Demystifying Mathematics: Herd Immunity to the Non Mathematician
Have you ever wondered why policymakers need evidence from research before they venture into the implementation of policies? Yes, sometimes certain problems call for immediate and prompt action but would you rather make any decision rather than an informed one?
Mathematics has been a backbone for most of the researches that have provided the best of solutions in the world. From the design of the experiment to calculation and selection of samples to discussions and conclusions. Mathematical concepts cannot be overlooked. They provide the best aid to researchers to find solutions to the most pressing world issues.
Epidemiologists have been the most sought-after Applied Mathematicians in recent times due to the existing epidemic. One of the concepts they keep on preaching is the concept of Herd Immunity. In simple terms, the Herd Immunity Concept estimates the number of people to be immunized in a population to halt transmission of disease. This is considered because of the cost and sometimes the ready availability of the vaccines needed for the immunization. The herd immunity concept may seem so complicated or mathematical. Take it this way, if hundred people were to be on a walking journey and it suddenly began to rain, how many umbrellas will be sufficient to shield them from the rain? Not a hundred pieces. It is in the same spirit that it might not be necessary to vaccinate every individual in the population. Herd immunity, therefore, has to be the threshold to meet to cover everyone from the disease.
The next question then is; who gets vaccinated? This brings about another Mathematical Concept called Disease Mapping. In marketing, there is what is called segmentation. This allows marketers to find the right customers to target to get the best of sales. A similar idea is used by epidemiologists to find the hotspots of common disease and this is called Disease Mapping. The approach to disease mapping is such that the region, say the country of is divided into smaller regions, in our case districts and the risk of each of the districts assigned to it on a map. Districts with high risks are therefore targeted to channel relief programmes and support. The risk defined here is a ratio of the observed disease mortality to the expected mortality from the disease in the respective districts. To find this expected mortality, the overall disease-induced mortality risk is multiplied by the respective population.
The population and the disease prevalence in the districts help Epidemiologists to map the disease risk and these help policymakers to do the most appropriate thing.
