tetano
Editor, Senior Moderator
One Health
. 2025 May 29:20:101091.
doi: 10.1016/j.onehlt.2025.101091. eCollection 2025 Jun. Integrating behavioural science and epidemiology to improve early detection of zoonotic swine influenza in the Netherlands
Juliette Fraser[SUP] 1 [/SUP], Ewa Pacholewicz[SUP] 2 [/SUP], Peter Hobbelen[SUP] 2 [/SUP], Thomas Hagenaars[SUP] 2 [/SUP], Ron Bergevoet[SUP] 1 [/SUP], Michel Counotte[SUP] 2 [/SUP]
Affiliations
Background and objectives: The 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 methods: Within 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.
Results: The 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 conclusion: The 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.
Keywords: Earl detection; Epidemiology; Social sciences; Swine influenza; Zoonoses; the Netherlands.
. 2025 May 29:20:101091.
doi: 10.1016/j.onehlt.2025.101091. eCollection 2025 Jun. Integrating behavioural science and epidemiology to improve early detection of zoonotic swine influenza in the Netherlands
Juliette Fraser[SUP] 1 [/SUP], Ewa Pacholewicz[SUP] 2 [/SUP], Peter Hobbelen[SUP] 2 [/SUP], Thomas Hagenaars[SUP] 2 [/SUP], Ron Bergevoet[SUP] 1 [/SUP], Michel Counotte[SUP] 2 [/SUP]
Affiliations
- PMID: 40520961
- PMCID: PMC12167494
- DOI: 10.1016/j.onehlt.2025.101091
Background and objectives: The 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 methods: Within 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.
Results: The 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 conclusion: The 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.
Keywords: Earl detection; Epidemiology; Social sciences; Swine influenza; Zoonoses; the Netherlands.