Amy Hurford’s research while affiliated with Memorial University of Newfoundland and other places

Background The COVID-19 pandemic underlined the need for pandemic planning but also brought into focus the use of mathematical modelling to support public health decisions. The types of models needed (compartment, agent-based, importation) are described. Best practices regarding biological realism (including the need for multidisciplinary expert advisors to modellers), model complexity, consideration of uncertainty and communications to decision-makers and the public are outlined. Methods A narrative review was developed from the experiences of COVID-19 by members of the Public Health Agency of Canada External Modelling Network for Infectious Diseases (PHAC EMN-ID), a national community of practice on mathematical modelling of infectious diseases for public health. Results Modelling can best support pandemic preparedness in two ways: 1) by modelling to support decisions on resource needs for likely future pandemics by estimating numbers of infections, hospitalized cases and cases needing intensive care, associated with epidemics of “hypothetical-yet-plausible” pandemic pathogens in Canada; and 2) by having ready-to-go modelling methods that can be readily adapted to the features of an emerging pandemic pathogen and used for long-range forecasting of the epidemic in Canada, as well as to explore scenarios to support public health decisions on the use of interventions. Conclusion There is a need for modelling expertise within public health organizations in Canada, linked to modellers in academia in a community of practice, within which relationships built outside of times of crisis can be applied to enhance modelling during public health emergencies. Key challenges to modelling for pandemic preparedness include the availability of linked public health, hospital and genomic data in Canada.

Setting: Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, modelling approaches, and data infrastructure across provinces provides a unique opportunity to understand the factors that shaped modelling strategies. Intervention: Provinces implemented stringent pandemic interventions to mitigate SARS-CoV-2 transmission, considering evidence from epidemic models. This study aimed to summarize provincial COVID-19 modelling efforts. We identified modelling teams working with provincial decision-makers, through referrals and membership in Canadian modelling networks. Information on models, data sources, and knowledge translation were abstracted using standardized instruments. Outcomes: We obtained information from six provinces. For provinces with sustained community transmission, initial modelling efforts focused on projecting epidemic trajectories and healthcare demands, and evaluating impacts of proposed interventions. In provinces with low community transmission, models emphasized quantifying importation risks. Most of the models were compartmental and deterministic, with projection horizons of a few weeks. Models were updated regularly or replaced by new ones, adapting to changing local epidemic dynamics, pathogen characteristics, vaccines, and requests from public health. Surveillance datasets for cases, hospitalizations and deaths, and serological studies were the main data sources for model calibration. Access to data for modelling and the structure for knowledge translation differed markedly between provinces. Implication: Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts and modulated by available resources. Strengthening Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and ensuring earlier access to linked and timely surveillance data could help improve pandemic preparedness.

Public health responses to the COVID-19 pandemic varied across the world. Some countries (e.g. mainland China, New Zealand and Taiwan) implemented elimination strategies involving strict travel measures and periods of rigorous non-pharmaceutical interventions (NPIs) in the community, aiming to achieve periods with no disease spread; while others (e.g. many European countries and the USA) implemented mitigation strategies involving less strict NPIs for prolonged periods, aiming to limit community spread. Travel measures and community NPIs have high economic and social costs, and there is a need for guidelines that evaluate the appropriateness of an elimination or mitigation strategy in regional contexts. To guide decisions, we identify key criteria and provide indicators and visualizations to help answer each question. Considerations include determining whether disease elimination is: (1) necessary to ensure healthcare provision; (2) feasible from an epidemiological point of view and (3) cost-effective when considering, in particular, the economic costs of travel measures and treating infections. We discuss our recommendations by considering the regional and economic variability of Canadian provinces and territories, and the epidemiological characteristics of different SARS-CoV-2 variants. While elimination may be a preferable strategy for regions with limited healthcare capacity, low travel volumes, and few ports of entry, mitigation may be more feasible in large urban areas with dense infrastructure, strong economies, and with high connectivity to other regions.

Mosquitoes are important vectors for the transmission of some major infectious diseases of humans, i.e., malaria, dengue, West Nile Virus and Zika virus. The burden of these diseases is different for different regions, being highest in tropical and subtropical areas, which have high annual rainfall, warm temperatures, and less pronounced seasonality. The life cycle of mosquitoes consists of four distinct stages: eggs, larvae, pupae, and adults. These life stages have different mortality rates and only adults can reproduce. Seasonal weather may affect the population dynamics of mosquitoes, and the relative abundance of different mosquito stages. We developed a stage-structured model that considers laboratory experiments describing how temperature and rainfall affects the reproduction, maturation and survival of different Anopheles mosquito stages, the species that transmits the parasite that causes malaria. We consider seasonal temperature and rainfall patterns and describe the stage-structured population dynamics of the Anopheles mosquito in Ain Mahbel, Algeria, Cape Town, South Africa, Nairobi, Kenya and Kumasi, Ghana. We find that neglecting seasonality leads to significant overestimation or underestimation of mosquito abundance. We find that depending on the region, mosquito abundance: peaks one, two or four times a year, periods of low abundance are predicted to occur for durations ranging from six months (Ain Mahbel) to not at all (Nairobi); and seasonal patterns of relative abundance of stages are substantially different. The region with warmer temperatures and higher rainfall across the year, Kumasi, Ghana, is predicted to have higher mosquito abundance, which is broadly consistent with reported malaria deaths relative to the other countries considered by our study. Our analysis reveals distinct patterns in mosquito abundance across different months and regions. Control strategies often target one specific life stage, for example, applying larvicides to kill mosquito larvae, or spraying insecticides to kill adult mosquitoes. Our findings suggest that differences in seasonal weather affect mosquito stage structure, and that the best approaches to vector control may differ between regions in timing, duration, and efficacy.

Bumble bees are important pollinators of many crops around the world. In recent decades, agricultural intensification has resulted in significant declines in bumble bee populations and the pollination services they provide. Empirical studies have shown that this trend can be reversed, however, by enhancing the agricultural landscape with natural habitat, such as adding wildflower patches adjacent to crops. Despite the empirical evidence, the mechanisms behind these positive effects are not fully understood, and the specific characteristics of the enhanced natural habitat that would maximize benefits are unclear at this time. Theoretical studies, in the form of mathematical models, have proven useful in elucidating the underlying mechanisms and determining the optimal natural habitat configurations. Existing models, however, generally focus only on particular aspects of bumble bee behaviour; some models are accurate at describing population dynamics, while others are accurate at describing their spatial distribution. In this work, we build a unique model coupling population dynamics, using a whole-colony Dynamic Energy Budget (DEB) approach, to a spatial distribution model based on the maximum energy principle. This coupling gives valuable new insights into the effects of spatial arrangements on population dynamics, and vice-versa. With our model, we answer questions such as when, how much, or what type of wildflower patches should be planted to maximize crop pollination services and minimize bee decline. We find that planting wildflowers that bloom before and after crop bloom is crucial to achieve high pollination services and preserving wild pollinator populations. We also find that small quantities of natural habitat are needed when the crop is nutritionally rich, but higher quantities are most beneficial when the crop is nutritionally deficient.

Public health responses to the COVID-19 pandemic varied across the world. Some countries (e.g., mainland China, New Zealand, and Taiwan) implemented elimination strategies involving strict travel measures and periods of rigorous non-pharmaceutical interventions (NPIs) in the community, aiming to achieve periods with no disease spread; while others (e.g., many European countries and the United States of America) implemented mitigation strategies involving less strict NPIs for prolonged periods, aiming to limit community spread. Travel measures and community NPIs have high economic and social costs, and there is a need for guidelines that evaluate the appropriateness of an elimination or mitigation strategy in regional contexts. To guide decisions, we identify key criteria and provide indicators and visualizations to help answer each question. Considerations include determining whether disease elimination is: (1) necessary to ensure health care provision; (2) feasible from an epidemiological point of view; and (3) cost effective when considering, in particular, the economic costs of travel measures and treating infections. We discuss our recommendations by considering the regional and economic variability of Canadian provinces and territories, and the epidemiological characteristics of different SARS-CoV-2 variants.

Empirical observations and mathematical models show that climate warming can lead to the northern (or, more generally, poleward) spread of host species ranges and their corresponding diseases. Here, we consider an unexpected possibility whereby climate warming facilitates disease spread in the opposite direction to the directional shift in the host species range. To explore this possibility, we consider two host species, both susceptible to a disease, but spatially isolated due to distinct thermal niches, and where prior to climate warming the disease is endemic in the northern species only. Previous theoretical results show that species’ distributions can lag behind species’ thermal niches when climate warming occurs. As such, we hypothesize that climate warming may increase the overlap between northern and southern host species ranges, due to the northern species lagging behind its thermal tolerance limit. To test our hypothesis, we simulate climate warming as a reaction-diffusion equation model with a Susceptible-Infected (SI) epidemiological structure, for two competing species with distinct temperature-dependent niches. We show that climate warming, by shifting both species’ niches northwards, can facilitate the southward spread of disease, due to increased range overlap between the two populations. As our model is general, our findings may apply to viral, bacterial, and prion diseases that do not have thermal tolerance limits and are inextricably linked to their hosts’ distributions, such as the spread of rabies from arctic to red foxes.

During the COVID-19 pandemic, the World Health Organization (WHO) updated guidelines advising that a risk-assessment framework considering local epidemiology and health services capacity be used to determine if travel measures should be implemented. Data, analysis, and models are needed to support these updated WHO guidelines. In 2020 and 2021, the Canadian province of Newfoundland and Labrador (NL) implemented travel measures that affected most travelers, including non-residents of NL, and NL residents that work outside the province. We used multiple data sources to estimate the total travel volume arriving in NL before and during the pandemic. We found that during the pandemic, travel to NL decreased by 82%, and the percentage of travelers arriving from given origins changed with Quebec decreasing from 14 to 4%, and Alberta increasing from 7 to 17%. We formulated an importation model including many epidemiological details, however, a less detailed statistical model considering the product of infection prevalence and travel volume for each Canadian province and the territories better predicted daily travel-related cases of Canadian origin (R ² = 0.55). We conclude that the accuracy of importation models are limited more by data availability, particularly travel-related case data, and data quality, particularly between-country differences in infection reporting, than by the complexity and details of importation models. Our results are evidence that will inform future risk-assessment frameworks to support travel measure implementation decisions during public health emergencies.

Background Case underreporting during the coronavirus disease 2019 (COVID-19) pandemic has been a major challenge to the planning and evaluation of public health responses. School children were often considered a less vulnerable population and underreporting rates may have been particularly high. In January 2022, the Canadian province of Newfoundland and Labrador (NL) was experiencing an Omicron variant outbreak (BA.1/BA.2 subvariants) and public health officials recommended that all returning students complete two rapid antigen tests (RATs) to be performed three days apart. Methods To estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we asked parents and guardians to report the results of the RATs completed by K–12 students (approximately 59,000 students) using an online survey. Results When comparing the survey responses with the number of cases and tests reported by the NL testing system, we found that one out of every 4.3 (95% CI, 3.1–5.3) positive households were captured by provincial case count, with 5.1% positivity estimated from the RAT results and 1.2% positivity reported by the provincial testing system. Of positive test results, 62.9% (95% CI, 44.3–83.0) were reported for elementary school students, and the remaining 37.1% (95% CI, 22.7–52.9) were reported for junior high and high school students. Asymptomatic infections were 59.8% of the positive cases. Given the low survey participation rate (3.5%), our results may suffer from sample selection biases and should be interpreted with caution. Conclusion The underreporting ratio is consistent with ratios calculated from serology data and provides insights into infection prevalence and asymptomatic infections in school children; a currently understudied population.