Math has been in the spotlight in recent weeks as the Covid-19 pandemic has spread across the world, causing widespread devastation. When you go to a news page, you’ll be bombarded with graphs and charts like you’ve never seen before. As cases double, exponential growth is becoming well-known, and a single carrier can lead to massive infection chains.
With the arrival of COVID-19, researchers have been using and formulating mathematical models as a technique for gaining insight into the pandemic’s mode of spread, transmission, impact, prevention and control, and the impact of preventive measures ranging from hand washing with a disinfectant such as a hand sanitizer, 2 to 5 metres social distance, on the pandemic.
Mathematical modelling is a useful technique for deciphering Covid-19 transmission and examining various scenarios. Understanding how a virus spreads within a population can be done through mathematical modelling. The goal of mathematical modelling is to create a collection of mathematical equations that closely resemble reality.
These are then solved for specific values of the equations’ parameters. When we apply information that we already know about the virus spread, such as available statistics on the reported number of infections, the reported number of hospitalizations, or the confirmed number of deaths owing to the infection, the mathematical model’s answers can be refined.
This model refinement (or calibration) process can be repeated until the mathematical equations’ solutions agree with what we already know about virus spread. The calibrated model can then be used to predict how the virus will propagate in the future. The anticipated epidemic curve, which represents the number of infections induced by the virus over time, is one of the outcomes of mathematical models. The expected epidemic curve can be changed by changing model parameters, which may illustrate alternative interventions, or calibrating the model against different data.
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