Evolutionary Anthropology Staff: Publications since January 2023

%% Uelmen, Johnny   
@article{fds371620,
   Author = {Uelmen, JA and Mapes, CD and Prasauskas, A and Boohene, C and Burns, L and Stuck, J and Carney, RM},
   Title = {A Habitat Model for Disease Vector Aedes aegypti in the
             Tampa Bay Area, FloridA.},
   Journal = {Journal of the American Mosquito Control
             Association},
   Volume = {39},
   Number = {2},
   Pages = {96-107},
   Year = {2023},
   Month = {June},
   url = {http://dx.doi.org/10.2987/22-7109},
   Abstract = {Within the contiguous USA, Florida is unique in having
             tropical and subtropical climates, a great abundance and
             diversity of mosquito vectors, and high rates of human
             travel. These factors contribute to the state being the
             national ground zero for exotic mosquito-borne diseases, as
             evidenced by local transmission of viruses spread by Aedes
             aegypti, including outbreaks of dengue in 2022 and Zika in
             2016. Because of limited treatment options, integrated
             vector management is a key part of mitigating these
             arboviruses. Practical knowledge of when and where mosquito
             populations of interest exist is critical for surveillance
             and control efforts, and habitat predictions at various
             geographic scales typically rely on ecological niche
             modeling. However, most of these models, usually created in
             partnership with academic institutions, demand resources
             that otherwise may be too time-demanding or difficult for
             mosquito control programs to replicate and use effectively.
             Such resources may include intensive computational
             requirements, high spatiotemporal resolutions of data not
             regularly available, and/or expert knowledge of statistical
             analysis. Therefore, our study aims to partner with mosquito
             control agencies in generating operationally useful mosquito
             abundance models. Given the increasing threat of
             mosquito-borne disease transmission in Florida, our analytic
             approach targets recent Ae. aegypti abundance in the Tampa
             Bay area. We investigate explanatory variables that: 1) are
             publicly available, 2) require little to no preprocessing
             for use, and 3) are known factors associated with Ae.
             aegypti ecology. Out of our 4 final models, none required
             more than 5 out of the 36 predictors assessed (13.9%).
             Similar to previous literature, the strongest predictors
             were consistently 3- and 4-wk temperature and precipitation
             lags, followed closely by 1 of 2 environmental predictors:
             land use/land cover or normalized difference vegetation
             index. Surprisingly, 3 of our 4 final models included one or
             more socioeconomic or demographic predictors. In general,
             larger sample sizes of trap collections and/or citizen
             science observations should result in greater confidence in
             model predictions and validation. However, given disparities
             in trap collections across jurisdictions, individual county
             models rather than a multicounty conglomerate model would
             likely yield stronger model fits. Ultimately, we hope that
             the results of our assessment will enable more accurate and
             precise mosquito surveillance and control of Ae. aegypti in
             Florida and beyond.},
   Doi = {10.2987/22-7109},
   Key = {fds371620}
}

@article{fds370207,
   Author = {Uelmen, JA and Kopsco, H and Mori, J and Brown, WM and Smith,
             RL},
   Title = {Modeling community COVID-19 transmission risk associated
             with U.S. universities.},
   Journal = {Scientific reports},
   Volume = {13},
   Number = {1},
   Pages = {1428},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1038/s41598-023-28212-z},
   Abstract = {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.},
   Doi = {10.1038/s41598-023-28212-z},
   Key = {fds370207}
}

@article{fds370208,
   Author = {Holcomb, KM and Mathis, S and Staples, JE and Fischer, M and Barker, CM and Beard, CB and Nett, RJ and Keyel, AC and Marcantonio, M and Childs, ML and Gorris, ME and Rochlin, I and Hamins-Puértolas, M and Ray, EL and Uelmen, JA and DeFelice, N and Freedman, AS and Hollingsworth, BD and Das, P and Osthus, D and Humphreys, JM and Nova, N and Mordecai, EA and Cohnstaedt, LW and Kirk, D and Kramer, LD and Harris, MJ and Kain, MP and Reed, EMX and Johansson, MA},
   Title = {Evaluation of an open forecasting challenge to assess skill
             of West Nile virus neuroinvasive disease
             prediction.},
   Journal = {Parasites & vectors},
   Volume = {16},
   Number = {1},
   Pages = {11},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1186/s13071-022-05630-y},
   Abstract = {<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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).},
   Doi = {10.1186/s13071-022-05630-y},
   Key = {fds370208}
}

@article{fds370209,
   Author = {Uelmen, JA and Lamcyzk, B and Irwin, P and Bartlett, D and Stone, C and Mackay, A and Arsenault-Benoit, A and Ryan, SJ and Mutebi, J-P and Hamer, GL and Fritz, M and Smith, RL},
   Title = {Human biting mosquitoes and implications for West Nile virus
             transmission.},
   Journal = {Parasites & vectors},
   Volume = {16},
   Number = {1},
   Pages = {2},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1186/s13071-022-05603-1},
   Abstract = {<h4>Background</h4>West Nile virus (WNV), primarily vectored
             by mosquitoes of the genus Culex, is the most important
             mosquito-borne pathogen in North America, having infected
             thousands of humans and countless wildlife since its arrival
             in the USA in 1999. In locations with dedicated mosquito
             control programs, surveillance methods often rely on
             frequent testing of mosquitoes collected in a network of
             gravid traps (GTs) and CO<sub>2</sub>-baited light traps
             (LTs). Traps specifically targeting oviposition-seeking
             (e.g. GTs) and host-seeking (e.g. LTs) mosquitoes are
             vulnerable to trap bias, and captured specimens are often
             damaged, making morphological identification
             difficult.<h4>Methods</h4>This study leverages an
             alternative mosquito collection method, the human landing
             catch (HLC), as a means to compare sampling of potential WNV
             vectors to traditional trapping methods. Human collectors
             exposed one limb for 15 min at crepuscular periods
             (5:00-8:30 am and 6:00-9:30 pm daily, the time when Culex
             species are most actively host-seeking) at each of 55 study
             sites in suburban Chicago, Illinois, for two summers (2018
             and 2019).<h4>Results</h4>A total of 223 human-seeking
             mosquitoes were caught by HLC, of which 46 (20.6%) were
             mosquitoes of genus Culex. Of these 46 collected Culex
             specimens, 34 (73.9%) were Cx. salinarius, a potential WNV
             vector species not thought to be highly abundant in upper
             Midwest USA. Per trapping effort, GTs and LTs collected >
             7.5-fold the number of individual Culex specimens than HLC
             efforts.<h4>Conclusions</h4>The less commonly used HLC
             method provides important insight into the complement of
             human-biting mosquitoes in a region with consistent WNV
             epidemics. This study underscores the value of the HLC
             collection method as a complementary tool for surveillance
             to aid in WNV vector species characterization. However,
             given the added risk to the collector, novel mitigation
             methods or alternative approaches must be explored to
             incorporate HLC collections safely and strategically into
             control programs.},
   Doi = {10.1186/s13071-022-05603-1},
   Key = {fds370209}
}

@article{fds373495,
   Author = {Leosari, Y and Uelmen, JA and Carney, RM},
   Title = {Spatial evaluation of healthcare accessibility across
             archipelagic communities of Maluku Province,
             Indonesia.},
   Journal = {PLOS global public health},
   Volume = {3},
   Number = {3},
   Pages = {e0001600},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1371/journal.pgph.0001600},
   Abstract = {The Maluku Province is an underdeveloped region in Indonesia
             with over 1,340 scattered islands. Due to the limited health
             facilities and transportation infrastructure, access to
             healthcare is very challenging. Here, we combined data from
             various sources to locate the population clusters, health
             facilities, roads, and ports/docks, and then utilize
             geographic information systems (GIS) to estimate distances
             from residents to health facilities. Health workforce
             distribution data was then integrated to elucidate overall
             healthcare equity among districts in the province. The
             average distances to puskesmas (primary health clinics) were
             8.89 km (by land) and 18.43 km (by land and water)
             respectively, and the average distances to hospitals were
             56.19 km (by land) and 73.09 km (by land and water), with
             large disparities within and among districts. Analysis of
             health workforce data shows that 65% of 207 puskesmas lack
             physicians, while 49% lack midwives. Ambon, Tual, and
             Southeast Maluku have the highest health equity, while East
             Ceram, Buru, and South Buru have the lowest. In general,
             this study demonstrates the utility of GIS and spatial
             analyses, which can help identify problem areas in
             healthcare accessibility and equity in archipelago settings,
             and provide recommendations to stakeholders such as public
             health officials and district administrators.},
   Doi = {10.1371/journal.pgph.0001600},
   Key = {fds373495}
}

@article{fds374605,
   Author = {Carney, RM and Long, A and Low, RD and Zohdy, S and Palmer, JRB and Elias,
             P and Bartumeus, F and Njoroge, L and Muniafu, M and Uelmen, JA and Rahola,
             N and Chellappan, S},
   Title = {Citizen Science as an Approach for Responding to the Threat
             of Anopheles stephensi in Africa},
   Journal = {Citizen Science: Theory and Practice},
   Volume = {8},
   Number = {1},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.5334/cstp.616},
   Abstract = {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.},
   Doi = {10.5334/cstp.616},
   Key = {fds374605}
}

@article{fds373661,
   Author = {Wan, GW and Allen, J and Ge, W and Rawlani, S and Uelmen, J and Mainzer,
             LS and Smith, RL},
   Title = {Two-Step Light Gradient Boosted Model to identify human West
             Nile Virus infection risk factor in Chicago},
   Volume = {19},
   Number = {1},
   Pages = {e0296283},
   Booktitle = {medRxiv},
   Year = {2023},
   url = {http://dx.doi.org/10.1101/2023.05.09.23289737},
   Abstract = {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.},
   Doi = {10.1101/2023.05.09.23289737},
   Key = {fds373661}
}