Machine Learning to Fight COVID-19
Viral pandemics are a serious threat to humanity, and COVID-19 is not the first, nor will it be the last pandemic. However, we are now better equipped than ever before to collect and share information about the virus. Hundreds of research teams worldwide are working together to gather data and develop solutions.
This article highlights the role of machine learning in combating COVID-19 by helping us to identify who is most at risk, diagnose patients, develop drugs faster, find existing drugs that can help, predict the spread of the disease, understand viruses better, map where viruses come from, and predict the next pandemic. The article also aims to promote research to combat the current pandemic and prepare for the next one.
Identifying who is most at risk from COVID-19
Machine learning is essential in predicting risks in various fields, including medical risks. With respect to COVID-19, machine learning can predict infection risk, severity risk, and outcome risk. Early experiments using machine learning to predict all three risks are promising, but COVID-19-specific machine learning research is still in its infancy. As more and better data become available, we can expect more practical applications of machine learning for predicting infection risk.
Predicting the risk of infection
Early statistics indicate that several factors influence an individual’s likelihood of contracting COVID-19, including age, pre-existing conditions, general hygiene habits, social habits, the number of human interactions, frequency of interactions, location and climate, and socio-economic status. Prevention measures such as wearing masks, washing hands, and social distancing are likely to influence overall risk as well. Although research is still in its early stages, researchers used machine learning to build an initial Vulnerability Index for COVID-19. As more and better data become available and ongoing studies produce results, we will likely see more practical applications of machine learning for predicting infection risk.
Predicting who is at risk of developing a severe case
After a person or group contracts the virus, the next step is to predict the risk of complications or requiring advanced medical care. Although some people experience only mild symptoms, others develop severe lung disease or acute respiratory distress syndrome (ARDS), which can be potentially fatal. It is not feasible to treat and monitor everyone with mild symptoms, but early treatment is more effective for severe cases. Researchers have shown that machine learning can predict the likelihood of a patient developing ARDS and the risk of mortality by analyzing initial symptoms. The study’s limitations include the size of the dataset and a limited spectrum of severity. However, this research is essential groundwork for applying machine learning once more data becomes available.
Predicting treatment outcomes
Predicting the outcome of a treatment is critical, and machine learning can be useful in predicting treatment outcomes for COVID-19 patients. Machine learning can personalize treatment plans, and it has been used to predict treatment outcomes for patients with epilepsy and predict responses to cancer immunotherapy. Although treatment options for COVID-19 are still evolving, machine learning can predict outcomes for specific treatments, allowing doctors to treat patients more effectively.
Screening patients and diagnosing COVID-19
Testing on a large scale for COVID-19 is challenging and likely to be expensive, especially at the beginning of a pandemic. Instead of taking medical samples from each patient and waiting for slow, expensive lab reports to come back, a simpler, faster, and cheaper test would be useful in gathering data on a larger scale. This data could be used for further research, as well as for screening and triaging patients. Machine learning can assist in diagnosing COVID-19 by using face scans to identify symptoms, wearable technology such as smartwatches to look for tell-tale patterns in a patient’s resting heart rate, and machine learning-powered chat.
Diagnosing individuals during a new pandemic is challenging due to the difficulty of large-scale testing, the cost of tests, and the similarity of symptoms to other diseases. To gather data on a larger scale, a faster and cheaper test, even if less accurate, can be useful. Promising research areas for using machine learning to diagnose COVID-19 include using face scans to identify symptoms, wearable technology to detect resting heart rates, and chatbots to screen patients. Machine learning is more suited to screening patients than reliably diagnosing them. In drug development, machine learning can speed up the process of finding potential candidates for testing and help identify effective existing drugs by building knowledge graphs and predicting interactions between drugs and viral proteins.
The use of machine learning in the field of virology has gained significant attention in recent times. Machine learning models have shown promise in various areas, such as predicting the spread of infectious diseases, understanding viruses through proteins, figuring out how to attack the virus, and identifying hosts in the natural world. In this article, we will explore these areas in more detail.
Predicting the spread of infectious disease
Using social networks during a pandemic is crucial to know where the disease is spreading and how quickly it is spreading. While the government and health agencies provide regular updates on the number of cases, there can be a time lag in reporting, making it challenging to track the spread of the disease effectively. Machine learning models trained on social media data can help predict the spread of infectious diseases in real time by analyzing public interactions and assessing the likelihood of virus contamination. This information can be useful in decision-making processes during a rapidly evolving pandemic.
Understanding viruses through proteins
Proteins play a critical role in determining how viruses interact with the human body, and analyzing them can help us understand the virus better. Machine learning models can help in this regard by predicting protein-protein interactions (PPIs) between viruses and human body cells. The virus-host interactome, which is the entire map of interactions between a virus’s and a host’s proteins, can be challenging to map. Machine learning models can significantly reduce the effort required to map the whole virus-host interactome. Furthermore, predicting protein folding, which is critical in determining the protein’s role in the cell, has been a challenging task. However, artificial neural networks have shown promise in predicting protein structures, making it feasible to identify protein structures using computational methods.
Figuring out how to attack the virus
Epitopes are clusters of amino acids found on the outside of a virus that antibodies bind to. Identifying and classifying epitopes is essential in developing effective vaccines, as they help in determining which part of a molecule to target. Machine learning models have proven to be faster and more accurate than human researchers at identifying epitopes. Epitope-based vaccines are safer than traditional vaccines and can prevent disease without the risk of potentially deadly side effects.
Identifying hosts in the natural world
Zoonotic pandemics, such as COVID-19, originate in different species and spread to humans. Identifying the reservoir hosts, which are animals that carry the virus but are unaffected by the disease, is vital in controlling the spread of the disease and preventing future outbreaks. Machine learning models can use genome sequencing data and expert knowledge to pinpoint the species that most likely acted as hosts for the disease. This process can significantly reduce the time and effort required to find reservoir hosts and prevent future pandemics.
Machine learning has shown tremendous potential in the field of virology. By predicting the spread of infectious diseases in real-time, understanding viruses through proteins, figuring out how to attack the virus, and identifying hosts in the natural world, machine learning models can help combat pandemics and save lives.