A5078718261- Data-Driven Disease Surveillance
- COVID-19 epidemiological studies
- Influenza Virus Research Studies
- Mosquito-borne diseases and control
- Solar and Space Plasma Dynamics
- Atmospheric Ozone and Climate
- Climate variability and models
- COVID-19 Pandemic Impacts
- High-Velocity Impact and Material Behavior
- Hydrology and Drought Analysis
- Viral Infections and Vectors
- Solar Radiation and Photovoltaics
- Forecasting Techniques and Applications
- Climate Change and Health Impacts
- Statistical Methods and Bayesian Inference
- Structural Response to Dynamic Loads
- Calibration and Measurement Techniques
- Malaria Research and Control
- Ionosphere and magnetosphere dynamics
- Gaussian Processes and Bayesian Inference
- Healthcare Policy and Management
- Statistical Methods and Inference
- Wikis in Education and Collaboration
- HIV/AIDS Research and Interventions
- Human Mobility and Location-Based Analysis
Los Alamos National Laboratory
2017-2024
University of Colorado System
2024
University of Colorado Boulder
2024
Iowa State University
2014-2019
University of California, Irvine
2018
Prevention Institute
2018
University of Zurich
2018
Statistical Service
2016
Influenza infects an estimated 9-35 million individuals each year in the United States and is a contributing cause for between 12,000 56,000 deaths annually. Seasonal outbreaks of influenza are common temperate regions world, with highest incidence typically occurring colder drier months year. Real-time forecasts transmission can inform public health response to outbreaks. We present results multiinstitution collaborative effort standardize collection evaluation forecasting models 2010/2011...
Abstract Background The COVID-19 pandemic has driven demand for forecasts to guide policy and planning. Previous research suggested that combining from multiple models into a single “ensemble” forecast can increase the robustness of forecasts. Here we evaluate real-time application an open, collaborative ensemble deaths attributable in U.S. Methods Beginning on April 13, 2020, collected combined one- four-week ahead cumulative jurisdictions standardized, probabilistic formats generate...
Seasonal influenza results in substantial annual morbidity and mortality the United States worldwide. Accurate forecasts of key features epidemics, such as timing severity peak incidence a given season, can inform public health response to outbreaks. As part ongoing efforts incorporate data advanced analytical methods into decision-making, Centers for Disease Control Prevention (CDC) has organized seasonal forecasting challenges since 2013/2014 season. In 2017/2018 22 teams participated. A...
Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields recent insights epidemiology, one maximise the predictive performance such if multiple models are combined into an ensemble. Here, we report ensembles predicting COVID-19 cases deaths across Europe between 08 March 2021 07 2022.
Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, deaths. The overall burden of captured by the Centers for Disease Control Prevention’s influenza-like illness network, which provides invaluable information about current incidence. This used provide decision support regarding prevention response efforts. Despite relatively rich surveillance data recurrent nature seasonal influenza, forecasting timing...
Abstract Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve reliability of outputs. Here we report insights from ten weeks collaborative short-term forecasting in Germany Poland (12 October–19 December 2020). The study period covers onset second wave both countries, with tightening non-pharmaceutical interventions (NPIs) subsequently a decay (Poland) or plateau renewed...
During the COVID-19 pandemic, forecasting trends to support planning and response was a priority for scientists decision makers alike. In United States, coordinated by large group of universities, companies, government entities led Centers Disease Control Prevention US Forecast Hub ( https://covid19forecasthub.org ). We evaluated approximately 9.7 million forecasts weekly state-level cases predictions 1–4 weeks into future submitted 24 teams from August 2020 December 2021. assessed coverage...
Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control are limited, making forecasting a public health priority. However, little research been done compare strengths weaknesses disease on national scale. We used forecasts submitted 2020 Forecasting Challenge, an open challenge organized by Centers for Disease Control Prevention, assess status...
Timely and accurate forecasts of seasonal influenza would assist public health decision-makers in planning intervention strategies, efficiently allocating resources, possibly saving lives. For these reasons, are consequential. Producing timely forecasts, however, have proven challenging due to noisy limited data, an incomplete understanding the disease transmission process, mismatch between process data-generating process. In this paper, we introduce a dynamic Bayesian (DB) flu forecasting...
The ability to produce timely and accurate flu forecasts in the United States can significantly impact public health. Augmenting with internet data has shown promise for improving forecast accuracy timeliness controlled settings, but results practice are less convincing, as models augmented have not consistently outperformed without data. In this paper, we perform a experiment, taking into account backfill, improve clarity on benefits limitations of augmenting an already good forecasting...
Abstract Influenza forecasting in the United States (US) is complex and challenging due to spatial temporal variability, nested geographic scales of interest, heterogeneous surveillance participation. Here we present Dante, a multiscale influenza model that learns rather than prescribes spatial, temporal, data structure generates coherent forecasts across state, regional, national scales. We retrospectively compare Dante’s short-term seasonal for previous flu seasons Dynamic Bayesian Model...
Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow expensive. Recent internet-based approaches promise real-time cheap, few parameters. However, the question of when how these work remains open. We addressed this using Wikipedia access logs category links. Our experiments, replicable extensible our open source code data, test effect semantic...
We present a generic method for automatically calibrating computer code to an experiment, with uncertainty, given “training” set of runs. The calibration technique is general and probabilistic, meaning the uncertainty represented in form probability distribution. demonstrate by combined Finite-Discrete Element Method (FDEM) Split Hopkinson Pressure Bar (SHPB) experiment granite sample. probabilistic combines runs FDEM simulation range settings experimental develop statistical emulator....
Abstract This paper presents a comprehensive exploration of the Interstellar Boundary Explorer energetic neutral atom (ENA) ribbon, focusing on its spatial and temporal variations over 14 yr. Methodological advancements, including refined map modeling procedure new ribbon separation technique with appropriate error propagation, enable detailed investigation ribbon’s features. Utilizing statistically robust metrics, this study reveals details across energy time. Key findings include energy-...
Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- interseasonal dynamics. Here, we present framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that relevant to forecasting efforts, focusing on outbreak shape, timing, pairwise correlations magnitude onset. In addition, use combination of 18 satellite remote sensing imagery, weather,...
Summary Fracture propagation plays a key role for number of applications interest to the scientific community, from dynamic fracture processes like spallation and fragmentation in metals failure ceramics, airplane wings, etc. Simulations material deformation rely on accurate knowledge characteristics such as strength amount energy being dissipated during process. Within combined finite‐discrete element method (FDEM) framework behavior is typically described through parametrized softening...
Abstract Background Dengue fever is a mosquito-borne infection transmitted by Aedes aegypti and mainly found in tropical subtropical regions worldwide. Since its re-introduction 1986, Brazil has become hotspot for dengue experienced yearly epidemics. As notifiable infectious disease, uses passive epidemiological surveillance system to collect report cases; however, burden underestimated. Thus, Internet data streams may complement activities providing real-time information the face of...
While the number of human cases mosquito-borne diseases has increased in North America last decade, accurate modeling mosquito population density remained a challenge. Longitudinal trap data over many years needed for model calibration, and validation is relatively rare. In particular, capturing relative changes abundance across seasons necessary predicting risk disease spread as it varies from year to year. We developed discrete, semi-stochastic, mechanistic process-based that captures...
Abstract Relationships exist between radiation belt electron flux intensities and solar drivers such as wind speed, ion density, magnetic fields. The particulars of these relationships, however, are not well understood. Many forecasting models have been developed in the last 25 years, attempting to make sense relationships produce accurate forecasts for intensities. We discuss some inherent limitations that many (e.g., static models) possess when trying untangle intricate dynamic levels...
Scientists often predict physical outcomes, e.g., experimental results, with the assistance of computer codes that, at their best, only coarsely approximate reality. Coarse predictions are challenging in large part due to multitude seemingly arbitrary yet consequential decisions that must be made such as choice relevant data, calibration code parameters, and construction empirical discrepancy forms. In this paper, we present a case study context inertial confinement fusion (ICF) implosion...
Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning consider forecasting methods short-term staffing other resources. With overwhelming burden imposed by health care system, an emergent need exists accurately forecast hospitalization needs within actionable timeframe.