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Abstract Phenological shifts due to climate change have been extensively studied in plants and animals. Yet, the responses of fungal spores—organisms important to ecosystems and major airborne allergens—remain understudied. This knowledge gap limits our understanding of their ecological and public health implications. To address this, we analyzed a long‐term (2003–2022), large‐scale (the continental US) data set of airborne fungal spores collected by the US National Allergy Bureau. We first pre‐processed the spore data by gap‐filling and smoothing. Afterward, we extracted 10 metrics describing the phenology (e.g., start and end of season) and intensity (e.g., peak concentration and integral) of fungal spore seasons. These metrics were derived using two complementary but not mutually exclusive approaches—ecological and public health approaches, defined as percentiles of total spore concentration and allergenic thresholds of spore concentration, respectively. Using linear mixed‐effects models, we quantified annual shifts in these metrics across the continental US. We revealed a significant advancement in the onset of the spore seasons defined in both ecological (11 days, 95% confidence interval: 0.4–23 days) and public health (22 days, 6–38 days) approaches over two decades. Meanwhile, total spore concentrations in an annual cycle and in a spore allergy season tended to decrease over time. The earlier start of the spore season was significantly correlated with climatic variables, such as warmer temperatures and altered precipitations. Overall, our findings suggest possible climate‐driven advanced fungal spore seasons, highlighting the importance of climate change mitigation and adaptation in public health decision‐making.
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This content will become publicly available on April 11, 2026
Aligning Time-series by Local Trends: Applications in Public Health
Individual models of infectious diseases or trajectories coming from different simulations may vary considerably, making it challenging for public communication and supporting policy-making. Therefore, it is common in public health to first create a consensus across multiple models and simulations through ensembling. However, current methods are limited to mean and median ensembles that perform aggregation of scale (cases, hospitalizations, deaths) along the time axis, which often misrepresents the underlying trajectories -- e.g., they underrepresent the peak. Instead, we wish to create an ensemble that represents aggregation simultaneously over both time and scale and thus better preserves the properties of the trajectories. This is particularly useful for public health where time-series have a sequence of meaningful local trends that are ordered, e.g., a surge to an increase to a peak to a decrease. We propose a novel alignment method DTW+SBA, which combines a representation of local trends along with dynamic time warping barycenter averaging. We prove key properties of this method that ensure appropriate alignment based on local trends. We demonstrate on real multi-model outputs that our approach preserves the properties of underlying trajectories. We also show that our alignment leads to a more sensible clustering of epidemic trajectories.
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- PAR ID:
- 10590699
- Publisher / Repository:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 39
- Issue:
- 27
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 28405 to 28412
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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