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IBM EdTech Youth Challenge projects that help scientists and humanity combat pressing health issues, such as COVID-19, to support healthier lives and protect our region's biodiversity.
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Please note: entries to the IBM EdTech Challenge have now closed. The winning teams will be announced on Friday 12 November 2021.
The World Health Organisation (WHO) has identified preparing for epidemics, the climate crisis and investing in health infrastructure as urgent health challenges for the next decade. Air pollution alone is estimated to kill 7 million people a year while extreme weather events fuel malnutrition and the spread of infectious disease.
How can new technologies in digital health using AI help us revolutionise our ability to not only address but prevent these urgent challenges?
Watch and read the information and definitions below to learn more about zoonotic diseases, the significant threat to global health and security they pose and the importance of combatting their emergence as a public health priority. Below you will find examples of potential AI applications such as AI based virtual assistance, identifying illegal wildlife trade activity through classifying visual and audio content, and text and data mining for disease hotspot detection and prediction. There is also information on next steps of the challenge and additional resources to guide and inspire the focus of your IBM EdTech Youth Challenge project issue and serve as a springboard for further brainstorming ideas that can improve the health outcomes of our community.
A zoonosis is a disease that spreads between animals and humans. Currently there are over 200 known types of zoonoses and make up a large percentage of new and existing diseases.
Wildlife genomics is the use of tissue samples from wildlife populations to analyse the genetic information from DNA for species identification, conservation, population management and identification of zoonotic disease sources.
Biodiversity comes from two words Bio meaning life and diversity meaning variability.
The Atlas of Living Australia (ALA) is a collaborative, digital, open infrastructure that pulls together Australian biodiversity data from multiple sources, making it accessible and reusable.
The ALA helps to create a more detailed picture of Australia’s biodiversity for scientists, policy makers, environmental planners and land managers, industry and the general public, and enables them to work more efficiently.
The ALA is an ideal resource of open source data sets that maybe used in your IBM EdTech Youth Challenge project! You can explore your area and discover species that live near you, and collect information for mapping, visualisation and analysis of the relationships between species, location and environment.
AI and design thinking to combat health issues
Welcome to the IBM EdTech Youth Challenge. One of the themes of the challenge is about health concerns, and how you can use AI solutions to help our frontline health workers. The emergence of the COVID-19 pandemic has brought global health concerns into sharp focus. It's demonstrated that an animal-born disease can disrupt our society on an almost unthinkable scale. How can artificial intelligence assist in the diagnosis of diseases and recommend treatments.
Chief Scientist and Director of the Australian Museum Research Institute, Professor Kris Helgen, will speak to the guiding research undertaken by natural history museums, and how they contribute to the identification of zoonotic diseases like COVID-19. Dr David Alquezar, Manager of the Australian Centre for Wildlife Genomics, discusses how AI and machine learning are already being used for diagnosis and about possibilities for future applications.
At the Australian Museum Research Institute we study the world around us - life sciences, earth sciences, and the cultures across our globe. My specialty is in the study of biodiversity, the richness of life on earth. There are millions of species of animals, plants and other kinds of living organisms on our planet. How do we know how many there are? How do we tell them apart? Where do they live? Which species might carry a particular disease, or play a certain role in the environment? These are the kinds of questions that scientists at this museum are working to find out. Over the last 12 months we've seen one of the central challenges to our global society emerge in the form of a pandemic disease – the coronavirus we call COVID-19. This disease, as best we understand it, seems to have come from a population of wild bats, and this is often how disease in humans first occurs. Most species of infectious diseases that have ever existed in humanity originally came to us from another animal species. Why is it that diseases like the virus that causes COVID-19 seem to be on the increase? As scientists we're realising more and more that human health, animal health, and environmental health are all intricately intertwined.
The typical methods for disease diagnosis such as genetic methods are not perfect. They have their issues and sometimes results may not be definitive. Artificial Intelligence and machine learning have several applications across medicine but in particular they've been heavily used in the diagnosis of disease. Throughout the COVID-19 pandemic C.T. scans and X-rays have been used to diagnose disease. In certain examples artificial intelligence and machine learning can assist in the diagnostic process. AI has been able to analyse C.T. scans at a much higher rate than what your typical radiologist may be able to do. Also, it has its benefits when certain health laboratories are under a lot of pressure to analyse a lot of samples. AI and machine learning techniques can be used to predict new vaccines as well as new therapeutic agents. Perhaps in your EdTech Youth Challenge you could also come up with some ideas where AI may be able to be applied for health concerns. When you're thinking about your EdTech Youth Challenge, think about how the use of images could be applied. Think about how machine learning or artificial intelligence might be applied to the range of images of museum materials that could be generated through the study of collections behind the scenes in museums. Think about technological solutions to better compare the distribution of bats, the species involved, and the sequences of DNA that many different labs have studied around the world from bats in Southeast Asia.
Hearing about this Australian Museum research may inspire your project focus for the IBM EdTech Youth Challenge and how you can apply artificial intelligence to deliver solutions that address a local health issue. To learn more about the role of artificial intelligence in tackling health concerns you can review the additional resources on the Australian Museum website. These resources identify how AI is already being applied to help health workers diagnose cases of COVID-19. Is there a health issue your team would like to tackle in this challenge? Follow the design thinking methodology through the project log book to develop your idea for an AI solution. And don't forget our experts are available to help answer your questions about project ideas. Simply use the online inquiry form on the Australian Museum IBM EdTech Youth Challenge website.
COVID-19 and zoonotic diseases
The emergence of a new pandemic coronavirus has shown the world, across a matter of months, that an animal-borne disease can serve as a global disruptor on an almost unthinkable scale, bringing connections between wildlife, disease, environment, and our global society and economy into sharpest focus. Professor Kris Helgen (Chief scientist and Director, Australian Museum Research Institute)
Zoonotic diseases (germs that spread between animals and people) account for approximately 75% of all emerging infectious diseases today. Professor Kris Helgen (Chief scientist and Director, AMRI) writes in COVID-19 and zoonotic diseases, that one of the vital scientific contributions made by natural history museums like the AM is the study of connections between animals and human diseases.
Large biodiversity collections from natural history museums are important resources for understanding the biology of pandemic diseases that arise from animals. They give us insights into the identification of disease sources and vectors (an agent that carries and transmits disease) and molecular information that helps in detection and surveillance.
The destruction and degradation of wildlife habitat, climate change impacts, urbanisation and wildlife trade all factor into the increasing emergence of zoonotic diseases. Are similar activities happening in your community? Can you identify an AI process to help scientists capture data or address risks of such issues in the IBM EdTech Youth Challenge?
How zoonotic diseases are transmitted
Understanding how human behaviour or impact on our local environment contributes to health concerns, or by capturing data that documents connections contributing to known diseases, could be a focus in your team's challenge: to predict potential sources of health concerns using AI.
Zoonotic diseases for example are transmitted when a pathogen (virus or bacterium) found in one species is passed onto a human host. The below diagram illustrates how a zoonotic disease can be transmitted, and could be translated into a machine learning tool like a virtual assistant or chatbot to diagnose or identify potential sources through queries to map species connections.
Timeline of zoonotic diseases
The below timeline gives a snapshot of the geographical and animal origins of the most notable zoonotic diseases in the last 50 years. This represents existing research into past diseases that could be harnessed by AI to predict future scenarios. The occurrence of emerging infectious disease (EID) events have risen significantly over time and is thought to be driven largely by socio-economic, environmental and ecological factors. 
The Spanish flu pandemic was the first true global pandemic and the first to occur in the context of modern medicine. The origins of the disease are debated with scientists believing it to have originated in either pigs or birds.
SARS (severe acute respiratory syndrome)
SARS is a viral respiratory illness first reported in Asia and found to have been transmitted from civet cats to humans.
H5N1 (Avian flu)
Avian influenza is a disease caused by infection with avian (bird) influenza (flu). These viruses occur naturally among wild aquatic birds and can infect domestic poultry and other animals species. The first known transmission of H5N1 to humans occurred in Hong Kong.
H1N1 (swine flu)
Swine influenza is a respiratory disease of pigs that regularly causes outbreaks in pig herds. The first known transmission of H1N1 to humans occurred in Mexico.
West Nile Virus
The West Nile Virus (WNV) is transmitted in nature between birds and mosquitoes. Human transmission is usually caused by infected mosquitoes.
Ebola first appeared in South Sudan and is a a rare but often fatal illness. The virus is transmitted to people from non-human primates, fruit bats, forest antelope or porcupines.
MERS (Middle East respiratory syndrome)
MERS is a virus transferred to humans from infected Arabian camels first appearing in several countries in the Middle East, Africa and South Asia.
COVID-19 (Coronavirus disease)
COVID-19 is an infectious disease caused by a newly discovered coronavirus. The disease was first identified in the Chinese city of Wuhan and most researchers think it originated in bats.
Role of AI in health
Amidst the global health emergency caused by COVID-19, the opportunities for AI and machine learning applications to help in epidemic modelling, prediction and diagnosis is increasing. Some examples of possible applications are outlined below to serve as a springboard for further brainstorming ideas.
|AI based virtual assistance||AI can provide virtual assistance as a symptom checker to help users get instant and accurate medical information. AI systems can provide support to reach remote regions with limited access to healthcare as well as the translation of applications into any language.|
|Classifying visual and audio content||Using social media data collected via APIs, AI can be used to identify illegal wildlife trade activity online by processing audio and visual content eg. pangolin scales or rhinoceros horns. Using metadata specifying geographic location and time stamps, AI can also inform us of trade patterns and routes over time.|
|Text data mining and processing||By extracting and processing data from digital sources such as GPS, credit card transactions and social media, contact tracing information can be used to determine disease 'hot spots' and implement adequate control measures to break the transmission chain.|
Describe a local environmental sustainability problem.
Knowledge of AI
Is there an AI solution to assist in solving this problem?
Understanding the user
Who will benefit from the solution and how.
Document how creative and critical thinking were used to brainstorm, with one solution being prioritised.
Identify potential data sources, and investigate a data sample and privacy issues.
CSIRO - Protecting Australia from emerging infectious diseases
National Center for Emerging and Zoonotic Infectious Diseases (NCEZID)
Why museums are a keystone in fighting future pandemics: Kristofer Helgen
Journals & publications:
DNA sonication inverse PCR for genome scale analysis of uncharacterized flanking sequences
Alquezar-Planas D, Lober U, Cui P, Quedenau C, Chen W & Greenwood A, 'DNA sonication inverse PCR for genome scale analysis of uncharacterized flanking sequences': Methods in Ecology and Evolution.
The role of artificial intelligence in tackling COVID-19
Arora N, Banerjee A & Narasu M, 2020, The role of artificial intelligence in tackling COVID-19,: Future Virology.
Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.
Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X, Deng L, Zheng C, Zhou J, Shi H & Feng J, 2020, 'Development and evaluation of an artificial intelligence system for COVID-19 diagnosis': Nature Communications.
Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study
Randhawa G, P. M. Soltysiak M, Roz H, P. E. Souza C, A. Hill K & Kari L, 'Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study': Plos One.
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19
Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, He K, Shi Y and Shen D, 'Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19': IEEE Xplore.
Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data
Villarreal-González R, J. Acosta-Hoyos A, A. Garzon-Ochoa J, J. Galán-Freyle N, Amar-Sepúlveda P and C. Pacheco-Londoño L, 'Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data': Molecules.
Do you have a question?
- Nature, Global hotspots and correlates of emerging zoonotic diseases, October 2017
- WHO, Zoonoses, July 2020
- ICOPHAI, The global one health paradigm, November 2014
- Nature, Global trends in emerging infectious diseases, February 2008