Back

The Pandemics in Artificial Society: Agent-Based Model to Reflect Strategies on COVID-19

Situngkir, H.

2020-07-29 epidemiology
10.1101/2020.07.27.20162511 medRxiv
Show abstract

Various social policies and strategies have been deliberated and used within many countries to handle the COVID-19 pandemic. Some of those basic ideas are strongly related to the understanding of human social interactions and the nature of disease transmission and spread. In this paper, we present an agent-based approach to model epidemiological phenomena as well as the interventions upon it. We elaborate on micro-social structures such as social-psychological factors and distributed ruling behaviors to grow an artificial society where the interactions among agents may exhibit the spreading of the virus. Capturing policies and strategies during the pandemic, four types of intervention are also applied in society. Emerged macro-properties of epidemics are delivered from sets of simulations, lead to comparisons between each policy/strategy,s effectivity.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 10%
18.7%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 4%
9.8%
Similar papers in this journal
3
Royal Society Open Science
● 214 papers in training set
Top 0.8%
4.4%
Similar papers in this journal
4
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.2%
4.1%
Similar papers in this journal
5
PLOS Computational Biology
● 1863 papers in training set
Top 10%
3.3%
Similar papers in this journal
6
Frontiers in Applied Mathematics and Statistics
○ 11 papers in training set
Top 0.1%
2.8%
7
JMIRx Med
◐ 32 papers in training set
Top 0.5%
2.7%
8
The European Physical Journal Plus
○ 13 papers in training set
Top 0.1%
2.1%
9
Frontiers in Public Health
○ 148 papers in training set
Top 3%
2.1%
50% of probability mass above
10
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 2%
2.1%
11
Physical Biology
○ 46 papers in training set
Top 0.3%
2.0%
12
Nonlinear Dynamics
○ 10 papers in training set
Top 0.2%
1.7%
13
Frontiers in Genetics
○ 230 papers in training set
Top 2%
1.7%
14
Chaos: An Interdisciplinary Journal of Nonlinear Science
● 17 papers in training set
Top 0.1%
1.7%
15
Heliyon
○ 152 papers in training set
Top 4%
1.7%
16
Infectious Disease Modelling
○ 54 papers in training set
Top 0.8%
1.5%
17
PeerJ
◐ 308 papers in training set
Top 6%
1.5%
18
Applied Sciences
○ 25 papers in training set
Top 0.4%
1.4%
19
Physica A: Statistical Mechanics and its Applications
○ 13 papers in training set
Top 0.2%
1.1%
20
Journal of Theoretical Biology
○ 162 papers in training set
Top 2%
1.1%
21
Frontiers in Physics
○ 21 papers in training set
Top 0.1%
1.1%
22
Quantitative Biology
○ 12 papers in training set
Top 0.1%
1.1%
23
IEEE Access
● 35 papers in training set
Top 1%
1.0%
24
Mathematical Biosciences
○ 49 papers in training set
Top 1%
1.0%
25
Journal of Clinical Medicine
○ 97 papers in training set
Top 4%
1.0%
26
Epidemiology and Infection
◐ 89 papers in training set
Top 2%
1.0%
27
Biology
○ 45 papers in training set
Top 0.6%
0.9%
28
iScience
○ 1154 papers in training set
Top 35%
0.9%
29
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 6%
0.9%
30
Mathematical Biosciences and Engineering
● 23 papers in training set
Top 0.8%
0.9%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.