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The Severe Acute Respiratory Syndrome Coronavirus 2 pandemic has challenged medical systems to the brink of collapse around the globe. In this paper, logistic regression and three other artificial intelligence models (XGBoost, Artificial Neural Network and Random Forest) are described and used to predict mortality risk of individual patients. The database is based on census data for the designated area and co-morbidities obtained using data from the Ontario Health Data Platform. The dataset consisted of more than 280,000 COVID-19 cases in Ontario for a wide-range of age groups; 0–9, 10–19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, and 90+. Findings resulting from using logistic regression, XGBoost, Artificial Neural Network and Random Forest, all demonstrate excellent discrimination (area under the curve for all models exceeded 0.948 with the best performance being 0.956 for an XGBoost model). Based on SHapley Additive exPlanations values, the importance of 24 variables are identified, and the findings indicated the highest importance variables are, in order of importance, age, date of test, sex, and presence/absence of chronic dementia. The findings from this study allow the identification of out-patients who are likely to deteriorate into severe cases, allowing medical professionals to make decisions on timely treatments. Furthermore, the methodology and results may be extended to other public health regions.

With the recent outbreak of COVID-19, the reach and scale of COVID-19 cases is top of mind for everyone and many research groups are actively monitoring and exploring the potential spread. A positive consequence of past epidemics and pandemics is that there are sound epidemiological compartmental modelling approaches that can effectively model disease spread. With minor changes to the underlying dynamical system of equations, many different strategies and situations can be explored. In particular, one such strategy of social distancing is top of mind for many Canadians as our political leaders, local businesses, and fellow Canadians promote and adopt this approach with the hopes that it will effectively 'flatten the curve' and reduce or prevent further spread. In this paper, the baseline model is introduced with its close counterpart, the model. Social distancing is modelled through the isolation of a subset of the susceptible population and comparative studies are performed considering a range in the proportion of individuals isolated. Robust and accurate numerical approximation techniques are used to simulate the pessimistic base case for which no preventative measures are taken and for various social distancing regimes. The results of social distancing are consolidated into two groups-those that flatten the curve and those that completely halt the disease spread. Mathematical formulations show that the turning point between these two regimes is when the effective reproductive rate, denoted , is equal to 1. Conclusions are made regarding the impacts and extent of the spread in relation to the severity of social distancing measures.

The Severe Acute Respiratory Syndrome COVID-19 virus (SARS-CoV-2) has had enormous impacts, indicating need for non-pharmaceutical interventions (NPIs) using Artificial Intelligence (AI) modeling. Investigation of AI models and statistical models provides important insights within the province of Ontario as a case study application using patients' physiological conditions, symptoms, and demographic information from datasets from Public Health Ontario (PHO) and the Public Health Agency of Canada (PHAC). The findings using XGBoost provide an accuracy of 0.9056 for PHO, and 0.935 for the PHAC datasets. Age is demonstrated to be the most important variable with the next two variables being Hospitalization and Occupation. Further, AI models demonstrate identify the importance of improved medical practice which evolved over the six months in treating COVID-19 virus during the pandemic, and that age is absolutely now the key factor, with much lower importance of other variables that were important to mortality near the beginning of the pandemic. An XGBoost model is shown to be fairly accurate when the training dataset surpasses 1000 cases, indicating that AI has definite potential to be a useful tool in the fight against COVID-19 even when caseload numbers needed for effective utilization of AI model are not large.

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The rapid spread of COVID-19 and its variants have devastated communities worldwide, and as the highly transmissible Omicron variant becomes the dominant strain of the virus in late 2021, the need to characterize and understand the difference between the new variant and its predecessors has been an increasing priority for public health authorities. Artificial Intelligence has played a significant role in the analysis of various facets of COVID-19 since the early stages of the pandemic. This study proposes the use of AI, specifically an XGBoost model, to quantify the impact of various medical risk factors (or “population features”) on the possibility of a patient outcome resulting in hospitalization, ICU admission, or death. The results are compared between the Delta and Omicron COVID-19 variants. Results indicated that older age and an unvaccinated patient status most consistently correspond as the most significant population features contributing to all three scenarios (hospitalization, ICU, death). The top 15 features for each variant-outcome scenario were determined, which most frequently included diabetes, cardiovascular disease, chronic kidney disease, and complications of pneumonia as highly significant population features contributing to serious illness outcomes. The Delta/Hospitalization model returned the highest performance metric scores for the area under the receiver operating characteristic (AUROC), F1, and Recall, while Omicron/ICU and Omicron/Hospitalization had the highest accuracy and precision values, respectively. The recall was found to be above 0.60 in most cases (with only two exceptions), indicating that the total number of false positives was generally minimized (accounting for more of the people who would theoretically require medical care).

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