2.2. The Experiments of Information Disclosing
The simulation steps will be: (Figure 2 brief overviews these steps):Generate a random information network and a random physical network, the former illustrates the information relationship between people, and the latter records the coordinates of people M0 and gathering spots on the map.
Assign values to initial information and individual threshold. The initial information is the source of all information, which denotes the medical awareness of the virus; the individual threshold is a parameter to distinguish the population by groups set above; the smaller it is, the higher the level of public health awareness.
Assign values to the disclosing threshold. The disclosing threshold, chosen by the government, measures its relative priority to speed and accuracy in information dissemination. One of the objectives of our experiment is to ascertain the optimal disclosing threshold. Government prioritizes speed more as its threshold is lower.
Generate random individual nodes with initial information and random initial infected nodes.
Enter period 1. (a) Each individual node with information sends out information to neighbors.
(b) Each individual node will update its information (weighted) based on Equation (1).
(c) The government initiates a censoring and screening and enters stage d after a lag period, only for the first time does it receive the above-threshold information. If the government never receives above-threshold information, skip c, d, and go to e.
(d) Government discloses information to the public, which induces another round of information update for individual nodes based on Equation (2).
(e) The population is grouped into infected and healthy people by health status, and into panic-prone and non panic-prone by how much information one has compared with the individual threshold.
(f) Each individual node moves in a physical layer following the routine of the subgroup it is in with probability based on its final information.
(g) Reset the infection status of healthy individual within the transmission radius of an infected one according to the infection probability.
Return to step 5, initiate a new round for 50 times, that is, run the experiment for 50 periods. The data show a stability after 40 periods, so we stopped at 50.
Output the final overall infection rate at the end of period 50.
Repeat steps 4–7 for 50 times to reduce the randomness, record the mean, and standard deviation of the final infection rate.
Reassign for the disclosing threshold discrete values that equally divide the interval 0,1 into 11 parts, and repeat steps 3–8 for each value, that is, 11 times, to find the final infection rates for different disclosing threshold scenarios.
Reassign for initial information a discrete array 0.4,0.6,0.8,1.0, and reassign for an individual threshold the same values reassigned for the disclosing threshold in the previous step. Then, repeat steps 2–9, that is, 44 times.
Now, we have conducted an experiment with a full parameter space for each initial condition. A total of 484 different conditions were simulated for 24,200 repetitions of the experiment, each lasts for 50 periods, which adds up to a total of 1,210,000 periods of experiments. They essentially cover all possible scenarios under different external constraints. Table 1 lists the definitions, values, and distributions of all parameters in the model.