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.
Dengue fever, a tropical vector-borne disease, is a leading cause of hospitalization and death in many parts of the world, especially in Asia and Latin America. Where timely dengue surveillance exists, decision-makers can better implement public health measures and allocate resources. Reliable near-term forecasts may help anticipate healthcare demands and promote preparedness. We propose ensemble modeling approaches combining mechanistic, statistical, and machine learning models to forecast dengue cases 1 to 3 mo ahead at the province level across multiple countries. We assess these models’ predictive ability out-of-sample and retrospectively in over 180 locations worldwide, including provinces in Brazil, Colombia, Malaysia, Mexico, Thailand, plus Iquitos, Peru, and San Juan, Puerto Rico, during at least 2 to 3 y. We also evaluate ensemble approaches in a real-time, prospective dengue forecasting platform during 2022–2023, considering data availability limitations. Our ensemble modeling leads to an improvement to previous efforts that may help decision-making in the context of large uncertainties. This contrasts with the variable performance of individual component models across locations and time. No single model achieves optimal predictions across all scenarios, but while ensemble models may not always perform best in specific locations, they consistently rank among the top 3 performing models both retrospectively and prospectively.
ImportanceScreening measures of depressive symptoms (eg, 9-item Patient Health Questionnaire [PHQ-9]) are increasingly used in surveys and remote applications, where shorter versions would be valuable. ObjectiveTo derive shorter versions of the PHQ-9 that maximize the variability in total depressive symptom severity captured. Design, Setting, and ParticipantsThis survey study used data from 4 waves of a 50-state nonprobability web-based survey conducted between November 2, 2023, and July 21, 2024. Survey respondents were aged 18 years or older. The first survey wave data were used to identify shortened question subsets capturing variance in the PHQ-9 and estimating a PHQ-9 score of 10 or higher. Resulting models (eg, 3-item version of the PHQ [PHQ-3]) were validated in subsequent survey waves. Main Outcome and MeasurePerformance of PHQ-3 in the full sample and across subgroups of age, gender, race and ethnicity, and educational levels. Depressive symptom severity was measured with the PHQ-9 (total score range: 0-27, with a score ≥10 indicating moderate or greater depressive symptoms). ResultsIn the 4 survey waves, there were 96 234 total participants (mean [SD] age, 47.3 [17.1] years; 55 245 [57.4%] identifying as women). In the full sample, 4401 participants (4.6%) identified as Asian American, 12 699 (13.2%) as Black or African American, 9776 (10.2%) as Hispanic or Latino, and 65 309 (67.9%) as White individuals, with 4049 (4.2%) who identified as having other race or ethnicity. Among these participants, the mean (SD) PHQ-9 score was 6.5 (6.6), and 25 411 (26.4%) met the criteria for moderate or greater depressive symptoms (PHQ-9 score ≥10). The optimal 3-item version, PHQ-3, used items 2 (subject: depressed mood), 6 (self-esteem or failure), and 1 (interest), yielding a Cronbach α of 0.88 (95% CI, 0.88-0.88) and Pearson correlation with the PHQ-9 total score of 0.93 (95% CI, 0.93-0.94). At a threshold of 3 or greater, the PHQ-3 sensitivity was 0.98 (95% CI, 0.97-0.98) and specificity was 0.76 (95% CI, 0.75-0.76) for moderate or greater depressive symptoms. Performance was consistent across sociodemographic subgroups and survey waves. Conclusions and RelevanceThis survey study of US adults identified a 3-item scale that remained highly correlated with the full PHQ-9 instrument. The reduced set of questions could enable more widespread and efficient incorporation of depressive symptom measurement in general population samples.
Conspiratorial thinking in a 50-state survey of American adults Roy H. Perlis, Ata Uslu, Sergio A. Barroilhet, Paul A. Vohringer, Anudeepa K. Ramachandiran, Mauricio Santillana, Matthew A. Baum, James N. Druckman, Katherine Ognyanova, David Lazer. Journal of Affective Disorders, Volume 390, 2025, 119915, ISSN 0165-0327. https://doi.org/10.1016/j.jad.2025.119915
Abstract
Conspiratorial thoughts as a cognitive aspect are understudied outside small clinical cohorts. We conducted a 50-state non-probability internet survey of respondents age 18 and older, who completed the American Conspiratorial Thinking Scale (ACTS) and the 9-item Patient Health Questionnaire (PHQ-9). Across the 6 survey waves, there were 123,781 unique individuals. After reweighting, a total of 78.6 % somewhat or strongly agreed with at least one conspiratorial idea; 19.0 % agreed with all four of them. More conspiratorial thoughts were reported among those age 25–54, males, individuals who finished high school but did not start or complete college, and those with greater levels of depressive symptoms. Endorsing more conspiratorial thoughts was associated with a significantly lower likelihood of being vaccinated against COVID-19. The extent of correlation with non-vaccination suggests the importance of considering such thinking in designing public health strategies.
Objectives: Due to political polarization, adherence to public health measures varied across US states during the COVID-19 pandemic. Although social media posts have been shown effective in anticipating COVID-19 surges, the impact of political leaning on the effectiveness of different topics for early warning remains mostly unexplored. Our study examines the spatio-temporal early warning potential of different geo-social media topics across republican, democrat, and swing states. Methods: Using keyword filtering, we identified eight COVID-19-related geo-social media topics. We then utilized Chatterjee's rank correlation to assess their early warning capability for COVID-19 cases 7 to 42 days in advance across six infection waves. A mixed-effect model was used to evaluate the impact of timeframe and political leaning on the early warning capabilities of these topics. Results: Many topics exhibited significant spatial clustering over time, with quarantine and vaccination-related posts occurring in opposing spatial regimes in the second timeframe. We also found significant variation in the early warning capabilities of geo-social media topics over time and across political clusters. In detail, quarantine related geo-social media post were significantly less correlated to COVID-19 cases in republican states than in democrat states. Further, preventive measure and quarantine-related posts exhibited declining correlations to COVID-19 cases over time, while the correlations of vaccine and virus-related posts with COVID-19 infections. Conclusion: Our results highlight the need for a dynamic spatially targeted approach that accounts for both how regional geosocial media topics of interest change over time and the impact of local political ideology on their epidemiological early warning capabilities.
The novel coronavirus (COVID-19) pandemic has had a devastating global impact, profoundlyaffecting daily life, healthcare systems, and public health infrastructure. Despite the availability oftreatments and vaccines, hospitalizations and deaths continue. Real-time surveillance of infectiontrends supports resource allocation and mitigation strategies, but reliable forecasting remains achallenge. While deep learning has advanced time-series forecasting, its effectiveness relies on largedatasets, a significant obstacle given the pandemic’s evolving nature. Most models use national orstate-level data, limiting both dataset size and the granularity of insights. To address this, we proposethe Fine-Grained Infection Forecast Network (FIGI-Net), a stacked bidirectional LSTM structuredesigned to leverage county-level data to produce daily forecasts up to two weeks in advance. FIGI-Net outperforms existing models, accurately predicting sudden changes such as new outbreaks orpeaks, a capability many state-of-the-art models lack. This approach could enhance public healthresponses and outbreak preparedness.
Google Trends reports how frequently specific queries are searched on Google over time. It is widely used in research and industry to gain early insights into public interest. However, its data generation mechanism introduces missing values, sampling variability, noise, and trends. These issues arise from privacy thresholds mapping low search volumes to zeros, daily sampling variations causing discrepancies across historical downloads, and algorithm updates altering volume magnitudes over time. Data quality has recently deteriorated, with more zeros and noise, even for previously stable queries. We propose a comprehensive statistical methodology to preprocess Google Trends search information using hierarchical clustering, smoothing splines, and detrending. We validate our approach by forecasting U.S. influenza hospitalizations up to three weeks ahead with several statistical and machine learning models. Compared to omitting exogenous variables, our results show that preprocessed signals enhance forecast accuracy, while raw Google Trends data often degrades performance in statistical models.
The global shipping network, which moves over 80% of the world's goods, is not only a vital backbone of the global economy but also one of the most polluting industries. Studying how this network operates is crucial for improving its efficiency and sustainability. While the transport of solid goods like packaged products and raw materials has been extensively researched, far less is known about the competitive trade of crude oil and petroleum, despite these commodities accounting for nearly 30% of the market. Using 4 years of high-resolution data on oil tanker movements, we employ sequential motif mining and dynamic mode decomposition to uncover global spatio-temporal patterns in the movement of individual ships. Across all ship classes, we demonstrate that maximizing the proportion of time ships spend carrying cargo -- a metric of efficiency -- is achieved through strategic diversification of routes and the effective use of intra-regional ports for trips without cargo. Moreover, we uncover a globally stable travel structure in the fleet, with pronounced seasonal variations linked to annual and semi-annual regional climate patterns and economic cycles. Our findings highlight the importance of integrating high-resolution data with innovative analysis methods not only to improve our understanding of the underlying dynamics of shipping patterns, but to design and evaluate strategies aimed at reducing their environmental impact.
Accurate, real-time forecasts of influenza hospitalizations would facilitate prospective resource allocation and public health preparedness. State-of-the-art machine learning methods are a promising approach to produce such forecasts, but they require extensive historical data to be properly trained. Unfortunately, data on influenza hospitalizations, for the 50 states in the United States, are only available since the beginning of 2020. In addition, the data are far from perfect as they were under-reported for several months before health systems began consistently submitting their data. To address these issues, we propose a transfer learning approach. We extend the currently available two-season dataset for state-level influenza hospitalizations by an additional ten seasons. Our method leverages influenza-like illness (ILI) data to infer historical estimates of influenza hospitalizations. This data augmentation enables the implementation of advanced machine learning techniques, multi-horizon training, and an ensemble of models to improve hospitalization forecasts. We evaluated the performance of our machine learning approaches by prospectively producing forecasts for future weeks and submitting them in real time to the Centers for Disease Control and Prevention FluSight challenges during two seasons: 2022–2023 and 2023–2024. Our methodology demonstrated good accuracy and reliability, achieving a fourth place finish (among 20 participating teams) in the 2022–23 and a second place finish (among 20 participating teams) in the 2023–24 CDC FluSight challenges. Our findings highlight the utility of data augmentation and knowledge transfer in the application of machine learning models to public health surveillance where only limited historical data is available.
Background: The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises. However, previous studies on the early warning capabilities of geosocial media data have largely been constrained by coarse spatial resolutions or short temporal scopes, with limited understanding of how local political beliefs may influence these capabilities. Objective: This study aimed to assess how the epidemiological early warning capabilities of geosocial media posts for COVID-19 vary over time and across US counties with differing political beliefs. Methods: We classified US counties into 3 political clusters, democrat, republican, and swing counties, based on voting data from the last 6 federal election cycles. In these clusters, we analyzed the early warning capabilities of geosocial media posts across 6 consecutive COVID-19 waves (February 2020-April 2022). We specifically examined the temporal lag between geosocial media signals and surges in COVID-19 cases, measuring both the number of days by which the geosocial media signals preceded the surges in COVID-19 cases (temporal lag) and the correlation between their respective time series. Results: The early warning capabilities of geosocial media data differed across political clusters and COVID-19 waves. On average, geosocial media posts preceded COVID-19 cases by 21 days in republican counties compared with 14.6 days in democrat counties and 24.2 days in swing counties. In general, geosocial media posts were preceding COVID-19 cases in 5 out of 6 waves across all political clusters. However, we observed a decrease over time in the number of days that posts preceded COVID-19 cases, particularly in democrat and republican counties. Furthermore, a decline in signal strength and the impact of trending topics presented challenges for the reliability of the early warning signals. Conclusions: This study provides valuable insights into the strengths and limitations of geosocial media data as an epidemiological early warning tool, particularly highlighting how they can change across county-level political clusters. Thus, these findings indicate that future geosocial media based epidemiological early warning systems might benefit from accounting for political beliefs. In addition, the impact of declining geosocial media signal strength over time and the role of trending topics for signal reliability in early warning systems need to be assessed in future research.
Irritability and Social Media Use in US Adults Roy H. Perlis, MD, MSc; Ata Uslu, MS; Jonathan Schulman, MS; Faith M. Gunning, PhD; Mauricio Santillana, PhD; Matthew A. Baum, PhD; James N. Druckman, PhD; Katherine Ognyanova, PhD; David Lazer, PhD JAMA Netw Open. 2025;8(1):e2452807. doi:10.1001/jamanetworkopen.2024.52807
Abstract
ImportanceEfforts to understand the complex association between social media use and mental health have focused on depression, with little investigation of other forms of negative affect, such as irritability and anxiety. ObjectiveTo characterize the association between self-reported use of individual social media platforms and irritability among US adults. Design, Setting, and ParticipantsThis survey study analyzed data from 2 waves of the COVID States Project, a nonprobability web-based survey conducted between November 2, 2023, and January 8, 2024, and applied multiple linear regression models to estimate associations with irritability. Survey respondents were aged 18 years and older. ExposureSelf-reported social media use. Main Outcomes and MeasuresThe primary outcome was score on the Brief Irritability Test (range, 5-30), with higher scores indicating greater irritability. ResultsAcross the 2 survey waves, there were 42 597 unique participants, with mean (SD) age 46.0 (17.0) years; 24 919 (58.5%) identified as women, 17 222 (40.4%) as men, and 456 (1.1%) as nonbinary. In the full sample, 1216 (2.9%) identified as Asian American, 5939 (13.9%) as Black, 5322 (12.5%) as Hispanic, 624 (1.5%) as Native American, 515 (1.2%) as Pacific Islander, 28 354 (66.6%) as White, and 627 (1.5%) as other (ie, selecting the other option prompted the opportunity to provide a free-text self-description). In total, 33 325 (78.2%) of the survey respondents reported daily use of at least 1 social media platform, including 6037 (14.2%) using once a day, 16 678 (39.2%) using multiple times a day, and 10 610 (24.9%) using most of the day. Frequent use of social media was associated with significantly greater irritability in univariate regression models (for more than once a day vs never, 1.43 points [95% CI, 1.22-1.63 points]; for most of the day vs never, 3.37 points [95% CI, 3.15-3.60 points]) and adjusted models (for more than once a day, 0.38 points [95% CI, 0.18-0.58 points]; for most of the day, 1.55 points [95% CI, 1.32-1.78 points]). These associations persisted after incorporating measures of political engagement. Conclusions and RelevanceIn this survey study of 42 597 US adults, irritability represented another correlate of social media use that merits further characterization, in light of known associations with depression and suicidality.
