Paper Sample on Social Distancing: An Effective Measure to Fight Coronavirus (COVID-19)

Introduction

As the spread of Coronavirus (COVID-19) rapidly increases, nations worldwide have been requested to practice social distancing between people. This social distancing art means limiting close physical contact between individuals and has been declared an efficient way of minimizing the spread of the virus. The principal justification of the practice of social distancing is to reduce transmission of the coronavirus among people. Theoretically, social distancing brings about fewer spreads, hence fewer infections by the disease, and finally better care for the fewer people who get infected (Whitebloom, 2020). This activity developed within the 21st century means that people should stay at their homes as much as possible and avoid crowded places where there are higher chances of close physical contact among people.

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Critique

Mathematical modelling is applied when we try to solve a word problem and change the verbal statements into equations. Say, in an equation, there is need for an independent variable X whose variations or movement affects the movement of a dependent variable Y (El-Sahili, Al-Sharif, & Khanafer, 2015). Social distancing is good in mathematical modelling because it can be used as one parameter to determine the spread of Covid-19; it can satisfactorily be used as an independent variable. Social distancing is being practiced in real life for instance; many countries have gone into lockdown phases where they are banned from going outside their houses, meetings of over ten people have been canceled worldwide, enterprises like shops, restaurants, and bars have been temporarily closed down, and a more significant percentage of schools have moved into online learning.

In this problem, mathematical modeling comes about in keeping a safe distance of two meters or six feet apart. Modeling can also be incorporated into helping us determine how many people could be infected by already infected persons. For example, an infected person that does not practice social distancing could transmit the virus to around three people in five days; those three persons have a probability of transmitting the virus to another three people. Approximately 1,093 individuals will have been infected with the disease in a span of six weeks. However, when an infected person practices social distancing, it has been proven that they can reduce contacts by one third and therefore infect around 127 people in six weeks. On the other hand, if an infected person decides to stay home, the likelihood of infecting others would reduce by 75%. Exposure would approximate to around three or less infections after every six. Vividly, modeling shows that social distancing is active and can safely be used to prevent and curb the spread of coronavirus.

Model Modification

Modifying this problem would mean total isolation of infected persons, where the probability of coming into contact with anyone is 0%. This would, therefore, mean that the infected person will not have a chance to transmit the disease, thus infecting zero people in five days as well as in thirty days. This would mean that social distancing can completely curb and flatten the curve of COVID-19 spread. However, in real life, this is almost not applicable because mathematics only helps predict future events without executing them. The above scenarios are examples of mathematical modeling since they took into account particular circumstances and projected their most likely outcome. Without the model, people might be concerned about why the cases of infections increase daily. Governments have put into force quarantine measures, and global travel bans and mathematics have supported these decisions. This proves that reducing physical contact among people helps reduce the spread of the virus and can be used as a preventive measure.

Reflection

During this extraordinary time, Mathematics is the only known method to predict future scientifically. We are not sure when this pandemic will elapse; therefore, we should use mathematical models to predict how the future will be. This social distance problem can be illustrated and shared with students using graphs; these graphs can entail, probability of physical contact against the number of people infected. In scenario one, the chart will show a steep ascending curve since no social distancing is practiced, thus higher infections. The second graph will show a less steep curve when physical contact is reduced by one third, meaning fewer people are likely to be infected. The next chart, with a reduced probability of close contact of 75%, will reflect an almost flattened curve. When there is a zero probability of contact, the last graph will show a complete flat curve meaning the rate of transmission is wholly suppressed.

That would help the students understand the importance of mathematical modeling in making real-life decisions. In mathematical modeling, there is a consideration of crucial characteristics that must be put in place for a model to be effective (Erbas et al., 2014). For instance, from the above scenarios, we used the probability of physical contact among individuals and the number of daily infections to determine the spread of the Corona Virus. Mathematical modeling can be used to evaluate, predict future events, and probably establish potential solutions to specific problems. For instance, with the Covid-19 case, mathematical models helped different governments worldwide and World Health Organization to come up with possible measures of curbing and flattening the curve of the spread of the coronavirus with social distancing being one of them.

Mathematical models would help the students explore and understand the meaning of equations and functional relationships in real-world scenarios (Akgün, 2015). From the above model, the students would see the functional relationship between social distancing and Covid-19; that is, reduced social distancing increases the chances of contracting Covid-19. For instance, in this case, Covid-19 is a dependent variable, say Y, that relies on the movement of social distance independent variable X. After students have estimated a quantitative outcome using a specific math model, say an equation, it will be easier to compare these results with real-life observational data (Bautista et al., 2014). This would help them identify whether the model is strong or weak.

References

Akgün, L. (2015). Prospective Mathematics Teachers' Opinions about Mathematical Modeling Method and Applicability of This Method. International Journal of Progressive Education, 11(2).

Bautista, A., Wilkerson-Jerde, M. H., Tobin, R. G., & Brizuela, B. M. (2014). Mathematics Teachers' Ideas about Mathematical Models: A Diverse Landscape. PNA, 9(1), 1-28.

El-Sahili, A., Al-Sharif, N., & Khanafer, S. (2015). Mathematical Creativity: The Unexpected Links. The Mathematics Enthusiast, 12(1), 417-464.

Erbas, A. K., Kertil, M., Çetinkaya, B., Cakiroglu, E., Alacaci, C., & Bas, S. (2014). Mathematical Modeling in Mathematics Education: Basic Concepts and Approaches. Educational Sciences: Theory and Practice, 14(4), 1621-1627.

Whitebloom, S. (2020). Social distancing works: Here's the math. Retrieved from University of Oxford: https://www.ox.ac.uk/news/science-blog/social-distancing-works-here-s-maths