Background Influenza and respiratory syncytial virus (RSV) are major contributors to the burden of seasonal influenza-like illnesses (ILI) in the US. The prevention and treatment of ILI varies substantially across age groups and in cost and administration schedule. Clearly identifying the times when healthcare resources are most needed to mitigate the effects of seasonal RSV and influenza outbreaks will improve public health responses before and during ILI seasons. Methods We implemented stacked-regression linear models to infer the contribution of each of these diseases to seasonal ILI syndromic indicators. We further implemented anomaly-detection algorithms on data from the US Centers for Disease Control and Prevention National Syndromic Surveillance Program to identify the timing of onsets and peaks of RSV, influenza, and COVID-19. Findings A total of 148 state-ILI seasons were analyzed. In 114 out of 148 (77.0%) of analyzed seasons, volume of RSV emergency department (ED) visits peaked before influenza ED visits. The median time difference between peaks of RSV and peaks of influenza was +3.0 weeks. The timing of RSV and influenza onsets were found to occur more synchronously in the 2023-2024 and 2024-2025 ILI seasons. Interpretations RSV epidemics frequently reach peak volume before influenza epidemics across the US. Healthcare professionals and public health authorities should anticipate increases in RSV cases and hospitalizations at the start of the annual ILI season and establish infrastructure and planning to handle incoming surges of both RSV and influenza appropriately. Funding No specific funding was provided for this study. Summary Epidemics of respiratory pathogens such as influenza or RSV drive the influenza-like illness season in the US. We show that RSV epidemics peak before influenza epidemics in most states, with about a one to three week difference separating the epidemics.
Insect pathogenic fungi present a promising alternative to chemical insecticides for controlling insecticide-resistant mosquitoes. One proposed method involves releasing male Anopheles mosquitoes contaminated with transgenic Metarhizium pingshaense(Met-Hybrid) to lethally infect females during mating. This study presents a novel deterministic mathematical model to evaluate the impact of this control approach in malaria-endemic areas. The model incorporates two fungus transmission pathways: mating-based transmission and indirect transmission through contact with fungus- colonized mosquito cadavers. We found that the fungus cannot establish in the mosquito population without transmission from infected cadavers (in this scenario, the reproduction number of the model is zero). However, if transmission from colonized cadavers is possible, the fungus can persist in the local mosquito population when the reproduction number exceeds one. Simulations of periodic releases of infected male mosquitoes, parameterized using Met-Hybrid-exposed mosquito data from Burkina Faso, show that an 86% reduction in the local female mosquito population can be achieved by releasing 10 Met-Hybrid- exposed male mosquitoes per wild mosquito every three days over six months. This matches the efficiency of some genetic mosquito control approaches. However, a 90% reduction in the wild mosquito population requires, for instance, daily releases of the fungal-treated mosquitoes in a 6:1 ratio for about 5 months, which proves less efficient than some genetic approaches. This study concludes that fungal programs with periodic releases of infected males may complement other methods or serve as an alternative to genetic-based mosquito control methods, where regulatory, ethical, or public acceptance concerns restrict genetically-modified mosquito releases.
Parameter Inference from Black Hole Images using Deep Learning in Visibility Space Franc O, Pavlos Protopapas, Dominic W. Pesce, Angelo Ricarte, Sheperd S. Doeleman, Cecilia Garraffo, Lindy Blackburn, Mauricio Santillana. Monthly Notices of the Royal Astronomical Society. staf1843. October 30, 2025 https://doi.org/10.1093/mnras/staf1843
Abstract
Using very long baseline interferometry, the Event Horizon Telescope (EHT) collaboration has resolved the shadows of two supermassive black holes. Model comparison is traditionally performed in image space, where imaging algorithms introduce uncertainties in the recovered structure. Here, we develop a deep learning framework to perform parameter inference in visibility space, directly using the data measured by the interferometer without introducing potential errors and biases from image reconstruction. First, we train and validate our framework on synthetic data derived from general relativistic magnetohydrodynamics (GRMHD) simulations that vary in magnetic field state, spin, and Rhigh. Applying these models to the real data obtained during the 2017 EHT campaign, and only considering total intensity, we do not derive meaningful constraints on either of these parameters. At present, our method is limited both by theoretical uncertainties in the GRMHD simulations and variation between snapshots of the same underlying physical model. However, we demonstrate that spin and Rhigh could be recovered using this framework through continuous monitoring of our sources, which mitigates variations due to turbulence. In future work, we anticipate that including spectral or polarimetric information will greatly improve the performance of this framework.
When a new pathogen emerges, public health authorities must act rapidly to mitigate its spread while minimizing socioeconomic disruption. Despite extensive debate on localized epidemic control, no model-based study has systematically evaluated county-level interventions for statewide epidemic suppression in the United States. We present a metapopulation model that integrates county-level mobility data to identify epidemic hotspots and assess targeted intervention strategies for pandemic preparedness. We identify epidemic hotspots as counties that generate disproportionately large statewide epidemics when serving as outbreak origins. These hotspots align with population-dense and highly connected locations but provide sharper spatial contrast than traditional centrality metrics. Targeted interventions reducing the basic reproduction number (R0) only at hotspots achieve substantial epidemic control—reducing statewide epidemics by 60–90% in four representative states (Oklahoma, New York, Florida, and California)—without requiring broad lockdown measures. Hybrid strategies combining moderate reductions in R0 (10–30%) with partial mobility restrictions (30–80%) from hotspot counties, while preserving activity elsewhere, achieve control equivalent to full suppression at hotspots. This framework demonstrates that strategic, location-specific interventions can replace blanket pandemic responses. It provides state and county decision-makers with a quantitative tool for prospective pandemic planning, enabling rapid hotspot identification and intervention design grounded in empirical mobility networks.
A majority of the American public recognizes the value of universities and opposes federal funding cuts. However, they also express concern about campus issues, including costs and free speech. Communications can emphasize higher education’s highly valued contributions while distinctly acknowledging strategies to address concerns. Our first report examines public attitudes towards colleges and universities in the United States. We find that higher education enjoys broad public support, but this strength is tempered by serious vulnerabilities.Most Americans recognize universities as vital for science (90%), technology (91%), healthcare (83%), and economic growth (83%). Moreover, 72% see them as important for democracy, and majorities across parties value local contributions in health and the economy. This translates into strong opposition to federal funding cuts in science, health, and education, with disapproval ratios of roughly 4 or 5 to 1.
Respiratory disease outbreaks burden U.S. healthcare systems with over one million hospitalizations annually, yet current surveillance systems lag 1-2 weeks behind real-time conditions, preventing timely intervention. We developed a machine learning early warning system that combines Google search trends with traditional epidemiological data using ensemble voting algorithms to predict the timing of outbreak onsets and peaks across multiple respiratory pathogens. The system applies anomaly detection and transfer learning to monitor syndromic Influenza-like illnesses (ILI), and hospitalizations caused by respiratory syncytial virus (RSV) or Influenza, simultaneously, across all 50 US states. During operational real-time deployment from August 2024 through the 2024-2025 season, the system detected 98.0% of outbreak onsets with 5-week average lead time and 97.0% of peaks with 2-week average lead time, achieving positive predictive values that exceed 82%. This framework transforms reactive public health responses into proactive epidemic preparedness by reducing historical timing uncertainty from 10-20 weeks to consistent 2-6 week prediction windows, providing a scalable approach for monitoring both seasonal outbreaks and emerging respiratory threats.
Carbapenem-resistant Enterobacteriaceae (CRE) infections pose a major public health threat with limited treatment options and high mortality. Using national surveillance data from South Korea (2018–2021), we conducted a time-series analysis to assess associations between monthly CRE incidence, minimum temperature, and meropenem usage. A 10°C rise in minimum temperature was associated with a 9.4% increase in CRE incidence (95% Confidence Interval: 5.0–13.9%). Findings suggest temperature as an environmental driver of antimicrobial resistance, supporting integration of climate data into surveillance.
The effective reproduction number R is a predominant statistic for tracking the transmissibility of infectious diseases and informing public health policies. An estimated R=1 is universally interpreted as indicating epidemic stability and is a critical threshold for deciding whether infections will grow (R>1) or fall (R<1). We demonstrate that this threshold, which is typically computed over coarse spatial scales, seldom signifies stability because those scales frequently average stochastic infections from groups with heterogeneous transmission characteristics. Groups with falling and rising infections counteract and early-warning signals from resurging groups are obscured by noisy fluctuations from stable groups with larger infections. We prove that an estimated R=1 is consistent with a vast space of epidemiologically diverse scenarios, often leading to false-positive stability signals that diminish its predictive and policymaking value. In contrast, we show that a popular, alternative definition of transmissibility, relating to the next-generation matrix of the groups, overcorrects for this issue and yields false-negative stability signals by maximising sensitivity to stochasticity. We find a recently developed statistic, E, derived from R using experimental design theory, rigorously constrains the space of scenarios corresponding to stability, while limiting noise sensitivity. We establish that E=1 is a more practical and meaningful stability threshold.
Carbapenem-resistant Enterobacteriaceae (CRE) infections pose a major public health threat with limited treatment options and high mortality. Using national surveillance data from South Korea (2018–2021), we conducted a time-series analysis to assess associations between monthly CRE incidence, minimum temperature, and meropenem usage. A 10°C rise in minimum temperature was associated with a 9.4% increase in CRE incidence (95% Confidence Interval: 5.0–13.9%). Findings suggest temperature as an environmental driver of antimicrobial resistance, supporting integration of climate data into surveillance.
