Stochastic method to control Mycobacterium tuberculosis epidemic

Saurav Mandal; Shakti Nath Singh; Md Zubbair Malik; R K Brojen Singh

doi:10.1016/j.compbiolchem.2020.107250

Stochastic method to control Mycobacterium tuberculosis epidemic

Comput Biol Chem. 2020 May 18:87:107250. doi: 10.1016/j.compbiolchem.2020.107250. Online ahead of print.

Authors

Saurav Mandal¹, Shakti Nath Singh¹, Md Zubbair Malik², R K Brojen Singh³

Affiliations

¹ School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India.
² School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India. Electronic address: zubbairmalik@jnu.ac.in.
³ School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India. Electronic address: brojen@jnu.ac.in.

PMID: 32590222
DOI: 10.1016/j.compbiolchem.2020.107250

Abstract

We study the origin of TB (tuberculosis) epidemic and complex distributions of various populations of TB infection within the stochastic framework. The stochastic nature of this disease infection could be linked to the stochastic behaviour at genome level which is exhibited in SNP (single nucleotide polymorphism) distributions of experimentally identified hotspot driver genes. Our results show the emergence of random clusters, and well-defined discrete domains of the respective species populations in the model driven by demographic stochasticity and intrinsic complex species interaction. The multifractal analysis of the time series of the species populations indicate that TB epidemic could be mainly caused by contact communication and is directional. We propose that any TB epidemic may have high chance of approximately periodic recurrence and can be controlled by optimizing some of the parameters involved in the system modelling.

Keywords: Master equation; Multifractal analysis; Random clusters; SNP; Stochastic TB model.