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.

Background: In the fight against antimicrobial resistance, mathematical transmission models have been shown as a valuable tool to guide intervention strategies in public health. Objective: This review investigates the persistence of modelling gaps identified in earlier studies. It expands the scope to include a broader range of control measures, such as monoclonal antibodies, and examines the impact of secondary infections. Methods: This review was conducted according to the PRISMA guidelines. Gaps in model focus areas, dynamics, and reporting were identified and described. The TRACE paradigm was applied to selected models to discuss model development and documentation to guide future modelling efforts. Results: We identified 170 transmission studies from 2010 to May 2022; Mycobacterium tuberculosis (n = 39) and Staphylococcus aureus (n = 27) resistance transmission were most commonly modelled, focusing on multi-drug and methicillin resistance, respectively. Forty-one studies examined multiple interventions, predominantly drug therapy and vaccination, showing an increasing trend. Most studies were population-based compartmental models (n = 112). The TRACE framework was applied to 39 studies, showing a general lack of description of test and verification of modelling software and comparison of model outputs with external data. Conclusion: Despite efforts to model antimicrobial resistance and prevention strategies, significant gaps in scope, geographical coverage, drug-pathogen combinations, and viral-bacterial dynamics persist, along with inadequate documentation, hindering model updates and consistent outcomes for policymakers. This review highlights the need for robust modelling practices to enable model refinement as new data becomes available. Particularly, new data for validating modelling outcomes should be a focal point in future modelling research.

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.

Extended-spectrum cephalosporin-resistant Escherichia coli (ESC-EC) poses a significant public health concern, with its presence increasingly detected in healthy humans and various animal species. This study explores the transmission dynamic of ESC-EC within the Danish population as well as the transmission impact of a range of food and animal sources. We developed a compartmental model encompassing farmers, pet owners, and the general population. Additionally, we applied an established source attribution model to estimate the contributions to the transmission of different sources using Danish surveillance data on the distribution of resistance genes in E. coli. Our findings highlight the central role of human-to-human transmission while also showing the significant contributions of food and animal sources to the spread of ESC-EC in sporadic human infections. Imported food, pets, and livestock were estimated to contribute importantly to human infections. The results emphasize the complexity of ESC-EC transmission dynamics and the critical value of employing a One Health approach in modeling disease transmission and in the development of targeted intervention strategies.