Application of advanced machine learning and structure-based approaches for repurposing & discovering therapeutics for COVID-19
The novel COVID-19 pandemic caused by SARS-CoV-2 is a global health emergency of international concern with an estimated death toll in millions worldwide. The development, testing and approval of the COVID-19 vaccine may take at least 12-18 months. By then, the virus could mutate and reduce the vaccine’s efficacy. This project aims to leverage the latest advances in AI to repurpose existing drugs and identify novel ones that could be further developed as COVID-19 therapies in an extremely condensed manner. We will utilize Apollo 1060, 99andBeyond’s AI-augmented decision-making platform that can rapidly search a chemical space that is orders of magnitude larger than competitors. We will collaborate with experimentalists, and aim to have a set of compounds confirmed in a set of in vitro COVID-19 assays within the next six months. The proposed set of compounds and their corresponding biological activity will be openly published to help the community build powerful predictive models for COVID-19 targets. Their further testing and development will require the engagement of collaborating physicians in the hospitals and may attract partnerships with leading pharma and biotech in the US and Canada.
Cloud-based Framework for Analysing COVID-19 Data
Edge computing (EC) has shown to have a significant impact on offloading traffic/computations/AI tasks from backhaul links/servers and improving users’ Quality of Service (QoS). It is anticipated that EC in general and its use to implement AI at the edge (a.k.a. edge intelligence, EI) will be necessary components of all digital business by 2022 and that 40% of large enterprises will integrate EC/EI into their production systems by 2021.
The objective of the proposed research is to transform the already existing yet latent computing resources into on-demand clusters of distributed EC/EI workers, with a focus on distributed learning and intelligence. The proposed research will develop foundational elements of the COVID-19 and future pandemic analysis platform using KDS’s Distributed Compute Protocol (DCP). DCP allows massive computing resources to be accessed in parallel.
The proposed DCP COVID-19 project will support the Looking Glass project, a free and open platform to better inform remediation strategies, public health interventions, and vaccine campaign strategies against COVID-19 – as well as time strategies to relax lockdown measures during the recovery phase – for municipalities, state/federal authorities by modelling transmission patterns of diseases based on reports from epidemiologists fused with economic report data, and high-resolution mobility data.
Identifying disease clusters, spatial patterns, and methods of transmission of endemic, epidemic and pandemic diseases are essential to inform policymakers, programs, and interventions at both local and global scales. Health authorities depend on alerts provided by front-line employees or by members of the public when there is a disease cluster. The recent emergence of the COVID-19 as a global pandemic is one example of a critical public health threat that challenged management systems.
Computer aided diagnosis of COVID-19 symptoms using medical sensors
The challenge that currently has arisen because of COVID is that in-person appointments with doctors are not possible and everyone has to use Telemedicine. Though Telemedicine is a great convenience, the problem with current Telemedicine systems is that they cannot be used to virtually examine patients and identify if COVID-19 symptoms are present. The main reason for this is because our current Telemedicine systems lack integration with medical devices that allow capturing physiological signals over the web. We are working towards integrating these medical devices into Telemedicine platforms so that the diagnostic utility of Telemedicine can be improved, and these platforms can be used to virtually assess patients over the web.
Our software platform integrates digital medical devices into Telemedicine video conference platforms that allows doctors to assess COVID-19 symptoms from captured physiological signals and provides large scale machine learning aided COVID screening at home. Imagine Telemedicine appointments where in addition to just consulting with your doctor via video tele-conference you can have them hear your heart/lung sounds, take your temperature, blood pressure, weight, blood oxygenation all in real-time and over the internet. We are building the software infrastructure that will allow Telemedicine platforms to seamlessly integrate the plethora of digital medical devices on the market, which enable this functionality, natively into their software ecosystems. Additionally, our platform also offers an intelligent layer of machine learning software that aids doctors in consolidating patient data, clinical decision making, computer aided diagnosis and carrying out appropriate follow-ups and referrals.
COVID-19 AI based screening and monitoring of COVID-19 respiration patterns using acoustic sensors
This project addresses the pressing need for remote monitoring of long-term care homes to ensure potential cases of COVID-19 are identified early, isolated and treated.
Ryerson University’s Xiao-Ping Zhang, in concert with Altum View Systems Inc., will develop AI-based algorithms and systems to screen and monitor acoustic respiration patterns for COVID-19 in real-time, using customer mobile devices (e.g., mobile phones, wireless headphones, smart wrist-watches, etc.), low-cost electronic stethoscopes, and professional respiration monitor diagnostic devices. The COVID-19 acoustic respiration pattern screening and monitoring system will be incorporated into and complement Altum View Systems Inc.’s current camera-based health monitoring system for home care and long-term care facilities.
COVID-19: Agent-based framework for modelling pandemics in urban environment
The development of COVID-19 pandemic raises important questions on optimal policy design for managing and controlling the number of people affected. In order to answer these questions, one needs to better understand determinants of pandemic dynamics. Indeed, the development of epidemics depends on various factors including the intensity and frequency of social contacts and the amount of care and protection applied during those contacts. In particular, one area where the disease can be transmitted is the urban space of a large city such as Toronto.
The goal of the project is to create an agent-based framework for building virtual models of an urban area. This framework will be used as a virtual laboratory for testing various scenarios and their implications for the development of pandemics. In order for conclusions to be reliable, the models (known in the literature as synthetic population models or digital twins) have to be up to scale, with the number of agents comparable with the population of the city. This, in turn, requires implementations ready to be run in a large-scale distributed computing environment in the cloud as the algorithms behind the engine need high-performance computing power.
The framework will allow us to evaluate different COVID-19 mitigation policy designs. This includes possible decisions such as decreasing proneness to wearing masks, closing down some non-essential, high-contact, social network nodes (for example, hairdressers), limiting the number of people having simultaneous social gatherings or reducing the number of people on streets altogether via promoting actions such as #stayathome.
Creating a Predictive Financial Model to Optimize Digital Marketing Budget of SMEs Post COVID-19
In recent years, digital marketing has surpassed offline marketing and made marketing technology (Martech) the number one spending priority of businesses that didn’t exist ten years ago (Johnsen, 2017). Moreover, in the coming months, businesses are going to become more reliant than ever on their digital strategy. Without wanting to sound too alarmist, in many cases it will be the deciding factor in whether they make it through the tough times ahead. One thing is clear: marketers in the post-COVID-19 era will have to rethink what technologies they really need, which ones can help them save money, and which ones can help them transform their businesses that have been altered by this crisis. Data science can help companies acquire more customers, it can tell when, where and how to pitch target audience to maximize yields and minimize waste (it is estimated that on average 26% of marketing budgets are wasted on ineffective channels and strategies (Blake, Nosko, Tadelis, 2015)).
Using the right data sources, we can build simple (and more complex) models to predict impact on the customers’ behaviors and PPC (pay-per-click) conversion rates respectively, if you run a campaign at a certain point in time. These predictive analytics can estimate when the desired action will happen and what can impact it, and what would be the financial value.
The objective of this research is to introduce a predictive financial model to help Genius Camp and other SMEs in the Education industry, which are disrupted by COVID-19 situation, to allocate their digital marketing budget more efficiently. The data-backed solutions from this model will equip them with tools to make more informed decisions about going virtual and their resulting digital marketing initiatives. (i.e. how to structure their social media campaigns to achieve the highest ROI)
Design and Development of Autonomous Disinfecting Embedded Systems for COVID-19
One of the major challenges during the COVID-19 pandemic is frequent disinfecting. This is very critical for places like hospitals and long-term care. In most places, human operators perform the cleaning but it may cause them to be infected with the virus because of the shortages of personal protective equipment (PPE) and many of the unknowns of COVID-19. The aim of thisCOVID-19 project is to improve Cyberworks Robotics’ navigation technology on existing (a) floor disinfection machines (e.g. wet floor scrubbers) of various types used in hospitals, (b) high-intensity UV disinfection machines, and (c) chemical mist disinfection machines. This would allow hospitals to disinfect the hospital surfaces on a more frequent basis than is possible with human cleaners (due to both the cost and availability of human operators) and also simultaneously to increase the quality of cleaning by ensuring that some surfaces are not missed due to human error and neglect
Development of a COVID-19 in pregnancy data respository and prognostication algorithm
Professors Dafna Sussman & Rasha Kashef of Ryerson University are teaming up with Mount Sinai Hospital to tackle the very difficult problem of achieving successful early intervention for pregnant women diagnosed with COVID-19.
The project aims to support medical professionals who are directly treating COVID-19 pregnancies. A comprehensive, anonymized data repository will be deployed in conjunction with dedicated prediction algorithms to score patients for their risk of severe deterioration. The repository together with the algorithm are expected to radically transform Canadian and, potentially, international healthcare providers’ ability to identify, manage and treat cases of COVID-19 in pregnant patients.
Development of severity index of exacerbation for COVID-19 symptoms from abnormal respiratory patterns
Struggling to breathe has been a challenge that existing monitoring devices have faced, for identifying early symptoms of respiratory diseases such as novel Coronavirus (COVID-19). Measuring respiratory rate alone would result in potentially missing if a patient was unable to inhale a full breath.
To overcome this challenge, a novel technology solution invented in Canada is used through an Intelligent Bed Sheet, with the ability to continuously monitor if a patient has normal, shallow, or irregular breathing.
This collaboration with York University will revolve around developing a Severity Index by identifying earlier when a patient is struggling to breathe. Using the most advanced electronic fabric technology, the SOSCIP Platform will support computing challenges by using large amounts of breathing data in a constantly changing environment, to precisely identify before a patient’s breathing becomes more severe.
Establishing best practices for generating synthetic pediatric health data
Professor Aaron Smith of the University of Ottawa, working with the CHEO Research Institute, will develop software to generate synthetic electronic health records (EHR) that have similar statistical properties to real EHR while preserving patient privacy.
The ability to share patient data without compromising patient privacy will allow more researchers to search for medically and COVID relevant insights and broad-based sharing will allow researchers to check their ideas about fighting COVID with both greater speed and accuracy.
The final result of this project will be a software package that can generate synthetic electronic health records using minimal computational resources for moderately sized datasets.
Forecasting Covid-19 Epidemic in Canada with Spatial-Temporal Models That Exploit Population Behaviour on Twitter
The Covid-19 pandemic is creating unprecedented damage to the public health and world economy. Being able to accurately forecast the spread of Covid-19 is critical for the federal and provincial governments of Canada to devise policies and measures maximally protecting the lives of Canadians and rapidly reviving the Canadian economy. Not only important at present, developing advanced epidemic projection algorithms and techniques also helps prevent future epidemics of other infectious diseases. In this project, we set out to develop advanced forecasting algorithms for the spread of Covid-19 across Canada. Specifically, we will exploit the behaviour information of the population revealed on social media as well as the correlation of the Covid-19 spread across different regions of Canada. Our development will integrate modern AI techniques in data analytics, machine learning and natural language processing with the conventional mathematical models for infectious diseases. The developed algorithms will be hosted on a web platform, which will provide accurate daily predictions of Covid-19 spread and release them to the public. These predictions will assist the governments to strategically adjust policies for the protection of Canadian lives and the revival of the Canadian economy. Individuals, families, schools and businesses will all benefit from this new source of information in their planning processes.
Getting ahead of the curve: a novel way to find people who are likely to be asymptomatic carriers of COVID-19 before they infect others
The spread of COVID-19 has been slowed by physical distancing, self-isolation, lockdowns, masks and travel restrictions. Doing more testing and contact tracing of known cases has helped contain the spread. However, these approaches are ‘behind the virus’ – by at least 5-10 days. We need a way of getting ahead of the virus, before it spreads further. We know that about half the infections are transmitted by people who do not have any symptoms, and this is sufficient to cause a growing epidemic. Therefore, we need to find, isolate and test at least some asymptomatic people. Like intelligence officers hunting hidden terrorists, we need to prevent attacks before they happen, instead of chasing the culprits after the fact. In other words, we need to identify carriers before outbreaks occur, not after. Our team proposes a new way to do this.
The overall goal of this project is to develop models and an intervention prototype to predict which individuals are most likely to be exposed to COVID-19 and are therefore most at risk of onward transmission. We will apply statistical modeling and Artificial Intelligence (AI) methods to demographic, occupational, social networking and geolocation data. In simulations, we will test different ‘smart isolation and testing strategies’ that could be used by public health officials to determine which would most effectively reduce viral transmission. Applying AI-informed strategies could enable partial relaxation of confinement rules for lower-risk segments, and allow greater reopening of economic and social life without risking a second wave and further lockdowns.
Image detection for infection control
In a world of pandemics such as SARS and COVID-19, our health care environments have shown their vulnerability. Unfortunately, we have seen outbreaks in long term care facilities in Quebec, Alberta, BC and Ontario, with dozens of seniors at each site contracting and passing from COVID. In addition to life lost, health care workers themselves are put at risk which leads to a shortage of workers during urgent times. Additional costs are realized as patients require more investigations, longer stays in hospitals and additional treatments. Utilizing a MaskRCNN model to separate objects in video, we propose utilizing real-time image detection of humans (visitors and health care workers) using protective wear (I.e. masks, gloves, and gowns) in health care settings to safeguard workers and patients. Our model is also being trained to determine if PPE is donned and doffed accurately, as literature has shown that roughly 40% of Canadian healthcare workers do this incorrectly, further increasing transmission. The results of this project will provide MedDuck with a novel form of image detectionability that will have immediate real-world application.
In Silico Design of Inhibitory Peptides Targeting SARS-CoV-2
The COVID-19 pandemic has claimed millions of lives worldwide and could continue to pose problems in the future. In fact, a number of epidemiological models have shown that the virus may very well become endemic. The challenges associated with the development of therapies against infections with the SARS-CoV-2 virus are compounded by the sporadic apparition of novel variants and strains. SARS-CoV-2 virus depends on the interaction between its Spike protein and the human Angiotensin-Converting Enzyme 2 (hACE2) protein to enter the cells. Once in the cell, viral proteins are involved in a variety of processesthat trigger inflammation and an exaggerated immune response –the so-called “cytokine storm”. Preliminary work completed by our lab has allowed us to identify a high-quality prediction of the comprehensive inter-species interactome, and to identify relevant biological pathways that contribute to the pathological phenotype of the COVID-19 disease. Our current work aims to design short peptides that can effectively disrupt relevant interactions we previously identified between SARS-CoV-2 proteins and human proteins. To this end, we will develop evolutionary algorithmsthat leverage bothin silico protein-protein interaction predictors and in vitropeptidearrays in an iterativeway. Not only will the methodology developed in this work be applicable for the development of therapeutics against COVID-19 and its variants, it will also provide a systematic pipeline that can be deployed for the design anti-viral peptides more broadly.Such a pipeline will be critical to rapidly address future pandemics.
Machine learning and blockchain-backed optimized assignment matching for PSWs to improve understaffing and risk during the COVID-19 outbreak
TriNetra Systems Inc. (TriNetra) is an agile and fast-growing enterprise software development and services company specializing in IT-business alignment for service-oriented architectures, with clients like e-Health Ontario. TriNetra is developing blockchain tools and solutions to aid in establishing a system of trust and transparency in professional healthcare market enterprise architecture, DevOps, cloud and mobile technologies, IT processes, and governance. Their project called “Octochain” uses blockchain technology for an online, fast, easy and reliable way to confirm credentials. TriNetra is collaborating with ConnexHealth, a Personal Support Worker (PSW) placement company, to implement a system for verifying PSW candidate qualifications, achievements, certifications and résumés. TriNetra is proposing to collaborate with Seneca to extend the features of this new blockchain system to include machine learning/artificial intelligence (ML/AI) capabilities to match candidates to job assignments based on their entire profile, including certifications, training, geography, work history, and availability. It increases trust between the PSWs and employers, and thus improves social assistance and medical service planning for elderly and disabled individuals. It empowers PSWs by providing suitable assignments and by improving pay, because of validated credentials and experience. Responding to the vastly changed PSW market and constraints caused by the COVID-19 pandemic, this project will empower, efficiently mobilize and sustainably deploy individual PSWs.
Machine learning and blockchain-backed optimized assignment matching for PSWs to improve understaffing and risk during the COVID-19 outbreak
TriNetra Systems Inc. (TriNetra) is an agile and fast-growing enterprise software development and services company specializing in IT-business alignment for service-oriented architectures, with clients like e-Health Ontario. TriNetra is developing blockchain tools and solutions to aid in establishing a system of trust and transparency in professional healthcare market enterprise architecture, DevOps, cloud and mobile technologies, IT processes, and governance. Their project called “Octochain” uses blockchain technology for an online, fast, easy and reliable way to confirm credentials. TriNetra is collaborating with ConnexHealth, a Personal Support Worker (PSW) placement company, to implement a system for verifying PSW candidate qualifications, achievements, certifications and résumés. TriNetra is proposing to collaborate with Seneca to extend the features of this new blockchain system to include machine learning/artificial intelligence (ML/AI) capabilities to match candidates to job assignments based on their entire profile, including certifications, training, geography, work history, and availability. It increases trust between the PSWs and employers, and thus improves social assistance and medical service planning for elderly and disabled individuals. It empowers PSWs by providing suitable assignments and by improving pay, because of validated credentials and experience. Responding to the vastly changed PSW market and constraints caused by the COVID-19 pandemic, this project will empower, efficiently mobilize and sustainably deploy individual PSWs.
Remote production monitoring in a post COVID manufacturing environment
Given COVID-19 spread around the world, the importance of maintaining health is further understood through maintaining social distancing as when one coughs, sneezes or speaks, they spray small liquid droplets from their nose or mouth, which may contain the virus. If someone is too close to a person, he or she can breathe in the droplets, including the COVID-19 virus. This project proposes an approach to provide a real-time computer vision and deep learning method for inspection of social distancing as well as optimizing production operations while non-essential personnel, i.e. operations managers and manufacturing/production engineers, work remotely for manufacturing organizations. It will be the ideal solution for Canadian manufacturers that are re-starting production post-COVID-19 ensuring that worker safety is maintained without sacrificing production output and quality.
The bandwidth bottleneck has become worse post-COVID-19 as more people are working from home and thereby taking up much bandwidth. Small manufacturers, who cannot afford high-speed internet, are more critically impacted by this limitation. Hence, the goal of this project is to move video processing from the cloud to manufacturing sites via a reliable gateway device design and deployment that does not sacrifice quality and reliability for speed. This means videos do not have to be streamed to the cloud, and only the vital information extracted from the video feeds will be uploaded. Therefore, data transfer to the cloud can be accomplished with low internet bandwidth to ensure live-stream video feeds can be processed in near real-time without loss of quality or reliability for the required monitoring systems at hand.