No abstract

Containment syringe sharing among individuals is considered to be the most contributing factor to human immunodeficiency virus (HIV). It’s well recognized that sharing syringes significantly contributes to the transmission of diseases amongst individuals. This study examines how syringe sharing may contribute to HIV infection and spread among injectable drug users. Sharing syringes greatly aids in the spread of infections among people, as is widely acknowledged. The model was calibrated using data from Malaysia from 2000 to 2011 on the incidence of HIV among drug injectors. Through the use of the Markov chain Monte Carlo simulation approach, the parameters are estimated using Bayesian inference. The basic reproduction number for HIV disease suggests that the disease-free equilibrium was stable during the 12 years. This is a good indicator from the public health point of view since the goal is to stabilize the infection rate. Our findings emphasized the potential involvement of syringe sharing in the transmission of HIV among injectable drug users and the need for more research into this infection rate in order to improve strategies for reducing the incidence of individual HIV cases among people who inject drugs.

Campylobacter spp. are the most common cause of bacterial gastrointestinal infection in humans both in Denmark and worldwide. Studies have found microbial subtyping to be a powerful tool for source attribution, but comparisons of different methodologies are limited. In this study, we compare three source attribution approaches (Machine Learning, Network Analysis, and Bayesian modeling) using three types of whole genome sequences (WGS) data inputs (cgMLST, 5-Mers and 7-Mers). We predicted and compared the sources of human campylobacteriosis cases in Denmark. Using 7mer as an input feature provided the best model performance. The network analysis algorithm had a CSC value of 78.99% and an F1-score value of 67%, while the machine-learning algorithm showed the highest accuracy (98%). The models attributed between 965 and all of the 1224 human cases to a source (network applying 5mer and machine learning applying 7mer, respectively). Chicken from Denmark was the primary source of human campylobacteriosis with an average percentage probability of attribution of 45.8% to 65.4%, representing Bayesian with 7mer and machine learning with cgMLST, respectively. Our results indicate that the different source attribution methodologies based on WGS have great potential for the surveillance and source tracking of Campylobacter. The results of such models may support decision makers to prioritize and target interventions.