{"id":14603,"date":"2021-08-23T10:21:44","date_gmt":"2021-08-23T14:21:44","guid":{"rendered":"https:\/\/cnr.ncsu.edu\/geospatial\/?p=14603"},"modified":"2024-09-07T13:37:39","modified_gmt":"2024-09-07T17:37:39","slug":"pops-simulation","status":"publish","type":"post","link":"https:\/\/cnr.ncsu.edu\/geospatial\/news\/2021\/08\/23\/pops-simulation\/","title":{"rendered":"New PoPS Border Simulation Supports Front Lines of Pest Detection"},"content":{"rendered":"\n\n\n\n\n

UPDATE: May 12, 2022 \u2013\u2013<\/strong> The first peer-reviewed paper using the PoPS Border simulation (v 1.0.2) was published today in the international journal <\/em>Risk Analysis:“Contaminated consignment simulation to support risk-based inspection design<\/a>“<\/em><\/p>\n\n\n\n

Developers at the Center for Geospatial Analytics<\/a> at North Carolina State University have released new software to inform border inspections for insect pests and plant disease at ports of entry. The release, PoPS Border 1.0<\/a>, is the newest in the Pest or Pathogen Spread (PoPS) suite of models developed at the Center.<\/p>\n\n\n\n

PoPS Border simulates the outcomes of border inspections\u2013\u2013that is, how many pests in a shipment are missed versus detected\u2013\u2013depending on user-specified inputs such as shipment size, packaging type, levels and arrangement of pest contamination, and inspection protocol (sample size and selection).<\/p>\n\n\n\n

The simulation can be used to assess hypothetical scenarios of shipment contamination or can be based on records from real ports, using known sampling methods and pest detections to determine the potential for pests to slip through unnoticed.<\/p>\n\n\n\n

Measurements of undetected pests are \u201cvery valuable and difficult to come by for biological invasions research in general,\u201d explains Kellyn Montgomery, lead researcher of the PoPS Border project and recent graduate of the Geospatial Analytics Ph.D. program; she and Geospatial Research Software Engineer Vaclav Petras<\/a> developed the PoPS Border code.<\/p>\n\n\n\n

PoPS Border \u201cfills a big need, a gap, that phytosanitary agencies have,\u201d Montgomery says. Border inspection agents operate in a fast-paced environment, under constant pressure to keep the flow of commerce moving. \u201cThey are constantly confronted with changing conditions and it can be difficult to measure the impact of their decisions,\u201d she explains. <\/p>\n\n\n\n

The PoPS Border simulation, meanwhile, allows analysts to \u201cconduct experiments that you really can\u2019t do in an operational environment,\u201d Montgomery says. It \u201cprovides an environment where you can test\u2026combinations of factors\u201d that influence how well an inspection protocol performs.<\/p>\n\n\n\n

For example, PoPS Border can identify scenarios in which different protocols optimally balance available resources, inspection effort and pest detection. In situations where required effort would outstrip capacity, PoPS Border can help with \u201cdesigning alternative strategies that reduce workload and are still effective for intercepting pests,\u201d Montgomery explains.<\/p>\n\n\n\n

\"\"
The PoPS Border framework consists of three components: generating consignments (batches of goods packaged as shipments, boxes and items), consignment contamination and consignment inspection. The user provides configuration parameters for each component, and the simulation outputs measurements of inspection effort and effectiveness. Image credit: Kellyn Montgomery.<\/figcaption><\/figure>\n\n\n\n

\u201cWhat-if\u201d experiments conducted with PoPS Border can quantify the effectiveness of different inspection protocols under varying conditions, as well as provide a mechanism for \u201cestimating how many pests are making it past inspection,\u201d Montgomery says.<\/p>\n\n\n\n

Measurements of missed detections can then inform spread simulations, like PoPS 1.0<\/a> released last year, by quantifying propagule pressure, or the number of individuals of a potentially invasive species introduced to an area. \u201cPropagule pressure is usually measured with a proxy like trade,\u201d Montgomery explains, but PoPS Border estimates pest numbers with greater specificity by considering variable contamination rates and missed detections from that trade.<\/p>\n\n\n\n

The PoPS Border simulation<\/a> is flexible, open source and can be broadly applied beyond border protection to other quality assurance or inspection scenarios. It was developed based on inspection methods used in the United States but can be expanded for international use as well.<\/p>\n\n\n\n

\u201cThe ideal case would be if someone extended the open source project we established by adding country-specific inspection methods we haven\u2019t included yet,\u201d says Petras.<\/p>\n\n\n\n

\u201cAny addition someone would make would be useful to all users,\u201d Montgomery adds. <\/p>\n\n\n\n

To get started, users can follow the tutorials and installation instructions provided in the PoPS Border README file<\/a>.<\/p>\n","protected":false,"raw":"\n\n\n\n\n

UPDATE: May 12, 2022 \u2013\u2013<\/strong> The first peer-reviewed paper using the PoPS Border simulation (v 1.0.2) was published today in the international journal <\/em>Risk Analysis:\"Contaminated consignment simulation to support risk-based inspection design<\/a>\"<\/em><\/p>\n\n\n\n

Developers at the Center for Geospatial Analytics<\/a> at North Carolina State University have released new software to inform border inspections for insect pests and plant disease at ports of entry. The release, PoPS Border 1.0<\/a>, is the newest in the Pest or Pathogen Spread (PoPS) suite of models developed at the Center.<\/p>\n\n\n\n

PoPS Border simulates the outcomes of border inspections\u2013\u2013that is, how many pests in a shipment are missed versus detected\u2013\u2013depending on user-specified inputs such as shipment size, packaging type, levels and arrangement of pest contamination, and inspection protocol (sample size and selection).<\/p>\n\n\n\n

The simulation can be used to assess hypothetical scenarios of shipment contamination or can be based on records from real ports, using known sampling methods and pest detections to determine the potential for pests to slip through unnoticed.<\/p>\n\n\n\n

Measurements of undetected pests are \u201cvery valuable and difficult to come by for biological invasions research in general,\u201d explains Kellyn Montgomery, lead researcher of the PoPS Border project and recent graduate of the Geospatial Analytics Ph.D. program; she and Geospatial Research Software Engineer Vaclav Petras<\/a> developed the PoPS Border code.<\/p>\n\n\n\n

PoPS Border \u201cfills a big need, a gap, that phytosanitary agencies have,\u201d Montgomery says. Border inspection agents operate in a fast-paced environment, under constant pressure to keep the flow of commerce moving. \u201cThey are constantly confronted with changing conditions and it can be difficult to measure the impact of their decisions,\u201d she explains. <\/p>\n\n\n\n

The PoPS Border simulation, meanwhile, allows analysts to \u201cconduct experiments that you really can\u2019t do in an operational environment,\u201d Montgomery says. It \u201cprovides an environment where you can test\u2026combinations of factors\u201d that influence how well an inspection protocol performs.<\/p>\n\n\n\n

For example, PoPS Border can identify scenarios in which different protocols optimally balance available resources, inspection effort and pest detection. In situations where required effort would outstrip capacity, PoPS Border can help with \u201cdesigning alternative strategies that reduce workload and are still effective for intercepting pests,\u201d Montgomery explains.<\/p>\n\n\n\n

\"\"
The PoPS Border framework consists of three components: generating consignments (batches of goods packaged as shipments, boxes and items), consignment contamination and consignment inspection. The user provides configuration parameters for each component, and the simulation outputs measurements of inspection effort and effectiveness. Image credit: Kellyn Montgomery.<\/figcaption><\/figure>\n\n\n\n

\u201cWhat-if\u201d experiments conducted with PoPS Border can quantify the effectiveness of different inspection protocols under varying conditions, as well as provide a mechanism for \u201cestimating how many pests are making it past inspection,\u201d Montgomery says.<\/p>\n\n\n\n

Measurements of missed detections can then inform spread simulations, like PoPS 1.0<\/a> released last year, by quantifying propagule pressure, or the number of individuals of a potentially invasive species introduced to an area. \u201cPropagule pressure is usually measured with a proxy like trade,\u201d Montgomery explains, but PoPS Border estimates pest numbers with greater specificity by considering variable contamination rates and missed detections from that trade.<\/p>\n\n\n\n

The PoPS Border simulation<\/a> is flexible, open source and can be broadly applied beyond border protection to other quality assurance or inspection scenarios. It was developed based on inspection methods used in the United States but can be expanded for international use as well.<\/p>\n\n\n\n

\u201cThe ideal case would be if someone extended the open source project we established by adding country-specific inspection methods we haven\u2019t included yet,\u201d says Petras.<\/p>\n\n\n\n

\u201cAny addition someone would make would be useful to all users,\u201d Montgomery adds. <\/p>\n\n\n\n

To get started, users can follow the tutorials and installation instructions provided in the PoPS Border README file<\/a>.<\/p>\n"},"excerpt":{"rendered":"

Software developers at the Center for Geospatial Analytics have released the first version of a pest or pathogen spread (PoPS) simulation that can quantify the effectiveness of inspections at ports of entry and reveal how many pests are missed.<\/p>\n","protected":false},"author":119,"featured_media":14605,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"source":"","ncst_custom_author":"","ncst_show_custom_author":false,"ncst_dynamicHeaderBlockName":"ncst\/default-post-header","ncst_dynamicHeaderData":"{\"showAuthor\":true,\"showDate\":true,\"showFeaturedVideo\":false,\"caption\":\"PoPS Border enables analysts to assess the outcomes of hypothetical inspection scenarios at ports of entry. Users can vary contamination levels, contaminant arrangements, sampling approaches, and cargo configurations to test inspection protocols before deploying them. Image credit: Kellyn Montgomery, Shannon Jones, Devon Gaydos.\"}","ncst_content_audit_freq":"","ncst_content_audit_date":"","ncst_content_audit_display":false,"ncst_backToTopFlag":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[53,7,48,13,44,10],"tags":[38,56],"class_list":["post-14603","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-creating-near-real-time-decision-analytics","category-faculty-and-staff","category-geospatial-analytics-phd","category-new-research","category-newswire","category-spotlight","tag-open-source","tag-software-release"],"displayCategory":null,"acf":[],"_links":{"self":[{"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/posts\/14603","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/users\/119"}],"replies":[{"embeddable":true,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/comments?post=14603"}],"version-history":[{"count":14,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/posts\/14603\/revisions"}],"predecessor-version":[{"id":22606,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/posts\/14603\/revisions\/22606"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/media\/14605"}],"wp:attachment":[{"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/media?parent=14603"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/categories?post=14603"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cnr.ncsu.edu\/geospatial\/wp-json\/wp\/v2\/tags?post=14603"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}