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Objectives: The effectiveness of public health interventions for mitigation of the COVID-19 pandemic depends on individual attitudes, compliance, and the level of support available to allow for compliance with these measures. The aim of this study was to describe attitudes and behaviours towards the Canadian COVID-19 public health response, and identify risk-modifying behaviours based on socio-demographic characteristics., Methods: A cross-sectional online survey was administered in May 2020 to members of a paid panel representative of the Canadian population by age, gender, official language, and region of residence. A total of 4981 respondents provided responses for indicators of self-reported risk perceptions, attitudes, and behaviours towards COVID-19 public health measures., Results: More than 90% of respondents reported confidence in the ability to comply with a variety of public health measures. However, only 51% reported preparedness for illness in terms of expectation to work if sick or access to paid sick days. Risk perceptions, attitudes, and behaviours varied by demographic variables. Men, younger age groups, and those in the paid workforce were less likely to consider public health measures to be effective, and had less confidence in their ability to comply. Approximately 80% of respondents reported that parents provided childcare and 52% reported that parents in the workforce provided childcare while schools were closed., Conclusion: Policies to help address issues of public adherence include targeted messaging for men and younger age groups, social supports for those who need to self-isolate, changes in workplace policies to discourage presenteeism, and provincially co-ordinated masking and safe school policies.

Background: Traditional processes for the production of pandemic influenza vaccines are not capable of producing a vaccine that could be deployed sooner than 5-6 months after strain identification. Plant-based vaccine technologies are of public health interest because they represent an opportunity to begin vaccinating earlier., Methods: We used an age- and risk- structured disease transmission model for Canada to evaluate the potential impact of a plant-produced vaccine available for rapid deployment (within 1-3 months) compared to an egg-based vaccine timeline., Results: We found that in the case of a mildly transmissible virus (R0 = 1.3), depending on the amount of plant-based vaccine produced per week, severe clinical outcomes could be decreased by 60-100 % if vaccine was available within 3 months of strain identification. However, in the case of a highly transmissible virus (R0 = 2.0), a delay of 3 months does not change clinical outcomes regardless of the level of weekly vaccine availability. If transmissibility is high, the only strategy that can impact clinical outcomes occurs if vaccine production is high and available within 2 months., Conclusions: Pandemic influenza vaccines produced by plants, change the timeline of pandemic vaccine availability in a way that could significantly mitigate the impact of the next influenza pandemic.

We describe the application of mathematical models in the study of disease epidemics with particular focus on pandemic influenza. We outline the general mathematical approach and the complications arising from attempts to apply it for disease outbreak management in a real public health context.

Introduction: Public health preparedness is based on timely and accurate information. Time series forecasting using disease surveillance data is an important aspect of preparedness. This study compared two approaches of time series forecasting: seasonal auto‐regressive integrated moving average (SARIMA) modelling and the artificial neural network (ANN) algorithm. The goal was to model weekly seasonal influenza activity in Canada using SARIMA and compares its predictive accuracy, based on root mean square prediction error (RMSE) and mean absolute prediction error (MAE), to that of an ANN. Methods: An initial SARIMA model was fit using automated model selection by minimizing the Akaike information criterion (AIC). Further inspection of the autocorrelation function and partial autocorrelation function led to 'manual' model improvements. ANNs were trained iteratively, using an automated process to minimize the RMSE and MAE. Results: A total of 378, 462 cases of influenza was reported in Canada from the 2010–2011 influenza season to the end of the 2019–2020 influenza season, with an average yearly incidence risk of 20.02 per 100,000 population. Automated SARIMA modelling was the better method in terms of forecasting accuracy (per RMSE and MAE). However, the ANN correctly predicted the peak week of disease incidence while the other models did not. Conclusion: Both the ANN and SARIMA models have shown to be capable tools in forecasting seasonal influenza activity in Canada. It was shown that applying both in tandem is beneficial, SARIMA better forecasted overall incidence while ANN correctly predicted the peak week. [ABSTRACT FROM AUTHOR]

Background: Public health guidelines for chlamydia testing are not sex specific, but young females test at a disproportionally higher rate than males and other age groups. This study aims to describe testing trends across age and sex subgroups, then estimate a test-adjusted incidence of chlamydia in these subgroups to identify gaps in current testing practices. Methods: We used a population-based study to examine observed chlamydia rates by age and sex subgroups: 15–19 years, 20–29 years, 30–39 years and older than 40 years. The study included diagnostic test results recorded by Public Health Ontario Laboratories between Jan. 1, 2010, and Dec. 31, 2018, for individuals living in Peel region, Ontario. We then employed meta-regression models as a method of standardization to estimate the effect of sex and age on standardized morbidity ratio, testing ratio and test positivity, then calculate a test-adjusted incidence of chlamydia for each subgroup. Results: Over the study period, infection, testing and test positivity varied across age and sex subgroups. Observed incidence and testing were highest in females aged 20–29 years, whereas males had the highest standardized test positivity across all age groups. After estimating test-adjusted incidence for each age–sex subgroup, males in the 15–19-year and 30–39-year age groups had an increase in incidence of 60.2% and 9.7%, respectively, compared with the observed incidence. Interpretation: We found that estimated test-adjusted incidence was higher than observed incidence in males aged 15–19 years and 30–39 years. This suggests that infections in males are likely being missed owing to differential testing, and this may be contributing to the persistent increase in reported cases in Canada. Public health programming that targets males, especially in high-risk settings and communities, and use of innovative partner notification methods could be critical to curbing overall rates of chlamydia. [ABSTRACT FROM AUTHOR]

Giardia duodenalis is an intestinal parasite which causes diarrhoeal illness in people. Zoonotic subtypes found in livestock may contribute to human disease occurrence through runoff of manure into multi-use surface water. This study investigated temporal associations among selected environmental variables and G. duodenalis occurrence in livestock reservoirs on human giardiasis incidence using data collected in the Waterloo Health Region, Ontario, Canada. The study objectives were to: (1) evaluate associations between human cases and environmental variables between 1 June 2006 and 31 December 2013, and (2) evaluate associations between human cases, environmental variables and livestock reservoirs using a subset of this time series, with both analyses controlling for seasonal and long-term trends. Human disease incidence exhibited a seasonal trend but no annual trend. A Poisson multivariable regression model identified an inverse association with water level lagged by 1 month (IRR = 0.10, 95% CI 0.01, 0.85, P < 0.05). Case crossover analysis found varying associations between lagged variables including livestock reservoirs (1 week), mean air temperature (3 weeks), river water level (1 week) and flow rate (1 week), and precipitation (4 weeks). This study contributes to our understanding of epidemiologic relationships influencing human giardiasis cases in Ontario, Canada. [ABSTRACT FROM AUTHOR]

Background: Middle East respiratory syndrome coronavirus (MERS-CoV) is endemic in dromedary camels in the Arabian Peninsula, and zoonotic transmission to people is a sporadic event. In the absence of epidemiological data on the reservoir species, patterns of zoonotic transmission have largely been approximated from primary human cases. This study aimed to identify meteorological factors that may increase the risk of primary MERS infections in humans.Methods: A case-crossover design was used to identify associations between primary MERS cases and preceding weather conditions within the 2-week incubation period in Saudi Arabia using univariable conditional logistic regression. Cases with symptom onset between January 2015 - December 2017 were obtained from a publicly available line list of human MERS cases maintained by the World Health Organization. The complete case dataset (N = 1191) was reduced to approximate the cases most likely to represent spillover transmission from camels (N = 446). Data from meteorological stations closest to the largest city in each province were used to calculate the daily mean, minimum, and maximum temperature (οC), relative humidity (%), wind speed (m/s), and visibility (m). Weather variables were categorized according to strata; temperature and humidity into tertiles, and visibility and wind speed into halves.Results: Lowest temperature (Odds Ratio = 1.27; 95% Confidence Interval = 1.04-1.56) and humidity (OR = 1.35; 95% CI = 1.10-1.65) were associated with increased cases 8-10 days later. High visibility was associated with an increased number of cases 7 days later (OR = 1.26; 95% CI = 1.01-1.57), while wind speed also showed statistically significant associations with cases 5-6 days later.Conclusions: Results suggest that primary MERS human cases in Saudi Arabia are more likely to occur when conditions are relatively cold and dry. This is similar to seasonal patterns that have been described for other respiratory diseases in temperate climates. It was hypothesized that low visibility would be positively associated with primary cases of MERS, however the opposite relationship was seen. This may reflect behavioural changes in different weather conditions. This analysis provides key initial evidence of an environmental component contributing to the development of primary MERS-CoV infections. [ABSTRACT FROM AUTHOR]

Middle East respiratory syndrome coronavirus (MERS‐CoV) is an emerging zoonotic pathogen discovered in 2012. The purpose of this scoping review was to summarize the empirical evidence for MERS‐CoV in animals in order to map knowledge gaps and to extract data for modelling disease transmission in dromedary camels. A review protocol was developed a priori, and a systematic search, data extraction and summary were conducted using the Arksey and O'Malley framework. Ninety‐nine publications were identified for full review out of 1,368 unique records. Of these publications, 71 were articles in scientific journals. Ninety of the studies were observational and the remaining nine were experimental. We summarize characteristics of animal studies including study design, study population and outcomes of interest for future transmission modelling in the reservoir population. The majority of field studies reported measures of prevalence, while experimental studies provided estimates of transmission parameters that pertain to the natural course of disease. [ABSTRACT FROM AUTHOR]

Background: In Canada, active tuberculosis (TB) disease rates remain disproportionately higher among the Indigenous population, especially among the Inuit in the north. We used mathematical modeling to evaluate how interventions might enhance existing TB control efforts in a region of Nunavut.Methods: We developed a stochastic, agent-based model of TB transmission that captured the unique household and community structure. Evaluated interventions included: (i) rapid treatment of active cases; (ii) rapid contact tracing; (iii) expanded screening programs for latent TB infection (LTBI); and (iv) reduced household density. The outcomes of interest were incident TB infections and total diagnosed active TB disease over a 10- year time period.Results: Model-projected incidence in the absence of additional interventions was highly variable (range: 33-369 cases) over 10 years. Compared to the 'no additional intervention' scenario, reducing the time between onset of active TB disease and initiation of treatment reduced both the number of new TB infections (47% reduction, relative risk of TB = 0.53) and diagnoses of active TB disease (19% reduction, relative risk of TB = 0.81). Expanding general population screening was also projected to reduce the burden of TB, although these findings were sensitive to assumptions around the relative amount of transmission occurring outside of households. Other potential interventions examined in the model (school-based screening, rapid contact tracing, and reduced household density) were found to have limited effectiveness.Conclusions: In a region of northern Canada experiencing a significant TB burden, more rapid treatment initiation in active TB cases was the most impactful intervention evaluated. Mathematical modeling can provide guidance for allocation of limited resources in a way that minimizes disease transmission and protects population health. [ABSTRACT FROM AUTHOR]

Background: Communicable disease outbreaks of novel or existing pathogens threaten human health around the globe. It would be desirable to rapidly characterize such outbreaks and develop accurate projections of their duration and cumulative size even when limited preliminary data are available. Here we develop a mathematical model to aid public health authorities in tracking the expansion and contraction of outbreaks with explicit representation of factors (other than population immunity) that may slow epidemic growth. Methodology: The Incidence Decay and Exponential Adjustment (IDEA) model is a parsimonious function that uses the basic reproduction number R0, along with a discounting factor to project the growth of outbreaks using only basic epidemiological information (e.g., daily incidence counts). Principal Findings: Compared to simulated data, IDEA provides highly accurate estimates of total size and duration for a given outbreak when R0 is low or moderate, and also identifies turning points or new waves. When tested with an outbreak of pandemic influenza A (H1N1), the model generates estimated incidence at the i+1th serial interval using data from the ith serial interval within an average of 20% of actual incidence. Conclusions and Significance: This model for communicable disease outbreaks provides rapid assessments of outbreak growth and public health interventions. Further evaluation in the context of real-world outbreaks will establish the utility of IDEA as a tool for front-line epidemiologists. [ABSTRACT FROM AUTHOR]

Background: The Canadian National Antiviral Stockpile (NAS) contains treatment for 17.5% of Canadians. This assumes no concurrent intervention strategies and no wastage due to non-influenza respiratory infections. A dynamic model can provide a mechanism to consider complex scenarios to support decisions regarding the optimal NAS size under uncertainty. Methods: We developed a dynamic model for pandemic influenza in Canada that is structured by age and risk to calculate the demand for antivirals to treat persons with pandemic influenza under a wide-range of scenarios that incorporated transmission dynamics, disease severity, and intervention strategies. The anticipated per capita number of acute respiratory infections due to viruses other than influenza was estimated for the full pandemic period from surveys based on criteria to identify potential respiratory infections. Results: Our results demonstrate that up to two thirds of the population could develop respiratory symptoms as a result of infection with a pandemic strain. In the case of perfect antiviral allocation, up to 39.8% of the population could request antiviral treatment. As transmission dynamics, severity and timing of the emergence of a novel influenza strain are unknown, the sensitivity analysis produced considerable variation in potential demand (median: 11%, IQR: 2–21%). If the next pandemic strain emerges in late spring or summer and a vaccine is available before the anticipated fall wave, the median prediction was reduced to 6% and IQR to 0.7–14%. Under the strategy of offering empirical treatment to all patients with influenza like symptoms who present for care, demand could increase to between 65 and 144%. Conclusions: The demand for antivirals during a pandemic is uncertain. Unless an accurate, timely and cost-effective test is available to identify influenza cases, demand for antivirals from persons infected with other respiratory viruses will be substantial and have a significant impact on the NAS. [ABSTRACT FROM AUTHOR]

Dog overpopulation in developing countries has negative implications for the health and safety of people, including the transmission of zoonotic diseases, physical attacks and intimidation to humans and animals, as well as impacts on canine welfare. Understanding the ecology and demographic characteristics of a dog population can help in the planning and monitoring of canine population control programs. Little data exist regarding demography and dynamics of domestic dog populations in semi-urban areas in Mexico. A cross-sectional study was carried out between October 21 and November 7, 2015, to characterize the dog ecology and demography in Villa de Tezontepec, Hidalgo, Mexico. A face-to-face survey was used to collect data from randomly selected households in four contiguous communities using stratified two-stage cluster sampling. Within each household, adults answered questions related to their dogs and their experiences with dog bites and aggression. A total of 328 households were interviewed, representing a participation rate of 90.9% (328/361) and 1,450 people. Approximately 65.2% of the households owned one or more dogs, with a mean of 1.3 (SD = 1.5) and 2.0 (SD = 1.5) owned dogs in all participant households and dog-owning households, respectively. The human: owned dog ratio for all participant households was 3.4:1 (1450/428), and for the dog-owning households was 2.3:1 (984/428). The owned dog male: female ratio was 1.4:1 (249/179). Approximately 74.4% (95.0% CI = 69.8% – 78.7%) of the owned dogs were older than one year (mean age: 2.9 years; SD = 2.5). The mean age of owned female dogs at first litter was 1.9 years (SD = 1.2) and the mean litter size was 4.2 puppies (SD = 2.1). Approximately 36.9% (95.0% CI = 31.8% – 46.4%) of the females were spayed, and 14.1% (95.0% CI = 10.7% – 19.7%) of the males were neutered. Only 44.9% (95.0% CI = 40.1% – 49.7%) were always confined when unsupervised. Approximately 84.4% (95.0% CI = 80.6% – 87.7%) were reported to have been vaccinated against rabies in 2015. The knowledge of owned dog demography and ecology provided by this study can inform local government planning of dog population control interventions, and could serve as a baseline for the development of agent-based models to evaluate the effects of different dog population control strategies on dog demography. [ABSTRACT FROM AUTHOR]