publications

2025

1. A minimum data standard for wildlife disease research and surveillance

   Collin J. Schwantes, Cecilia A. Sánchez, Tess Stevens, and 23 more authors

   Scientific Data, Jun 2025

   Publisher: Nature Publishing Group

   Abs

   Rapid and comprehensive data sharing is vital to the transparency and actionability of wildlife infectious disease research and surveillance. Unfortunately, most best practices for publicly sharing these data are focused on pathogen determination and genetic sequence data. Other facets of wildlife disease data – particularly negative results – are often withheld or, at best, summarized in a descriptive table with limited metadata. Here, we propose a minimum data and metadata reporting standard for wildlife disease studies. Our data standard identifies a set of 40 data fields (9 required) and 24 metadata fields (7 required) sufficient to standardize and document a dataset consisting of records disaggregated to the finest possible spatial, temporal, and taxonomic scale. We illustrate how this standard is applied to an example study, which documented a novel alphacoronavirus found in bats in Belize. Finally, we outline best practices for how data should be formatted for optimal re-use, and how researchers can navigate potential safety concerns around data sharing.

2023

1. EHA Modeling & Analytics Handbook

   Collin Schwantes, Noam Ross, Alix Augusto Armero Villanueva, and 6 more authors

   Jun 2023
2. Bees Needs Data Exploration App

   Collin Schwantes, Alexandra Rose, Virginia Scott, and 2 more authors

   Jun 2023
3. Mosquito Control Optimization App

   Collin Schwantes, Jeff Demers, Sharon Bewick, and 1 more author

   Oct 2023

   Abs

   This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling timeperiod, and the optimal time to apply a treatment or multiple treatments. Link to application: https://collin-schwantes.shinyapps.io/MosquitoApp/

2022

1. Working Group on NIH DMSP Guidance

   Levi Dolan, Lucy Carr Jones, Barrie Hayes, and 26 more authors

   Oct 2022

   Publisher: OSF

2021

1. "Integrating COVID Models at Different Scales for Infection Risk Estimation and Control Optimization"

   Collin J. Schwantes, Benno Lee, Benjamin Ortiz, and 2 more authors

   In , Jan 2021

   Abs

   SARS-COV-2 infections result from interactions at fine spatial and temporal scales; however, case clusters occur within a larger scale epidemiological context. Appropriately integrating the interactions between processes that occur across spatial and temporal scales is essential for simulating systems of disease transmission and understanding infection risk. Here we present a simulation engine that places a fine scale network based infection model within the broader epidemiological context of the study population. Using this system, we are able to simulate seeding events and fully explore the control strategy space. Our simulation engine can integrate a spatially explicit COVID-19 case estimation technique at the county scale with institution level disease transmission at a “building” scale. The case estimation technique takes into account location specific factors around infection control and population level movement to estimate disease burden in a given location. The institution level model uses a multigraph to integrate social and spatial contact networks under various hazard reduction strategies at two different time scales. This allows us to model individual level interactions in the local context of the COVID-19 pandemic, opening a closed system to external forces. Our model provides realistic estimates of SARS-COV-2 outbreaks and the ability to discover novel control strategies. By integrating models at multiple scales, our simulation engine empowers decision makers to develop location specific preparedness policies based on realistic estimates of how SARS-COV-2 will spread through their institutions.

2. Methods in Constrained Community Detection: An Integer Optimization Model and Heuristic Approach for Cohort Creation

   Danielle Heymann, Collin Schwantes, Viveca Pavon-Harr, and 1 more author

   In 2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS), Dec 2021

2020

1. Social determinates of health and COVID-19 mortality rates at the county level

   Sophia Lam, Elizabeth Leeds Hohman, Viveca Pavon-Harr, and 7 more authors

   In 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA), Oct 2020

2019

1. A New State Record of the Rare Bee, Cemolobus ipomoeae (Hymenoptera, Apidae), from Colorado, USA

   Mary A. Jamieson Adrian L. Carper

   Journal of the Kansas Entomological Society, Apr 2019

2018

1. Solitary Floral Specialists Do Not Respond to Cryptic Flower-Occupying Predators

   Collin J. Schwantes, Adrian L. Carper, and M. Deane Bowers

   Journal of Insect Behavior, Nov 2018

   Publisher: Springer Science and Business Media LLC
2. NBIC Collaboration at Multiple Jurisdictional Levels During the Zika Epidemic

   Emily Iarocci, Collin Schwantes, Anne Folley, and 3 more authors

   Online Journal of Public Health Informatics, May 2018

   Publisher: University of Illinois Libraries
3. NBIC Biofeeds: Deploying a New, Digital Tool for Open Source Biosurveillance across Federal Agencies

   Heather Baker, Asher Grady, Collin Schwantes, and 7 more authors

   Online Journal of Public Health Informatics, May 2018

   Publisher: University of Illinois Libraries

2017

1. An online photographic catalog of primary types of Platygastroidea (Hymenoptera) in the National Museum of Natural History, Smithsonian Institution

   Elijah J. Talamas, Joseph Thompson, Amy Cutler, and 15 more authors

   May 2017
2. Exploring variation in local and landscape factors driving grassland bee communities in Colorado

   Adrian L. Carper, Collin J. Schwantes, Stacy B. Endriss, and 3 more authors

   In , Aug 2017

   Abs

   Agricultural intensification for both food and fuel could have wide reaching impacts on native pollinator communities, especially in habitats vulnerable to human agricultural development such as grasslands. However, studies on pollinator communities, and the factors that drive pollinator community dynamics, are lacking from many agriculturally intensive regions. To explore how variation in local and landscape factors affect native bee communities in the most agriculturally intensive region of Colorado, we conducted a two-year survey of bees in 32 focal grassland sites located across five counties. We sampled bees using a combination of bee bowls, vane traps, and netting in 12 actively grazed rangelands and 20 Conservation Reserve Program (CRP) grasslands. Traps were deployed for 24 hours, once monthly (June-Sept in 2013 and May-Sept in 2014). We also measured plant diversity locally within sites and extracted agricultural land-cover from multiple scales surrounding each site. We explored how variation in local and landscape factors impacted bee communities using a combination of ordination and modeling techniques.

2015

1. Digitized data and the bees of Colorado

   Collin J. Schwantes

   In , Nov 2015

   Abs

   In conservation biology, it is essential to know the size of a population and where it can be found. To properly estimate population size and location, long-term and wide-ranging sampling must be conducted. Digitized collections provide invaluable supplemental sampling to improve estimates of population size and location. Using records in the Global Biodiversity Information Facility in combination with literature review, I was able to build a database with location, date, and natural history characteristics for the bee species of eastern Colorado. Using that database and two years of fi eld sampling, I estimated population size and location for each species. Including GBIF records signifi cantly improved model estimates. Th ese estimations will help us refi ne conservation eff orts for bees in eastern Colorado.

2014

1. Conservation grasslands support diverse native bee assemblages in agroecosystems

   Adrian L. Carper, Virginia L. Scott, Collin J. Schwantes, and 4 more authors

   In , Nov 2014

   Abs

   The U.S. Department of Energy projects that millions of acres of croplands will be converted to new bioenergy crops in coming years. Such shifts in crop production are expected to alter the agricultural landscape, potentially affecting organisms inhabiting these agroecosystems. In particular, there is growing concern about how bees will respond to changes in land-use. Our research examines how landscape attributes, such as vegetation cover, floral diversity, and land-use influence bee abundance and diversity. The goal of this project is to better understand how future land-use change may affect bees in agroecosystems located in the Colorado high plains region. In 2013, we sampled bees and characterized landscape attributes in grassland habitats at 32 field sites located in northeastern Colorado. In addition to rangelands, field sites included Conservation Reserve Program (CRP) grasslands, allowing us the opportunity to evaluate CRP plantings as habitat for wild and managed bees. These grassland habitats, in particular, had a diverse and abundant native bee community. Preliminary results indicate that CRP fields supported approximately 30% more bees than rangeland habitats. Long-horned bees (tribe Eucerini) were especially common along the edges of conservation plantings, likely due to the availability of sunflowers, which were dominant floral resources for these bees. We are currently exploring how landscape attributes drive community composition within vegetation types and how these habitats may be influenced by future changes in land-use.