{"id":9886,"date":"2022-11-28T06:04:02","date_gmt":"2022-11-28T11:04:02","guid":{"rendered":"https:\/\/demand-planning.com\/?p=9886"},"modified":"2022-12-06T09:06:04","modified_gmt":"2022-12-06T14:06:04","slug":"the-advancements-in-disease-forecasting-that-can-revolutionize-business-forecasting","status":"publish","type":"post","link":"https:\/\/demand-planning.com\/2022\/11\/28\/the-advancements-in-disease-forecasting-that-can-revolutionize-business-forecasting\/","title":{"rendered":"The Advancements In Disease Forecasting That Can Revolutionize Business Forecasting"},"content":{"rendered":"

 <\/p>\n

\n

On the IBF On Demand<\/a> podcast, I recently spoke to a real innovator doing great things in the forecasting space. He comes from a disease forecasting background and many of his predictive analytics techniques are directly applicable to business forecasting. We’re not just talking about what<\/em> will will happen but why<\/em>, and identifying changes in consumer behavior before<\/em> they happen.\u00a0<\/strong><\/p>\n

His name is John Cordier and he runs a consulting firm called Epistemix<\/a>. Backed by the National Institutes of Health and the Bill & Melinda Gates Foundation, Epsitemix models the spread of infectious of diseases, and now applies that expertise to business.<\/p>\n

There is much we can learn from him when it comes to maximizing the value of our own data. The following are some highlights of that conversation.<\/p>\n

Can you give a breakdown of how you approach epidemiological forecasting? Then we\u2019ll dive how those approaches can be applied to demand forecasting.<\/b><\/h4>\n

We approach forecasting from the bottom up. Those in our space would recognize agent-based modeling as our underlying technique, but all you need to know about how we forecast is that we represent every single person in the entire country and forecast based on their behaviors.<\/span><\/p>\n

In the infectious diseases space, our technique has found is that within the United States there are 9 \u201contological units of epidemicity.\u201d Meaning there are 9 regions in the US with distinct seasonal behaviors. The data tells us why in late spring COVID-19 cases go up in the South but not in the Northeast and then inverts in fall, and so on.<\/span><\/p>\n

We\u2019re able to generate these seasonal patterns from human behaviors. There’s so many behavioral variables you can build into a model. What we end up doing is calibrating to the one or two most important pieces of data that we prepare the model against. With COVID we disregarded cases because case counts was too noisy for us to have any accurate projections so we use hospitalizations and deaths which are more stable, yet not perfect.<\/span><\/p>\n

 <\/p>\n