Let's chat RSV: the outpatient burden and novel preventive interventions
Bibliographic record
Abstract
Respiratory syncytial virus (RSV) is a major cause of acute respiratory infections, posing the greatest risk to infants and older adults. Recent advancements in RSV prevention, including monoclonal antibodies and vaccines, offer opportunities to reduce its burden, but effective implementation requires a comprehensive understanding of RSV’s clinical and socioeconomic impact. With this thesis, we aimed to better characterize the outpatient RSV burden, focusing on young children and older adults. Previous research has focused on severe cases requiring hospitalization, yet most RSV infections are managed in outpatient settings. Our research showed that RSV accounts for one-third of childhood respiratory infections in primary care during winter, leading to frequent doctor visits, medication use, parental work absence, and significant costs. For older adults, RSV is often underrecognized as a significant pathogen. This thesis provides new insights into its impact in this age group, showing that while hospitalizations are uncommon, RSV infections lead to substantial illness duration, healthcare use, and costs, similar to influenza. Understanding this burden is key to informing RSV immunization strategies for older populations. Beyond epidemiological and economic insights, this thesis highlights the role of general practitioners (GPs) in RSV immunization efforts. Interviews with GPs revealed strong support for infant RSV immunization but uncertainty about its importance in older adults. Increasing GP awareness and education on RSV could improve vaccine uptake and implementation. As RSV prevention advances, ensuring equitable access to vaccines and monoclonal antibodies remains essential. Immunization access remains limited to high- and upper-middle-income countries, despite the greatest burden of life-threatening disease predominantly exists in LMICs. This inequitable distribution delays vaccine access for those who need it most. While much of this research focuses on high-income settings, addressing disparities in global vaccine distribution, particularly in low- and middle-income countries, is critical. Ultimately, the real-world impact of RSV immunizations will strongly depend on people’s willingness to receive them. Public awareness will be crucial for immunization uptake. Traditional public health campaigns may be insufficient in the face of growing vaccine hesitancy, increasing digitalization of health information, and the spread of online misinformation. These challenges call for innovative approaches to public engagement. The discussion of this thesis explores the potential of using Artificial Intelligence chatbots for communication on RSV and other vaccine-preventable diseases, inspired by the development of our RSV Chatbot. These tools tools could provide accessible, evidence-based information, but their effectiveness in influencing vaccine uptake remains uncertain. If guided by ethical principles, clear regulations, and strong scientific evaluation, these tools could become a vital in public health communication efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".