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

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