Apparent RSV-COVID interference is not robust to adjustment for shared testing propensity

BackgroundViral interference, in which infection by one pathogen reduces susceptibility to another at the population level, may shape respiratory virus dynamics. Inference from surveillance data is complicated by time-varying testing behavior that can induce correlated detection patterns without any biological interaction. MethodsI developed a two-pathogen renewal model augmented with a ratio penalty that constrains interference estimates to be consistent with observed log-odds ratios of pathogen positivity. The penalty treats other-pathogen positives as implicit controls for shared testing propensity, adapting test-negative design logic to aggregate surveillance. I applied the model to US NAAT surveillance data reported to NREVSS (RSV and COVID-19; October 2020 to February 2026), validated parameter recovery in synthetic experiments, and quantified uncertainty via block bootstrap. I note at the outset that the method is conservative by design: synthetic experiments confirm a bias toward null interference estimates, so near-zero findings should not be read as proof that interference is absent. ResultsWithout the ratio penalty, estimated interference was |{theta}|sum = 0.0082 for RSV [->]COVID. With the penalty, this decreased to 0.0016 (80% reduction). Bootstrap 95% intervals included zero for all direction xlag combinations. Synthetic validation confirmed high specificity at{theta} = 0 but revealed that the method cannot recover moderate interference ({theta} [≤] 0.05), because virus-specific transmissibility deviations absorb the interference signal during Stage 1 estimation. A diagnostic decomposition showed that the ratio penalty term amplifies this bias-to-null: at{theta} = 0.01 in real data, the ratio penalty contributes a -314,000 log-joint penalty, roughly 130 times the multinomial penalty alone. Two-stage estimation was justified empirically; joint MAP estimation failed to converge across all tested configurations. ConclusionsThe ratio penalty functions as a conservative diagnostic screen with high specificity but limited sensitivity. When applied to RSV-COVID surveillance, it substantially reduces interference point estimates, with confidence intervals spanning zero. These results indicate that apparent interference signals in these data are not robust to this particular adjustment, but the methods known conservative bias means biological interference cannot be excluded. The approach is best understood as a sensitivity analysis rather than a definitive test. Author SummaryWhen one respiratory virus circulates widely, it may temporarily suppress transmission of others, a phenomenon called viral interference. Detecting interference from disease surveillance data is difficult because testing behavior changes over time: when any respiratory illness surges, more people seek tests, potentially creating correlated patterns that mimic biological interaction. I developed a statistical method to probe this confounding. Borrowing logic from vaccine studies, the method penalizes the model when its predictions diverge from the observed ratio of positive tests across pathogens. The idea is that this ratio should be stable if testing propensity fluctuates but affects all pathogens similarly. Applied to five years of US surveillance data for RSV and COVID-19, this penalty reduced apparent interference by 80%, with statistical uncertainty intervals including zero. Crucially, the method is intentionally conservative: simulation experiments show it also diminishes real interference signals, because transmissibility parameters absorb the interference effect before it can be estimated. My near-zero estimates therefore do not prove interference is absent; rather, they indicate that apparent signals in these data are not robust to this particular adjustment for testing composition. This work highlights that surveillance-based interference estimates may be sensitive to testing artifacts and provides one approach for assessing this sensitivity.