In 2020, California banned the sale and agricultural use of chlorpyrifos, an organophosphate pesticide (OP) associated with neurotoxicity and other adverse health outcomes. We primarily assessed changes in chlorpyrifos associated with this policy and secondarily explored how other OP exposures changed. The participants were from California’s Central Valley, 18 years or older, and English- or Spanish-speaking. The surveys and urine samples were collected pre-ban (December 2020) and post-ban (February–April 2022). The urine samples were analyzed for a chlorpyrifos-specific metabolite (TCPy), six dialkyl phosphates (DEP, DMTP, DETP, DMDTP, DMP, DEDTP), and total DE and DM. The pre- and post-ban metabolite concentrations were compared via Wilcoxon signed-rank tests and natural log-transformed paired differences in linear mixed effects regression, adjusted for covariates. Forty-nine participants had repeated biomarker data. The mean age of the study population was 46.8 years (SD: 16), 61% female, 67% Spanish-speaking, 100% Hispanic/Latino(a), and 47% had less than a high school education. Six urinary metabolites (TCPy, DEP, DMP, DMTP, total DE and total DM) had sufficient variation for further analysis, while DMDTP, DEDTP, and DETP were undetected. The paired differences in adjusted models showed statistically significant increases in TCPy and DMP associated with the policy change (e.g., TCPy estimated ratio of geometric means: 4.53 (95% CI 2.66, 7.69)) Reductions in metabolites of chlorpyrifos exposure were not observed following California’s chlorpyrifos ban, suggesting ongoing exposure to chlorpyrifos from other sources.

Reliable assessment of personal exposure to air pollution remains a challenge due to the limitations of monitoring technology. Recent technology developments, such as reductions in the size and cost of samplers as well as the incorporation of continuous sensors for location, activity, and exposure (i.e., global positioning systems [GPS], accelerometers, and low-cost pollutant sensors), have advanced our ability to assess personal exposure to air pollution. This study evaluated the upgraded Ultrasonic Personal Air Sampler (UPAS v2.1 PLUS) as a tool for quantifying time-integrated indoor and personal exposure to particulate matter (PM) and black carbon (BC) among a panel of participants in California's Central Valley and exploring personal exposures in different microenvironments using time/location-resolved PM2.5 data. Three field campaigns demonstrated that filter-derived PM10, PM2.5, PM10 BC, and PM2.5 BC concentrations measured using the UPAS were linear, unbiased, and precise compared to those measured using conventional personal sampling equipment. Time-resolved PM2.5, GPS, and light intensity data from the UPAS allowed for personal PM2.5 exposure assessment across microenvironments. The majority of daily PM2.5 exposure occurred inside the home. Participants with higher out-of-home PM2.5 exposures received those exposures primarily in agricultural and in-transit environments, in accordance with their self-reported occupational exposures. This study demonstrated the UPAS v2.1 PLUS is a reliable and valid tool for characterizing indoor air pollution and personal exposures in both temporal and spatial dimensions. Its enhanced capabilities should reduce the burden of personal activity logging in the field and enable accurate and precise estimation of exposures for epidemiological and community-based research.Copyright (c) 2024 American Association for Aerosol Research

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter ( β : 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter ( β :0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

Carbon monoxide (CO) is released during biomass burning events, resulting in decreased air quality and leading to the formation of climate forcing pollutants. An increase in wildfires has resulted in a change to the CO seasonal cycle of the North American Pacific Northwest, when comparing 2012-2018 to 2002-2011. This trend was reported using data from the Measurements of Pollution in the Troposphere (MOPITT) instrument on NASA’s Terra satellite. Similarly, an increase in summertime CO values was identified with the Fourier Transform Infrared (FTIR) spectrometer at the University of Toronto Atmospheric Observatory (TAO), over the same time period. Studies have shown correlations between wildfire smoke exposure and healthcare utilization for cardiovascular and respiratory conditions. Monthly counts of Emergency Department admissions for cardiovascular and respiratory diseases for Alberta and Ontario are investigated in relation to wildfire events in Canada and the USA. MOPITT and TAO FTIR CO columns, the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product, and provincial burned areas from Natural Resources Canada are assessed to estimate wildfire smoke exposure in the study region. This work aims to evaluate if CO can be used as a complementary tracer for health impacts from wildfire smoke exposure.

Two inaccurate reference citations were included in the Discussion section of the originally published paper. First, the citation to Ref. 44 in the sentence “Climate scientists predict that northern climates will experience the most dramatic effects of climate change” should instead be Ref. 43: “Climate scientists predict that northern climates will experience the most dramatic effects of climate change.” Second, the citation to Ref. 35 in the sentence “Given that this is the average maximum summer temperature in Fairbanks and above average in Anchorage, it is likely that many Alaskans are not physically adapted to extreme heat above this threshold” should instead be Ref. 34: “Given that this is the average maximum summer temperature in Fairbanks and above average in Anchorage, it is likely that many Alaskans are not physically adapted to extreme heat above this threshold.” EHP regrets these errors.

School closures during the COVID-19 pandemic have highlighted the importance of in-person learning on child health and wellness. Improving indoor air quality (IAQ) is a critical undertaking to keep children in school during outbreak events. Our primary objective was to evaluate the impact of Enhanced IAQ credit achievement among LEED-certified schools on the ability to remain open during the pandemic. In this analysis, schools that achieved LEED Enhanced IAQ credits for increased ventilation or outdoor air delivery monitoring were assigned to the treatment group; LEED schools without these credits were in the control group. We used LASSO regression to select potential confounders for treatment models. Inverse treatment probability weights were used to control for confounding in mixed effects Poisson models in which the response variable was the number of COVID-related school closure days during the 2021-22 academic year. Although effect estimates for the treatment were not significant, they were consistently in the inverse direction (incidence rate ratio (IRR) [95% CI]: 0.90 [0.80, 1.01]). Models may have lacked power to detect significance due to many schools having zero COVID-related closures during the 2021-22 academic year. An important secondary finding was a 53% decrease in COVID-related closure days (95% CI: 7%–77%) associated with a 10% increase in county residents who reported 'Always' using a mask in public. This study contributes to the current understanding of the indoor environment and airborne disease transmission. Efforts to monitor and improve IAQ in schools are important avenues to support community, student, and staff health.

Organophosphate (OP) pesticides are widely used in California for agricultural pest and weed control despite their well-documented adverse health effects among infants, children, and adults. We sought to identify factors affecting urinary OP metabolites among families living in high-exposure communities. Our study included 80 children and adults who lived within 61 m (200 ft) of agricultural fields in the Central Valley of California in January and June 2019, which are pesticide non-spraying and spraying seasons, respectively. We collected one urine sample per participant during each visit to measure dialkyl phosphate (DAP) metabolites; these were coupled with in-person surveys to identify health, household, sociodemographic, pesticide exposure, and occupational risk factors. We used a data-driven, best subsets regression approach to identify key factors that influenced urinary DAPs. Participants were mostly Hispanic/Latino(a) (97.5 %), over half were female (57.5 %), and most households reported having a member who worked in agriculture (70.6 %). Among the 149 urine samples suitable for analysis, DAP metabolites were detected in 48.0 % and 40.5 % of samples during January and June, respectively. Total diethyl alkylphosphates (EDE) were only detected in 4.7 % (n = 7) of samples, but total dimethyl alkylphosphates (EDM) were detected in 41.6 % (n = 62) of samples. No differences were observed in urinary DAP levels by visit month or by occupational exposure to pesticides. Best subsets regression identified several individual- and household-level variables that influenced both urinary EDM and total DAPs: the number of years spent living at the current address, household use of chemical products to control mice/rodents, and seasonal employment status. Among adults only, we identified educational attainment (for total DAPs) and age category (for EDM) as significant factors. Our study found consistent urinary DAP metabolites among participants, regardless of spraying season, and identified potential mitigating factors that members of vulnerable populations can implement to protect their health against OP exposure.

Organophosphate (OP) pesticides are associated with numerous adverse health outcomes. Pesticide use data are available for California from the Pesticide Use Report (PUR), but household- and individual-level exposure factors have not been fully characterized to support its refinement as an exposure assessment tool. Unique exposure pathways, such as proximity to agricultural operations and direct occupational contact, further complicate pesticide exposure assessment among agricultural communities. We sought to identify influencing factors of pesticide exposure to support future exposure assessment and epidemiological studies. Household dust samples were collected from 28 homes in four California agricultural communities during January and June 2019 and were analyzed for the presence of OPs. Factors influencing household OPs were identified by a data-driven model via best subsets regression. Key factors that impacted dust OP levels included household cooling strategies, secondary occupational exposure to pesticides, and geographic location by community. Although PUR data demonstrate seasonal trends in pesticide application, this study did not identify season as an important factor, suggesting OP persistence in the home. These results will help refine pesticide exposure assessment for future studies and highlight important gaps in the literature, such as our understanding of pesticide degradation in an indoor environment.

Widely considered an anthropogenic phenomenon, antimicrobial resistance (AMR) is a naturally occurring mechanism that microorganisms use to gain competitive advantage. AMR represents a significant threat to public health and has generated criticism towards the overuse of antimicrobial drugs. Livestock have been proposed as important reservoirs for AMR accumulation. Here, we show that assemblages of AMR genes in cattle and ungulates from natural environments (Yellowstone and Rocky Mountain National Parks) are all dominated by genes conferring resistance to tetracyclines. However, cattle feces contained higher proportions of erm(A-X) genes conferring resistance to macrolide antibiotics. Medically important AMR genes differed between cattle and natural ungulates, but cumulatively were more predominant in natural soils. Our findings suggest that the commonly described predominance of tetracycline resistance in cattle feces is a natural phenomenon among multiple ungulate species and not solely a result of antimicrobial drug exposure. Yet, the virtual absence of macrolide resistance genes in natural ungulates suggests that macrolide usage in agriculture may enrich these genes in cattle. Our results show that antimicrobial use in agriculture may be promoting a potential reservoir for specific types of AMR (i.e., macrolide resistance) but that a significant proportion of the ungulate resistome appears to have natural origins.

Studies on health effects of air pollution from local sources require exposure assessments that capture spatial and temporal trends. To facilitate intraurban studies in Denver, Colorado, we developed a spatiotemporal prediction model for black carbon (BC). To inform our model, we collected more than 700 weekly BC samples using personal air samplers from 2018 to 2020. The model incorporated spatial and spatiotemporal predictors and smoothed time trends to generate point-level weekly predictions of BC concentrations for the years 2009-2020. Our results indicate that our model reliably predicted weekly BC concentrations across the region during the year in which we collected data. We achieved a 10-fold cross-validation R2 of 0.83 and a root-mean-square error of 0.15 μg/m3 for weekly BC concentrations predicted at our sampling locations. Predicted concentrations displayed expected temporal trends, with the highest concentrations predicted during winter months. Thus, our prediction model improves on typical land use regression models that generally only capture spatial gradients. However, our model is limited by a lack of long-term BC monitoring data for full validation of historical predictions. BC predictions from the weekly spatiotemporal model will be used in traffic-related air pollution exposure-disease associations more precisely than previous models for the region have allowed.

Comprehensive and spatially accurate poultry population demographic data do not currently exist in the United States; however, these data are critically needed to adequately prepare for, and efficiently respond to and manage disease outbreaks. In response to absence of these data, this study developed a national-level poultry population dataset by using a novel combination of remote sensing and probabilistic modelling methodologies. The Farm Location and Agricultural Production Simulator (FLAPS) (Burdett et al., 2015) was used to provide baseline national-scale data depicting the simulated locations and populations of individual poultry operations. Remote sensing methods (identification using aerial imagery) were used to identify actual locations of buildings having the characteristic size and shape of commercial poultry barns. This approach was applied to 594 U.S. counties with > 100,000 birds in 34 states based on the 2012 U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Census of Agriculture (CoA). The two methods were integrated in a hybrid approach to develop an automated machine learning process to locate commercial poultry operations and predict the number and type of poultry for each operation across the coterminous United States. Validation illustrated that the hybrid model had higher locational accuracy and more realistic distribution and density patterns when compared to purely simulated data. The resulting national poultry population dataset has significant potential for application in animal disease spread modelling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research.

Event Abstract Back to Event Modeling the domestic poultry population in the United States: a novel approach leveraging remote sensing and synthetically generated data Andrew Fox1*, Mary Jane Mccool-eye1, Kelly Patyk1, David South1, Chris Burdett2, Susan Maroney2, Sheryl Magzamen2 and Grace Kuiper2 1 Center for Epidemiology and Animal Health, Animal and Plant Health Inspection Service (USDA), United States 2 Department of Environmental and Radiological Health Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, United States Safeguarding the U.S. poultry industry from infectious disease is a top priority because of the potentially devastating effects of outbreaks to producers, the industry and the economy. Comprehensive and accurate poultry population demographic data are currently lacking in the United States; however, these data are critically needed to adequately prepare for, and efficiently respond to and manage disease outbreaks. This study developed a national-level poultry population dataset by using a novel combination of remote sensing and probabilistic modeling methodologies. The Farm Location and Agricultural Production Simulator (FLAPS) (Burdett et al., 2015) was used to provide baseline national-scale data depicting the simulated locations and populations of individual poultry farms. Remote sensing methods (identification using aerial imagery) were used to identify actual locations of buildings having the characteristic size and shape of commercial poultry barns in 594 U.S. counties with >100,000 birds in 34 states based on the 2012 USDA, NASS Census of Agriculture. The two methods were integrated in a hybrid approach to develop an automated machine learning process to locate commercial poultry operations and predict the number and type of poultry for each operation across the conterminous United States. Validation of the model illustrated that the hybrid model had higher locational accuracy and more realistic grouping patterns when compared to purely simulated data. The hybrid approach and the developed large-scale commercial poultry dataset have significant potential for application in animal disease spread modeling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research. Acknowledgements This work has been supported under USDA Cooperative Agreement 6000001724. References Andronico, A., Courcoul, A., Bronner, A., Scoizec, A., Lebouquin-Leneveu, S., Guinat, C., Paul, M.C., Durand, B., Cauchemez, S. 2019. Highly pathogenic avian influenza H5N8 in south-west France 2016-2017: A modeling study of control strategies. Epidemics. In press. https://doi.org/10.1016/j.epidem.2019.03.006. Backer, J.A., van Roermund, H.J.W., Fischer, E.A.J., van Asseldonk, M.A.P.M., Bergevoet, R.H.M. 2015. Controlling highly pathogenic avian influenza outbreaks: An epidemiological and economic model analysis. Prev Vet Med. 121. 142-150. http://dx.doi.org/10.1016/j.prevetmed.2015.06.006. Bell, D.D., Weaver, W.D. (eds), Commercial Poultry Meat and Egg Production, 2002. Kluwer Academic Publishers, 819-27 and 965-71. Blundell, S., Opitz, D., Morris, M., Rao, R., 2008. Feature Analyst V5.0. ASPRS 2008 Annual Conference, 1-9. Boender, G.J., Hagenaars, T.J., Bouma, A., Nodelijk, G., Elbers, A.R.W., de Jong, M.C.M., van Boven, M. 2007. Risk maps for the spread of highly pathogenic avian influenza in poultry. PLoS Comput Biol. 3(4): e71. https://doi:10.1371/journal.pcbi.0030071. Bonney, P.J., Malladi, S., Boender, G.J., Weaver, J.T., Ssematimba, A., Halvorson, D.A., Cardona, C.J. 2018. Spatial transmission of H5N2 highly pathogenic avian influenza between Minnesota poultry premises during the 2015 outbreak. PLoS ONE. 13(9): e0204262. https://doi.org/10.1371/journal.pone.0204262. Bradhurst, R.A., Roche, S.E., East, I.J., Kwan, P., Garner, M.G. 2015. A hybrid modeling approach to simulating foot-and-mouth disease outbreaks in Australian livestock. Front. Environ. Sci. 3:17. https://doi:10.3389/fenvs.2015.00017. Bruhn, M.C., Munoz, B., Cajka, J., Smith, G., Curry, R.J., Wagener, D.K., Wheaton, W.D. 2012. Synthesized population databases: A geospatial database of US poultry farms. Methods Rep RTI Press. http://doi:10.3768/rtipress.2012.mr.0023.1201. Burdett, C.L., Kraus, B.R., Garza, S.J., Miller, R.S., Bjork, K.E. 2015. Simulating the distribution of individual livestock farms and their populations in the United States: An example using domestic swine (Sus scrofa domesticus) farms. PLoS ONE. 10(11): e0140338. doi:10.1371/journal.pone.0140338. Dent, J.E., Kiss, I.Z., Kao, R., Arnold, M. 2011. The potential spread of highly pathogenic avian influenza virus via dynamic contacts between poultry premises in Great Britain. BMC Vet Res. 7:59. Emelyanova, I.A., Donald, G.E., Miron, D.J., Henry, D.A., Garner, M.G. 2009. Probabilistic modelling of cattle farm distribution in Australia. Environ Model Assess. 14:449-465. DOI 10.1007/s10666-008-9140-z ERS. 2019. Poultry and Eggs. United States Department of Agriculture, Economic Research Service. Fairchild, B.D., Basic Introduction to Broiler House Environmental Control, 2005 August. Retrieved from http://www.thepoultrysite.com/articles/386/basic-introduction-to-broiler-housing-environmental-control/ (Accessed: 16 February 2018). Harvey, N., Reeves, A., Schoenbaum, M.A., Zagmutt-Vergara, F.J., Dube, C., Hill, A.E., Corso, B.A., McNab, W.B., Cartwright, C.I., Salman, M.D. 2007. The North American Animal Disease Spread Model: A simulation model to assist decision making in evaluating animal disease incursions. Prev Vet Med. 82. 176-197. Johnson, K. K., Seeger, R. M., Marsh, T. L. 2016. Local economies and highly pathogenic avian influenza. Choices, Quarter 2. Available online: http://www.choicesmagazine.org/choices-magazine/theme-articles/economic-consequences-of-highly-pathogenic-avian-influenza/local-economies-and-highly-pathogenic-avian-influenza. Khan, S.U., O’Sullivan, T.L., Poljak, Z., Alsop, J., Greer, A.L. 2018. Modeling livestock population structure: a geospatial database for Ontario swine farms. BMC Vet Res. 14:31. doi.10.1186/s12917-018-1362-y. Maroney, S., McCool-Eye, M.J., Fox, A., Burdett, C.L. In prep. Identifying commercial poultry operations from high resolution imagery to support animal health emergencies. Martin, M.K., Helm, J., Patyk, K.A. 2015. An approach for de-identification of point locations of livestock premises for further use in disease spread modeling. Prev Vet Med. 120.131-140. http://dx.doi.org/10.1016/j.prevetmed.2015.04.010. Meadows, A.J., Mundt, C.C., Keeling, M.J., Tildesley, M.J. 2018. Disentangling the influence of livestock vs. farm density on livestock disease epidemics. Ecosphere 9(7):e02294. 10.1002/ecs2.2294. Melius, C., 2007. Developing poultry facility type information from USDA agricultural census data for use in epidemiologic and economic models. Department of Homeland Security. Lawrence Livermore National Laboratory. Livermore, CA. Melius, C., Robertson, A., Hullinger, P. 2006. Developing livestock facility type information from USDA agricultural census data for use in epidemiologic and economic models. Department of Homeland Security Lawrence Livermore National Laboratory. Livermore, CA. NASS. 2015. USDA Poultry Production Data. Fact Sheet. United States Department of Agriculture, National Agricultural Statistics Service. https://www.usda.gov/sites/default/files/documents/nass-poultry-stats-factsheet.pdf. NASS. 2019. 2017 Census of Agriculture. United States. Summary and State Data. Volume 1. Geographic Area Series. Part 51. AC-17-A-51. United States Department of Agriculture, National Agricultural Statistics Service. Neumann, K., Elbersen, B.S., Verburg, P.H., Staritsky, I., Perez-Soba, M., de Vries, W, Rienks, W.A. 2009. Modelling the spatial distribution of livestock in Europe. Landscape Ecol. 24:1207-1222. DOI 10.1007/s10980-009-9357-5. Patyk, K.A., Helm, J., Martin, M.K., Forde-Folle, K.N., Olea-Popelka, F.J., Hokanson, J.E., Fingerline, T., Reeves, A. 2013. An epidemiologic simulation model of the spread and control of highly pathogenic avian influenza (H5N1) among commercial and backyard poultry flocks in South Carolina, United States. Prev Vet Med. 110, 510-524. http://dx.doi.org/10.1016/j.prevetmed.2013.01.003. Porphyre, T., Auty, H.K., Tildesley, M.J., Gunn, G.J., Woolhouse, M.E.J. 2013. Vaccination against foot-and-mouth disesae: Do initial conditions affect its benefit? PloS ONE 8(10):e77616. https://doi:10.1371/journal.pone.0077616. Prosser, D.J., Wu, J., Ellis, E.C., Gale, F. Van Boeckel, T.P., Wint, W., Robinson, T., Xiao, X., Gilbert. M. 2011. Modelling the distribution of chickens, ducks, and geese in China. Agric Ecosyst Environ. 141(3-4):381-389. doi:10.1016/j.agee.2011.04.002. Reeves, A. 2012. Construction and evaluation of epidemiologic simulation modesl for the within- and among-unit spread and control of infectious diseases of livestock and poultry. PhD thesis. Colorado State University, Fort Collins, CO. Sanson, R. 1993. The development of a decision support system for an animal disease emergency. PhD thesis. Massey University, Palmerston North, NZ. Seitzinger, A.H., Paarlberg, P.L. 2016. Regionalization of the 2014 and 2015 Highly Pathogenic Avian Influenza Outbreaks. Choices, Quarter 2. Available online: http://www.choicesmagazine.org/choices-magazine/theme-articles/economic-consequences-of-highly-pathogenic-avian-influenza/regionalization-of-the-2014-and-2015-highly-pathogenic-avian-influenza-outbreaks. Stevenson, M.A., Sanson, R.L., Miranda, A.O., Lawrence, K.A., Morris, R.S. 2007. Decision support systems for monitoring and maintaining health in food animal populations. New Zealand Veterinary Journal. 55:6, 264-272. https://doi.org/10.1080/00480169.2007.36780. Stevenson, M.A., Sanson, R.L., Stern, M.W., O’Leary, B.D., Sujau, M., Moles-Benfell, N., Morris, R.S. 2013. InterSpread Plus: a spatial and stochastic simulation model of disease in animal populations. Prev Vet Med. 109. 10-24. Tildesley, M.J., House, T.A., Bruhn, M.C., Curry, R.J., O’Neil, M., Allpress, J.L.E., Smith, G., Keeling, M.J. 2010. Impact of spatial clustering on disease transmission and optimal control. PNAS. 107(3). 1041-1046. Tomassen, F.H.M., de Koeijer, A., Mourits, M.C.M., Dekker, A., Bouma, A., Huirne, R.B.M. 2002. A decision-tree to optimize control measures during the early state of a foot-and-mouth disease epidemic. Prev Vet Med. 54. 301-324. van Andel, M., Jewell, C., McKenzie, J., Hollings, T., Robinson, A., Burgman, M., Bingham, P., Carpenter, T. 2017. Predicting farm-level animal populations using environmental and socioeconomic variables. Prev Vet Med. 145. 121-132. https://doi.org/10.1016/j.prevetmed.2017.07.005. van Andel, M., Hollins, T., Bradhurst, R., Robinson, A., Burgman, M., Gates, M.C., Bingham, P., Carpenter, T. 2018. Does size matter to models? Exploring the effect of herd size on outputs of a herd-level disease spread simulator. Front Vet Sci. 5:78. doi:10.3389/fvets.2018.00078. Werkman, M., Tildesley, M.J., Brooks-Pollock, E., Keeling, M.J. 2016. Preserving privacy whilst maintaining robust epidemiological predictions. Epidemics. 17. 35-41. http://dx.doi.org/10.1016/j.epidem.2016.10.004. Keywords: Poultry, farm, population estimates, distribution modelling, remote sensing Conference: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019. Presentation Type: Poster-no session Topic: Spatial data sources, open data, accessibility and information integration Citation: Fox A, Mccool-eye M, Patyk K, South D, Burdett C, Maroney S, Magzamen S and Kuiper G (2019). Modeling the domestic poultry population in the United States: a novel approach leveraging remote sensing and synthetically generated data. Front. Vet. Sci. Conference Abstract: GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data. doi: 10.3389/conf.fvets.2019.05.00045 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 30 May 2019; Published Online: 27 Sep 2019. * Correspondence: Mx. Andrew Fox, Center for Epidemiology and Animal Health, Animal and Plant Health Inspection Service (USDA), Fort Collins, Colorado, United States, andrew.m.fox@aphis.usda.gov Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Andrew Fox Mary Jane Mccool-eye Kelly Patyk David South Chris Burdett Susan Maroney Sheryl Magzamen Grace Kuiper Google Andrew Fox Mary Jane Mccool-eye Kelly Patyk David South Chris Burdett Susan Maroney Sheryl Magzamen Grace Kuiper Google Scholar Andrew Fox Mary Jane Mccool-eye Kelly Patyk David South Chris Burdett Susan Maroney Sheryl Magzamen Grace Kuiper PubMed Andrew Fox Mary Jane Mccool-eye Kelly Patyk David South Chris Burdett Susan Maroney Sheryl Magzamen Grace Kuiper Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.