Examining the effects of voluntary avoidance behaviour and policy-mediated behaviour change on the dynamics of SARS-CoV-2: A mathematical model.

Background: Throughout the SARS-CoV-2 pandemic, policymakers have had to navigate between recommending voluntary behaviour change and policy-driven behaviour change to mitigate the impact of the virus. While individuals will voluntarily engage in self-protective behaviour when there is an increasing infectious disease risk, the extent to which this occurs and its impact on an epidemic is not known. Methods: This paper describes a deterministic disease transmission model exploring the impact of individual avoidance behaviour and policy-mediated avoidance behaviour on epidemic outcomes during the second wave of SARS-CoV-2 infections in Ontario, Canada (September 1, 2020 to February 28, 2021). The model incorporates an information feedback function based on empirically derived behaviour data describing the degree to which avoidance behaviour changed in response to the number of new daily cases COVID-19. Results: Voluntary avoidance behaviour alone was estimated to reduce the final attack rate by 23.1%, the total number of hospitalizations by 26.2%, and cumulative deaths by 27.5% over 6 months compared to a counterfactual scenario in which there were no interventions or avoidance behaviour. A provincial shutdown order issued on December 26, 2020 was estimated to reduce the final attack rate by 66.7%, the total number of hospitalizations by 66.8%, and the total number of deaths by 67.2% compared to the counterfactual scenario. Conclusion: Given the dynamics of SARS-CoV-2 in a pre-vaccine era, individual avoidance behaviour in the absence of government action would have resulted in a moderate reduction in disease however, it would not have been sufficient to entirely mitigate transmission and the associated risk to the population in Ontario. Government action during the second wave of the COVID-19 pandemic in Ontario reduced infections, protected hospital capacity, and saved lives.

Forecasting seasonal influenza activity in Canada-Comparing seasonal Auto-Regressive integrated moving average and artificial neural network approaches for public health preparedness.

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.

An Evaluation of the Impact of an OPEN Stewardship Generated Feedback Intervention on Antibiotic Prescribing among Primary Care Veterinarians in Canada and Israel.

An interrupted time-series study design was implemented to evaluate the impact of antibiotic stewardship interventions on antibiotic prescribing among veterinarians. A total of 41 veterinarians were enrolled in Canada and Israel and their prescribing data between 2019 and 2021 were obtained. As an intervention, veterinarians periodically received three feedback reports comprising feedback on the participants' antibiotic prescribing and prescribing guidelines. A change in the level and trend of antibiotic prescribing after the administration of the intervention was compared using a multi-level generalized linear mixed-effect negative-binomial model. After the receipt of the first (incidence rate ratios [IRR] = 0.88; 95% confidence interval (CI): 0.79, 0.98), and second (IRR = 0.85; 95% CI: 0.75, 0.97) feedback reports, there was a reduced prescribing rate of total antibiotic when other parameters were held constant. This decline was more pronounced among Israeli veterinarians compared to Canadian veterinarians. When other parameters were held constant, the prescribing of critical antibiotics by Canadian veterinarians decreased by a factor of 0.39 compared to that of Israeli veterinarians. Evidently, antibiotic stewardship interventions can improve antibiotic prescribing in a veterinary setting. The strategy to sustain the effect of feedback reports and the determinants of differences between the two cohorts should be further explored.

Development and validation of a farm- and province-level swine flow simulation model using discrete events and Ontario swine farm and provincial input data.

Infectious disease events can cause disruptions in service-based and agricultural industries. The list of possible events is long and varies from the incursion or emergence of a reportable animal pathogen to the recently documented interruptions caused by the COVID-19 pandemic. There is a need to develop models that can determine the impact of pathogens and mitigation measures on populations that are not directly affected by the pathogen in the case of a reportable disease, particularly when the health and welfare of these populations could be affected due to resulting disruptions in trade and supply chains. The primary objective of this study was to develop a discrete-event simulation (DES) model of swine production, including pork processing, for scenarios without major disruptions, which could be scaled from the level of an individual farm to the entire province of Ontario, Canada. The secondary objective was to validate the developed simulation against observed farm- and province-level statistics. A weekly discrete-event simulation consisting of 3 connected areas (a sow farm, a pig farm, and abattoirs) was developed using AnyLogic modelling software. Using Mann-Whitney tests, model outputs representative of the standard industry statistics were compared to data from 6 individual farms separately, as well as to provincial data from Ontario. A scalable discrete-event simulation of the swine production system for typical scenarios was accomplished. The model outputs were consistent with individual farm and industry statistics. As such, the model can be used to simulate swine production at distinct levels and could be further modified to represent swine marketing in other provinces or internationally.

Les maladies infectieuses peuvent provoquer des perturbations dans les industries de services et agricoles. La liste des événements possibles est longue et varie de l'arrivée ou de l'émergence d'un agent pathogène animal à déclaration obligatoire aux interruptions récemment documentées causées par la pandémie de COVID-19. Il est nécessaire d'élaborer des modèles permettant de déterminer l'impact des agents pathogènes et des mesures d'atténuation sur les populations qui ne sont pas directement affectées par l'agent pathogène dans le cas d'une maladie à déclaration obligatoire, en particulier lorsque la santé et le bien-être de ces populations pourraient être affectés en raison des conséquences dues aux perturbations du commerce et des chaînes d'approvisionnement. L'objectif principal de cette étude était de développer un modèle de simulation à événements discrets (DES) de la production porcine, y compris la transformation du porc, pour des scénarios sans perturbations majeures, qui pourraient être étendus du niveau d'une ferme individuelle à l'ensemble de la province de l'Ontario, Canada. L'objectif secondaire était de valider la simulation développée par rapport aux statistiques observées au niveau de la ferme et de la province. Une simulation à événements discrets hebdomadaire composée de 3 zones connectées (un élevage de truies, un élevage de porcs et des abattoirs) a été développée à l'aide du logiciel de modélisation AnyLogic. À l'aide des tests de Mann-Whitney, les résultats du modèle représentatifs des statistiques standards de l'industrie ont été comparés aux données de 6 fermes individuelles séparément, ainsi qu'aux données provinciales de l'Ontario. Une simulation à événements discrets évolutive du système de production porcine pour des scénarios typiques a été réalisée. Les résultats du modèle étaient cohérents avec les statistiques individuelles des exploitations et des industries. Ainsi, le modèle peut être utilisé pour simuler la production porcine à des niveaux distincts et pourrait être modifié davantage pour représenter la commercialisation du porc dans d'autres provinces ou à l'échelle internationale.(Traduit par Docteur Serge Messier).

Is scientific evidence enough? Using expert opinion to fill gaps in data in antimicrobial resistance research.

BACKGROUND: Antimicrobial Resistance (AMR) is a global problem with large health and economic consequences. Current gaps in quantitative data are a major limitation for creating models intended to simulate the drivers of AMR. As an intermediate step, expert knowledge and opinion could be utilized to fill gaps in knowledge for areas of the system where quantitative data does not yet exist or are hard to quantify. Therefore, the objective of this study was to identify quantifiable data about the current state of the factors that drive AMR and the strengths and directions of relationships between the factors from statements made by a group of experts from the One Health system that drives AMR development and transmission in a European context. METHODS: This study builds upon previous work that developed a causal loop diagram of AMR using input from two workshops conducted in 2019 in Sweden with experts within the European food system context. A secondary analysis of the workshop transcripts was conducted to identify semi-quantitative data to parameterize drivers in a model of AMR. MAIN FINDINGS: Participants spoke about AMR by combining their personal experiences with professional expertise within their fields. The analysis of participants' statements provided semi-quantitative data that can help inform a future of AMR emergence and transmission based on a causal loop diagram of AMR in a Swedish One Health system context. CONCLUSION: Using transcripts of a workshop including participants with diverse expertise across the system that drives AMR, we gained invaluable insight into the past, current, and potential future states of the major drivers of AMR, particularly where quantitative data are lacking.

Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020.

Background: Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada. Design and methods: We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period. Results: For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test. Conclusions: Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.

Psychology Meets Biology in COVID-19: What We Know and Why It Matters for Public Health.

Psychosocial factors are related to immune, viral, and vaccination outcomes. Yet, this knowledge has been poorly represented in public health initiatives during the COVID-19 pandemic. This review provides an overview of biopsychosocial links relevant to COVID-19 outcomes by describing seminal evidence about these associations known prepandemic as well as contemporary research conducted during the pandemic. This focuses on the negative impact of the pandemic on psychosocial health and how this in turn has likely consequences for critically relevant viral and vaccination outcomes. We end by looking forward, highlighting the potential of psychosocial interventions that could be leveraged to support all people in navigating a postpandemic world and how a biopsychosocial approach to health could be incorporated into public health responses to future pandemics.

Descriptive network analysis and the influence of timescale on centrality and cohesion metrics from a system of between-herd dairy cow movements in Ontario, Canada.

Previous research has demonstrated that static monthly networks of between-herd dairy cow movements in Ontario, Canada were highly fragmented, reducing potential for large-scale outbreaks. Extrapolating results from static networks can become problematic for diseases with an incubation period that exceeds the timescale of the network. The objectives of this research were to: 1) describe the networks of dairy cow movements in Ontario, and 2) describe the changes that occur among network analysis metrics when conducted at seven different timescales. Networks of dairy cow movements were created using Lactanet Canada milk recording data collected in Ontario between 2009 and 2018. Centrality and cohesion metrics were calculated after aggregating the data at seven timescales: weekly, monthly, semi-annual, annual, biennial, quinquennial, and decennial. There were 50,598 individual cows moved between Lactanet-enrolled farms, representing approximately 75% of provincially registered dairy herds. Most movements occurred over short distances (median = 39.18 km), with fewer long-range movements (maximum = 1150.80 km). The number of arcs increased marginally relative to the number of nodes with longer network timescales. Both mean out-degree, and mean clustering coefficients increased disproportionately with increasing timescale. Conversely, mean network density decreased with increasing timescale. The largest weak and strong components at the monthly timescale were small relative to the full network (267 and 4 nodes), whereas yearly networks had much higher values (2213 and 111 nodes). Higher relative connectivity in networks with longer timescales suggests pathogens with long incubation periods and animals with subclinical infection present increased potential for wide-spread disease transmission among dairy farms in Ontario. Careful consideration of disease-specific dynamics should be made when using static networks to model disease transmission among dairy cow populations.

Estimating the test-adjusted incidence of Chlamydia trachomatis infections identified through Public Health Ontario Laboratories in Peel region, Ontario, 2010-2018: a population-based study.

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.

Pandemic fatigue or enduring precautionary behaviours? Canadians' long-term response to COVID-19 public health measures.

The long-term dynamics of COVID-19 disease incidence and public health measures may impact individuals' precautionary behaviours as well as support for measures. The objectives of this study were to assess longitudinal changes in precautionary behaviours and support for public health measures. Survey data were collected online from 1030 Canadians in each of 5 cycles in 2020: June 15-July 13; July 22-Aug 8; Sept 7-15; Oct 14-21; and Nov 12-17. Precautionary behaviour increased over the study period in the context of increasing disease incidence. When controlling for the stringency of public health measures and disease incidence, mixed effects logistic regression models showed these behaviours did not significantly change over time. Odds ratios for avoiding contact with family and friends ranged from 0.84 (95% CI 0.59-1.20) in September to 1.25 (95% CI 0.66-2.37) in November compared with July 2020. Odds ratios for attending an indoor gathering ranged from 0.86 (95% CI 0.62-1.20) in August to 1.71 (95% CI 0.95-3.09) in October compared with July 2020. Support for non-essential business closures increased over time with 2.33 (95% CI 1.14-4.75) times higher odds of support in November compared to July 2020. Support for school closures declined over time with lower odds of support in September (OR 0.66 [95% CI 0.45-0.96]), October (OR 0.48 [95% CI 0.26-0.87]), and November (OR 0.39 [95% CI 0.19-0.81]) compared with July 2020. In summary, respondents' behaviour mirrored government guidance between July and November 2020 and supported individual precautionary behaviour and limitations on non-essential businesses over school closures.

Seasonality of influenza and coseasonality with avian influenza in Bangladesh, 2010-19: a retrospective, time-series analysis.

BACKGROUND: Seasonal and avian influenza viruses circulate among human and poultry populations in Bangladesh. However, the epidemiology of influenza is not well defined in this setting. We aimed to characterise influenza seasonality, examine regional heterogeneity in transmission, and evaluate coseasonality between circulating influenza viruses in Bangladesh. METHODS: In this retrospective, time-series study, we used data collected between January, 2010, and December, 2019, from 32 hospital-based influenza surveillance sites across Bangladesh. We estimated influenza peak timing and intensity in ten regions using negative binomial harmonic regression models, and applied meta-analytic methods to determine whether seasonality differed across regions. Using live bird market surveillance data in Dhaka, Bangladesh, we estimated avian influenza seasonality and examined coseasonality between human and avian influenza viruses. FINDINGS: Over the 10-year study period, we included 8790 human influenza cases and identified a distinct influenza season, with an annual peak in June to July each year (peak calendar week 27·6, 95% CI 26·7-28·6). Epidemic timing varied by region (I2=93·9%; p<0·0001), with metropolitan regions peaking earlier and epidemic spread following a spatial diffusion pattern based on geographical proximity. Comparatively, avian influenza displayed weak seasonality, with moderate year-round transmission and a small peak in April (peak calendar week 14·9, 95% CI 13·2-17·0), which was out of phase with influenza peaks in humans. INTERPRETATION: In Bangladesh, influenza prevention and control activities could be timed with annual seasonality, and regional heterogeneity should be considered in health resource planning. Year-round avian influenza transmission poses a risk for viral spillover, and targeted efforts will be crucial for mitigating potential reassortment and future pandemic threats. FUNDING: Canadian Institute of Health Research Vanier Canada Graduate Scholarship.

Examining the role of person-to-person transmission during a verocytotoxigenic Escherichia coli outbreak in Ontario, Canada.

OBJECTIVE: Person-to-person transmission can occur during outbreaks of verotoxigenic Escherichia coli (VTEC), however the impact of this transmission route is not well understood. This study aimed to examine the role of person-to-person transmission during a VTEC outbreak, and how targeting this route may reduce outbreak size. A deterministic compartmental model describing a VTEC outbreak was constructed and fit to data from a 2008 outbreak in Ontario, Canada. Using the best-fit model, simulations were run to calculate the: reduction in transmission rate after implementing interventions, proportion of cases infected through both transmission routes, and number of cases prevented by interventions. Latin hypercube sensitivity analysis was conducted to examine the sensitivity of the outbreak size to the model parameters. RESULTS: Based on the best-fit model, ~ 14.25% of the cases likely arose due to person-to-person transmission. Interventions reduced this transmission rate by ~ 73%, causing a reduction in outbreak size of ~ 17% (47 cases). Sensitivity analysis showed that the model was highly sensitive to changes in all parameters of the model. The model demonstrates that person-to-person could be an important transmission route during VTEC outbreaks. Targeting this route of transmission through hand hygiene and work exclusions could reduce the final outbreak size.

Sporadic SARS-CoV-2 cases at the neighbourhood level in Toronto, Ontario, 2020: a spatial analysis of the early pandemic period.

BACKGROUND: As the largest city in Canada, Toronto has played an important role in the dynamics of SARS-CoV-2 transmission in Ontario, and the burden of disease across Toronto neighbourhoods has shown considerable heterogeneity. The purpose of this study was to investigate the spatial variation of sporadic SARS-CoV-2 cases in Toronto neighbourhoods by detecting clusters of increased risk and investigating effects of neighbourhood-level risk factors on rates. METHODS: Data on sporadic SARS-CoV-2 cases, at the neighbourhood level, for Jan. 25 to Nov. 26, 2020, were obtained from the City of Toronto COVID-19 dashboard. We used a flexibly shaped spatial scan to detect clusters of increased risk of sporadic COVID-19. We then used a generalized linear geostatistical model to investigate whether average household size, population density, dependency ratio and prevalence of low-income households were associated with sporadic SARS-CoV-2 rates. RESULTS: We identified 3 clusters of elevated risk of SARS-CoV-2 infection, with standardized morbidity ratios ranging from 1.59 to 2.43. The generalized linear geostatistical model found that average household size (relative risk [RR] 2.17, 95% confidence interval [CI] 1.80-2.61) and percentage of low-income households (RR 1.03, 95% CI 1.02-1.04) were significant predictors of sporadic SARS-CoV-2 cases at the neighbourhood level. INTERPRETATION: During the study period, 3 clusters of increased risk of sporadic SARS-CoV-2 infection were identified, and average household size and percentage of low-income households were found to be associated with sporadic SARS-CoV-2 rates at the neighbourhood level. The findings of this study can be used to target resources and create policy to address inequities that are shown through heterogeneity of SARS-CoV-2 cases at the neighbourhood level in Toronto, Ontario.

Ranavirus Amplification in Low-Diversity Amphibian Communities.

In an era where emerging infectious diseases are a serious threat to biodiversity, epidemiological patterns need to be identified, particularly the complex mechanisms driving the dynamics of multi-host pathogens in natural communities. Many amphibian species have faced unprecedented population declines associated with diseases. Yet, specific processes shaping host-pathogen relationships within and among communities for amphibian pathogens such as ranaviruses (RV) remain poorly understood. To address this gap, we conducted a comprehensive study of RV in low-diversity amphibian communities in north-western Canada to assess the effects of biotic factors (species identity, species richness, abundance) and abiotic factors (conductivity, pH) on the pathogen prevalence and viral loads. Across 2 years and 18 sites, with communities of up to three hosts (wood frog, Rana sylvatica; boreal chorus frog, Pseudacris maculata; Canadian toad, Anaxyrus hemiophrys), we observed that RV prevalence nearly doubled with each additional species in a community, suggesting an amplification effect in aquatic, as well as terrestrial life-history stages. Infection intensity among infected wood frogs and boreal chorus frogs also significantly increased with an increase in species richness. Interestingly, we did not observe any effects of host abundance or abiotic factors, highlighting the importance of including host identity and species richness when investigating multi-host pathogens. Ultimately, only such a comprehensive approach can improve our understanding of complex and often highly context-dependent host-pathogen interactions.

Modelling the introduction and transmission of Campylobacter in a North American chicken flock.

Campylobacter is the second leading cause of foodborne illness in the United States. Although many food production animals carry Campylobacter as commensal bacteria, consumption of poultry is the main source of human infection. Previous research suggests that the biology of Campylobacter results in complete flock colonization within days. However, a recent systematic review found that the on-farm prevalence of Campylobacter varies widely, with some flocks reporting low prevalence. We hypothesized that the low prevalence of Campylobacter in some flocks may be driven by a delayed introduction of the pathogen. The objectives of this study were to (a) develop a deterministic compartmental model that represents the biology of Campylobacter, (b) identify the parameter values that best represent the natural history of the pathogen in poultry flocks and (c) examine the possibility that a delayed introduction of the pathogen is sufficient to replicate the observed low prevalence examples documented in the literature. A deterministic compartmental model was developed to examine the dynamics of Campylobacter in chicken flocks over a 56-day time period prior to movement to the abattoir. The model outcome of interest was the final population prevalence of Campylobacter at day 56. The resulting model that incorporated a high transmission rate (ß = 1.04) was able to reproduce the wide range of prevalence estimates observed in the literature when pathogen introduction time is varied. Overall, we established that the on-farm transmission rate of Campylobacter in chickens is likely high and can result in complete colonization of a flock when introduced early. However, delaying the time at which the pathogen enters the flock can reduce the prevalence observed at 56 days. These results highlight the importance of enforcing strict biosecurity measures to prevent or delay the introduction of the bacteria to a flock.

Epidemiology of norovirus and viral gastroenteritis in Ontario, Canada, 2009-2014.

BACKGROUND: Norovirus is the most common cause of acute gastroenteritis in Canada. The illness causes great morbidity and high societal costs. The objective of this article is to describe the epidemiology of norovirus in the province of Ontario, Canada from 2009 to 2014. METHODS: To assess activity of norovirus and viral gastroenteritis (VGE) in Ontario, three datasets were acquired from the provincial government: two traditional surveillance datasets (outbreak and laboratory) and syndromic surveillance data (telehealth), all spanning 2009-2014. All outbreaks, laboratory submissions and telehealth calls were first assessed for total VGE. Norovirus and norovirus-like illness totals were calculated as a proportion of VGE to estimate agent-specific activity levels. Affected institution types, sexes and age groups were also analyzed. RESULTS: Between 2009 and 2014, 41.5% of VGE outbreaks, 63.4% of VGE laboratory submissions and 36.6% of all acute gastroenteritis-related (not restricted to viral causes) telehealth calls were attributed to norovirus and norovirus-like illness in Ontario. The most commonly affected institution type was long-term care homes and the most commonly affected age groups were younger (younger than five years) and older (older than 65 years) individuals. Females were slightly more frequently affected than males. CONCLUSION: Norovirus and norovirus-like illnesses were the leading cause of VGE in Ontario between 2009 and 2014. They comprised the greatest percentage of VGE when compared with all other VGE-associated viruses. Additional work is needed to determine all component costs and necessary public health actions to reduce the burden of disease.

Quantifying contact patterns in response to COVID-19 public health measures in Canada.

BACKGROUND: A variety of public health measures have been implemented during the COVID-19 pandemic in Canada to reduce contact between individuals. The objective of this study was to provide empirical contact pattern data to evaluate the impact of public health measures, the degree to which social contacts rebounded to normal levels, as well as direct public health efforts toward age- and location-specific settings. METHODS: Four population-based cross-sectional surveys were administered to members of a paid panel representative of Canadian adults by age, gender, official language, and region of residence during May (Survey 1), July (Survey 2), September (Survey 3), and December (Survey 4) 2020. A total of 4981 (Survey 1), 2493 (Survey 2), 2495 (Survey 3), and 2491 (Survey 4) respondents provided information about the age and setting for each direct contact made in a 24-h period. Contact matrices were constructed and contacts for those under the age of 18 years imputed. The next generation matrix approach was used to estimate the reproduction number (Rt) for each survey. Respondents with children under 18 years estimated the number of contacts their children made in school and extracurricular settings. RESULTS: Estimated Rt values were 0.49 (95% CI: 0.29-0.69) for May, 0.48 (95% CI: 0.29-0.68) for July, 1.06 (95% CI: 0.63-1.52) for September, and 0.81 (0.47-1.17) for December. The highest proportion of reported contacts occurred within the home (51.3% in May), in 'other' locations (49.2% in July) and at work (66.3 and 65.4% in September and December). Respondents with children reported an average of 22.7 (95% CI: 21.1-24.3) (September) and 19.0 (95% CI 17.7-20.4) (December) contacts at school per day per child in attendance. CONCLUSION: The skewed distribution of reported contacts toward workplace settings in September and December combined with the number of reported school-related contacts suggest that these settings represent important opportunities for transmission emphasizing the need to support and ensure infection control procedures in both workplaces and schools.

Frequency and patterns of exposure to live poultry and the potential risk of avian influenza transmission to humans in urban Bangladesh.

Avian influenza is endemic in Bangladesh, where greater than 90% of poultry are marketed through live poultry markets (LPMs). We conducted a population-based cross-sectional mobile telephone survey in urban Dhaka, Bangladesh to investigate the frequency and patterns of human exposure to live poultry in LPMs and at home. Among 1047 urban residents surveyed, 74.2% (95% CI 70.9-77.2) reported exposure to live poultry in the past year, with the majority of exposure occurring on a weekly basis. While visiting LPMs was less common amongst females (40.3%, 95% CI 35.0-45.8) than males (58.9%, 95% CI 54.0-63.5), females reported greater poultry exposure through food preparation, including defeathering (13.2%, 95% CI 9.5-17.9) and eviscerating (14.8%, 95% CI 11.2-19.4) (p < 0.001). A large proportion of the urban population is frequently exposed to live poultry in a setting where avian influenza viruses are endemic in LPMs. There is thus not only ample opportunity for spillover of avian influenza infections into humans in Dhaka, Bangladesh, but also greater potential for viral reassortment which could generate novel strains with pandemic potential.

Population Health Surveillance Using Mobile Phone Surveys in Low- and Middle-Income Countries: Methodology and Sample Representativeness of a Cross-sectional Survey of Live Poultry Exposure in Bangladesh.

BACKGROUND: Population-based health surveys are typically conducted using face-to-face household interviews in low- and middle-income countries (LMICs). However, telephone-based surveys are cheaper, faster, and can provide greater access to hard-to-reach or remote populations. The rapid growth in mobile phone ownership in LMICs provides a unique opportunity to implement novel data collection methods for population health surveys. OBJECTIVE: This study aims to describe the development and population representativeness of a mobile phone survey measuring live poultry exposure in urban Bangladesh. METHODS: A population-based, cross-sectional, mobile phone survey was conducted between September and November 2019 in North and South Dhaka City Corporations (DCC), Bangladesh, to measure live poultry exposure using a stratified probability sampling design. Data were collected using a computer-assisted telephone interview platform. The call operational data were summarized, and the participant data were weighted by age, sex, and education to the 2011 census. The demographic distribution of the weighted sample was compared with external sources to assess population representativeness. RESULTS: A total of 5486 unique mobile phone numbers were dialed, with 1047 respondents completing the survey. The survey had an overall response rate of 52.2% (1047/2006) and a co-operation rate of 89.0% (1047/1176). Initial results comparing the sociodemographic profile of the survey sample to the census population showed that mobile phone sampling slightly underrepresented older individuals and overrepresented those with higher secondary education. After weighting, the demographic profile of the sample population matched well with the latest DCC census population profile. CONCLUSIONS: Probability-based mobile phone survey sampling and data collection methods produced a population-representative sample with minimal adjustment in DCC, Bangladesh. Mobile phone-based surveys can offer an efficient, economic, and robust way to conduct surveillance for population health outcomes, which has important implications for improving population health surveillance in LMICs.

Spatio-Temporal Variation in the Prevalence of Major Mastitis Pathogens Isolated From Bovine Milk Samples Between 2008 and 2017 in Ontario, Canada.

An understanding of the spatio-temporal distribution of several groups of mastitis pathogens can help to inform programs for the successful control and management of mastitis. However, in the absence of an active surveillance program such information is not readily available. In this retrospective study we analyzed passive surveillance data from a diagnostic laboratory with an aim to describe the spatio-temporal trend of major mastitis pathogens between 2008 and 2017 in Ontario dairy cattle. Data for all milk culture samples submitted to the Animal Health Laboratory (AHL) at the University of Guelph between 2008 and 2017 was accessed. Descriptive analyses were conducted to identify the major pathogens and Chi-square goodness-of-fit tests were used to compare between multiple proportions. Likewise, univariable logistic regression analysis was performed to determine if there was a change in the probability of isolating the major mastitis pathogens depending on geography or time. Seasonality was assessed by calculating the seasonal relative risk (RR). Of a total of 85,979 milk samples examined, more than half of the samples (61.07%) showed no growth and the proportion of samples that showed no growth almost halved during the study period. Of the samples (36.21%, n = 31,133) that showed any growth, the major bacterial pathogens were Staphylococcus aureus (15.60%), Non-aureus Staphylococci (NAS) (5.04%), Corynebacterium spp. (2.96%), and Escherichia coli (2.00%). Of the NAS, the major species reported were Staphylococcus chromogenes (69.02%), Staphylococcus simulans (14.45%), Staphylococcus epidermidis (12.99%), and Staphylococcus hyicus (2.13%). A temporal change in the prevalence of contagious pathogens like S. aureus and Corynebacterium spp. was observed with an increasing odds of 1.06 and 1.62, respectively. Likewise, except for Trueperella pyogenes, the prevalence of all the major environmental mastitis pathogens increased during the study period. The isolation of most of the pathogens peaked in summer, except for S. aureus, T. pyogenes, and Streptococcus dysgalactiae which peaked in spring months. Interestingly, a regional pattern of isolation of some bacterial pathogens within Ontario was also observed. This study showed a marked spatio-temporal change in the prevalence of major mastitis pathogens and suggests that a regional and seasonal approach to mastitis control could be of value.