NetLogo User Community Models

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## What is it?

This model builds a Tokyo-based down-scaled simulation environment to explain the eight epidemic trends using agent-based modelling and extended SEIR denotation. Four key factors are being considered, that are 1. vaccination, 2. virus mutation, 3. government policy and 4. PCR test. Simulation period is 2020.01.24 ~ 2023.05.08.

## How it works?

This model reads an external csv file called 'data1.csv', import information related to the four key factors, conduct simulation, output plots and key numbers at each time step.

## How to use it?

Use it in conjuction with the 'data1.csv' file. click 'setup' then click 'go' in the interface. In order to obtain the results faster, it is recommended to unclick 'view updates' so that the simulation speed shall improve dramatically.

## Things to notice

It is noticed that the number of vaccinated agents in 'Vaccination and Antibody' plot drop during the later period of simulation because this model considers population movements, both inbound and outbound. Some may move out of Tokyo after receiving vaccinations.

## Things to try

Readers may compare the simulated results obtained from 'COVID+' plot and the scaled infection cases of Tokyo (population in this model : population of Tokyo Metropolitan Area = 12,527:13,920,000 approximately).

Readers may also try to scale up and down to check the model's stability and verify the feasibility of linear scaling. To do so, readers should change all together the followings: the initial population / simulation environment / hospital capacity and the doses of vaccination/ PCR tests, inbound/outbound population.

Besides, readers are encouraged to modifty the model to reproduce regional COVID-19 epidemic trends, given that data have been correctly collected and regional circumstances have been fully considered.

## Extending the model

Currently this model does not consider the work-from-home rate of Tokyo citizens. But readers may try to complete the function to study the impact of WFH policy during Coronavirus outbreak.

## Related models
The HIV model in the models library has inspired me to develop into the current TokyoCovSim-VVGP model.

## Credits and references
There are two papers related to this model.
1. Simulation of SARS-CoV-2 epidemic trends in Tokyo considering vaccinations, virus mutations, government policies and PCR tests written by Jianing Chu, Hikaru Morikawa, Yu Chen.
2. Evaluating the Multifactorial Effects on SARS-CoV-2 Spread in Tokyo Metropolitan Area with an Agent-based Model written by Jianing Chu and Yu Chen.

## Acknowledgement
This model would not have been possible without the support from a number of important individuals. First and foremost, I am grateful to Dr. Zhiyi Zhang from INSA Lyon for his invaluable support and insightful discussin during the global pandemic. His sharing of relevant articles and simulation results greatly inspired me to develop this research. I also thank M.D. Ariel Israel for his inspiring paper about IgG titer regression and feedback about my constructed model. I also thank my families Aiguo Chu, Yun Liu and Xiaomi Chu, Yicheng Xiao as well as my instructor Dr. Prof Yu Chen. I would like to express my gratitude to the WINGS-CFS Program and the Japan Society for the Promotion of Science for providing research funding. Last but not the least, hope that complex system simulation can save the world. El Psy Congroo!

## Contact
Jianing Chu (Sonata)
1st yr PhD Student @ UTokyo as of 03/2024
j-chu@g.ecc.u-tokyo.ac.jp