Estimating cumulative incidence from partially missing time series of respiratory viral infections
6 affiliations
- 1BPM Evidence Statistics, BioPharmaceuticals Medical, AstraZeneca, Cambridge, United Kingdom
- 2Model Health Ltd, Brixham, United Kingdom
- 3Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Mölndal, Sweden
- 4Kaiser Permanente Center for Health Research, Portland, Oregon, United States of America
- 5Medical Evidence, Vaccines and Immune Therapies, BioPharmaceuticals Medical, AstraZeneca, Cambridge, United Kingdom
- 6Medical Evidence, Vaccines and Immune Therapies, BioPharmaceuticals Medical, AstraZeneca, Gaithersburg, Maryland, United States of America
Abstract
Background: Seasonal respiratory viruses, including respiratory syncytial virus (RSV) and human metapneumovirus (hMPV), are major contributors to global respiratory infection burden. Estimating viral incidence using real-world data (RWD) is challenging as misalignment of data collection with seasonal circulation can lead to underestimated incidence and hinder inter-study comparisons. Methods: We developed a regression-based approach to retrospectively correct partial-season time series with contiguous missing weeks at the start or end of a season. Weekly RSV clinical incidence (per 100,000) was extracted from U.S. RWD sources (Optum® CDM and PharMetrics Plus, adults ≥65 years; Kaiser Permanente Northwest [KPNW], all ages) for seasons 2018–19 to 2023–24 and aligned to RSV‑Net hospitalization surveillance. Weekly early- and late-season missingness scenarios were created (approximately 10%, 15%, 20%, and 25% of weeks removed). Models were fit within each season using harmonic terms and the surveillance-aligned covariate. Uncertainty was quantified using moving block bootstrap 95% prediction intervals. Weekly accuracy was evaluated using RMSE and normalized RMSE, and seasonal burden reconstruction accuracy using cumulative percent error and signed difference. For hMPV, we evaluated an illustrative extension using Optum® CDM (adults ≥65 years; 2022–23 and 2023–24) and NREVSS test positivity; because positivity is bounded and depends on testing volume, we used a denominator-aware binomial/logit model (positives out of tests). Results: For RSV, weekly prediction error increased with missingness level, while reconstructed seasonal burden generally remained close to observed totals in applicable scenarios. Early-season gaps were associated with larger cumulative errors than late-season gaps in Optum® CDM and PharMetrics Plus, whereas late-season median percent errors were closer to zero across missingness levels. Applicability was reduced primarily for higher early-season missingness in KPNW due to peak overlap (e.g., early 25% scenarios not applicable). For hMPV test positivity (illustrative; two seasons), weekly prediction RMSE was on the order of ~1–2 percentage points across missingness scenarios. Conclusion: Across multiple RSV seasons and data sources, reconstructed seasonal burden generally agreed with observed totals when missingness was confined to contiguous early- or late-season weeks and did not overlap peak activity. This approach provides a transparent method to improve the utility and comparability of partial-season respiratory virus time series. However, applicability is reduced when peak weeks are missing, and hMPV findings are illustrative given limited seasons and the use of test positivity.
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BibTeX
@article{venkatesan2026estimating,
title = {{Estimating cumulative incidence from partially missing time series of respiratory viral infections}},
author = {Sudhir Venkatesan and Lisa White and Hyewon Nina Koo and John Dickerson and Weiming Hu and Jennifer Kuntz and Mark Schmidt and Sylvia Taylor and Carla Talarico},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0353681},
url = {https://doi.org/10.1371/journal.pone.0353681},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Estimating cumulative incidence from partially missing time series of respiratory viral infections
AU - Sudhir Venkatesan
AU - Lisa White
AU - Hyewon Nina Koo
AU - John Dickerson
AU - Weiming Hu
AU - Jennifer Kuntz
AU - Mark Schmidt
AU - Sylvia Taylor
AU - Carla Talarico
PY - 2026
DA - 2026/09/15
JO - PLOS One
DO - 10.1371/journal.pone.0353681
UR - https://doi.org/10.1371/journal.pone.0353681
AB - Background: Seasonal respiratory viruses, including respiratory syncytial virus (RSV) and human metapneumovirus (hMPV), are major contributors to global respiratory infection burden. Estimating viral incidence using real-world data (RWD) is challenging as misalignment of data collection with seasonal circulation can lead to underestimated incidence and hinder inter-study comparisons. Methods: We developed a regression-based approach to retrospectively correct partial-season time series with contiguous missing weeks at the start or end of a season. Weekly RSV clinical incidence (per 100,000) was extracted from U.S. RWD sources (Optum® CDM and PharMetrics Plus, adults ≥65 years; Kaiser Permanente Northwest [KPNW], all ages) for seasons 2018–19 to 2023–24 and aligned to RSV‑Net hospitalization surveillance. Weekly early- and late-season missingness scenarios were created (approximately 10%, 15%, 20%, and 25% of weeks removed). Models were fit within each season using harmonic terms and the surveillance-aligned covariate. Uncertainty was quantified using moving block bootstrap 95% prediction intervals. Weekly accuracy was evaluated using RMSE and normalized RMSE, and seasonal burden reconstruction accuracy using cumulative percent error and signed difference. For hMPV, we evaluated an illustrative extension using Optum® CDM (adults ≥65 years; 2022–23 and 2023–24) and NREVSS test positivity; because positivity is bounded and depends on testing volume, we used a denominator-aware binomial/logit model (positives out of tests). Results: For RSV, weekly prediction error increased with missingness level, while reconstructed seasonal burden generally remained close to observed totals in applicable scenarios. Early-season gaps were associated with larger cumulative errors than late-season gaps in Optum® CDM and PharMetrics Plus, whereas late-season median percent errors were closer to zero across missingness levels. Applicability was reduced primarily for higher early-season missingness in KPNW due to peak overlap (e.g., early 25% scenarios not applicable). For hMPV test positivity (illustrative; two seasons), weekly prediction RMSE was on the order of ~1–2 percentage points across missingness scenarios. Conclusion: Across multiple RSV seasons and data sources, reconstructed seasonal burden generally agreed with observed totals when missingness was confined to contiguous early- or late-season weeks and did not overlap peak activity. This approach provides a transparent method to improve the utility and comparability of partial-season respiratory virus time series. However, applicability is reduced when peak weeks are missing, and hMPV findings are illustrative given limited seasons and the use of test positivity.
SN - 1932-6203
ER -