About

Dr. Johnny Uelmen is a researcher involved in disease ecology, with a focus on ecological modeling and forecasting of tick-borne diseases. He has been awarded a Wisconsin Partnership Program (WPP) New Investigator Program Grant for his work on improving ecological modeling and forecasting of ticks and tick-borne diseases in Wisconsin. Dr. Uelmen is actively engaged in hosting symposia, such as 'The Changing Epidemiology of Oropouche Virus in the Americas,' and his lab has welcomed new graduate students, indicating ongoing research and mentorship activities. He has also been recognized as a SMPH STRIDE Scholar, highlighting his contributions to the field.

Research topics

• Computer Science
• Medicine
• Virology
• Geography
• Environmental health
• Cartography
• Data science
• Operations research
• Risk analysis (engineering)
• Pathology
• Nursing
• Telecommunications
• Engineering
• Environmental resource management
• Demography
• Biology
• Ecology

Selected publications

• Ticks and tickborne diseases in the upper Midwestern United States: role for citizen science in assessing exposure risk

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-15

  articleOpen access

  Abstract Tickborne diseases are a significant burden in many parts of the world. In the upper Midwestern United States, Lyme disease is the most common tickborne disease. It is carried by Ixodes scapularis. This vector can also transmit the pathogens causing anaplasmosis, babesiosis, ehrlichiosis, and several more tickborne diseases in this region. There is also concern for other tick species, such as Amblyomma americanum, that are expanding their ranges northward. We launched a citizen science passive tick surveillance program in 2024 to investigate tick species ranges in the upper Midwest, as well as the pathogens carried by I. scapularis. We received over 12,000 ticks in the first two years of this program, primarily from Wisconsin. While we received submissions of adult A. americanum outside of their endemic range, we did not see evidence of establishment in our study area. We measured pathogen prevalence in adult female I. scapularis (n=707) and observed 51% positivity for Borrelia burgdorferi, 9% for Babesia microti, 9% for Anaplasma phagocytophilum, and 3% for Ehrlichia muris eauclairensis. Multiple pathogens were identified in 14% of tested specimens, and significant associations were observed between B. burgdorferi and B. microti, and B. burgdorferi and E. muris eauclairensis. Pathogen prevalences varied across time and geography. Our results can begin to inform risk assessment for tickborne diseases in our region. A non-technical version of this document with interactive maps is available here : https://storymaps.arcgis.com/stories/8008c9d710b5400599f3c6cf88b2c546 Our online data dashboard is available here : redcap.link/TICS

  PublisherDOI

• Exploring Evolutionary Medicine through Bibliometrics: Research Insights and Future Opportunities

  Evolution Medicine and Public Health · 2025-01-01 · 1 citations

  articleOpen access

  Abstract Background Evolutionary medicine applies principles of evolutionary biology to elucidate the origins of human health and disease. Despite rapid growth since its emergence in the 1990s, the field lacks systematic bibliometric evaluation. Methods We conducted the first comprehensive bibliometric analysis of evolutionary medicine using the Web of Science Core Collection. Two search strategies captured general literature (n = 885) and publications from Evolution, Medicine, and Public Health (EMPH, n = 358). We analyzed citation patterns, thematic clusters, and collaboration networks using Bibliometrix and VOSviewer. Results The field exhibits steady growth, with high citation impact from review articles and a dominant presence of contributions from the USA, UK, and Germany. Six major keyword clusters were identified: drug resistance, infection, evolutionary mismatch, cancer, cognition, and mental health. However, topics such as clinical translation, One Health, Planetary Health, and race-related issues remain underrepresented. Moreover, standard database queries failed to capture most EMPH articles, highlighting a lack of field identification in metadata. Conclusions This bibliometric overview reveals strengths and gaps in the evolutionary medicine literature. To enhance visibility, equity, and clinical relevance, future research should promote interdisciplinary integration, broader international collaboration, and more consistent field labeling in publications. These efforts are vital to advancing evolutionary perspectives in global biomedical and public health discourse.

  PublisherOA PDFDOI

• Artificial Intelligence and Citizen Science as a Tool for Global Mosquito Surveillance: Madagascar Case Study

  Insects · 2025-10-28

  articleOpen access

  Anopheles stephensi is an invasive and deadly malaria vector with the ability to use artificial containers as larval habitats. This ability is unique for malaria vectors in Africa and requires distinct surveillance strategies for early detection and rapid response. In this study, we trained a variety of artificial intelligence (AI) image recognition algorithms, using thousands of smartphone photos of laboratory-authenticated An. stephensi and seven endemic mosquito species, to develop a citizen science-friendly tool for An. stephensi detection. In Antananarivo, Madagascar, citizen science observations of >132 Anopheles spp. larvae from multiple artificial containers—including one closeup photo of a larva, from a tire—were submitted via NASA’s GLOBE Observer app in March 2020 and discovered years later. Given that genetic testing was no longer possible, this photo was used as a proof-of-concept to determine whether the AI species identification could be used on citizen science-generated images. The tire larva was classified as An. stephensi by all 11 species models, which yielded high accuracy and confidence (up to 99.34%) and included a false positive rate of <1%. Furthermore, explainable AI (XAI) heat maps led to the discovery of dark spots in abdominal segment VI corresponding to testes, corroborating a separate classification of the tire larva as male by the sex model. All available evidence suggests that AI image identification would have flagged this larva as a suspect An. stephensi, which could have been submitted to a molecular laboratory for further confirmation. Results demonstrate the power of integrating citizen science and AI—for which we provide free online tools—as a low-cost signal for malaria programs to confirm and respond to, and as complementary surveillance to fill the critical knowledge gaps in the distribution of invasive An. stephensi across Africa and beyond.

  PublisherDOI

• Review 4: "Yellow Fever in Ghana: Predicting Emergence and Ecology from Historical Outbreaks"

  2024-03-23

  peer-reviewOpen access1st authorCorresponding

  This preprint can be summarized as: 1.Despite the frequency of cases of yellow fever, there are few studies that evaluate the characteristics of human cases; 2. the dynamic of cases in Ghana have shifted, largely attributed to human decisions based on vaccination adherence; 3. Habitat suitability models of DF are strongly predicted, but need to be further assessed to include a lot of other important information pertaining to the sylvatic and savannah cycles.Overall, this was a very well written preprint that would make an excellent contribution to a much-needed, and neglected, disease system.There are lots of assumptions made with notable limitations, but the authors provided these clearly and honestly.Any habitat suitability model will have numerous choices for inputs and corresponding inclusion criteria -but this is highly informative and makes a strong case for several, key points that are important for taking the steps necessary to reduce the burden of YF in Ghana.Smaller points to consider:

  PublisherOA PDFDOI

• Infectious disease responses to human climate change adaptations

  Global Change Biology · 2024-08-01 · 6 citations

  reviewOpen access

  Many recent studies have examined the impact of predicted changes in temperature and precipitation patterns on infectious diseases under different greenhouse gas emissions scenarios. But these emissions scenarios symbolize more than altered temperature and precipitation regimes; they also represent differing levels of change in energy, transportation, and food production at a global scale to reduce the effects of climate change. The ways humans respond to climate change, either through adaptation or mitigation, have underappreciated, yet hugely impactful effects on infectious disease transmission, often in complex and sometimes nonintuitive ways. Thus, in addition to investigating the direct effects of climate changes on infectious diseases, it is critical to consider how human preventative measures and adaptations to climate change will alter the environments and hosts that support pathogens. Here, we consider the ways that human responses to climate change will likely impact disease risk in both positive and negative ways. We evaluate the evidence for these impacts based on the available data, and identify research directions needed to address climate change while minimizing externalities associated with infectious disease, especially for vulnerable communities. We identify several different human adaptations to climate change that are likely to affect infectious disease risk independently of the effects of climate change itself. We categorize these changes into adaptation strategies to secure access to water, food, and shelter, and mitigation strategies to decrease greenhouse gas emissions. We recognize that adaptation strategies are more likely to have infectious disease consequences for under-resourced communities, and call attention to the need for socio-ecological studies to connect human behavioral responses to climate change and their impacts on infectious disease. Understanding these effects is crucial as climate change intensifies and the global community builds momentum to slow these changes and reduce their impacts on human health, economic productivity, and political stability.

  PublisherOA PDFDOI

• Two-step light gradient boosted model to identify human west nile virus infection risk factor in Chicago

  PLoS ONE · 2024-01-05 · 1 citations

  articleOpen accessCorresponding

  West Nile virus (WNV), a flavivirus transmitted by mosquito bites, causes primarily mild symptoms but can also be fatal. Therefore, predicting and controlling the spread of West Nile virus is essential for public health in endemic areas. We hypothesized that socioeconomic factors may influence human risk from WNV. We analyzed a list of weather, land use, mosquito surveillance, and socioeconomic variables for predicting WNV cases in 1-km hexagonal grids across the Chicago metropolitan area. We used a two-stage lightGBM approach to perform the analysis and found that hexagons with incomes above and below the median are influenced by the same top characteristics. We found that weather factors and mosquito infection rates were the strongest common factors. Land use and socioeconomic variables had relatively small contributions in predicting WNV cases. The Light GBM handles unbalanced data sets well and provides meaningful predictions of the risk of epidemic disease outbreaks.

  PublisherOA PDFDOI

• Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction

  Parasites & Vectors · 2023-01-12 · 45 citations

  articleOpen access

  BACKGROUND: West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. METHODS: We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. RESULTS: Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. CONCLUSIONS: Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases).

  PublisherOA PDFDOI

• Citizen Science as an Approach for Responding to the Threat of Anopheles stephensi in Africa

  Citizen Science Theory and Practice · 2023-01-01 · 16 citations

  articleOpen accessCorresponding

  Even as novel technologies emerge and medicines advance, pathogen-transmitting mosquitoes pose a deadly and accelerating public health threat. Detecting and mitigating the spread of Anopheles stephensi in Africa is now critical to the fight against malaria, as this invasive mosquito poses urgent and unprecedented risks to the continent. Unlike typical African vectors of malaria, An. stephensi breeds in both natural and artificial water reservoirs, and flourishes in urban environments. With An. stephensi beginning to take hold in heavily populated settings, citizen science surveillance supported by novel artificial intelligence (AI) technologies may offer impactful opportunities to guide public health decisions and community-based interventions. Coalitions like the Global Mosquito Alert Consortium (GMAC) and our freely available digital products can be incorporated into enhanced surveillance of An. stephensi and other vector-borne public health threats. By connecting local citizen science networks with global databases that are findable, accessible, interoperable, and reusable (FAIR), we are leveraging a powerful suite of tools and infrastructure for the early detection of, and rapid response to, (re)emerging vectors and diseases.

  PublisherOA PDFDOI

• Modeling community COVID-19 transmission risk associated with U.S. universities

  Scientific Reports · 2023-01-25 · 1 citations

  articleOpen access1st authorCorresponding

  The ongoing COVID-19 pandemic is among the worst in recent history, resulting in excess of 520,000,000 cases and 6,200,000 deaths worldwide. The United States (U.S.) has recently surpassed 1,000,000 deaths. Individuals who are elderly and/or immunocompromised are the most susceptible to serious sequelae. Rising sentiment often implicates younger, less-vulnerable populations as primary introducers of COVID-19 to communities, particularly around colleges and universities. Adjusting for more than 32 key socio-demographic, economic, and epidemiologic variables, we (1) implemented regressions to determine the overall community-level, age-adjusted COVID-19 case and mortality rate within each American county, and (2) performed a subgroup analysis among a sample of U.S. colleges and universities to identify any significant preliminary mitigation measures implemented during the fall 2020 semester. From January 1, 2020 through March 31, 2021, a total of 22,385,335 cases and 374,130 deaths were reported to the CDC. Overall, counties with increasing numbers of university enrollment showed significantly lower case rates and marginal decreases in mortality rates. County-level population demographics, and not university level mitigation measures, were the most significant predictor of adjusted COVID-19 case rates. Contrary to common sentiment, our findings demonstrate that counties with high university enrollments may be more adherent to public safety measures and vaccinations, likely contributing to safer communities.

  PublisherOA PDFDOI

• Global mosquito observations dashboard (GMOD): creating a user-friendly web interface fueled by citizen science to monitor invasive and vector mosquitoes

  International Journal of Health Geographics · 2023-10-28 · 19 citations

  articleOpen access1st authorCorresponding

  BACKGROUND: Mosquitoes and the diseases they transmit pose a significant public health threat worldwide, causing more fatalities than any other animal. To effectively combat this issue, there is a need for increased public awareness and mosquito control. However, traditional surveillance programs are time-consuming, expensive, and lack scalability. Fortunately, the widespread availability of mobile devices with high-resolution cameras presents a unique opportunity for mosquito surveillance. In response to this, the Global Mosquito Observations Dashboard (GMOD) was developed as a free, public platform to improve the detection and monitoring of invasive and vector mosquitoes through citizen science participation worldwide. METHODS: GMOD is an interactive web interface that collects and displays mosquito observation and habitat data supplied by four datastreams with data generated by citizen scientists worldwide. By providing information on the locations and times of observations, the platform enables the visualization of mosquito population trends and ranges. It also serves as an educational resource, encouraging collaboration and data sharing. The data acquired and displayed on GMOD is freely available in multiple formats and can be accessed from any device with an internet connection. RESULTS: Since its launch less than a year ago, GMOD has already proven its value. It has successfully integrated and processed large volumes of real-time data (~ 300,000 observations), offering valuable and actionable insights into mosquito species prevalence, abundance, and potential distributions, as well as engaging citizens in community-based surveillance programs. CONCLUSIONS: GMOD is a cloud-based platform that provides open access to mosquito vector data obtained from citizen science programs. Its user-friendly interface and data filters make it valuable for researchers, mosquito control personnel, and other stakeholders. With its expanding data resources and the potential for machine learning integration, GMOD is poised to support public health initiatives aimed at reducing the spread of mosquito-borne diseases in a cost-effective manner, particularly in regions where traditional surveillance methods are limited. GMOD is continually evolving, with ongoing development of powerful artificial intelligence algorithms to identify mosquito species and other features from submitted data. The future of citizen science holds great promise, and GMOD stands as an exciting initiative in this field.

  PublisherOA PDFDOI

Frequent coauthors

• Rebecca L. Smith

  19 shared

• Patrick Irwin

  12 shared

• Charles L. Nunn

  Duke Institute for Health Innovation

  12 shared

• William M. Brown

  9 shared

• Surendra Karki

  8 shared

• Marilyn O. Ruiz

  University of Illinois Urbana-Champaign

  7 shared

• Alexander C. Keyel

  Wadsworth Center

  6 shared

• Jameson Mori

  University of Illinois Urbana-Champaign

  6 shared

Labs

• Uelmen Disease Ecology LabPI

Awards & honors

• Wisconsin Partnership Program (WPP) New Investigator Program…