Original title: Antivirals for seasonal influenza in the US: modeling direct and indirect effects with variable uptake
Sinead Morris, Samantha Dean, Louis Yat Hin Chan, Shikha Garg +4 · September 2026
Flu antiviral medicines can stop severe illness in patients who take them quickly. They might also stop patients from passing the virus to others, which protects the wider public. However, earlier studies predicted huge benefits by assuming unrealistic numbers of people actually take these drugs on time. Researchers built a computer model of the United States to track how flu spreads and how antivirals change disease outcomes. Based on typical healthcare visits, prescriptions, and pill-taking habits, only 1.1–9.7% of sick individuals start and finish prompt treatment. Because so few people take them correctly, the overall population impact stays small. Antivirals prevented just 0.9% to 3.8% of hospitalizations in simulated seasons. Preventing >20% of hospitalizations requires far more people to start and finish treatment promptly than currently do.
Imagine a town where a charity offers free repairs for any broken roof, but only if you call within two days of the leak starting. Most people wait a week or never call at all. Even though the repair team is fast, they only fix 1 in every 100 roofs in time. The town still floods every storm. In this paper, antivirals are the roof repair, and the storm is flu season—the fix works, but almost nobody uses it quickly enough to matter.
Every flu season, hospitals fill up with severe cases that might have been prevented. This study shows that the real problem isn't the drugs themselves—it's that almost nobody takes them in time. Unless many more people get treated promptly, antivirals won't make a dent in hospitalizations, even if they work perfectly in theory. That shifts the conversation from developing better drugs to figuring out how to get people treated faster.
The model relies on reported patterns of care-seeking, prescribing, and adherence, which may vary by season and population. It does not capture potential variation in antiviral efficacy against different influenza strains, nor does it model effects in high-risk subgroups separately. The indirect effect estimates assume a specific mechanism of transmission reduction that may not hold in all settings.
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Antivirals for seasonal influenza in the US: modeling direct and indirect effects with variable uptake — Sinead Morris, Samantha Dean, Louis Yat Hin Chan et al. — medRxiv — doi:10.64898/2026.09.10.26362674
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.