Project Summary/AbstractThis project will help reduce foodborne illness in the Municipality of Anchorage (MOA)by optimizing the methodology of when regulated food facilities are inspected. Thisproject will utilize and adapt an open source predictive data analytics platformdeveloped by the City of Chicago to predict which regulated food facilities are mostlikely to have critical violations. By using this innovative technology, the MOA canchange its inspection approach to conduct inspections of facilities that are predicted bythe model to be higher risk and have critical violations. This will help the MOA targetfacilities that have a higher risk of foodborne illness and help protect the community asa whole. Data collected from many sources will be used to optimize the order in whichinspections are done. These sources include previous inspection history, how long ithas been since the last inspection, whether or not the establishment has a liquor licenseand a host of other data points that help rank the risk of violations occurring in eachfacility. The data model will then rank all the facilities based on how many factors thedata model finds. Then the model will rank the facilities so that they can prioritized forinspection sooner than they would be using the traditional method of inspection order.This will allow the MOA to find facilities with problems sooner than normal. In theChicago study they found that facilities were inspected seven days sooner than usingtraditional methods for inspection order.This proposal will also allow the MOA to develop new educational materials and shortvideos that will be used to help educate facilities found with critical violations withmethods on how to prevent these violations and thus lower the risk of foodborne illness.Once this data model is operating the program will be able to track over time whetherthe intervention methods put into place are effective by looking at whether facilitiesremain high risk or improve. By tracking progress of facilities over time the program willbe able to adjust interventions to improve compliance and reduce the risk of foodborneillness across the MOA.