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The conservation of insect pollinators is drawing attention because of reported declines in bee species and the 'ecosystem services' they provide. This issue has been brought to a head by recent devastating losses of honey bees throughout North America (so called, 'Colony Collapse Disorder'); yet, we still have little understanding of the cause(s) of bee declines. Wild bumble bees (Bombus spp.) have also suffered serious declines and circumstantial evidence suggests that pathogen 'spillover' from commercially reared bumble bees, which are used extensively to pollinate greenhouse crops, is a possible cause. We constructed a spatially explicit model of pathogen spillover in bumble bees and, using laboratory experiments and the literature, estimated parameter values for the spillover of Crithidia bombi, a destructive pathogen commonly found in commercial Bombus. We also monitored wild bumble bee populations near greenhouses for evidence of pathogen spillover, and compared the fit of our model to patterns of C. bombi infection observed in the field. Our model predicts that, during the first three months of spillover, transmission from commercial hives would infect up to 20% of wild bumble bees within 2 km of the greenhouse. However, a travelling wave of disease is predicted to form suddenly, infecting up to 35-100% of wild Bombus, and spread away from the greenhouse at a rate of 2 km/wk. In the field, although we did not observe a large epizootic wave of infection, the prevalences of C. bombi near greenhouses were consistent with our model. Indeed, we found that spillover has allowed C. bombi to invade several wild bumble bee species near greenhouses. Given the available evidence, it is likely that pathogen spillover from commercial bees is contributing to the ongoing decline of wild Bombus in North America. Improved management of domestic bees, for example by reducing their parasite loads and their overlap with wild congeners, could diminish or even eliminate pathogen spillover.

Extensive non-pharmaceutical and physical distancing measures are currently the primary interventions against coronavirus disease 2019 (COVID-19) worldwide. It is therefore urgent to estimate the impact such measures are having. We introduce a Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing, with the timing of distancing measures informed by survey data on attitudes to distancing and COVID-19. We fit our model to reported COVID-19 cases in British Columbia (BC), Canada, and five other jurisdictions, using an observation model that accounts for both underestimation and the delay between symptom onset and reporting. We estimated the impact that physical distancing (social distancing) has had on the contact rate and examined the projected impact of relaxing distancing measures. We found that, as of April 11 2020, distancing had a strong impact in BC, consistent with declines in reported cases and in hospitalization and intensive care unit numbers; individuals practising physical distancing experienced approximately 0.22 (0.11–0.34 90% CI [credible interval]) of their normal contact rate. The threshold above which prevalence was expected to grow was 0.55. We define the “contact ratio” to be the ratio of the estimated contact rate to the threshold rate at which cases are expected to grow; we estimated this contact ratio to be 0.40 (0.19–0.60) in BC. We developed an R package ‘covidseir’ to make our model available, and used it to quantify the impact of distancing in five additional jurisdictions. As of May 7, 2020, we estimated that New Zealand was well below its threshold value (contact ratio of 0.22 [0.11–0.34]), New York (0.60 [0.43–0.74]), Washington (0.84 [0.79–0.90]) and Florida (0.86 [0.76–0.96]) were progressively closer to theirs yet still below, but California (1.15 [1.07–1.23]) was above its threshold overall, with cases still rising. Accordingly, we found that BC, New Zealand, and New York may have had more room to relax distancing measures than the other jurisdictions, though this would need to be done cautiously and with total case volumes in mind. Our projections indicate that intermittent distancing measures—if sufficiently strong and robustly followed—could control COVID-19 transmission. This approach provides a useful tool for jurisdictions to monitor and assess current levels of distancing relative to their threshold, which will continue to be essential through subsequent waves of this pandemic.

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.

In socially living animals, individuals interact through complex networks of contact that may influence the spread of disease. Whereas traditional epidemiological models typically assume no social structure, network theory suggests that an individual's location in the network determines its risk of infection. Empirical, especially experimental, studies of disease spread on networks are lacking, however, largely due to a shortage of amenable study systems. We used automated video-tracking to quantify networks of physical contact among individuals within colonies of the social bumble bee Bombus impatiens. We explored the effects of network structure on pathogen transmission in naturally and artificially infected hives. We show for the first time that contact network structure determines the spread of a contagious pathogen (Crithidia bombi) in social insect colonies. Differences in rates of infection among colonies resulted largely from differences in network density among hives. Within colonies, a bee's rate of contact with infected nestmates emerged as the only significant predictor of infection risk. The activity of bees, in terms of their movement rates and division of labour (e.g., brood care, nest care, foraging), did not influence risk of infection. Our results suggest that contact networks may have an important influence on the transmission of pathogens in social insects and, possibly, other social animals.

Postmarketing evaluations have linked myocarditis to SARS-CoV-2 mRNA vaccines. We sought to estimate the incidence of myocarditis after mRNA vaccination against SARS-CoV-2, and to compare the incidence with expected rates based on historical background rates in British Columbia. We conducted an observational study using population health administrative data from the BC COVID-19 Cohort from Dec. 15, 2020, to Mar. 10, 2022. The primary exposure was any dose of an mRNA vaccine against SARS-CoV-2. The primary outcome was incidence of hospital admission or emergency department visit for myocarditis or myopericarditis within 7 and 21 days postvaccination, calculated as myocarditis rates per 100 000 mRNA vaccine doses, expected rates of myocarditis cases and observedto-expected ratios. We stratified analyses by age, sex, vaccine type and dose number. We observed 99 incident cases of myocarditis within 7 days (0.97 cases per 100 000 vaccine doses; observed v. expected ratio 14.81, 95% confidence interval [CI] 10.83–16.55) and 141 cases within 21 days (1.37 cases per 100 000 vaccine doses; observed v. expected ratio 7.03, 95% CI 5.92–8.29) postvaccination. Cases of myocarditis per 100 000 vaccine doses were higher for people aged 12–17 years (2.64, 95% CI 1.54–4.22) and 18–29 years (2.63, 95% CI 1.94–3.50) than for older age groups, for males compared with females (1.64, 95% CI 1.30–2.04 v. 0.35, 95% CI 0.21–0.55), for those receiving a second dose compared with a third dose (1.90, 95% CI 1.50–2.39 v. 0.76, 95% CI 0.45–1.30) and for those who received the mRNA-1273 (Moderna) vaccine compared with the BNT162b2 (Pfizer-BioNTech) vaccine (1.44, 95% CI 1.06–1.91 v. 0.74, 95% CI 0.56–0.98). The highest observed-to-expected ratio was seen after the second dose among males aged 18–29 years who received the mRNA-1273 vaccine (148.32, 95% CI 95.03–220.69). Although absolute rates of myocarditis were low, vaccine type, age and sex are important factors to consider when strategizing vaccine administration to reduce the risk of postvaccination myocarditis. Our findings support the preferential use of the BNT162b2 vaccine over the mRNA-1273 vaccine for people aged 18–29 years.

Background Vibrio growth in the environment is related to sea surface temperature (SST). The incidence of human Vibrio illness increased sharply in British Columbia (BC) between 2008 and 2015 for unknown reasons, culminating in the largest outbreak of shellfish-associated Vibrio parahaemolyticus (Vp) in Canadian history in 2015. Our objective was to assess the relationship between SST and Vibrio illness in BC, Canada during 1992–2017 and assess the role of SST and other environmental factors in the 2015 Vp outbreak. Methods Cases of Vibrio infection reported to the BC Centre for Disease Control during 1992–2017 were used. SST data were obtained from NOAA and NASA. We assessed changes in incidence trend of annual Vibrio cases during 1992–2017 using a Poisson regression. We assessed the correlation between annual Vibrio cases and the average annual maximum SST using a Spearman rank-order correlation. We modeled the association between weekly Vp case counts, SST and other environmental factors during 2007–2017 using a Poisson regression. Results There was a significant increase in Vibrio cases between 2008 and 2015 (annual slope = 0.163, P  < 0.001). Increased Vibrio incidence was observed in most El Niño years. There was a significant correlation between annual Vibrio cases and maximum SST from 1992 to 2017 ( r  = 0.46, P  = 0.018). Our model captured observed seasonal variation in shellfish-associated Vp in most years, but underestimated the 2015 Vp outbreak. Conclusions Vibrio incidence has been increasing concurrently with increasing SST in BC during 2008–2015. The 2015 Vp outbreak was not fully explained by climatic factors and may in part have been associated with other factors. Vp subtyping would be useful in the future to understand the combined effects of SST changes and strain emergence.

Diet has a significant effect on pathogen infections in animals and the consumption of secondary metabolites can either enhance or mitigate infection intensity. Secondary metabolites, which are commonly associated with herbivore defense, are also frequently found in floral nectar. One hypothesized function of this so-called toxic nectar is that it has antimicrobial properties, which may benefit insect pollinators by reducing the intensity of pathogen infections. We tested whether gelsemine, a nectar alkaloid of the bee-pollinated plant Gelsemium sempervirens, could reduce pathogen loads in bumble bees infected with the gut protozoan Crithidia bombi. In our first laboratory experiment, artificially infected bees consumed a daily diet of gelsemine post-infection to simulate continuous ingestion of alkaloid-rich nectar. In the second experiment, bees were inoculated with C. bombi cells that were pre-exposed to gelsemine, simulating the direct effects of nectar alkaloids on pathogen cells that are transmitted at flowers. Gelsemine significantly reduced the fecal intensity of C. bombi 7 days after infection when it was consumed continuously by infected bees, whereas direct exposure of the pathogen to gelsemine showed a non-significant trend toward reduced infection. Lighter pathogen loads may relieve bees from the behavioral impairments associated with the infection, thereby improving their foraging efficiency. If the collection of nectar secondary metabolites by pollinators is done as a means of self-medication, pollinators may selectively maintain secondary metabolites in the nectar of plants in natural populations.

The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our modeling framework has the flexibility of determining mobility between regions based on the distances between the regions or using data from mobile devices. In addition, our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in Metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions.

A Canadian health authority implemented a multisectoral intervention designed to control severe acute respiratory coronavirus virus 2 (SARS-CoV-2) transmission during long-term care facility (LTCF) outbreaks. The primary objective was to evaluate the effectiveness of the intervention 14 days after implementation. Quasi-experimental, segmented regression analysis. A series of outbreak measures classified into 4 categories: case and contact management, proactive case detection, rigorous infection control practices and resource prioritization and stewardship. A mixed-effects segmented Poisson regression model was fitted to the incidence rate of coronavirus disease 2019 (COVID-19), calculated every 2 days, within each facility and case type (staff vs residents). For each facility, the outbreak time period was segmented into an early outbreak period (within 14 days of the intervention) and postintervention period (beyond 14 days following the intervention). Model outputs quantified COVID-19 incidence trend and rate changes between these 2 periods. A secondary model was constructed to identify effect modification by case type. The significant upward trend in COVID-19 incidence rate during the early outbreak period (rate ratio [RR], 1.07; 95% confidence interval [CI], 1.03-1.11; P < .001) reversed during the postintervention period (RR, 0.73; 95% CI, 0.67-0.80; P < .001). The average trend did not differ by case type during the early outbreak period (P > .05) or the postintervention period (P > .05). However, staff had a 70% larger decrease in the average rate of COVID-19 during the postintervention period than residents (RR, 0.30; 95% CI, 0.10-0.88; P < .05). Our study provides evidence for the effectiveness of this intervention to reduce the transmission of COVID-19 in LTCFs. This intervention can be adapted and utilized by other jurisdictions to protect the vulnerable individuals in LTCFs.

ObjectivesThis study describes the 2016 expansion of the British Columbia Take Home Naloxone (BCTHN) programme quantitatively and explores the challenges, facilitators and successes during the ramp up from the perspectives of programme stakeholders.DesignMixed-methods study.SettingThe BCTHN programme was implemented in 2012 to reduce opioid overdose deaths by providing naloxone kits and overdose recognition and response training in BC, Canada. An increase in the number of overdose deaths in 2016 in BC led to the declaration of a public health emergency and a rapid ramp up of naloxone kit production and distribution. BCTHN distributes naloxone to the five regional health authorities of BC.ParticipantsFocus groups and key informant interviews were conducted with 18 stakeholders, including BC Centre for Disease Control staff, urban and rural site coordinators, and harm reduction coordinators from the five regional health authorities across BC.Primary and secondary outcome measuresTake Home Naloxone (THN) programme activity, qualitative themes and lessons learnt were identified.ResultsIn 2016, BCTHN responded to a 20-fold increase in demand of naloxone kits and added over 300 distribution sites. Weekly numbers of overdose events and overdose deaths were correlated with increases in THN kits ordered the following week, during 2013–2017. Challenges elicited include forecasting demand, operational logistics, financial, manpower and policy constraints. Facilitators included outsourcing kit production, implementing standing orders and policy changes in naloxone scheduling, which allowed for easier hiring of staff, reduced paperwork and expanded client access.ConclusionFor THN programmes preparing for potential increases in naloxone demand, we recommend creating an online database, implementing standing orders and developing online training resources for standardised knowledge translation to site staff and clients.