Editor-in-chief Vitaly Volpert of the Camille Jordan Institute of the University of Lyon, France, examines how the new public clinical discipline, mathematical modeling, is being used to monitor and combat the COVID-19 pandemic.
Predicting the long term has been risky, but never more than in the early 2020.COVID-19, a disease that in January deserved a little footnote in medical journals, exploded in less than a year on each and every continent except Antarctica, killing more than three-quarters of a million other people in August 2020.
An unforeseen effect of the pandemic is an unprecedented public interest in understanding the evolution of the disease and, therefore, on mathematical modelling.
In a recent opinion piece on the special factor in Natural Phenomenon Mathematical Modeling, editor-in-chief Vitaly Volpert describes how modeling is used to counter the virus.
COVID-19 has been compared to influenza, but differs from influenza in several respects: the incubation age is longer, mortality is higher (perhaps 2 to 4%) and the diversity of severity is superior, from the absence of symptoms to severe pneumonia, multiorgan insufficiency and death.
In the case of influenza, for which there are effective vaccines, the number of inflamed and recovered Americans plus those vaccinated will give more quickly or later “collective immunity”.”Simply put, there’s no one else who inflames and the disease goes away,” Says Volpert.
The absence of a COVID-19 vaccine means that collective immunity will only occur once other people are infected: currently, only 5-10% of the maximum populations appear to have been in contact with the virus, allowing it to spread enough to create collective immunity will overwhelm any fitness system.
Modeling the progression of such an epidemic is relatively simple, the number of cases over time is governed by a differential equation with an exponential service as a solution.
The key number of this service is R0, the average number of other people inflamed for each new case: when R0 is greater than one, the infection grows, below one decreases.R0 cannot be known in advance, but you will need to adjust to the data; can be replaced quickly, as in the UK when modelers persuaded the government to leave because of the blockade.
Exponential styles have their limits on the expectation of the progression of the epidemic, because their parameters have characteristics such as seasonality, the progression of immunity and, most complicated of all, the behavior of people.The epidemic has shown once behind us that we should not stylize.and manage complex systems by adding collective behaviors.”
“The vast majority of paintings on the COVID-19 epidemic deal with epidemiology; some issues, such as the immune reaction to coronavirus or the pathophysiology of coronavirus disease, are left out of modeling efforts due to the complexity of these phenomena,” volpert.Explains.
However, beyond epidemiology, mathematical models can also perceive the disease procedure and how our immune formula counteracts it.For example, in severe cases, COVID-19 causes inflammation of lung tissue (pneumopathy) leading to the formation of blood clots in the lungs..
Mathematical models are used to waiting for the spread of clots and the effects of anticoagulant drugs.The main difficulties of these models stem from the variation between patients.
“In practice, we want to design the lungs of an ‘average’ patient; the ideal of personalized medicine has not yet been achieved,” says Volpert.
Other teams design the effect of the virus on cells that secrete mucus in the airways.Cells inflamed with the virus secrete less mucus and this mucus moves less successfully through the tract, causing other breathing problems.
COVID-19 can also cause the body’s immune formula to overreact, a ‘cytokine typhoon’, which can be fatal.The mathematical models of this reaction are progressive, but there is still much knowledge to validate.
However, some useful data can be extrapolated from very similar models of SARS coronavirus, which, such as COVID-19 coronavirus, enters a receptor called ACE2 into its host cells.By modeling how the virus interacts with this receptor and those cells, the researchers hope.to be more informed about the progression of the disease.
Another vital point is an imaginable interaction between COVID-2 and influenza, which is feared to lead to “spikes” in serious illness during the winter months. Modeling at the molecular level of the protein spike on the surface of the virus that binds to the receptor is another promising study direction that may provide useful data for drug and vaccine design.
Now what? Months after the outbreak of the pandemic, there’s good news and bad news.Although the number of cases continues to increase, there are fewer deaths because doctors are learning to better treat the disease.
We now know of drugs developed for other diseases that can fuel the storm of calming and healing cytokines; However, express drugs and vaccines against COVID-19 are still far away and the timing of their progression is uncertain.
However, as Volpert concludes: “Scientific studies are a long and complicated process, and progress is slow, but without it, there is no progress.We must not forget this, not only the dramatic infectious epidemics, but also among them..”
SciencePOD
Volpert, V., (2020) Mathematical modeling in the coronavirus (Six months in a new reality). Mathematical modeling of naturels.doi.org/10.1051/mmnp/2020027 phenomena.
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