News and Announcement

Basic Idea of Epidemiological Modelling

Saturday, April 27, 2024

We are honored to announce that Dr. Andrew Omame, Assistant Professor at Department of Mathematics, Federal University of Technology Owerri Nigeria, will be the distinguished guest speaker for this event.  Dr. Omame   is a renowned expert in the field, and his talk promises valuable insights into mathematical modeling and especially in Epidemiological Modelling. Epidemiological modeling is a crucial tool in public health that helps us understand and predict the spread of diseases within populations. At its core, it's about using mathematical equations and computational techniques to simulate how diseases move through communities over time. Here's a breakdown of the basic idea:

Guest Speaker: Dr. Andrew Omame

Affiliation: Assistant Professor at Department of Mathematics, Federal University of Technology Owerri Nigeria


Moderator: Dr. Muhammad Imran Asjad

Director CMAP, School of Science SSC, UMT


Why is Epidemiological Modelling important? 

Population Structure: Epidemiological models start by defining the population being studied. This can include factors such as demographics (age, gender), geographic location, and social connections.

Disease Dynamics: The next step is to understand the dynamics of the disease being studied. This involves factors such as the incubation period, infectious period, transmission routes (e.g., respiratory droplets, direct contact), and the effectiveness of interventions like vaccines or treatments.

Compartmental Models: One common approach to epidemiological modeling is compartmental models, which divide the population into compartments based on their disease status. The most basic compartmental model is the SIR model, which divides the population into three compartments: Susceptible (S), Infected (I), and Recovered (R). As the disease spreads, individuals move between these compartments based on factors such as infection rates and recovery rates.

Parameters and Variables: Epidemiological models rely on various parameters and variables, such as transmission rates, recovery rates, and mortality rates. These parameters are often estimated from data or inferred from previous studies.

Simulation and Analysis: Once the model is set up with appropriate parameters, it's simulated over time to see how the disease spreads within the population. By analyzing the simulation results, researchers can gain insights into important questions such as the potential impact of different interventions (e.g., social distancing, vaccination campaigns) and the likely trajectory of the epidemic.

Validation and Refinement: Epidemiological models are constantly refined and validated as new data becomes available. This process helps improve the accuracy of the models and their ability to inform public health decision-making.

Overall, epidemiological modeling provides a powerful framework for understanding the dynamics of infectious diseases and guiding public health responses to outbreaks..

We look forward to your participation and to sharing this insightful seminar with you.

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