Ⅰ. What is "Tokyo flu"

or

This data set is applicable to Costomized City Network.

The historical raw data of Tokyo region was downloaded through Tokyo Metropolitan Infectious Disease Surveillance Center (Link: http://survey.tokyo-eiken.go.jp/epidinfo/weeklyhc.do).

For each ward or district, the raw data were averaged in terms of the total number of clinics within the ward/district. This normalization process is directly related to the population of each ward/district, since the population is roughly proportional to the number of clinics.

Ⅱ. Input Data format

TPD(Costomized City Network) can read two types of files: .csv or .txt. The CSV file contains the data of each node at different times. The TXT file contains the network structure, that is, the adjacency of nodes.

Example Data format:
flu.csv: Influenza data from 23 wards in Tokyo, Japan (2009-2018). Row: number of clinic visits per ward over weeks. Column: number of clinic visits in 23 wards in the same week. Columns 1-53 Show 2009 influenza data , and so on. According to the characteristics of influenza outbreak, it is recommended to set the start and end columns of data to 12 and 64.
network.txt: Based on the geographic distribution of 23 wards and their adjacent relationship, a 23‐node neighboring network model is constructed.

Ⅲ. Parameter Setting

Data upload: flu.csv;

Network upload: network.txt;

Method selection:conventional Dynamical network biomarker (cDNB) or Single-sample Landscape Entropy(SLE).

Data selection: Group data by column. Column: number of clinic visits in 23 wards in the same week. Columns 1-54 are 2009 data, and so on.

Ⅳ. Output

Landscape with Tipping Point:The x-axis represents different weeks, and the y-axis represents different wards, and the z-axis represents the average DNB score of the current stage.

Line chart with Tipping Point:The x-axis represents different weeks, and the y-axis represents the average DNB score of the current stage.

Dynamic network:With the dynamic evolution of network graph, critical points are monitored in real time. The color of each node corresponds to its DNB score; the thickness of each edge corresponds to its correlation!