SARS-COV-2 PANDEMIC

We have completed a large number of epidemiological and modeling projects related to the disease dynamics of SARS-CoV-2 in Canada. This work was funded by the Canada Research Chairs program, Public Health Agency of Canada, University of Guelph, the National Collaborating Centre for Infectious Diseases, and the NSERC-EIDM networks

Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in dromedary camels in Egypt.

Dromedary camels have been implicated as the zoonotic source and reservoir of MERS-CoV. However, we have only a limited understanding of the dynamics of MERS-CoV in the camel population, and ultimately how those dynamics influence the zoonotic risk of transmission. Preliminary insights into this host-pathogen system suggest that the natural history parameters for the pathogen in the camel reservoir may be much more variable and complex than previously thought. Important differences may be related to our assumptions about the role of immunity and re-infection.

EARLY DYNAMICS OF PORCINE EPIDEMIC DIARRHEA VIRUS (PEDV) IN ONTARIO

We have applied our previously published, Incidence Decay and Exponential Adjustment (IDEA) model to better understand the 2014 PEDV outbreak in Ontario. Using our simple, 2-parameter IDEA model, we have evaluated the early epidemic dynamics of PEDV on Ontario swine farms. We estimated the best-fit R0 and control parameter (d) for the farm-to-farm transmission component of the outbreak by fitting the model to publically available cumulative incidence data. 

THE ECOLOGY AND DEMOGRAPHY OF FREE ROAMING DOGS IN HIDALGO, MEXICO

We have developed an improved understanding of the ecology and demography of semi-urban domestic dog population, as well as dog ownership practices in Villa de Tezontepec, Hidalgo, Mexico. In semi-urban areas of Mexico, free-roaming dogs present a public health risk to humans. These findings are an essential first step for the design of effective dog population control and rabies vaccination programs.

Pandemic Planning

Our group was heavily involved in the public health response during the 2009 influenza A/H1N1 pandemic in Canada. We were a part of the first group to describe the epidemiological parameters during the pandemic using data from the beginning of the outbreak in Ontario, Canada. This work represented an important early collaboration between academics and provincial public health officials during a public health crisis. The research findings helped to guide provincial and federal decision-making regarding optimal public health intervention strategies throughout the pandemic period in Canada. This paper is a seminal paper from the 2009 pandemic and as a result has been widely cited by other authors since it was published.   

We have also completed modeling work in collaboration with various Canadian Pandemic Influenza Plan (CPIP) Task Groups coordinated by the Public Health Agency of Canada.  This is the first time that mathematical modellers have played a major role in developing models in direct collaboration with a federal working group for the express purpose of using models to help guide discussion and decision-making.

Vaccine Preventable Diseases

We have developed mathematical models to determine the impact of vaccination strategies for  vaccine-preventable diseases including pandemic influenza and pertussis. These models have allowed us to examine a variety of “what-if” scenarios regarding alternative vaccination strategies, uptake in different age groups and different vaccine efficacy to suggest how best to optimize a vaccine intervention in order to minimize overall morbidity and mortality and/or disease transmission in the population. This work has contributed to Canadian recommendations regarding the provision of pertussis booster vaccination to healthcare workers. Additional work on pandemic influenza vaccine strategies was a significant factor in the development of the Canadian pandemic vaccine prioritization list in 2009 and was recognized internationally in 2010 when Ashleigh Tuite and Amy Greer were awarded the Senior Lupina Prize for Dynamic Modelling in Health Policy.

Vulnerable Populations

The burden of infectious diseases in Canada is distributed in a geographically heterogeneous fashion with northern populations having higher burdens for a variety of well-documented reasons. We have demonstrated that pandemic influenza was in fact, far more transmissible in Nunavut communities than other areas. We used empirical data to demonstrate that the differential severity of the pandemic in these regions can be explained partly by differential transmissibility. This finding suggested the need for more nuanced, targeted or population-specific control strategies to be considered when a public health crisis affects our vulnerable northern populations. This work was completed with the support of the Nunavut Department of Health.  We maintain ongoing collaborations with both Nunavut and the Yukon Territory to provide modeling expertise for applied public health problems. We have worked with the Yukon Territory to examine the potential health impacts of increasing Chlamydia screening in men within the Territory. 

Modeling for Non-Technical Audiences & Knowledge Translation

During the first wave of the 2009 pandemic, there was much discussion about using mathematical models to better understand optimal intervention strategies for pandemic influenza. Many public health professionals and clinicians were not familiar with modeling and we organized a significant number of knowledge translation activities for non-technical audiences to provide an introduction to the topic. In addition, we have contributed extensively to the training of non-technical staff at the Public Health Agency of Canada so that individuals in policy related positions can more effectively communicate and collaborate with mathematical modelers both within the Agency and within academia to address important public health problems.

evaluating the impact of environmental factors on human enteric disease

The emerging One Health paradigm regards human, animal and environmental health as inextricably inter-related, but practical linkages between human and veterinary realms remain limited in Canada and elsewhere. The potential health, food safety, and economic benefits of improved public health intelligence that would result from linkage of human and animal surveillance systems are expected to be large, and data derived from the linkage of existing human and animal surveillance systems could be used to parameterize, calibrate and validate mathematical models of infectious diseases, for risk analysis, evaluation of cost-effectiveness of preventive activities and exploration of areas of uncertainty, which could be prioritized for future research. 

Using simulation models to examine foreign animal disease risk to Ontario swine

Foreign animal disease (FAD) introductions are rare events in North America; however, the impact would be devastating. Outbreaks in economically valuable animal populations have social and economic costs, and erode consumer confidence in food products. Managing a FAD event within a resource-constrained system represents an important challenge since many of the required interventions involve depopulation. FAD outbreaks are particularly important because of the direct impact on production and also because of the destruction of healthy animals due to welfare slaughter, lost exports to trading partners, and the risk of a long-term export ban. Preparation for the response and eradication efforts to address a FAD in the Ontario swine industry requires a better understanding of the scenarios that might occur and careful planning to minimize welfare slaughter. Using a computer simulation approach for the farmed swine population in Ontario, we are simulating the range of possible outcomes that might occur following an introduction of Classical Swine Fever and evaluate intervention strategies to contain the outbreak, and minimize welfare cull. This project will provide Ontario, Canada, and the Ontario swine industry with a set of outcomes that are actionable and supported by the best available evidence.