A stochastic differential equation model for the spread of HIV amongst people who inject drugs

Liang, Yanfeng and Greenhalgh, David and Mao, Xuerong (2016) A stochastic differential equation model for the spread of HIV amongst people who inject drugs. Computational and Mathematical Methods in Medicine, 2016. 6757928. ISSN 1748-6718 (https://doi.org/10.1155/2016/6757928)

[thumbnail of Liang-etal-JCMMM-2015-A-stochastic-differential-equation-model-for-the-spread-of-HIV-amongst-people-who]
Preview
Text. Filename: Liang_etal_JCMMM_2015_A_stochastic_differential_equation_model_for_the_spread_of_HIV_amongst_people_who.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (1MB)| Preview

Abstract

In this paper, we introduce stochasticity into the deterministic differential equation model for the spread of HIV amongst people who inject drugs (PWIDs) studied by Greenhalgh and Hay [10]. This was based on the original model constructed by Kaplan [17] which analyses the behaviour of HIV/AIDS amongst a population of PWIDs. We derive a stochastic differential equation (SDE) for the fraction of PWIDs who are infected with HIV at time t. The stochasticity is introduced using the well-known standard technique of parameter perturbation. We first prove that the resulting SDE for the fraction of infected PWIDs has a unique solution in (0,1) provided that some infected PWIDs are initially present, and next construct the conditions required for extinction and persistence. Furthermore, we also show that there exists a stationary distribution for the persistence case. Simulations using realistic parameter values are then constructed to illustrate and support our theoretical results. Our results provide new insight into the spread of HIV amongst PWIDs. The results show that the introduction of stochastic noise into a model for the spread of HIV amongst PWIDs can cause the disease to die out in scenarios where deterministic models predict disease persistence. Hence in situations where stochastic noise is important predictions of control measures such as needle cleaning or reduction of needle sharing rates needed to eliminate disease may be overly conservative.

ORCID iDs

Liang, Yanfeng ORCID logoORCID: https://orcid.org/0000-0002-0592-876X, Greenhalgh, David ORCID logoORCID: https://orcid.org/0000-0001-5380-3307 and Mao, Xuerong ORCID logoORCID: https://orcid.org/0000-0002-6768-9864;