Will we ever be rid of COVID?
28 September 2020
I think it’s about time to address the elephant in the room. Yes, we are now two blogs deep, soon to be three, and yet to comment on the current climate.
It’s everywhere (practically).
It’s life altering (literally).
It’s invisible (well, microscopically).
Like almost everyone, I too have been wondering when this particular episode of Black Mirror will end. However, it appears, unlike Bandersnatch, we are seemingly unable to rewind 10 minutes prior to disaster striking and pick an alternative ending. Isn’t hindsight a wonderful thing.
But, I think many of us are wondering: “What comes next?”
Short answer: no one fully knows, but there are ways to scientifically ‘guess’.
Let me elaborate.
Where maths marries medicine
Mathematical modelling of disease outbreak and progression is not a novel concept, having been practiced since the 18th century. However, the power of such statistical models has been significantly increased due to the technological advancements seen over recent decades. Indeed, collaboration between clinical and computational scientists has allowed for accurate predictions of disease onset, progression and trends to be made.1
It’s all very well preaching the importance of complex scientific principles, but hard to see how it may be relevant to the average person’s daily life. Let me give an example.
We all know that every year, close to Christmas time, there will be an outbreak of the flu. We also know that every year the strain of flu going around is different from the previous year. Finally, it is also common knowledge that a vaccine will be available to help protect us against this form of the flu virus. This is not information we were born knowing. Indeed, despite it all now feeling like common sense, it is something we came to learn based on the application of computational concepts in infectious disease research.2
Hopefully, it is now abundantly clear how essential informatics is in infection control.
Computing COVID: are we finally seeing 2020
Now I have introduced informatics positive place in in disease prediction, it is time to focus on its role in the current pandemic.
So, to go back to my initial, title, question: why can’t we say when this is going to end and what the end result will be?
Many computational models have been made since the COVID-19 outbreak was first identified in Wuhan (China) late 2019. However, there is high variability between such models, making an overall consensus on the nature and thus ‘end result’ of the disease unclear. This can be attributed to a lack of available data, especially at the beginning of the pandemic. Even now, with increasing cases and thus more accessible information on the condition, there is such high variability in results between individuals it makes assessing the number of cases that have occurred/are occurring very difficult.3 Indeed, combining this with the virus’ rapid primary progression, high infection rate, and a divergence in approach between different countries on how to handle the pandemic, making a truly accurate prediction is challenging. It is hoped, however, that with the continual assessment of new, daily generated data, disease-related predictions increase in accuracy over time.
So why should we care about computing?
I can appreciate how it may seem technology is failing us a little in current climate, however, despite teething issues, the role of informatics in present times is vital. Much like making a vaccine, it takes time to optimise a mathematical model.
And, although current times are extremely challenging, this does not mean the future will also be as bleak. Present efforts in trouble shooting relevant technology have been tremendous. Indeed, current models are being regularly revaluated to best predict the nature of the disease. So, to end on a positive note (something we all perhaps need), here are just some of the ways that computing is and will continue to aid us in the ‘fight’ against COVID-19.
- Models are being constructed that anticipate the order symptoms will appear.4 This will allow for better screening of the disease, allowing us to know when we have COVID as opposed to the common cold or flu. Overall, this will make the quarantining process more efficient.
- Computational immunology has revolutionised the way vaccine can be developed.5 This is a field of science where principles of informatics and mathematical modelling have been applied to immunology, the study of the immune system and its afflicting ailments. So, because computing had already been used in past to demonstrate how SARS-CoV-2 (COVID-19) affects the immune system, vaccine development could start immediately, fast tracking years of work that would have been needed otherwise.
- Modelling is being used and developed to predict when social distancing measures should be changed. Indeed, such predictions have already played into our daily lives through the introduction of the 2m distancing rule.5 Although, at times, difficult, these regulations are essential in keeping us all as safe as possible.
Although obvious, I find it necessary to reiterate current advice. Whilst efforts are being made by health care and scientific staff alike (and of course, all other key workers), to resolve our current situation, it is essential we all continue to play our part in stopping the spread of COVID-19. We can do this by maintaining good hygiene, social distancing and following our local COVID-related guidelines.
- Burke, D.S. Computational Modeling and Simulation of Epidemic Infectious Diseases. In Microbial Threats to Health: Emergence, Detection, and Response. Washington (DC): National Academic Press; 2003.
- Guo, D., Li, K., Peters, T., Snively, B.M., Poehling, K.A. & Zhou, X. Multi-scale modeling for the transmission of influenza and the evaluation of interventions toward it. Sci Rep. 5, 8980 (2015).
- Roda WC, Varughese MB, Han D, Li MY. Why is it difficult to accurately predict the COVID-19 epidemic? Infect Dis Model. 2020; 5:271-281.
- Larsen, J.R., Martin, M.R, Martin, J.D, Kuhn, B. & Hicks, J.B. Modeling the Onset of Symptoms of COVID-19. Front Public Health. 2020; 8:473.
- Nature. How computation immunology changed the face of COVID-19 vaccine development [Accessed 25th September 2020].
- The Centre for Evidence Based Medicine (CEBM), The University of Oxford. What is the evidence to support the 2-metre social distancing rule to reduce COVID-19 transmission? [Accessed 25th September 2020].
About the author
I achieved a First-Class Honours in Neuroscience at the University of Edinburgh, graduating in 2017. Following this, I worked in the Clinical Neuroscience Department at The University of Cambridge. I am currently in my third year of studying Medicine at The University of Glasgow. I am interested in the role of online platforms in medical education and science communication and research.
Caitlin's LinkedIn profile