associated with lower adult lice levels from weeks 5 to 7 after treatment. This relationship appeared to be linear, suggesting that higher levels of synchronization may result in lower adult sea lice levels during these weeks. These findings suggest that synchronization can improve the performance of immersion delousing treatments by keeping sea lice levels low for a longer period of time. Our results may be applicable to other regions of the world where immersion treatments are widely used.

Sea lice are common ectoparasites of farmed and wild salmonids and can cause substantial morbidity and mortality in their hosts. While sea lice infections are common in estuarine areas with variable salinity, the effects of salinity on population dynamics are poorly understood. We used existing literature to parameterize salinity-dependent logistic mortality curves for different life stages of sea lice. We then used population matrix models to characterize the effects of temperature and salinity on sea louse population growth. Our models showed that low salinity decreases survival, while low temperature retards sea louse development. In contrast with the linear effects of temperature on sea louse development, salinity has a nonlinear effect on sea louse survival; values below 20 psu cause mortality, while values above 20 psu have little effect on survival. Simulations showed that sea louse population growth can be greatest in zones that are intermediate between estuarine and oceanic. In these cases population growth is not limited by the low salinities found in more estuarine sites or the low temperatures found in more oceanic sites.

We applied the model-guided fieldwork framework to the Caribbean mongoose rabies system by parametrizing a spatially-explicit, individual-based model, and by performing an uncertainty analysis designed to identify parameters for which additional empirical data are most needed. Our analysis revealed important variation in output variables characterizing rabies dynamics, namely rabies persistence, exposure level, spatiotemporal distribution, and prevalence. Among epidemiological parameters, rabies transmission rate was the most influential, followed by rabies mortality and location, and size of the initial infection. The most influential landscape parameters included habitat-specific carrying capacities, landscape heterogeneity, and the level of resistance to dispersal associated with topography. Movement variables, including juvenile dispersal, adult fine-scale movement distances, and home range size, as well as life history traits such as age of independence, birth seasonality, and age- and sex-specific mortality were other important drivers of rabies dynamics. We discuss results in the context of mongoose ecology and its influence on disease transmission dynamics. Finally, we suggest empirical approaches and study design specificities that would provide optimal contributing data addressing the knowledge gaps identified by our approach, and would increase our potential to use epidemiological models to guide mongoose rabies control and management in the Caribbean.

The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department’s data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial scales. Furthermore, including sociodemographic predictors is likely to be helpful in capturing longer-term dengue trends., Author summary Dengue virus has the highest disease burden of all mosquito-borne viral diseases, infecting 390 million people annually in 128 countries. Forecasting is an important warning mechanism that can help with proactive planning and response for clinical and public health services. In this study, we compare two different machine learning approaches to dengue forecasting: random forest (RF) and artificial neural networks (ANN). National (pooling across all departments) and local (department-specific) models were compared and used to predict future dengue cases in Colombia. In Colombia, the departments are administrative divisions formed by a grouping of municipalities. The results demonstrated that the counts of future dengue cases were more accurately estimated by RF than by ANN. It was also shown that environmental and meteorological predictors were more important for forecast accuracy for shorter-term forecasts while socio-demographic predictors were more important for longer-term forecasts. Finally, the national pooled model applied to local data was more accurate in dengue forecasting compared to the department-specific model. This research contributes to the field of disease forecasting and highlights different considerations for future forecasting studies.

Lyme disease, the most commonly reported vector-borne disease in North America, is caused by the spirochete Borrelia burgdorferi sensu stricto, which is transmitted by Ixodes scapularis in eastern Canada and Ixodes pacificus in western Canada. Recently, the northward range expansion of I. scapularis ticks, in south-eastern Canada, has resulted in a dramatic increase in the incidence of human Lyme disease. Detecting emerging areas of Lyme disease risk allows public health to target disease prevention efforts. We analysed passive tick surveillance data from Ontario and Manitoba to i) assess the relationship between the total numbers of I. scapularis submissions in passive surveillance from humans, and the number of human Lyme disease cases, and ii) develop province-specific acarological indicators of risk that can be used to generate surveillance-based risk maps. We also assessed associations between numbers of nymphal I. scapularis tick submissions only and Lyme disease case incidence. Using General Estimating Equation regression, the relationship between I. scapularis submissions (total numbers and numbers of nymphs only) in each census sub-division (CSD) and the number of reported Lyme disease cases was positively correlated and highly significant in the two provinces (P ≤ 0.001). The numbers of I. scapularis submissions over five years discriminated CSDs with ≥ 3 Lyme disease cases from those with < 3 cases with high accuracy when using total numbers of tick submission (Receiver Operating Characteristics area under the curve [AUC] = 0.89) and moderate accuracy (AUC = 0.78) when using nymphal tick submissions only. In Ontario the optimal cut-off point was a total 12 tick submissions from a CSD over five years (Sensitivity = 0.82, Specificity = 0.84), while in Manitoba the cut-off point was five ticks (Sensitivity = 0.71, Specificity = 0.79) suggesting regional variability of the risk of acquiring Lyme disease from an I. scapularis bite. The performances of the acarological indicators developed in this study for Ontario and Manitoba support the ability of passive tick surveillance to provide an early signal of the existence Lyme disease risk areas in regions where ticks and the pathogens they transmit are expanding their range.

Connectivity in an aquatic setting is determined by a combination of hydrodynamic circulation and the biology of the organisms driving linkages. These complex processes can be simulated in coupled biological-physical models. The physical model refers to an underlying circulation model defined by spatially-explicit nodes, often incorporating a particle-tracking model. The particles can then be given biological parameters or behaviors (such as maturity and/or survivability rates, diel vertical migrations, avoidance, or seeking behaviors). The output of the bio-physical models can then be used to quantify connectivity among the nodes emitting and/or receiving the particles. Here we propose a method that makes use of kernel density estimation (KDE) on the output of a particle-tracking model, to quantify the infection or infestation pressure (IP) that each node causes on the surrounding area. Because IP is the product of both exposure time and the concentration of infectious agent particles, using KDE (which also combine elements of time and space), more accurately captures IP. This method is especially useful for those interested in infectious agent networks, a situation where IP is a superior measure of connectivity than the probability of particles from each node reaching other nodes. Here we illustrate the method by modeling the connectivity of salmon farms via sea lice larvae in the Broughton Archipelago, British Columbia, Canada. Analysis revealed evidence of two sub-networks of farms connected via a single farm, and evidence that the highest IP from a given emitting farm was often tens of kilometers or more away from that farm. We also classified farms as net emitters, receivers, or balanced, based on their structural role within the network. By better understanding how these salmon farms are connected to each other via their sea lice larvae, we can effectively focus management efforts to minimize the spread of sea lice between farms, advise on future site locations and coordinated treatment efforts, and minimize any impact of farms on juvenile wild salmon. The method has wide applicability for any system where capturing infectious agent networks can provide useful guidance for management or preventative planning decisions.

Animal movement influences the spatial spread of directly transmitted wildlife disease through host–host contact structure. Wildlife disease hosts vary in home range‐associated foraging and social behaviours, which may increase the spread and intensity of disease outbreaks. The consequences of variation in host home range movement and space use on wildlife disease dynamics are poorly understood, but could help to predict disease spread and determine more effective disease management strategies.We developed a spatially explicit individual‐based model to examine the effect of spatiotemporal variation in host home range size on the spatial spread rate, persistence and incidence of rabies virus (RABV) in raccoons (Procyon lotor). We tested the hypothesis that variation in home range size increases RABV spread and decreases vaccination effectiveness in host populations following pathogen invasion into a vaccination zone.We simulated raccoon demography and RABV dynamics across a range of magnitudes and variances in weekly home range size for raccoons. We examined how variable home range size influenced the relative effectiveness of three components of oral rabies vaccination (ORV) programmes targeting raccoons—timing and frequency of bait delivery, width of the ORV zone and proportion of hosts immunized.Variability in weekly home range size increased RABV spread rates by 1.2‐fold to 5.2‐fold compared to simulations that assumed a fixed home range size. More variable host home range sizes decreased relative vaccination effectiveness by 71% compared to less variable host home range sizes under conventional vaccination conditions. We found that vaccination timing was more influential for vaccination effectiveness than vaccination frequency or vaccination zone width.Our results suggest that variation in wildlife home range movement behaviour increases the spatial spread and incidence of RABV. Our vaccination results underscore the importance of prioritizing individual‐level space use and movement data collection to understand wildlife disease dynamics and plan their effective control and elimination., The authors developed a spatially explicit model of raccoon population and rabies virus infection dynamics, and show that variation in host home range size increases rabies spread and incidence, decreasing the population‐level effectiveness of oral rabies vaccination. The results underscore the importance of host movement and space use heterogeneity on disease dynamics and elimination efforts.

Background Zika virus (ZIKV) spread rapidly in the Americas in 2015. Targeting effective public health interventions for inhabitants of, and travellers to and from, affected countries depends on understanding the risk of ZIKV emergence (and re-emergence) at the local scale. We explore the extent to which environmental, social and neighbourhood disease intensity variables influenced emergence dynamics. Our objective was to characterise population vulnerability given the potential for sustained autochthonous ZIKV transmission and the timing of emergence. Logistic regression models estimated the probability of reporting at least one case of ZIKV in a given municipality over the course of the study period as an indicator for sustained transmission; while accelerated failure time (AFT) survival models estimated the time to a first reported case of ZIKV in week t for a given municipality as an indicator for timing of emergence. Results Sustained autochthonous ZIKV transmission was best described at the temporal scale of the study period (almost one year), such that high levels of study period precipitation and low mean study period temperature reduced the probability. Timing of ZIKV emergence was best described at the weekly scale for precipitation in that high precipitation in the current week delayed reporting. Both modelling approaches detected an effect of high poverty on reducing/slowing case detection, especially when inter-municipal road connectivity was low. We also found that proximity to municipalities reporting ZIKV had an effect to reduce timing of emergence when located, on average, less than 100 km away. Conclusions The different modelling approaches help distinguish between large temporal scale factors driving vector habitat suitability and short temporal scale factors affecting the speed of spread. We find evidence for inter-municipal movements of infected people as a local-scale driver of spatial spread. The negative association with poverty suggests reduced case reporting in poorer areas. Overall, relatively simplistic models may be able to predict the vulnerability of populations to autochthonous ZIKV transmission at the local scale. Electronic supplementary material The online version of this article (10.1186/s13071-018-2867-8) contains supplementary material, which is available to authorized users.

Seagrasses are ecosystem engineers of essential marine habitat. Their populations are rapidly declining worldwide. One potential cause of seagrass population declines is wasting disease, which is caused by opportunistic pathogens in the genus Labyrinthula. While infection with these pathogens is common in seagrasses, theory suggests that disease only occurs when environmental stressors cause immunosuppression of the host. Recent evidence suggests that host factors may also contribute to disease caused by opportunistic pathogens. In order to quantify patterns of disease, identify risk factors, and investigate responses to infection, we surveyed shoot density, shoot length, epiphyte load, production of plant defenses (phenols), and wasting disease prevalence in eelgrass Zostera marina across 11 sites in the central Salish Sea (Washington state, USA), a region where both wasting disease and eelgrass declines have been documented. Wasting disease was diagnosed by the presence of necrotic lesions, and Labyrinthula cells were identified with histology. Disease prevalence among sites varied from 6 to 79%. The probability of a shoot being diseased was higher in longer shoots, in patches of higher shoot density, and in shoots with higher levels of biofouling from epiphytes. Phenolic concentration was higher in diseased leaves. We hypothesize that this results from the induction of phenols during infection. Additional research is needed to evaluate whether phenols are an adaptive defense against Labyrinthula infection. The high site-level variation in disease prevalence emphasizes the potential for wasting disease to be causing some of the observed decline in eelgrass beds.

Sea lice infestation levels on wild chum and pink salmon in the Broughton Archipelago region are known to vary spatially and temporally; however, the locations of areas associated with a high infestation level had not been investigated yet. In the present study, the multivariate spatial scan statistic based on a Poisson model was used to assess spatial clustering of elevated sea lice (Caligus clemensi and Lepeophtheirus salmonis) infestation levels on wild chum and pink salmon sampled between March and July of 2004 to 2012 in the Broughton Archipelago and Knight Inlet regions of British Columbia, Canada. Three covariates, seine type (beach and purse seining), fish size, and year effect, were used to provide adjustment within the analyses. The analyses were carried out across the five months/datasets and between two fish species to assess the consistency of the identified clusters. Sea lice stages were explored separately for the early life stages (non-motile) and the late life stages of sea lice (motile). Spatial patterns in fish migration were also explored using monthly plots showing the average number of each fish species captured per sampling site. The results revealed three clusters for non-motile C. clemensi, two clusters for non-motile L. salmonis, and one cluster for the motile stage in each of the sea lice species. In general, the location and timing of clusters detected for both fish species were similar. Early in the season, the clusters of elevated sea lice infestation levels on wild fish are detected in areas closer to the rivers, with decreasing relative risks as the season progresses. Clusters were detected further from the estuaries later in the season, accompanied by increasing relative risks. In addition, the plots for fish migration exhibit similar patterns for both fish species in that, as expected, the juveniles move from the rivers toward the open ocean as the season progresses The identification of space-time clustering of infestation on wild fish from this study can help in targeting investigations of factors associated with these infestations and thereby support the development of more effective sea lice control measures.

Parasite transmission between captive and wild fish is mediated by spatial, abiotic, biotic, and management factors. More effective population management and conservation strategies can result from multivariable assessments of factors associated with spatial dynamics of parasite spillover. Our study characterised spatial patterns of sea lice (Lepeophtheirus salmonis, Caligus clemensi) infection on out-migrating chum (Oncorhynchus keta) and pink (O. gorbuscha) salmon in an area with Atlantic salmon (Salmo salar) farming. A multivariable statistical model for sea louse parasitism of out-migrating chum and pink salmon was developed from 166,316 wild salmon sampled in the Broughton Archipelago, British Columbia, Canada from 2003 to 2012. We assessed for factors hypothesized to influence sea lice infection levels, at the non-motile life stage, including spatial scales of infection sources. Fish length, sampling year and method were strong explanatory factors. Infection was greatest in higher salinity water. Farmed and wild juvenile salmon infection levels were correlated, on average, within 30 km. Except for 2004, sea lice infection on farms were typically well below the regulatory level (3 motiles per fish). Average intensity of non-motile infections observed on the wild fish were 6.36 (SD = 9.98) in 2004 compared to 1.66 (SD = 1.25) for the other years. Accuracy of future model estimates will benefit by including hydrodynamic data accounting for anisotropic spread of sea lice from sources. Multivariable statistical modelling over long time series data strengthens understanding of factors impacting wild juvenile salmon infection levels and informs spatial patterns of aquatic epidemiology.

Background: The risk of contracting Lyme disease (LD) can vary spatially because of spatial heterogeneity in risk factors such as social-behavior and exposure to ecological risk factors. Integrating these risk factors to inform decision-making should therefore increase the effectiveness of mitigation interventions. Objectives: The objective of this study was to develop an integrated social-behavioral and ecological risk-mapping approach to identify priority areas for LD interventions. Methods: The study was conducted in the Montérégie region of Southern Quebec, Canada, where LD is a newly endemic disease. Spatial variation in LD knowledge, risk perceptions, and behaviors in the population were measured using web survey data collected in 2012. These data were used as a proxy for the social-behavioral component of risk. Tick vector population densities were measured in the environment during field surveillance from 2007 to 2012 to provide an index of the ecological component of risk. Social-behavioral and ecological components of risk were combined with human population density to create integrated risk maps. Map predictions were validated by testing the association between high-risk areas and the current spatial distribution of human LD cases. Results: Social-behavioral and ecological components of LD risk had markedly different distributions within the study region, suggesting that both factors should be considered for locally adapted interventions. The occurrence of human LD cases in a municipality was positively associated with tick density (p

Zika virus (ZIKV) infection is emerging globally, currently causing outbreaks in the Caribbean, and Central and South America, and putting travellers to affected countries at risk. Model-based estimates for the basic reproduction number (R 0 ) of ZIKV in affected Caribbean and Central and South American countries, obtained from 2015 to 2016 human case surveillance data, were compared by logistic regression and Receiver-Operating Characteristic (ROC), with the prevalence of ZIKV-positive test results in Canadians who travelled to them. Estimates of R 0 for each country were a good predictor of the ZIKV test result (ROC area under the curve = 0.83) and the odds of testing positive was 11-fold greater for travellers visiting countries with estimated R 0 ≥ 2.76, compared to those visiting countries with R 0

Effective disease management can benefit from mathematical models that identify drivers of epidemiological change and guide decision-making. This is well illustrated in the host–parasite system of sea lice and salmon, which has been modelled extensively due to the economic costs associated with sea louse infections on salmon farms and the conservation concerns associated with sea louse infections on wild salmon. Consequently, a rich modelling literature devoted to sea louse and salmon epidemiology has been developed. We provide a synthesis of the mathematical and statistical models that have been used to study the epidemiology of sea lice and salmon. These studies span both conceptual and tactical models to quantify the effects of infections on host populations and communities, describe and predict patterns of transmission and dispersal, and guide evidence-based management of wild and farmed salmon. As aquaculture production continues to increase, advances made in modelling sea louse and salmon epidemiology should inform the sustainable management of marine resources.