Frontier California, Inc. is a Frontier Communications-owned operating company providing telephone service in former Verizon regions. This included Southern California cities such as Long Beach, Seal Beach, Lakewood, Norwalk and Santa Monica.
For infectious disease forecasting challenges, individual model performance typically varies across space and time. This phenomenon raises the question: are there properties of the target time series that contribute to a particular season, location, or disease being more difficult to forecast? Here we characterize a time series' future predictability using a forecastability metric that calculates the spectral entropy of the time series. Forecastability of syndromic influenza hospital admissions for the state of California varied widely across seasons and was positively correlated with peak burden. Next, using archived U.S. state and national forecasts targeting laboratory-confirmed COVID-19 and influenza hospital admissions, we investigated the relationship between forecastability and: (i) population size of the forecasting target, and (ii) forecast performance as measured by mean absolute error, weighted interval score (WIS), and scaled relative WIS. Forecastability increased with increasing population size of the forecasting target, and forecasting performance generally improved with higher forecastability when mitigating the effects of population size across scales. These preliminary results support the idea that some targets and respiratory virus seasons may be inherently more difficult to forecast and could help explain inter-seasonal variation in model performance.
The tobacco cyst nematode, Globodera tabacum, is considered a serious and important pest of shade and broadleaf tobacco. In our study we provide phylogenetic analyses of 51 COI and 66 ITS rRNA gene sequences of G. tabacum populations from different geographical areas using statistical parsimony. These sequences included 17 new COI and 34 new ITS rRNA gene sequences obtained from 19 samples from the USA, France, Spain and Italy. Statistical parsimony COI gene sequence network confirmed that G. tabacum is a polytypic species consisting of four subspecies rather than three as previously reported. The four subspecies include the three known subspecies: G. tabacum tabacum, G. t. virginiae and G. t. solanacearum and a new subspecies, G. tabacum argentinensis subsp. n. Maximal intraspecific COI gene sequence diversity for G. tabacum was 14.1%. If COI gene sequences clearly differentiated subspecies showing large genetic distance between them, the statistical parsimony analysis of the ITS rRNA gene sequences differentiate four subspecies based on a few nucleotides only. Moreover, this discrimination could be compromised by the presence of recombinant ITS rRNA gene sequences due to subspecies hybridisation. The new subspecies, G. t. argentinensis subsp. n., presently found in Argentina and Italy, is proposed and morphologically described.
Infectious disease surveillance is an essential component of public health for preventing and mitigating outbreaks. Systematically applying statistical methods for anomaly detection to surveillance data can expedite outbreak response through early warning. A commonly used approach is the usage of spatiotemporal scan statistics as implemented in SaTScan, a software that analyzes spatiotemporal data to identify clusters of events over space and time that deviate from expected values. Some health departments identify outbreaks and prioritize resources using SaTScan for early cluster detection for diseases such as salmonellosis, legionellosis, and COVID-19. However, as a standalone software, SaTScan v10.2.1 does not provide functionality to easily disseminate visual cluster results over time in a way that is tailored to epidemiologists’ needs for real-time disease surveillance. We developed an open source dashboard that provides a customizable framework for displaying results and facilitating the use of SaTScan for public health surveillance. The California Clustering for Operational Real-time Visualization of Infectious Diseases (CalCORVID) dashboard is built using RShiny, is specifically designed for SaTScan outputs, and can be easily adapted to display any jurisdiction’s results. This dashboard features a map and corresponding results table, the option to view historical results, integration of the Social Vulnerability Index (SVI) to contextualize clusters, and interactive elements to enhance usability for epidemiologists. We present CalCORVID as a complementary tool to native outputs of SaTScan v10.2.1, allowing users to visualize, customize, and distribute their results for specific public health use cases. Epidemiologists currently using SaTScan can adapt the provided code repository and dashboard template to display their own jurisdictions’ results, facilitating dissemination of cluster results for real-time, ongoing disease surveillance.
Background: During the COVID-19 pandemic, U.S. federal hospitalization surveillance systems provided a new data source for lab-confirmed influenza hospital admissions; these data were important for monitoring hospital capacity for combined respiratory virus pathogen burden and served as a new forecasting target for seasonal influenza forecasting efforts. Methods: For influenza surveillance in the state of California, from February 2, 2022 through May 31, 2025, we explored: (1) how lab-confirmed hospital admissions correlated with other syndromic and sentinel surveillance data; (2) which signals could serve as a potential leading input for predicting hospital admissions; and (3) how these relationships varied across respiratory virus seasons and geography. Results: Despite varying across seasons and regions, influenza surveillance data sources in California were strongly correlated with laboratory-confirmed influenza hospitalizations (Spearman's ρ ≥ 0.8). These correlations generally strengthened through time from the 2021-2022 season to the most recent 2024-2025 season, especially for death, influenza-like illness, wastewater, and clinical lab data. Most of these data sources neither consistently led nor lagged hospital admissions across all four seasons, but electronic laboratory reporting provided consistent leads of one to two weeks relative to the admissions signal across all four seasons (Spearman's ρ ≥ 0.89). Principal component analysis suggests that 93% of the variation in data signals can be explained by a single axis. Conclusions: Influenza surveillance data sources have inherent trade-offs in geographic coverage, temporal resolution, and reporting frequency. Understanding the relationship between different data sources will inform future predictions of influenza burden, including forecasting and scenario modeling. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the California Department of Public Health. The findings and conclusions in this article are those of the author(s) and do not necessarily represent the views or opinions of the California Department of Public Health or the California Health and Human Services Agency. This study used the California Patient Discharge Dataset. The interpretation and reporting of these data are the sole responsibility of the authors. The authors acknowledge the California Department of Healthcare Access and Information for compilation of these data. This work was funded by Centers for Disease Control and Prevention, Epidemiology and Laboratory Capacity for Infectious Diseases, Cooperative Agreement Number 6 NU50CK000539. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The California Health and Human Services Agency Committee for the Protection of Human Subjects (CPHS) has determined that this research (project number 2024-210) is classified as exempt under the federal Common Rule. This decision is issued under the California Health and Human Services Agency's Federalwide Assurance #00000681 with the Office of Human Research Protections (OHRP). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Code and scaled data to reproduce the analyses are available at: https://github.com/cdphmodeling/flu\_correlations. NSSP data at the state-level are publicly available at: https://data.cdc.gov/Public-Health-Surveillance/NSSP-Emergency-Department-Visit-Trajectories-by-St/rdmq-nq56/about\_data NHSN data are publicly available at: https://healthdata.gov/dataset/Weekly-United-States-Hospitalization-Metrics-by-Ju/kruc-9unj/about\_data and https://data.cdc.gov/Public-Health-Surveillance/Weekly-Hospital-Respiratory-Data-HRD-Metrics-by-Ju/ua7e-t2fy/about\_data State-level wastewater data are available from the CDC at: https://www.cdc.gov/nwss/rv/InfluenzaA-statetrend.html Syndromic influenza hospitalization data derived from the California Department of Healthcare Access and Information (HCAI) are available upon a data request: https://hcai.ca.gov/data/request-data/
Disease outbreaks in wild populations worldwide can result in widespread mortality within populations, with the recovery of individuals being rare. An example of this population is the sunflower sea star Pycnopodia helianthioides o to an unidentified disease known as sea star wasting disease. Pycnopodia down control of kelp grazers in rocky reefs across the Northeastern Pacific coast. This, combined with the massive declines in kelp coverage observed during the 2015-2016 marine heat wave observed in the Northeastern Pacific, has sparked an interest in reintroducing Pycnopodia individuals on the coast to potentially assist in the recovery of the populations. However, the epidemiological implications of reintroducing healthy sea stars into the wild populations are an under-explored question. This work explores this question using a dynamical population model of Pycnopodia. We also find. This analysis provides valuable information on timing and intensity for restoring Pycnopodia populations. This article is part of the theme issue 'Managing infectious marine diseases in wild populations'.
