Predicting regional influenza epidemics with uncertainty estimation using commuting data in Japan.

ABSTRACT: Obtaining an accurate prediction of the number of influenza patients in specific areas is a crucial task undertaken by medical institutions. Infections (such as influenza) spread from person to person, and people are rarely confined to a single area. Therefore, creating a regional influenza prediction model should consider the flow of people between different areas. Although various regional flu prediction models have previously been proposed, they do not consider the flow of people among areas. In this study, we propose a method that can predict the geographical distribution of influenza patients using commuting data to represent the flow of people. To elucidate the complex spatial dependence relations, our model uses an extension of the graph convolutional network (GCN). Additionally, a

SUBMITTER: Murayama T 

PROVIDER: S-EPMC8062106 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature