It\u2019s called PoPS, the Pest or Pathogen Spread forecast, a sophisticated yet user-friendly tool that can be used to predict the spread of potentially any species.<\/p>\n\n\n\n
Predicting the spread of pests with big data and models<\/strong><\/h3>\n\n\n\nComputer models are the go-to method for predicting the spread of insect pests and the pathogens that cause plant disease. Running these models typically requires a lot of data\u2013\u2013gigabytes and gigabytes of data\u2013\u2013and being fluent in the language of computer code. Assorted software is also usually needed, and so is a familiarity with the ins and outs of model calibration and validation: that is, making sure that a model is well-matched to the data feeding it and checking that the model is doing a good job of predicting what it\u2019s supposed to.<\/p>\n\n\n\n
For managers who need answers quickly, without being expert coders, the task can be daunting. Under most circumstances, preparing the data and models to simulate a pest\u2019s spread can take months to a year. Enter PoPS, a nearly fully automated framework that asks its users for a few simple inputs to output spread predictions and comparisons of management scenarios.<\/p>\n\n\n\n
What makes the new PoPS model different?<\/strong><\/h3>\n\n\n\nWith PoPS, managers simply provide three years\u2019 worth of infestation information and adjust a few values on the web-based dashboard to answer pressing questions about when, where and how much a pest problem is likely to grow, and how much it may cost to stop it.<\/p>\n\n\n\n![\"\"](\"https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/12\/2019\/07\/PoPS_Dashboard_Example.png\")
The new PoPS model and its user-friendly dashboard allows managers to quickly and easily simulate a pest or pathogen\u2019s spread and then test different strategies for containing it, before doing any actual management. Image by Shannon Jones \/ Center for Geospatial Analytics<\/figcaption><\/figure>\n\n\n\n\u201cYou can plug in your data and PoPS will do everything, and the validation will tell you how accurate it is,\u201d says Chris Jones<\/u><\/a>, a research associate at the Center for Geospatial Analytics and lead developer of PoPS. The framework uses two years of the input data to calibrate its mathematical model, and the third year of data to validate it. Weather data and maps of host plants are pulled from existing repositories, and PoPS even reclassifies raw values to ones the model can use. \u201cMost models don\u2019t help you do this,\u201d Jones points out.<\/p>\n\n\n\nBy using a computer mouse to draw shapes on a map of predicted spread, a user can even tell PoPS where they would like to apply management\u2013\u2013such as eliminating host plants or setting targeted insect traps\u2013\u2013and the system will calculate the financial cost as well as the impact on spread, to help compare treatment scenarios. Essentially, the system helps a user assess which locations are best to treat and the expected return on investment.<\/p>\n\n\n\n
Harnessing big data and PoPS to contain the spotted lanternfly<\/strong><\/h3>\n\n\n\nOne of PoPS\u2019 first tests has been predicting the spread of spotted lanternfly<\/u><\/a> for the USDA and Pennsylvania Department of Agriculture. Native to Asia, the species was first discovered in southeast Pennsylvania in 2014; it has since spread to four other states, motivating the quarantine of sixteen counties.<\/p>\n\n\n\nSpotted lanternflies suck the life out of plants by drinking the fluids flowing through the stems. Their favorite food plants are also highly prized by people\u2013\u2013including grapevines, hops and hardwood trees used for timber\u2013\u2013threatening billion-dollar industries in multiple states.<\/p>\n\n\n\n![\"\"](\"https:\/\/cnr.ncsu.edu\/geospatial\/wp-content\/uploads\/sites\/12\/2019\/07\/30776344918_efc4bc1a2f_k-1500x844-1024x577.jpg\")
Spotted lanternfly (Lycorma delicatula) winged adult and red nymph in Pennsylvania. Photo by Stephen Ausmus \/ USDA<\/figcaption><\/figure>\n\n\n\nThe USDA Animal and Plant Health Inspection Service (APHIS) and Pennsylvania Department of Agriculture are aggressively targeting this emerging pest<\/u><\/a> and outlined a budget for curbing its spread, but they also needed a geospatial tool to help them decide where to focus their efforts and to determine how much money would be needed to make an impact.<\/p>\n\n\n\nJones and his team took on the problem, using PoPS to generate what-if scenarios of management within and outside of the quarantine zone, and with different levels of funding. In preliminary runs with PoPS, \u201cspending an extra 16% on management led to a greater than 50% reduction in the total infested area, even when management was randomly distributed within the management zone,\u201d Jones reports. A scenario with no management saw spotted lanternfly explode across the map, but strategic investment of limited management dollars saw the pest restricted to the containment area.<\/p>\n\n\n\n
What\u2019s next?<\/strong><\/h3>\n\n\n\nAn initial partnership between NC State\u2019s Center for Geospatial Analytics and USDA APHIS to build a model prototype \u201chas blossomed,\u201d Jones says, into a multi-dimensional project aimed at generating urgently needed decision analytics.<\/p>\n\n\n\n
Currently a whole team of Geospatial Analytics doctoral students, postdoctoral scholars and research associates are hard at work optimizing the PoPS web interface, improving host maps and how the model functions and evaluating its many potential uses.<\/p>\n\n\n\n
In April 2019, the research team hosted the APHIS Plant Protection and Quarantine deputy director on NC State\u2019s Centennial Campus to unveil the PoPS dashboard, and in June the group took PoPS to APHIS headquarters, outside of Washington, D.C., to demonstrate its capabilities to more agency leaders.<\/p>\n\n\n\n
From the beginning, the project has been a collaborative one. \u201cThis is a participatory process,\u201d Jones says. Feedback from government scientists, analysts and on-the-ground managers has been integral to the design of the framework, and continues to inspire even more directions the research can take. \u201cThis project has grown immensely since we started two years ago,\u201d Jones says. \u201cAPHIS is definitely planning for this to be a key decision tool for them.\u201d<\/p>\n\n\n\n
A new web interface will soon debut on the PoPS website<\/u><\/a>, making the framework accessible to a wider audience of users. Project partners hope that it will drive progress in a range of applications. \u201cThis is not just for management,\u201d Jones says. \u201cIdeally I would like other researchers to use it too.\u201d<\/p>\n\n\n\nFor example, predictions from PoPS could be used to plan experiments and field sampling designs. Extension agents too could use the model to help farmers understand and predict the spread of insect pests and plant diseases across agricultural fields on time scales of weeks to months or years.<\/p>\n\n\n\n
All of these uses have real-world implications, and harness data and computing for making on-the-ground decisions. Says Jones, \u201cThis is the direction I want all of my research to go\u2013\u2013something people will use.\u201d<\/p>\n","protected":false,"raw":"\n\n\n\n\n
Spotted lanternfly, emerald ash borer, hemlock woolly adelgid, Asian longhorned beetle. These are but a few of the hundreds of exotic insect pests that have been accidentally introduced to North America, causing millions of dollars in damage, not to mention ecological havoc, across agricultural fields and forests.<\/p>\n\n\n\n
Management agencies like the US Department of Agriculture work to contain these pests, searching for ways to curb outbreaks and control their impact. But how quickly is a pest likely to spread to a new area? And where?<\/p>\n\n\n\n
Researchers at the Center for Geospatial Analytics<\/u><\/a> at North Carolina State University recently developed a new forecasting technology that can help the USDA and other agencies answer these questions, more quickly and easily than ever before.<\/p>\n\n\n\nIt\u2019s called PoPS, the Pest or Pathogen Spread forecast, a sophisticated yet user-friendly tool that can be used to predict the spread of potentially any species.<\/p>\n\n\n\n
Predicting the spread of pests with big data and models<\/strong><\/h3>\n\n\n\nComputer models are the go-to method for predicting the spread of insect pests and the pathogens that cause plant disease. Running these models typically requires a lot of data\u2013\u2013gigabytes and gigabytes of data\u2013\u2013and being fluent in the language of computer code. Assorted software is also usually needed, and so is a familiarity with the ins and outs of model calibration and validation: that is, making sure that a model is well-matched to the data feeding it and checking that the model is doing a good job of predicting what it\u2019s supposed to.<\/p>\n\n\n\n
For managers who need answers quickly, without being expert coders, the task can be daunting. Under most circumstances, preparing the data and models to simulate a pest\u2019s spread can take months to a year. Enter PoPS, a nearly fully automated framework that asks its users for a few simple inputs to output spread predictions and comparisons of management scenarios.<\/p>\n\n\n\n
What makes the new PoPS model different?<\/strong><\/h3>\n\n\n\nWith PoPS, managers simply provide three years\u2019 worth of infestation information and adjust a few values on the web-based dashboard to answer pressing questions about when, where and how much a pest problem is likely to grow, and how much it may cost to stop it.<\/p>\n\n\n\nThe new PoPS model and its user-friendly dashboard allows managers to quickly and easily simulate a pest or pathogen\u2019s spread and then test different strategies for containing it, before doing any actual management. Image by Shannon Jones \/ Center for Geospatial Analytics<\/figcaption><\/figure>\n\n\n\n\u201cYou can plug in your data and PoPS will do everything, and the validation will tell you how accurate it is,\u201d says Chris Jones<\/u><\/a>, a research associate at the Center for Geospatial Analytics and lead developer of PoPS. The framework uses two years of the input data to calibrate its mathematical model, and the third year of data to validate it. Weather data and maps of host plants are pulled from existing repositories, and PoPS even reclassifies raw values to ones the model can use. \u201cMost models don\u2019t help you do this,\u201d Jones points out.<\/p>\n\n\n\nBy using a computer mouse to draw shapes on a map of predicted spread, a user can even tell PoPS where they would like to apply management\u2013\u2013such as eliminating host plants or setting targeted insect traps\u2013\u2013and the system will calculate the financial cost as well as the impact on spread, to help compare treatment scenarios. Essentially, the system helps a user assess which locations are best to treat and the expected return on investment.<\/p>\n\n\n\n
Harnessing big data and PoPS to contain the spotted lanternfly<\/strong><\/h3>\n\n\n\nOne of PoPS\u2019 first tests has been predicting the spread of spotted lanternfly<\/u><\/a> for the USDA and Pennsylvania Department of Agriculture. Native to Asia, the species was first discovered in southeast Pennsylvania in 2014; it has since spread to four other states, motivating the quarantine of sixteen counties.<\/p>\n\n\n\nSpotted lanternflies suck the life out of plants by drinking the fluids flowing through the stems. Their favorite food plants are also highly prized by people\u2013\u2013including grapevines, hops and hardwood trees used for timber\u2013\u2013threatening billion-dollar industries in multiple states.<\/p>\n\n\n\nSpotted lanternfly (Lycorma delicatula) winged adult and red nymph in Pennsylvania. Photo by Stephen Ausmus \/ USDA<\/figcaption><\/figure>\n\n\n\n
With PoPS, managers simply provide three years\u2019 worth of infestation information and adjust a few values on the web-based dashboard to answer pressing questions about when, where and how much a pest problem is likely to grow, and how much it may cost to stop it.<\/p>\n\n\n\n \u201cYou can plug in your data and PoPS will do everything, and the validation will tell you how accurate it is,\u201d says Chris Jones<\/u><\/a>, a research associate at the Center for Geospatial Analytics and lead developer of PoPS. The framework uses two years of the input data to calibrate its mathematical model, and the third year of data to validate it. Weather data and maps of host plants are pulled from existing repositories, and PoPS even reclassifies raw values to ones the model can use. \u201cMost models don\u2019t help you do this,\u201d Jones points out.<\/p>\n\n\n\n By using a computer mouse to draw shapes on a map of predicted spread, a user can even tell PoPS where they would like to apply management\u2013\u2013such as eliminating host plants or setting targeted insect traps\u2013\u2013and the system will calculate the financial cost as well as the impact on spread, to help compare treatment scenarios. Essentially, the system helps a user assess which locations are best to treat and the expected return on investment.<\/p>\n\n\n\n One of PoPS\u2019 first tests has been predicting the spread of spotted lanternfly<\/u><\/a> for the USDA and Pennsylvania Department of Agriculture. Native to Asia, the species was first discovered in southeast Pennsylvania in 2014; it has since spread to four other states, motivating the quarantine of sixteen counties.<\/p>\n\n\n\n Spotted lanternflies suck the life out of plants by drinking the fluids flowing through the stems. Their favorite food plants are also highly prized by people\u2013\u2013including grapevines, hops and hardwood trees used for timber\u2013\u2013threatening billion-dollar industries in multiple states.<\/p>\n\n\n\n The USDA Animal and Plant Health Inspection Service (APHIS) and Pennsylvania Department of Agriculture are aggressively targeting this emerging pest<\/u><\/a> and outlined a budget for curbing its spread, but they also needed a geospatial tool to help them decide where to focus their efforts and to determine how much money would be needed to make an impact.<\/p>\n\n\n\n Jones and his team took on the problem, using PoPS to generate what-if scenarios of management within and outside of the quarantine zone, and with different levels of funding. In preliminary runs with PoPS, \u201cspending an extra 16% on management led to a greater than 50% reduction in the total infested area, even when management was randomly distributed within the management zone,\u201d Jones reports. A scenario with no management saw spotted lanternfly explode across the map, but strategic investment of limited management dollars saw the pest restricted to the containment area.<\/p>\n\n\n\n An initial partnership between NC State\u2019s Center for Geospatial Analytics and USDA APHIS to build a model prototype \u201chas blossomed,\u201d Jones says, into a multi-dimensional project aimed at generating urgently needed decision analytics.<\/p>\n\n\n\n Currently a whole team of Geospatial Analytics doctoral students, postdoctoral scholars and research associates are hard at work optimizing the PoPS web interface, improving host maps and how the model functions and evaluating its many potential uses.<\/p>\n\n\n\n In April 2019, the research team hosted the APHIS Plant Protection and Quarantine deputy director on NC State\u2019s Centennial Campus to unveil the PoPS dashboard, and in June the group took PoPS to APHIS headquarters, outside of Washington, D.C., to demonstrate its capabilities to more agency leaders.<\/p>\n\n\n\n From the beginning, the project has been a collaborative one. \u201cThis is a participatory process,\u201d Jones says. Feedback from government scientists, analysts and on-the-ground managers has been integral to the design of the framework, and continues to inspire even more directions the research can take. \u201cThis project has grown immensely since we started two years ago,\u201d Jones says. \u201cAPHIS is definitely planning for this to be a key decision tool for them.\u201d<\/p>\n\n\n\n A new web interface will soon debut on the PoPS website<\/u><\/a>, making the framework accessible to a wider audience of users. Project partners hope that it will drive progress in a range of applications. \u201cThis is not just for management,\u201d Jones says. \u201cIdeally I would like other researchers to use it too.\u201d<\/p>\n\n\n\n For example, predictions from PoPS could be used to plan experiments and field sampling designs. Extension agents too could use the model to help farmers understand and predict the spread of insect pests and plant diseases across agricultural fields on time scales of weeks to months or years.<\/p>\n\n\n\n All of these uses have real-world implications, and harness data and computing for making on-the-ground decisions. Says Jones, \u201cThis is the direction I want all of my research to go\u2013\u2013something people will use.\u201d<\/p>\n","protected":false,"raw":"\n\n\n\n\n Spotted lanternfly, emerald ash borer, hemlock woolly adelgid, Asian longhorned beetle. These are but a few of the hundreds of exotic insect pests that have been accidentally introduced to North America, causing millions of dollars in damage, not to mention ecological havoc, across agricultural fields and forests.<\/p>\n\n\n\n Management agencies like the US Department of Agriculture work to contain these pests, searching for ways to curb outbreaks and control their impact. But how quickly is a pest likely to spread to a new area? And where?<\/p>\n\n\n\n Researchers at the Center for Geospatial Analytics<\/u><\/a> at North Carolina State University recently developed a new forecasting technology that can help the USDA and other agencies answer these questions, more quickly and easily than ever before.<\/p>\n\n\n\n It\u2019s called PoPS, the Pest or Pathogen Spread forecast, a sophisticated yet user-friendly tool that can be used to predict the spread of potentially any species.<\/p>\n\n\n\n Computer models are the go-to method for predicting the spread of insect pests and the pathogens that cause plant disease. Running these models typically requires a lot of data\u2013\u2013gigabytes and gigabytes of data\u2013\u2013and being fluent in the language of computer code. Assorted software is also usually needed, and so is a familiarity with the ins and outs of model calibration and validation: that is, making sure that a model is well-matched to the data feeding it and checking that the model is doing a good job of predicting what it\u2019s supposed to.<\/p>\n\n\n\n For managers who need answers quickly, without being expert coders, the task can be daunting. Under most circumstances, preparing the data and models to simulate a pest\u2019s spread can take months to a year. Enter PoPS, a nearly fully automated framework that asks its users for a few simple inputs to output spread predictions and comparisons of management scenarios.<\/p>\n\n\n\n With PoPS, managers simply provide three years\u2019 worth of infestation information and adjust a few values on the web-based dashboard to answer pressing questions about when, where and how much a pest problem is likely to grow, and how much it may cost to stop it.<\/p>\n\n\n\n \u201cYou can plug in your data and PoPS will do everything, and the validation will tell you how accurate it is,\u201d says Chris Jones<\/u><\/a>, a research associate at the Center for Geospatial Analytics and lead developer of PoPS. The framework uses two years of the input data to calibrate its mathematical model, and the third year of data to validate it. Weather data and maps of host plants are pulled from existing repositories, and PoPS even reclassifies raw values to ones the model can use. \u201cMost models don\u2019t help you do this,\u201d Jones points out.<\/p>\n\n\n\n By using a computer mouse to draw shapes on a map of predicted spread, a user can even tell PoPS where they would like to apply management\u2013\u2013such as eliminating host plants or setting targeted insect traps\u2013\u2013and the system will calculate the financial cost as well as the impact on spread, to help compare treatment scenarios. Essentially, the system helps a user assess which locations are best to treat and the expected return on investment.<\/p>\n\n\n\n One of PoPS\u2019 first tests has been predicting the spread of spotted lanternfly<\/u><\/a> for the USDA and Pennsylvania Department of Agriculture. Native to Asia, the species was first discovered in southeast Pennsylvania in 2014; it has since spread to four other states, motivating the quarantine of sixteen counties.<\/p>\n\n\n\n Spotted lanternflies suck the life out of plants by drinking the fluids flowing through the stems. Their favorite food plants are also highly prized by people\u2013\u2013including grapevines, hops and hardwood trees used for timber\u2013\u2013threatening billion-dollar industries in multiple states.<\/p>\n\n\n\nHarnessing big data and PoPS to contain the spotted lanternfly<\/strong><\/h3>\n\n\n\n
What\u2019s next?<\/strong><\/h3>\n\n\n\n
Predicting the spread of pests with big data and models<\/strong><\/h3>\n\n\n\n
What makes the new PoPS model different?<\/strong><\/h3>\n\n\n\n
Harnessing big data and PoPS to contain the spotted lanternfly<\/strong><\/h3>\n\n\n\n