{"id":23787,"date":"2023-07-06T11:38:32","date_gmt":"2023-07-06T16:38:32","guid":{"rendered":"https:\/\/uwm.edu\/letters-science\/?p=23787"},"modified":"2023-12-04T13:35:45","modified_gmt":"2023-12-04T19:35:45","slug":"mathematical-models-put-food-in-mouths-math-students-research-aids-local-food-banks","status":"publish","type":"post","link":"https:\/\/uwm.edu\/letters-science\/in-focus-2023\/mathematical-models-put-food-in-mouths-math-students-research-aids-local-food-banks\/","title":{"rendered":"Mathematical models put food in mouths: Math student\u2019s research aids local food banks"},"content":{"rendered":"\n

Food pantries have an inventory problem.<\/p>\n\n\n\n

Each month, a food bank receives any number of visitors who are experiencing food insecurity. Each month, a food bank also receives donations that go to stock the shelves. Will the pantry receive enough donations to meet the demand of its clients? Will there be surplus, shelf-stable donations they can carry over into next month? Will there be an unusually high number of clients relying on the food pantry this month? How do you answer those questions?<\/p>\n\n\n\n

\u201cThe best way might be to use a Markov Chain,\u201d Gavin Sayrs mused.<\/p>\n\n\n\n

Sayrs is an undergraduate research student at 51ÁÔÆæ. He is double-majoring in mathematical sciences and German, and he\u2019s also the treasurer of the Living Hope Club, a campus organization that focuses on helping Milwaukee residents living below the poverty line.<\/p>\n\n\n\n

\u201cI thought, I really enjoy charity work, and I have a club that does charity work. Why don\u2019t I take my interest in math and combine it and see what happens?\u201d Sayrs said. \u201cThere\u2019s food going in and out of pantries constantly. How can we best help them know, in the future months, how much food they\u2019ll be receiving and how much food they might be losing?\u201d<\/p>\n\n\n\n

That was the inspiration for his undergraduate research project titled, \u201cLocal Food Pantry Hidden Markov Model Analysis.\u201d He presented his work at the 51ÁÔÆæ Undergraduate Research Symposium<\/a> in April. He was mentored by 51ÁÔÆæ mathematics professor Chao Zhu. Sayrs relied on data supplied by the Kinship Food Pantry<\/a> in Milwaukee.<\/p>\n\n\n\n

If you\u2019ve never heard of a Hidden Markov Chain, you\u2019re not alone, but it\u2019s a tool that\u2019s being increasingly used in supply-chain and logistics fields. Think of the Markov chain like a matrix with many probabilities within it. Each matrix entry shows where the probability will go over time. You can use a Markov Chain to predict a sequence of unknown (hidden) variables based on a set of observed variables.<\/p>\n\n\n\n

\u201cTo summarize: Every single time a transition is made from an unobserved state to another unobserved state (it could possibly be the same state it\u2019s transitioning to), there is a signal emitted that can be seen that is the observed state,\u201d Sayrs explained.<\/p>\n\n\n\n

\u201cIn our situation, the observed state is how much inflow or outflow of food we\u2019re getting. That can either be a positive number where we\u2019re receiving more food than giving out, or a negative number, where we\u2019re giving out more food than we\u2019re receiving.<\/p>\n\n\n\n

\u201cFrom there, we can work backwards. We\u2019re only able to see data about how much food we\u2019re receiving,\u201d Sayrs added. \u201cWe can say what state we are in currently, and we can combine this data and say what (donation levels) will look like in the next year.\u201d<\/p>\n\n\n\n

In terms of inventory in a food pantry, Sayrs had plenty of observed variables, thanks to the meticulous records that Kinship Food Pantry keeps. Working with Krista Fuller, an intern at Kinship working as the Food Center Operations Coordinator, Sayrs gathered five years\u2019 worth of data showing the food bank\u2019s monthly donations and how much they served up to community members in need.<\/p>\n\n\n\n

\u201cWe see that in certain months, we don\u2019t receive as much food as in other months. There\u2019s a certain inflow\/outflow difference,\u201d he said. \u201cWe can record this difference by taking all of our monthly data and looking at it through a standard deviation. Is our monthly inflow\/outflow within one standard deviation, meaning everything\u2019s normal? Or is it greater than one standard deviation? \u2026From there, we can see how much food they\u2019re getting in relation to what they normally get.\u201d<\/p>\n\n\n\n

With this method, Sayrs said, the food pantries can predict which months might be leaner than others. For example, September, October, and November are all months where the food pantry sees an increased number of visitors. The food pantry may want to hold back surplus food in July and August to make sure there is enough food on the shelves in the coming months, or workers might try to reserve money in their budget to address the shortfall.<\/p>\n\n\n\n

There is still more research to do. While Sayrs is grateful that Kinship Food Pantry was generous in working with him (\u201cThey were so gracious,\u201d he said. \u201cThey helped out all that they could and it was a great experience.\u201d), he wants more than just the 5 years of data they were able to provide. To really build a good model, he said, Sayrs wants to look at 10 years\u2019 worth of pantry inflow\/outflow numbers.<\/p>\n\n\n\n

Even so, Sayrs said he\u2019s shared his finding with Kinship Food Pantry, and hopes that other food pantries in the city can also use this method to predict inventory surpluses and shortfalls. Hidden Markov Chains are already used in weather forecasting, estimating stock market prices, and even in projecting the spread of diseases.<\/p>\n\n\n\n

\u201cI know people who get involved in working with food pantries might not have math backgrounds. They have some social science background or something like that. That\u2019s good. We need people like that,\u201d Sayrs said. \u201cBut we also need to have solid data, because that helps them do what they\u2019re best at doing.\u201d<\/p>\n\n\n\n

By Sarah Vickery, College of Letters & Science<\/em><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

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