Every other day, the world is faced by a myriad of problems at the individual, society and/or global level. Although each of these problems is unique in its occurrence, all the problems cannot be solved using one method. In fact, most of today’s problems require an interdisciplinary approach for effective solution. Interdisciplinary approaches apply a combination of different subjects in the solution of problems through the use of totally new methods (English, 2008). As such, the approach assumes no single universal pattern in problem solving but appreciates the uniqueness of each problem thus customizing an appropriate solution for each of these problems. Most problems in the health, political, mathematics and engineering fields require a combined effort of the many disciplines to tackle them efficiently.

One of the most common problems facing the world today is the emergence of diseases that continue to cripple the global economy. Depending on the scale of the problems, solutions have been proposed but all settle on an interdisciplinary approach. For instance, if a lethal airborne disease was located in an African country, it would raise concern all over the world and lead to concerted efforts to defeat it at the very early stage. In the process of understanding its sources, causative agents and the reasons for its spread within that community, a multidisciplinary approach would have to be applied. In the first place, the subject of mathematics would be used to derive the statistics behind its spread. This would provide detailed insights into the number of people affected within a certain time and also trace the time when the disease first appeared. In addition, economics as well as mathematics would be used to analyze th social political aspects of the community in which the disease is most prevalent. Such instances can paint a clear picture of the environment in which the causative agents reside. For instance, it could be that the disease is caused due to poor waste disposal

The subject of biomedicine would come in very handy as the medical practitioners try to understand the disease. The genetic composition of the causative agents and the life cycle of the virus/bacteria would only be studied using the biomedicine subject. Moreover, the subject of computer science may be effective in coming up with computer models that conceptualize the disease in detail. This is very important as the history of the disease can be tracked down and the chances of infection ascertained with accuracy based on the identified parameters. Furthermore, the computer models can be used to ascertain the probability of future reemergence of the disease even after it has been wiped off. The models may also be used to predict future trends in the development of the disease including mutation of the causative agents when exposed to different environment and media.

The interpretation of the data and results of the studies may require the utilization data interpretation tools. These tools can only be made using the knowledge of computer science to come up with applications that can interpret the said data. For instance, the statistics in the mathematical calculations would need to be interpreted and converted into biomedicine terms that can be understood by the medical practitioners. Moreover, the calculation of the right dosage to treat the disease can also be done using applications from computer science knowledge. In addition to the subjects aforementioned, language is also essential in the understanding of the dynamics of the disease. For instance, the medical practitioners as well as the research team may require translators for effective communication with both the patients and other practitioners. It is therefore true that a better understanding of the problem would require the combination of biomedicine, mathematics, and computer science and language subjects.

References

English, L. D. (2008). Interdisciplinary problem solving: A focus on engineering experiences. In Proceedings of the 31st Annual Conference of the Mathematics Education Research Group of Australasia (Vol. 1, pp. 187-193).

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