Biology subjects

Hobbelen, P.

Publications and source records attributed to Hobbelen, P..

3 recordsLinked to original sources

bioRxiv2025

Mathematical modelling of in vitro replication dynamics for multiple highly pathogenic avian influenza clade 2.3.4.4 viruses in chicken and duck cells

The introduction and subsequent detection of highly pathogenic avian influenza (HPAI) in poultry is influenced by the virus replication fitness, transmission fitness, and virulence in poultry. These viral fitness parameters are important for implementing surveillance and control measures for poultry. This study investigates the potential application of an avian in vitro model using primary chicken embryo (CEF) and duck embryo fibroblasts (DEF) to identify the viral fitness for a reference panel of eight dominant HPAI clade 2.3.4.4 virus genotypes: four H5N1 viruses isolated between 2021 and 2024, as well as three H5N8 and one H5N6 virus isolated between 2014 and 2020. Infectious virus titre and cytopathogenicity were measured in the primary cell cultures over time and these data were analysed using a mathematical model which delineates cell populations into susceptible, latent, infectious, and dead compartments. In addition to obtaining "traditional" virological parameters such as peak virus replication and the time to 50% cell death, eight new parameters, key among those, the infecting time (tinf), generation time (tgen) and basic reproduction number (R0), were estimated using the mathematical model. Collectively, these parameters contribute to virus characterization, enhancing the resolution for comparing genetically similar viruses. This approach can allow for the evaluation of virus virulence, replication fitness, and, ideally, transmissibility fitness across different hosts. This study underscores the potential of integrating avian in vitro models with mathematical modeling and builds towards rapid risk assessments of novel HPAI viruses.

microbiology↗

bioRxiv2025

Integrating Behavioural Science and Epidemiology to Improve Early Detection of Zoonotic Swine Influenza in the Netherlands

Background and ObjectivesThe Netherlands faces zoonotic disease risks due to its dense human and livestock populations. The 2009 H1N1 outbreak highlighted the pandemic potential of influenza virus reassortment. Effective preparedness requires integrating behavioural and epidemiological models. Human behaviour, shaped by personal, social, and institutional factors, is critical in detecting, intervening, and treating diseases. Using the Theory of Planned Behaviour (TPB), a framework was developed integrating knowledge from the TPB to improve early detection and response, using (zoonotic) swine influenza as a case study. Material and MethodsWithin the framework we defined the desired outcome: timely detection and notification of symptomatic (and hypothetical zoonotic) swine influenza to prevent its spread. Actions, such as symptom recognition and disease reporting, were linked to key drivers extracted from the TPB and disease transmission modelling. Expert elicitation estimated the likelihood of action for different farmer profiles, while disease transmission modelling assessed farm-to-farm spread probabilities. Simulations integrated these probabilities to evaluate intervention effectiveness across different scenarios. ResultsThe framework successfully combined behavioural science and epidemiology, offering nuanced estimates of intervention effectiveness. For early detection, 95% of farmers were estimated to notify their veterinarian within 13 days post-infection. Key factors influencing action included symptom recognition and disease spread extent. The farmer profiles influenced response likelihood, while human infections linked to outbreaks had minimal impact. Farm density and assumptions about transmission probabilities significantly affected the likelihood of spread before notification. Discussion and ConclusionThe framework provides a systematic approach for integrating social and epidemiological insights to support evidence-based policies. The work can be further enhanced by complementing expert judgement with more extensive stakeholder surveys, randomized scenario presentations, and immersive methods. This pragmatic tool aids policymakers in designing targeted interventions for zoonotic disease preparedness.

systems biology↗

bioRxiv2023

The optimization of Salmonella surveillance programmes for pullet and layer farms using between-farm distance as a risk factor

Human salmonellosis cases are often caused by Salmonella serovars Enteritidis and Typhimurium and a large percentage of Salmonella outbreaks is associated with the consumption of eggs and egg products. For this reason, many countries implemented general surveillance programmes for the detection and control of Salmonella on pullet and layer farms. The infection risk however varies between farms and the identification of risk factors for Salmonella infection may be used to improve the performance of these surveillance programmes. The aims of this study are therefore to determine 1) whether local farm density is a risk factor for the infection of pullet and layer farms by Salmonella Enteritidis and Typhimurium and 2) whether the sampling effort of surveillance programmes can be reduced by accounting for this risk factor, while still providing sufficient control of target serovars Enteritidis and Typhimurium. To assess the importance of local farm density as a risk factor, we fitted different transmission kernels to Israeli surveillance data during the period from June 2017 to April 2019. The analysis showed that the distance to infected farms significantly increases the infection risk by serovar Enteritidis within an approximately 4 km radius and by Typhimurium within an approximately 0.3 km radius. We subsequently used these kernels to derive a model for the between-farm R0 and used it to optimise a surveillance programme that subdivided layer farms into groups at low and at high risk of between-farm transmission based on the local farm density and allowed the sampling frequency to vary between these groups. In this design, the pullet farms were always sampled one week prior to pullet distribution. Our analysis showed that the risk-based surveillance programme was able to keep the between-farm R0 of serovars Enteritidis and Typhimurium below 1 for all pullet and layer farms, using a sampling effort that was reduced by 32% compared to the currently implemented surveillance programme in Israel. The results of our study therefore indicate that local farm density is an important risk factor for infection of pullet and layer farms by Salmonella Enteritridis and Typhimurium and can be used to improve the performance of surveillance programmes.

ecology↗