Re-Thinking the Role of Government Information Intervention in the COVID-19 Pandemic: An Agent-Based Modeling Analysis
2.1. The Model of Information Disclosing
2.2. The Experiments of Information Disclosing
- 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 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.
- Each individual node with information sends out information to neighbors.
- Each individual node will update its information (weighted) based on Equation (1).
- 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.
- Government discloses information to the public, which induces another round of information update for individual nodes based on Equation (2).
- 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.
- Each individual node moves in a physical layer following the routine of the subgroup it is in with probability based on its final information.
- 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 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 , 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.
2.3. The Model and Experiments of Information Blocking
3. Results and Discussion
3.1. Modeling Framework
3.2. Intervention Dilemma in Disclosing Information
3.3. Intervention Dilemma in Blocking Information
3.4. Optimal Intervention under Different Government Types
- For information disclosing, governments face a trade-off between speed and accuracy. A better medical understanding of the virus and an inadequate public health awareness make accuracy outweigh speed; otherwise, a quick one is better.
- For information blocking, the optimal strategy is contingent on varying conditions: no blocking is usually optimal for a well-known virus and a higher public health awareness; otherwise, blocking is preferred.
- The optimal combination of disclosing and blocking is highly sensitive to the government preference and its governance capability. A government that is only responsible for the outcome of intervention will focus unilaterally on accuracy at the expense of speed; a risk-averse government that intends to minimize the maximum infection rate in uncertain scenarios will impose a more restrictive blocking; and the most restrictive blocking strategy might be best for governments with lower capability and credibility.
Data Availability Statement
Conflicts of Interest
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|maximum moving radius||2|
|maximum infection radius||1|
|population that can send information to government||5|
|numbers of gathering spots||10|
|population with initial information||1|
|M||numbers of nodes (area of the whole map)||2500|
|infection rate of one-time contact|
|information decay rate|
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Lu, Y.; Ji, Z.; Zhang, X.; Zheng, Y.; Liang, H. Re-Thinking the Role of Government Information Intervention in the COVID-19 Pandemic: An Agent-Based Modeling Analysis. Int. J. Environ. Res. Public Health 2021, 18, 147. https://doi.org/10.3390/ijerph18010147
Lu Y, Ji Z, Zhang X, Zheng Y, Liang H. Re-Thinking the Role of Government Information Intervention in the COVID-19 Pandemic: An Agent-Based Modeling Analysis. International Journal of Environmental Research and Public Health. 2021; 18(1):147. https://doi.org/10.3390/ijerph18010147Chicago/Turabian Style
Lu, Yao, Zheng Ji, Xiaoqi Zhang, Yanqiao Zheng, and Han Liang. 2021. "Re-Thinking the Role of Government Information Intervention in the COVID-19 Pandemic: An Agent-Based Modeling Analysis" International Journal of Environmental Research and Public Health 18, no. 1: 147. https://doi.org/10.3390/ijerph18010147