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Abstract
To estimate the probability of human immunodeficiency virus (HIV)-1 transmission from different key HIV population groups using probabilistic modelling. This study was conducted in December 2020. A probabilistic model was used to estimate the probability of HIV-1 transmission from different key HIV population groups in Larkana. Our model was run on three probabilistic assumptions: 1) each replication gave two conceivable results: 'true' or 'false'; 2) the chance of giving a 'true' result is the same for each replication; and 3) the replications are independent - 'true' in one will not impact the likelihood of 'true' in another. The results estimated the probability of HIV transmission in key HIV population groups in Larkana to range between 0.42-0.54 per trial, where the highest probability of transmission was predicted for men who have sex with men (MSM; 0.54 per trial), followed by transgender (TG; 0.46 per trial) and people who inject drugs (PWID; 0.457 per trial). Our results suggest that there is a high likelihood of HIV transmission by key population groups in Larkana, such as MSM, TG, and PWID. Mathematic models, such as one proposed in our study can aid the HIV and acquired immunodeficiency syndrome (AIDS) control programmes in evaluating and optimising the strategies in controlling transmission of HIV from the key population groups.
Highlights
Human Immunodeficiency Virus 1 (HIV-1) remains a major public health challenge worldwide.[1]
Since our model worked on the Figure-1: Binomial probability for human immunodeficiency virus (HIV) transmission in Larkana: Graph shows the probability of HIV transmission in different HIV key populations in Larkana, Pakistan, namely people who inject drugs (PWID), transgender (TG), men who have sex with men (MSM), and female sex workers (FSW)
This study aimed to use probabilistic modelling to estimate the chances of HIV-1 transmission by different key HIV population groups of Larkana, Pakistan
Summary
Human Immunodeficiency Virus 1 (HIV-1) remains a major public health challenge worldwide.[1]. Between 2000 and 2018, new HIV infections and HIV-related deaths decreased by 37% and 45%, respectively.[2] In countries such as Pakistan, the number of new HIV-1 cases have proportionally increased over the past decade, with a nearly 57% increase in the number of new infections between 2010 and 2017.2 There have been eight documented HIV outbreaks in the past two decades. Mathematical probabilistic models have been used previously to forecast HIV / acquired immunodeficiency syndrome (AIDS) transmissions.[6] They assume the initial HIV burden to model the spread of HIV infection and predict future cases.[7] One past study from Tanzania for example, applied a nonlinear mathematical model to study the effect of screening and treatment on transmission of HIV/AIDS infection in a population.[8] In areas with high HIV transmission, mathematical probabilistic modelling can be useful in determining which key population groups have the greatest risk of HIV transmission; such information can aid in establishing effective interventional strategies
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