Jarad Niemi
A5019728627- COVID-19 epidemiological studies
- Data-Driven Disease Surveillance
- Gaussian Processes and Bayesian Inference
- Statistical Methods and Bayesian Inference
- Soil Geostatistics and Mapping
- Influenza Virus Research Studies
- Advanced Multi-Objective Optimization Algorithms
- Statistical Methods and Inference
- Simulation Techniques and Applications
- Chromosomal and Genetic Variations
- Gene Regulatory Network Analysis
- Bayesian Methods and Mixture Models
- Gene expression and cancer classification
- Data Analysis with R
- CRISPR and Genetic Engineering
- Genetic Mapping and Diversity in Plants and Animals
- Soil Carbon and Nitrogen Dynamics
- Statistical Distribution Estimation and Applications
- Forecasting Techniques and Applications
- Ecology and Vegetation Dynamics Studies
- Soil Moisture and Remote Sensing
- COVID-19 Pandemic Impacts
- Census and Population Estimation
- Genomics and Chromatin Dynamics
- demographic modeling and climate adaptation
Iowa State University
2015-2024
National Laboratory for Agriculture and the Environment
2020
Minneapolis Institute of Arts
2020
Agricultural Research Service
2020
Universidad de la República
2019
University of California, Santa Barbara
2010-2011
Duke University
2007-2010
University of Minnesota
2002-2007
Decision Sciences (United States)
2007
Loss of biodiversity and degradation ecosystem services from agricultural lands remain important challenges in the United States despite decades spending on natural resource management. To date, conservation investment has emphasized engineering practices or vegetative strategies centered monocultural plantings nonnative plants, largely excluding native species cropland. In a catchment-scale experiment, we quantified multiple effects integrating strips prairie amid corn soybean crops, with...
Short-term probabilistic forecasts of the trajectory COVID-19 pandemic in United States have served as a visible and important communication channel between scientific modeling community both general public decision-makers. Forecasting models provide specific, quantitative, evaluable predictions that inform short-term decisions such healthcare staffing needs, school closures, allocation medical supplies. Starting April 2020, US Forecast Hub ( https://covid19forecasthub.org/ ) collected,...
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...
Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, United States Centers for Disease Control Prevention (CDC) partnered with academic research lab University of Massachusetts Amherst to create US Forecast Hub. Launched in April 2020, Hub is a dataset point probabilistic incident cases, hospitalizations, deaths, cumulative deaths due county, state,...
AbstractThis article develops a block composite likelihood for estimation and prediction in large spatial datasets. The (CL) is constructed from the joint densities of pairs adjacent blocks. This allows datasets to be split into many smaller datasets, each which can evaluated separately, combined through simple summation. Estimates unknown parameters are obtained by maximizing CL function. In addition, new method optimal under presented. Asymptotic variances both parameter estimates...
Abstract Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged into mean ensemble model compared them against predictions based on historical trends. highest seasonal peak intensity short-term forecasts,...
Abstract Short-term probabilistic forecasts of the trajectory COVID-19 pandemic in United States have served as a visible and important communication channel between scientific modeling community both general public decision-makers. Forecasting models provide specific, quantitative, evaluable predictions that inform short-term decisions such healthcare staffing needs, school closures, allocation medical supplies. Starting April 2020, US Forecast Hub ( https://covid19forecasthub.org/ )...
Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 seasons, 26 forecasting teams provided national jurisdiction-specific probabilistic predictions of weekly confirmed hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using Weighted Interval Score (WIS), relative WIS, coverage. Six out 23 models outperform baseline model across forecast locations in 12 18 2022-23. Averaging all...
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 An increasingly common component of studies in synthetic and systems biology is analysis dynamics gene expression at the single‐cell level, a context that heavily dependent on use time‐lapse movies. Extracting quantitative data temporal from such movies remains major challenge. Here, we describe novel methods for automating key steps single‐cell, fluorescent images—segmentation lineage reconstruction—to recognize track individual cells over time. The automated iteratively combines...
Covariance matrix estimation arises in multivariate problems including normal sampling models and regression where random effects are jointly modeled, e.g. random-intercept, random-slope models. A Bayesian analysis of these requires a prior on the covariance matrix. Here we compare an inverse Wishart, scaled hierarchical separation strategy as possible priors for We evaluate through simulation study application to real data set. Generally all work well with exception Wishart when true...
Especially when facing reliability data with limited information (e.g., a small number of failures), there are strong motivations for using Bayesian inference methods. These include the option to use from physics‐of‐failure or previous experience failure mode in particular material specify an informative prior distribution. Another advantage is ability make statistical inferences without having rely on specious (when failures small) asymptotic theory needed justify non‐Bayesian Users methods...
We explore how the big-three computing paradigms---symmetric multiprocessor, graphical processing units (GPUs), and cluster computing---can together be brought to bear on large-data Gaussian processes (GP) regression problems via a careful implementation of newly developed local approximation scheme. Our methodological contribution focuses primarily GPU computation, as this requires most care also provides largest performance boost. However, in our empirical work we study relative merits all...
Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, United States Centers for Disease Control Prevention (CDC) partnered with academic research lab University of Massachusetts Amherst to create US Forecast Hub. Launched in April 2020, Hub is a dataset point probabilistic incident hospitalizations, cases, deaths, cumulative deaths due national, state,...
A set of probabilities along with corresponding quantiles are often used to define predictive distributions or probabilistic forecasts. These quantile predictions offer easily interpreted uncertainty an event, and generally straightforward estimate using standard statistical machine learning methods. However, compared a distribution defined by probability density cumulative function, has less distributional information. When given estimated quantiles, it may be desirable fully continuous...
A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge its kinetic parameters. Despite recent experimental advances, estimation unknown parameter values from observed data still bottleneck obtaining accurate results. Many methods exist in deterministic systems; discrete stochastic systems are less well developed. Given probabilistic nature models, natural approach to choose that maximize probability with respect parameters, a.k.a. maximum likelihood...
Phylogenetic trees are widely used visual representations in the biological sciences and most important evolutionary biology. Therefore, phylogenetic have also become an component of biology education. We sought to characterize reasoning by introductory students interpreting taxa relatedness on trees, measure prevalence correct taxa-relatedness interpretations, determine how student correctness change response instruction over time. Counting synapomorphies nodes between were common forms...
Management policies for influenza outbreaks balance the expected morbidity and mortality costs versus cost of intervention policies. We present a methodology dynamic determination optimal in completely observed stochastic compartmental model with parameter uncertainty. Our approach is simulation-based searches full set sequential control strategies. For each time point, it generates policy map describing to implement as function outbreak state Bayesian posteriors. As running example, we...
Many crop fields in the United States Corn Belt continue to erode at rates excess of soil regeneration leading sediment being transported from farms adjacent surface water and degrading wildlife habitat. To reduce or eliminate loss, vegetative filter strips can be established perpendicular hillslope edge-of-field intercept runoff transporting sediment. The planted with native prairie vegetation out as well establishing high quality A long-term study Neal Smith Wildlife Refuge Farm central...
The profitability of farming varies based on factors such as a crop's market value, input costs and occurrence resistant pests, all capable altering the value pest management tactics in an integrated program. We provide framework for calculating expected yield net revenue scenarios, using soybean aphid (Aphis glycines) case study. Foliar insecticide host-plant resistance are effective preventing loss from outbreaks; however, pyrethroid-resistant populations pose challenge farmers. evaluated...
We describe a strategy for Markov chain Monte Carlo analysis of non-linear, non-Gaussian state-space models involving batch inference on dynamic, latent state variables and fixed model parameters. The key innovation is Metropolis-Hastings method the time series based sequential approximation filtering smoothing densities using normal mixtures. These mixtures are propagated through non-linearities an accurate, local mixture method, we use regenerating procedure to deal with potential...