HE Severe Acute Respiratory Syndrome COVID-19 virus (SARS-CoV-2) has had enormous impacts throughout the world, spreading at an alarming rate such that there have now been more than 28.6 million infections and 917 thousand deaths globally, including >6 million cases and 193 thousand deaths in the United States alone (Johns Hopkins, 2020). The world is collectively frustrated by the limited ability to predict and control future changes in the virus trajectory. The virus is pervasive, resulting in the need to develop a better understanding how alternative non-pharmaceutical interventions (NPIs) can be implemented to control the worldwide outbreak (including, but not limited to, shutdown of economies, and lockdown of peoples' movements), while keeping the virus under a controllable magnitude until a vaccine becomes available.
Mammoth changes in the operations of economies and populations have arisen as most countries have adopted selfisolation as a means of decreasing infectivity levels and holding down the curve ('planking' or 'flattening' the curve of the virus). While successful in some countries, other nations continue to evolve into resurgence of the virus as a result of 'opening up the economy' too quickly (e.g. the USA) or unable to enact social distancing and extensive poverty (e.g. India) . These examples emphasize the need for insights on how governments need a guide for allowable behavior controls and means of opening up the economy as society moves forward.
To provide insights into the effectiveness of alternative initiatives, the power of Artificial Intelligence (AI) as a technology is investigated as a means to improve prediction of the future impact on the medical care system. Internationally, and more specifically within Canada, the pandemic has caused widespread lockdowns on activities within economies. Further, enormous angst arose in New York city, as an example, where medical preparedness was pushed to the limit during the height of their pandemic, where ventilators to assist people with breathing problems proved to be in short supply and were under frequent re-distribution between cities within the US. These types of implications questioning whether available Personal Protective Equipment (PPE) resources were expressed, as the virus raged through various country populations. President Trump undertook efforts to restrict forwarding of PPE outside the US, much to the concern of foreign countries including Canada, where agreements had been established, and hence, represented an attempt to derail forwarding along of PPE materials; these types of actions will change the attitudes in terms of reliance on foreign countries to provide critical items at times of need (the capacities of medical systems are definitely limited).
Concerns continue, including issues of a potential resurgence of virus infections if removal of lockdown conditions occurs too early and a second wave of the virus commences. If too early an opening of the economy occurs, or relaxation of some measures such as opening daycare or summer day camps for children, these actions may increase the spread of the virus. Some states, such as Florida, Texas and Arizona, opened up their economies very early and were then confronted with the difficult task of trying to re-implement lockdown procedures in hopes of controlling the rapidly spreading virus.
There continues to be concern that Canada's hospital system will become overwhelmed, and this possibility underlines the need for forecasting models to provide the country's leaders with information (e.g., how many people are likely going to be admitted to hospitals, and how many will require intensive care and/or ventilators, who is most at risk, etc.) to help them make informed decisions. In response to the highly complex nature of the COVID-19 outbreak and variations in its behavior internationally, machine learning has potential to model the viral outbreak [1] .
In response, and to improve the understanding of the success of various initiatives related to COVID-19 attenuation, this paper describes research related to the development of AI and statistical models that provide innovative insights into the COVID-19 pandemic. This research was undertaken to compare alternative models regarding their ability to identify the mortality risk of COVID-19 patients in the province of Ontario. Data-based methodologies were implemented to enable widespread usage of the resultant model, adaptable to various public health units in their planning for inflated caseloads on medical systems as well as to inform the public about who is at greatest risk. This research was undertaken to create city-wide and region-wide models aimed at improving predictions of mortality within the province of Ontario.
Although the applications of AI modelling of the COVID-19 outbreak are still in their infancy, this paper demonstrates significant benefits of evolving these models to improve mortality predictions from COVID-19, and hospital caseloads within a province of Canada context. This paper is organized as follows. The use of machine learning to address COVID-19 is discussed in Section 2. The case study and problem setup are discussed in Section 3. Section 4 includes the experimental results and discussion of applying machine learning to study COVID-19. The paper is then concluded.
Machine Learning, as an approach to AI, enables improved predictions of the future impact on the healthcare system, stemming from the overall effect of the virus on the general population. In response to the highly complex nature of the COVID-19 outbreak and variations in its behavior internationally, researchers suggest machine learning is an effective tool to model the viral outbreak [1] . More precisely, multiple machine learning methods can be used to analyse COVID-19 cases in Ontario, with case information prevailing from two different publicly available datasets. These methods include Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), and Random Forest. Logistic Regression, a statistical regression-based model was also used for the predictions in this paper so that the Machine Learning results can be compared to the results of a simpler model. Other applications of the aforementioned Machine Learning algorithms have proven to be valuable in an enormous array of applications, including just as examples, landslide susceptibility models, predicted price changes in crude oil, and geographical origin analysis of music samples, respectively [2, 3, 4] .
The ability to make reasonably accurate predictions on the future outcomes of COVID-19 have the potential to have an immense impact on choices surrounding behavioural directives, as well as decisions made for Canada's healthcare system. Even with some degrees of self-isolation and social distancing, conditions are indicating a real possibility that Canada's hospital system may become overwhelmed [5] . To provide an acceptable response to the demand for hospital beds, it is imperative that an approach is developed to distinguish between high risk and low risk COVID-19 patients.
The role of AI in healthcare is being increasingly well established over the past decade. The insights available from interpreting diverse and vast datasets can produce lifesaving results. The advanced integration and instant access of technology allows for better management of patient data, spanning from the security and administration of electronic medical records, effortless linkage of data sources, and realtime information sharing. This elevated level of data collection consequently leads to improved data manipulation. However, to effectively make use of the large and complex datasets including in-depth health data, there is need for epidemiologists and health care professionals to incorporate computational techniques that identify patterns in data [6] . This reinforces the need for use of Machine Learning and other mathematical modelling as key analytical tools in healthcare epidemiology [7] . Studies have shown that AI can be a valuable tool in the modelling and prediction of viral infections within the population. A 2018 study effectively mapped the transmission of the mosquito-born Zika virus (ZIKV) using multiple Machine Learning models; Backward Propagation Neural Network (BPNN), Gradient Boosting Machine (GBM) and Random Forest [8] . These models were used to generate the probability of Zika outbreak at the global level, with an area under the curve (AUC) between 0.963 and 0.966, indicating a very high level of accuracy [8] . This application of Machine Learning for the 2015 Zika epidemic established that AI methods are effective for disease transmission prediction and risk assessment. Machine Learning tools are not only useful for modelling the spread of a virus, but can also predict patient outcomes. In a study supported by the U.S. National Institute on Aging, researchers generated a 'super learning model', an ensemble of multiple Machine Learning approaches, that collectively forms a predictive algorithm with the best cross-validated mean-squared-error [9] . The findings concluded that Machine Learning methods for epidemiological predictions are highly dependent on typical characteristics such as gender and age, plus demonstrating accuracy can be augmented with additional biostatistics such as smoking habits, underlying heart conditions, reported physical activity tendencies, level of education, household income, and weight [9] .
The various Machine Learning algorithms used in the study [11] , were also integrated within a 2019 forecast that monitored vital signs of Intensive Care Unit patients, and whether or not they would proceed to critical conditions [10] . Further, accuracy was found in the Random Forest Machine Learning algorithm for an analysis for predicting patient conditions remotely, improving existing single predictive models [11] .
Specific to COVID-19 results, public health authorities, as well as common media, are placing attention on traditional epidemiological and statistical models. Although quick to develop with fewer datasets and foci [12] , it has already been determined that standard models fitted to confirmed cases of COVID-19 are anticipated to have a high level of uncertainty, largely due to the deviation on reported dates for confirmed cases [13] . Moreover, the popularized Susceptible-Exposed-Infectious-Recovered (SEIR) model, as well as the simpler, SIR model, can both provide useful insights into the effectiveness of transmission prevention strategies, but can still be expected to derive results with considerable uncertainty [14] .
AI modelling of the pandemic has had good preliminary results. A study focussed on the development of AI predictions as a form of decision-making support had up to 80% accuracy when using a K-Nearest Neighbors algorithm [15] . Surprisingly, this study found that characteristics of COVID-19, including fever, certain features in radiographic lung images, and strong immune responses were not useful in predicting which of the sampled 53 patients (all evidencing with initially mild symptoms) would progress to develop severe lung disease. Age and gender were also ineffective factors in predicting the development of serious disease. Instead, the results indicated that a combination of small changes in levels of the liver enzyme alanine aminotransferase (ALT), patient-reported myalgia, and raised hemoglobin levels most accurately predicted the development of serious disease conditions.
Researchers at China's Huazhong University of Science and Technology have used Machine Learning (using XGBoost) to develop a prognostic prediction algorithm to forecast the likelihood of an individual surviving the infection [16] .
Overall, there are a number of primary areas of scope where AI has demonstrated significant potential in fighting COVID-19, including early warnings and alerts, tracking and prediction, data dashboards, diagnosis and prognosis, treatments, and cures, and societal control. However, the utility of AI models depends on the availability of pertinent data. Specifically, although AI models will not replace the role of experts or trusted epidemiological models, AI models have the potential to monitor and respond to the crisis -assisting in the quantification of medical caseloads and future global trends [17] . In a whitepaper by ttopstart, it was outlined that, regardless of the challenges in acquiring the necessary data and integrating the algorithms within existing medical workflows, AI will likely make immense differences in the future of healthcare administration and delivery [18] .
AI models for Ontario as described herein, were deve to assist hospitals and healthcare facilities to decide patients to prioritize in receiving medical attention, elev hospitalization, or triage when the system is overwhe The algorithm predicts the mortality risks based on pa physiological conditions, symptoms, and demog information. These data were collected from two so Public Health Ontario (PHO) and the Public Health Age Canada (PHAC), both of which cluster datasets by geog health regions. In Ontario, these 36 regionalized authorities are called Public Health Units, as depic Figure 1 . Due to large clusters of cases, Ontario is of interest for this application of AI modeling. [19] III. CASE STUDY AND COMPUTER EXPERIMENTS Cumulatively, the available data across all regio Ontario was for 39,794 individual cases (August 1, either grouped under recovery or mortality. One dimens these outbreaks, considered the largest, was withi Province's long-term care facilities. Between January 1 June 1, 2020, nearly 18% of all provincial COVID-19 were confirmed as long-term care residents [20] . Durin time, rates of COVID-19 within long-term care per 10 population reached as high as 72.8, as reported with Leeds, Grenville & Lanark District Health Unit [20] .
This research developed and contrasted various m learning and statistical models in order to predict the r mortality for Canadians infected with COVID-19. The m were calibrated using only publicly accessible Ca COVID-19 data to highlight the applicability of AI mod Specifically, two open access COVID-19 datasets varying attribute information, were used to build and co the models. The first dataset (PHO) was obtained Ontario Health Services [21] , while the second (PHAC obtained from Canada health services [22] . To ensur comparisons were made between datasets, the COV . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint
The copyright holder for this this version posted September 30, 2020. . https://doi.org/10.1101/2020.09.29.20201632 doi: medRxiv preprint cases were limited to only Ontario COVID-19 cases that were reported after January 19, 2020 and were resolved (recovered or died) by Aug 1, 2020. A summary of the two datasets and their attributes are listed below in Table 1 .
The Machine Learning models were adapted for Ontario to focus on three distinct case statuses, namely active, recovered, or reported deceased. PHO data reports 36, 988 cases between January 19 th and August 1 st [23] , PHAC report 27,778 cases in Ontario for the same period [24] . Data after August 1 st was not used as the caseloads prior to this were already substantial. Detailed characteristics of the website information in terms of variable definitions for both the PHO and PHAC datasets are as described in Table 1 . As evident in Table 1 , several variables are the same across both datasets (Gender), similar but with minor differences (age, date infected, case acquisition), and several variables are only present in one or the other (Public Health Unit, Date Reported, Occupation and Hospitalization). By comparing the accuracy of models built using the different publicly available datasets, the importance of specific variables can be highlighted as well as begin to predict how a more complete dataset may improve the models' accuracy.
Also worth noting, is that the number of COVID-19 cases that recovered or died between Jan 23-Aug 16, 2020 differ between datasets by 194 deaths. This highlights that data anomalies and/or differences in reporting requirements exist between the two datasets. More detailed description of the reporting requirements and methodology for each dataset would be useful to identify what is causing the difference in COVID-19 cases but with the enormous speed of the pandemic, data assembly challenges continue to exist.
Three machine learning models and one statistical regression model were developed to predict the mortality risk of COVID-19 patients within Ontario. The models selected include; artificial neural network (ANN) [25] , Random Forest [26] , extreme gradient boosting decision tree (XGBoost) [27] , and logistic regression [25] . These models were selected based on their prevalence in literature and accuracy in binary classification.
Before calibrating any of the models, the datasets were randomly divided into two parts; training dataset containing 70% of the data, and the testing dataset containing the remaining 30% of the data. The training dataset was used to calibrate each model, whereas the testing dataset was used to evaluate the final models' accuracy.
Pre-processing techniques were applied to the training dataset. First, numeric variables were centered and scaled, and categorical variables were converted into dummy (or binary) variables. The SMOTE resampling technique was applied to the training dataset to adjust for the class imbalance (i.e. only a small number of COVID-19 patients actually die from this disease) [28] .
Tuning parameters for the machine learning models (ANN, Random Forest, XGBoost) were determined using a grid search approach and assessed using a 10-fold cross-validation technique repeated three times for each model. Tuning parameters were optimized to produce the maximum area under the receiver operating characteristic curve (Area Under the Curve, or AUC) based on the average 10-fold crossvalidation technique.
The logistic regression's input variables were selected using a step-wise Akaike Information Criterion (AIC) function [25] , which selects the array of input variables to be used based on step-wise selection process which is evaluated using the AIC.
All models were developed and analyzed using the computer programming language R [29] . The models generate a probability of mortality for each recorded case outcome, and then decide the final predicted outcome, be it recovered or died, based on a 50% threshold value.
The AUC is an effective indicator measurement of accuracy to represent the performance of a classification model. In general, the AUC represents the degree to which the model can distinguish between different classes [30] , and in this case, the classes being either a patient has recovered or died. It is the area under the Receiver Operating Characteristics (ROC) curve, with 1.0 being a perfect measure of separability and 0.0 means that the model is only generating false predictions [31] . When the AUC is near 0.5, it means that the model cannot recognise the different possible classes and predicts outcomes at random.
The AUCs of each model for both datasets are depicted below in Figure 2 .
. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this this version posted September 30, 2020. . https://doi.org/10.1101/2020.09.29.20201632 doi: medRxiv preprint The Logistic Regression model has the second-highest AUC for both PHO and PHAC datasets. This is interesting since regression models are often found to be less accurate than machine learning models, especially when datasets contain a large number of input variables, although in this application, since there are only six variables, the advantage of machine learning models ability to identify complex relationships between input variables and the outcomes are somewhat limited. These findings indicate that not being able to incorporate the complex relationships between input variables and the outcome is important but not terribly deficient, when compared to machine learning models, as the logistic model produces results better than ANN and Random Forest.
As illustrated in Figure 2 , all models developed using the PHAC dataset outperformed the models developed using the PHO dataset. This indicates the variables included in the PHAC dataset provide greater information in predicting COVID-19 mortality, than the variables included in the PHO dataset. To characterize this further, a variable importance graph is provided in Figure 3 , indicating importance of individual variables for the most accurate model in both datasets (XGBoost). Figure 3 to most important variable for the XGBoost model, for datasets. The importance of the Age variable reflects th that long-term care facilities, where ages are typically experience much higher COVID-19 mortality risk (81 deaths from COVID-19 in Canada occurred in Long Care homes. (CBC, 2020) ).
The next two most important variables for the P XGBoost model are Hospitalization and Occupation. W someone is hospitalized, or their occupation is a heal worker, has a substantial impact on predicting the morta a COVID-19 patient. This also indicates that the inc accuracy noticed for the models built using the PHAC d is likely in large part due to the addition of these two var (hospitalization and occupation). The two variables lo only in the PHO dataset, namely Date Reported and Health Unit, appear to be only relatively important f model's prediction. This indicates that by creating a pu available dataset that contains all eight of these var improved accuracy of mortality prediction model wou attained. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint
The copyright holder for this this version posted September 30, 2020. . the ongoing pandemic, relative to (ii) the last five weeks reveals how the demographics of the pandemic shifted over time. In Error! Reference source not found. the cases and deaths reported in the first four weeks and last five weeks are contrasted. The number of deaths per confirmed case has dropped dramatically, from 0.047 fatalities per case in the winter to 0.0098 per case through the summer as evidenced in the data reports. Additionally, in the later period, all of the deaths were concentrated in the 80+ years and the 'age not stated' categories, while the earlier period recorded deaths in those as young as 50-59 years.
While the changes noted in Table 2 were influenced to some degree by the change in testing requirements which allowed the recording of a greater number of non-serious cases, the drop in mortalities also indicate there was considerable improvement (success) of treatment in hospitals. Specifically, as the pandemic progressed and many months have passed since COVID-19 was first reported in Canada, the global understanding of the disease, and improvements in treatment options occurred. Through research and medical practice, doctors in Canada now have more tools to fight serious cases of COVID-19 in hospitals. Steroids are now commonly used to significantly reduce deaths in patients with lung injuries as evidenced by studies from University of Oxford [32] . Additionally, anti-viral treatment Remedesivir has been approved in Canada to aid critically ill patients [32] .
Of interest in terms of exploring the power of the AI, training an XGBoost model on data from the two periods noted in Table 2 , the results showed there was better accuracy, AUC, for the earlier time period of 0.914, and for the later time frame, the AUC fell to 0.838, but demonstrate good results for both. Most importantly, the AI results demonstrate the ability to detect the importance of different variables and detect trends (proven, as able to characterize the implications of the research and medical practices). As indications of the adjustments identified over time, the results in Figure 4 . XGBoost: Variable Importance by Time Period illustrate the changes in variable importance of the characteristics used by the XGBoost model are illustrated for the early and later datasets. The model based on the earlier data used a number of characteristics such as hospitalization status, date infected and gender to a higher degree. The model based on later data relied a entirely on age and somewhat on hospitalization, with m consideration for any of the other categories. As the r indicate in Table 2 showed, all the deaths were among e people (80+ years), indicating very clearly that age identified as the very important issue for predicting mor the importance of hospitalization became dramatically l a result of the learning curve from improved understa resulting in a dramatic decrease of importanc hospitalization resulting in mortality. These takeaways course, now evident, from the reported results now ava but the key is that the AI model was able to identi implications of the improved medical systems understanding of the virus, to decrease the occasio mortality.
The accuracy of the models built in the previous se were calibrated using a large training dataset containing 27,000 patients. However, individual communities hospitals may wish to develop their own COVID-19 mo prediction model but may have significantly less available for AI model calibration. The following s identifies how the number of cases available for calib may impact the models overall accuracy and thus pro guidance for these communities.
The impact of dataset size was analyzed using the accurate model and dataset, XGboost -PHAC. Eight da with different sizes were created, using random sampli separate XGBoost model was trained on each dataset a AUC was calculated using a hold-out testing d containing 30%of the overall Covid-19 case data. To e consistency, the entire process was repeated three time the results averaged. Figure 4 below highlights the averaged results for each dataset. CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint
The copyright holder for this this version posted September 30, 2020. . https://doi.org/10.1101/2020.09.29.20201632 doi: medRxiv preprint As expected, the AUC of the model increases with the size of dataset used to train the model. With only 50 cases to train the model the AUC is 0.6791 which indicates the model is not that effective in discerning the risk of mortality for COVID-19 patients. However, an XGBoost model trained using 300 cases has an AUC of 0.8833, indicating the model is fairly accurate and after the training dataset surpasses 1000 cases, the increase in AUC is minimal, only increasing by 0.0015 by including more than 10,000 additional cases into the training dataset.
From the three Machine Learning models and the one statistical model, the XGBoost model provides the highest level of precision and accuracy from both the PHO and PHAC datasets, as seen in Error! Reference source not found.. This observation is critical to this study because it shows how an advanced machine learning model such as XGBoost, can provide more accurate COVID-19 outcomes in comparison to a statistical model, such as the logistics regression model. In many respects, it is not surprising that the XGBoost performed well, as it has been a favourite in applied machine learning competitions since its release in 2014 due to its execution speed and model performance (e.g. [32] [33] ).
Moreover, the results obtained using the national dataset (PHAC) are consistently higher than the results from the datasets release by the provincial government (PHO). The inconsistencies as to what information is made available between the two datasets are the cause of the different accuracy ranges, with key items being the inclusion of hospitalisation status and patient occupation in the PHAC database. However, the dataset available from PHO also produced significant results, with interesting attributes coming from the inclusion of location data pertaining to the Public Health Unit of the cases. The variable importance graph ( Figure 3) suggests the PHAC results are bettered by into consideration hospital status as it displays the seve the case; however, hospitalisation and ICU status m useful for the mortality prediction of already severe CO 19 cases, but this piece of data comes after the patient' has already reached a point of severity that requires m intervention, and therefore cannot be used to predict w or not a case will develop to the point of needing profes healthcare and possibly develop to the point of fatality.
AI has not yet been impactful against COVID-19 sin use of AI is hampered by availability of data related to of confidentiality to protect those and their families who or may be impacted by this dreadful virus. There is discussion about the mortality risk of COVID-19 patie the province of Ontario as may be apparent through bette and AI modeling, to assess which patients to priorit receiving medical attention. Overcoming the constrain data confidentiality will require a careful balance betwee privacy and public health concerns, and more rigorous h AI interaction.
The implications of the Severe Acute Respiratory Syn COVID-19 virus (SARS-CoV-2) at world scale have profound and the potential for initiation of a second w impacts from the virus are expected. The power of Ar Intelligence (AI) as a technology was demonstrated as a to improve prediction of the future impact on the medica system. The needs for forecasting models are obvio provide the country's leaders with information (e.g. many people are likely going to be admitted to hospital how many will require intensive care and/or ventilators is most at risk, etc.) to help them make informed decision Substantial success was demonstrated as evident in ter the accuracy of the XGBoost AI modeling, achieving an of 0.9056 for PHO and 0.935 for the PHAC datasets. As the value of the AI modeling as gained from the importa individual variables was demonstrated, providing imp insights into the most important variable for the XG model. Exploring the ability of the AI model to be sensi changing conditions, and in this case specifically improvements in the understanding how to treat the r the AI model was able to identify the dramatic importa the age of the mortality becomes dramatically important much lower impacts on the other variables that descri individual caseloads. The results relying only upon th five weeks of data showed that AI picks up the overwhe importance of age is clearly evident, as an indicati mortality from COVID-19. This is confirmed for the AI modelling wherein ther separate but very demonstrative evidence that showed term care facilities, where ages are typically high an morbidities are frequent, are the most important differentiating between COVID- 19 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint
The copyright holder for this this version posted September 30, 2020. . https://doi.org/10.1101/2020.09.29.20201632 doi: medRxiv preprint Whether someone is hospitalized, or their occupation as a healthcare worker has a substantial impact on predicting the mortality of a COVID-19 patient. As well, the AI modeling showed that an XGBoost model trained using 300 cases has an AUC of 0.8833, indicating the model is fairly accurate, and that data after 1000 cases, improved accuracies from data are limited, indicating that development of an AI model from a much lesser basis than was the primary data utilized for this research, is still able to provide informative and useful results.
As the analyses described herein indicate, AI has the potential to be a tool in the fight against COVID-19 and similar pandemics. However, as Georgios Petropoulos concludes, "AI systems are still at a preliminary stage, and it will take time before the results of such AI measures are visible." [34] 