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Abstract

Bayesian Inference for Infectious Disease Modeling: A Case of SIR Model

Presentation Date: Feb 14, 2026

AGSA Abstract

Abstract


The Susceptible–Infectious–Recovered (SIR) model is a foundational framework in epidemiological modelling, used to describe how infectious diseases spread within a population. This project demonstrates how Bayesian inference can be applied to estimate the key SIR parameters—the transmission rate ($\beta$) and the recovery rate ($\mu$)—from observed epidemic data. We implement this Bayesian framework in both Python and R using probabilistic programming tools.


Presenting Author


J

Jacob Kapita

Department of Mathematics, Louisiana State University, Baton Rouge, LA. USA.


Authors


L

Leonce Leandry

Department of Mathematics, University of Dar es Salaam, Dar es Salaam, Tanzania

G

Graceful Mulenga

Department of Mathematical and Statistical Sciences, Botswana International University of Science and Technology, Gaborone, Botswana

M

Mihle Mpofu

Department of Statistics, Nelson Mandela University, Port Elizabeth, South Africa

M

Mary Kiarie

Department of Mathematics and Statistics, Technical University of Kenya, Nairobi, Kenya

L

Luba Pascoe

School of Computational and Communication Sciences and Engineering, Nelson Mandela African Institution of Science and Technology, Arusha, Tanzana

I

Iurii Lebedev

E

Enrico Bibbona

Department of Mathematical Sciences, The Polytechnic of Turin, Torino, Italy

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