Real-Time Forecast of Influenza Outbreak Using Dynamic Network Marker Based on Minimum Spanning Tree

Algorithm 1

MST-DNM.

Input: one-year hospitalization data caused by influenza;

Output: the tipping point of the flu outbreak of this year.

 1: Model a city network for a specific city

 2: Map the hospitalization data into the corresponding nodes in the network

 3: for week in a certain year do

 4: for each edge do

 5:  Weight the edge with

 6: end for/obtained a weighted undirected graph /

 7: obtained the minimum spanning tree using Algorithm 2/

 8: Calculate the minimum spanning tree’s weight sum as the MST-DNB score

 9: ifthen /the parameter was trained by other year’s dataset/

10:  the week is deemed to the tipping point

11:  Break

12: end if

13: end for