Coronavirus is an infectious disease that spreads from person to person. When an infected person is out in the open, it puts the population at risk of disease. So it is important to calculate how a disease spreads from a single infected person to an entire herd of people. Assuming how much people will be infected and how the transmission affects a huge number of people can help to determine the transmission patterns, therefore helping to prevent a disastrous epidemic. Here, mathematics to prevent coronavirus can be an important step to control the damage.
Many variables are considered while calculating the assumptions of the outbreak and transmission. First, the number of days the contagious disease appears. For coronavirus, it is estimated to be 10-14 days after which prominent signs appear. The second important variable is the number of people each person comes in contact with per day. This helps to determine the possibility of transmission and infections a sick person can create. The third variable is the term “herd immunity” which means the level of immunity required in a population that’s needed to prevent an outbreak from happening. If one knows how much to immunize its population, the chain of transmission can stop. The most important variable is the “basic reproduction number” abbreviated as R0 of a particular microbe. It represents the number of people infected by each contagious individual.
These variables are taken into consideration and computed in different formats to prepare a particular conclusion or an outcome. For example: If the R0 of a particular microbe or virus is taken, then we can know how many secondary cases to expect from each infected person. This will help us to determine the level of herd immunity needed to prevent the virus from spreading within the population. This is measured by taking the reciprocal of R0 and subtracting from 1. For measles, with an R0 of 12 to 18, you need to have good immunity somewhere between 92 percent (1 – 1/12) and 95 percent (1 – 1/18) of the population to prevent the virus from the spread. It’s much lower for flu — only around 50 percent.
By pooling all these variables (host population size and dynamics, population immunity rates, the existence of treatments, microbial properties, and more), we can map and forecast the spread of the virus in a population using mathematical models. Sometimes these models overestimate the spread as happened in the case of Ebola in 2014. Whereas in some situations, it has been accurate such as in the case of Cholera outbreak in Yemen. But it is safe to say that Mathematics to prevent coronavirus can be an opening door towards cutting the chain of transmission and preventing major casualties.