Page 10 - Dream May 2020 English

COVID-19 SPECIAL
DISEASE DYNAMICS
Epidemiological model and its impact on public health policy: A COVID-19 PERSPECTIVE
Rintu Nath
Since the emergence of the novel coronavirus (SARS- CoV-2) in December 2019, it has made a profound impact globally within the last three months, making it the most serious pandemic in recent history.
Initially known as the ‘2019 novel coronavirus’, it was renamed on 11 February 2020 as “severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)” by the International Committee on Taxonomy of Viruses (ICTV). The World Health Organisation has declared “COVID-19” as the name of the disease it causes.
In the absence of any vaccine, the role of public health measures is limited to non-pharmaceutical interventions (NPIs), and therefore, epidemiologists, health workers and policymakers around the world are exploring strategies and public health measures to mitigate or reduce the impact of this pandemic and thereby minimise hundreds and thousands of deaths and the burden on existing resources of public health systems.
The World Health Organisation and public health departments across the globe are closely monitoring incidences related to the spread, the number of people getting the infection, recovery rate, death rate, and many other associated parameters like geographic location, population density, existing healthcare facilities and other factors that contribute to the dynamics of an epidemic. While some of this information may make news headlines, the actual use of this high-volume and high-dimensional information requires careful processing and interpretation to fight the virus in the real world!
To harness the high-volume information arising from an emerging epidemic, epidemiologists and health scientists adopt mathematical and statistical methodologies and construct epidemiological models to predict the paths that an epidemic would take. They develop the models using historical and existing data and explore how an epidemic would unfold under different scenarios – both in the absence and presence of various intervention strategies, thus providing valuable insight for the policymakers regarding the scope and opportunities of different interventions. In this article, we will discuss the basic ingredients of the epidemiological model development process and explore how these models play an important role in our war against the epidemic.
History of epidemiological models
Before the emergence of SARS-CoV-2, the world witnessed several outbreaks of infectious diseases – smallpox, polio, cholera, chickenpox, Zika, Ebola, and SARS to name a few.
Some of these diseases were eradicated completely and some were controlled, adopting guidelines based on epidemiological models.
The first mathematical model to understand an epidemic was done for smallpox. In 1766, a Swiss mathematician, Daniel Bernoulli, developed a mathematical model to analyse the mortality due to smallpox. Bernoulli created a mathematical model to defend the practice of inoculating against smallpox. The calculations from his model established that universal inoculation against smallpox would increase the life expectancy. The setting stone of modern epidemiological models was founded by two British epidemiologists, A.G. McKendrick and W.O. Kermack, who published their theory in a set of three seminal research articles between 1927 and 1933. It is interesting to know that their work was based on the plague epidemic in Bombay (now Mumbai). They are often credited as the first to develop modern models of disease dynamics; their approach has proven both flexible and robust and related to different variants of modern epidemiological models.
A simple epidemiological model (SIR)
There are several variants of epidemiological models. The spread of any infectious disease from person to person is captured by the ‘between-host’ model. For a pandemic situation, the global outbreak across several geographically separated populations is explained by the 'meta-population’ model. Last but not the least, the interaction of the virus within host cells is represented as 'within-host’ model. Here we will describe a simple between-host model, such as those used to model COVID-19 epidemic, using a simple framework called SIR compartmental disease model.
An SIR model is a compartmental disease model in which each individual in the population is assigned to one of the three compartments, namely “Susceptible”, “Infectious”, and “Recovered”. An individual moves from one compartment to another during the course of the epidemic. An SIR model computes the theoretical number of people infected with a contagious illness in a closed population over time.
Susceptible individuals do not have immunity to the disease; therefore, they can become infected. Susceptible individuals can move into the “Infectious” compartment through contact with an infectious person. Infectious people can spread the infection to other susceptible individuals, and after a certain
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