What relationships between biochemistry and epidemiology?

The epidemiological study of transmissible diseases translates biochemical concepts into empirical parameters: e.g., immunity, genetic variations of host cell's receptors, etc. To facilitate cross-fertilization, minimal understanding of epidemiology by biochemists is desirable.

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In order to apply biochemical, genetic, and medical concepts to the scale of the whole population, epidemiology makes use of empirical parameters whose relationship to the original concept may appear obscure to basic science experts. An elementary understanding of epidemiological concepts may greatly improve the contributions that basic science experts may provide to social medicine. At Sapienza University (Rome, Italy) a very simple implementation of the epidemiological model developed by Lowell Reed and Wade Hampton Frost has been realized to allow students of the course of Clinical Biochemistry (in the Medicine curriculum) to familiarize themselves with epidemiological concepts.

The model is interactive and allows the student to simulate the course of an epidemic and to manipulate and observe the effect of some parameters whose biochemical basis he or she is already familiar with, e.g., immunity or genetic variations of cell receptors responsible for the attack of the virus to the host's cells. The model is available for free use at this web page.

The Reed-Frost model is a very simple one and was developed by the authors around 1928 at Johns Hopkins University (Baltimore, USA) for teaching purposes. Its principles are statistically rigorous, and minimal modifications allow one to adjust several variables.  The model assumes that all the members of a population of size N have K potentially contagious encounters with each other in the serial generation time of the epidemics (the serial generation time being the average time interval between being infected and infecting a new member of the population). The term K is the upper limit of the parameter R0 of the epidemics. Each member of the population may assume only one of three states – Susceptible, Affected and Immune – and these are ordered in the irreversible progression S → A → I. The model defines the probability of transmission of the disease as p=K/(N-1) and iteratively calculates the number of new cases at any serial generation time using the formula: Ai = Si-1 x [1-(1-p)Ai-1]

The model allows students to manipulate some relevant parameters they are familiar with from courses of biochemistry and genetics. In particular, a student may set that a quota of the population is immune prior to the beginning of the epidemic (i.e. possesses antiviral antibodies) because of vaccination or prior exposure to the same virus; and may set that a quota of the population is resistant to the infection (i.e. has a reduced probability of developing the disease) because of a genetic variant of the cell receptor used by the virus. For further information and for using the model please access the web page: https://www.andreabellelli.it/ReedFrost.php or its mirror page: http://biomodel.uah.es/tecnicas/epidem/ReedFrost.php

I am indebted to Prof. Angel Herráez for many fruitful discussions on the subject matter presented in this post and its pertinence to the Educator channel of the FEBS network; moreover Prof. Herráez contributed the graphical elaboration presented in the program and the mirror web site. 

 

Andrea Bellelli

Prof., Sapienza University of Rome

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