The role of case importation in explaining differences in early SARS-CoV-2 transmission dynamics in Canada-A mathematical modeling study of surveillance data.

OBJECTIVE: The North American coronavirus disease-2019 (COVID-19) epidemic exhibited distinct early trajectories. In Canada, Quebec had the highest COVID-19 burden and its earlier March school break, taking place two weeks before those in other provinces, could have shaped early transmission dynamics. METHODS: We combined a semi-mechanistic model of SARS-CoV-2 transmission with detailed surveillance data from Quebec and Ontario (initially accounting for 85% of Canadian cases) to explore the impact of case importation and timing of control measures on cumulative hospitalizations. RESULTS: A total of 1544 and 1150 cases among returning travelers were laboratory-confirmed in Quebec and Ontario, respectively (symptoms onset ≤03-25-2020). Hospitalizations could have been reduced by 55% (95% CrI: 51%-59%) if no cases had been imported after Quebec's March break. However, if Quebec had experienced Ontario's number of introductions, hospitalizations would have only been reduced by 12% (95% CrI: 8%-16%). Early public health measures mitigated the epidemic spread as a one-week delay could have resulted in twice as many hospitalizations (95% CrI: 1.7-2.1). CONCLUSION: Beyond introductions, factors such as public health preparedness, responses and capacity could play a role in explaining interprovincial differences. In a context where regions are considering lifting travel restrictions, coordinated strategies and proactive measures are to be considered.

A population health perspective on artificial intelligence.

The burgeoning field of Artificial Intelligence (AI) has the potential to profoundly impact the public's health. Yet, to make the most of this opportunity, decision-makers must understand AI concepts. In this article, we describe approaches and fields within AI and illustrate through examples how they can contribute to informed decisions, with a focus on population health applications. We first introduce core concepts needed to understand modern uses of AI and then describe its sub-fields. Finally, we examine four sub-fields of AI most relevant to population health along with examples of available tools and frameworks. Artificial intelligence is a broad and complex field, but the tools that enable the use of AI techniques are becoming more accessible, less expensive, and easier to use than ever before. Applications of AI have the potential to assist clinicians, health system managers, policy-makers, and public health practitioners in making more precise, and potentially more effective, decisions.

Initial Usability Evaluation of a Knowledge-Based Population Health Information System: The Population Health Record (PopHR).

We report the baseline usability of a novel web-based application, the Population Health Record (PopHR), designed to facilitate the effective use of population health information by public health professionals and to support evidence-based decision-making. The usability test was conducted with ten potential users who each completed eight tasks using the PopHR system. Participant responses were recorded, including timestamps for each data entry. Overall, the task completion rate was 96% while the success rate was 88%. The average time-on-task was 3.11 minutes, with more time spent on tasks requiring a user to stratify data along multiple dimensions, such as age, sex, or geographical region. Usability scores indicated that the current version of PopHR has good usability. Potential improvements identified included adding supporting information, offering different visualizations, and enhancing system stability. These findings are examples of addressable usability problems encountered in developing a population health record system.

PopHR: a knowledge-based platform to support integration, analysis, and visualization of population health data.

Population health decision makers must consider complex relationships between multiple concepts measured with differential accuracy from heterogeneous data sources. Population health information systems are currently limited in their ability to integrate data and present a coherent portrait of population health. Consequentially, these systems can provide only basic support for decision makers. The Population Health Record (PopHR) is a semantic web application that automates the integration and extraction of massive amounts of heterogeneous data from multiple distributed sources (e.g., administrative data, clinical records, and survey responses) to support the measurement and monitoring of population health and health system performance for a defined population. The design of the PopHR draws on the theories of the determinants of health and evidence-based public health to harmonize and explicitly link information about a population with evidence about the epidemiology and control of chronic diseases. Organizing information in this manner and linking it explicitly to evidence is expected to improve decision making related to the planning, implementation, and evaluation of population health and health system interventions. In this paper, we describe the PopHR platform and discuss the architecture, design, key modules, and its implementation and use.

Addressing the challenge of encoding causal epidemiological knowledge in formal ontologies: a practical perspective.

The paper presents an overview of approaches to encoding uncertain causal knowledge in formal ontologies and demonstrates how these approaches can be used in a semantic-driven application for public health using the Population Health Record (PopHR) platform as an example.