Emily Howerton

Respiratory syncytial virus (RSV) and human metapneumovirus (hMPV) are closely related pathogens responsible for a significant burden of acute respiratory infections. Interactions between RSV and hMPV have been hypothesized, but the mechanisms of interaction are largely unknown. Here, we use a mathematical model to quantify the likelihood of interactions from population-level surveillance data and investigate whether interactions could lead to increases in hMPV burden under RSV medical interventions, including active and passive immunization. In Scotland, Korea, and three regions of Canada, annual hMPV outbreaks lag RSV outbreaks by up to 18 weeks; two Canadian regions show patterns consistent with out-of-phase biennial outbreaks. Using a two-pathogen transmission model, we show that a negative effect of RSV infection on hMPV transmissibility can explain these dynamics. We use post-pandemic RSV-hMPV rebound dynamics as an out of sample test for our model, and the model with interactions better predicts this period than a model where the pathogens are assumed to be independent. Finally, our model suggests that hMPV peak timing and magnitude may change under RSV interventions. Our analysis provides a foundation for detecting possible RSV-hMPV interactions at the population level, although such a model oversimplifies important complexities about interaction mechanisms. Using mathematical modeling of RSV and hMPV, this study found lowered transmissibility of hMPV and suppressive effects on hMPV transmission. Peak timing and magnitude of hMPV may shift due to RSV interventions, but the overall burden is small.

Probabilistic predictions support public health planning and decision making, especially in infectious disease emergencies. Aggregating outputs from multiple models yields more robust predictions of outcomes and associated uncertainty. While the selection of an aggregation method can be guided by retrospective performance evaluations, this is not always possible. For example, if predictions are conditional on assumptions about how the future will unfold (e.g. possible interventions), these assumptions may never materialize, precluding any direct comparison between predictions and observations. Here, we summarize literature on aggregating probabilistic predictions, illustrate various methods for infectious disease predictions via simulation, and present a strategy for choosing an aggregation method when empirical validation cannot be used. We focus on the linear opinion pool (LOP) and Vincent average, common methods that make different assumptions about between-prediction uncertainty. We contend that assumptions of the aggregation method should align with a hypothesis about how uncertainty is expressed within and between predictions from different sources. The LOP assumes that between-prediction uncertainty is meaningful and should be retained, while the Vincent average assumes that between-prediction uncertainty is akin to sampling error and should not be preserved. We provide an R package for implementation. Given the rising importance of multi-model infectious disease hubs, our work provides useful guidance on aggregation and a deeper understanding of the benefits and risks of different approaches.

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.