Chapter 2. An Agent-Based Model of Infectious Diseases That Incorporates the Roles of Immune Cells and Antibodies

$39.50

Shigeaki Ogibayashi
Chiba Institute of Technology, Chiba, Japan

Part of the book: Infectious Diseases: From Prevention to Control

Abstract

In this study, an agent-based infection model focusing on recovery process modeling was constructed, and the following results were obtained. The present model well reproduced the qualitative features of the chronological pattern in the numbers of newly infected, newly recovered, and total infected agents observed in the real world. The factors of the model that are indispensable for reproducing the actual pandemic are the roles of fever and antibodies, which increase the upper limit of the rate of virus replication for pandemic convergence. The role of fever is modeled as the effect of immunity increasing with an increasing number of viruses, and the role of antibodies is modeled as antibodies emerging when the immunity is insufficient to keep up with virus replication. The critical factor determining whether pandemic convergence occurs is whether the system includes severely infected agents whose immune response cannot keep up with the virus replication rate. Such severely infected agents are characterized by a high body temperature and a massive number of viruses. To control infection spread, it is essential to identify infected individuals, especially severely infected ones, and isolate them from the system. Measuring body temperature is effective in identifying severely infected individuals, rather than PCR tests, because fever is a sign of being infected and also provides information about the severity of infection, while PCR tests provide information only in a dichotomous positive/negative format. To overcome the pandemic while minimizing the economic impact, it is effective to identify and isolate infected individuals by monitoring body temperature and refusing entry of those infected at national borders, such as airports, and also in densely populated places. It is also effective for individuals to determine their own infection status by monitoring their own body temperature. Wearing masks and providing ventilation in densely populated spaces are also effective because they decrease the number of viruses at the time of infection.

Keywords: agent-based model, infectious disease, SARS-CoV-2, immune cell, antibody, fever, virus, viral particle, pandemic, convergence, infection, recovery


References


Anderson R. M. and May, R. M. (1991) Infectious Diseases of Humans: Dynamics and
Control, Oxford University Press, New York.
Bailey N. T. J. (1975) The Mathematical Theory of Infectious Diseases (Mathematics in
Medicine series) Graffin, London.
Britton T. and Ball F. (2019) Stochastic Epidemic Models with Inference (Lecture Notes in
Mathematics), Springer.
Eden David, (2020) “Beyond the antibodies: How our immune system may protect us
against COVID-19 infection,” Accessed August 18.
https://abcnews.go.com/Health/antivodies-/story?id=71879020.
Epstein J. M. and Axtell R. (1995) Growing Artificial Societies, (Social Science from the
Bottom Up), MIT Press, London.
Li M. Y. (2019) An Introduction to Mathematical Modeling of Infectious Diseases
(Mathematics of Planet Earth), Springer.
Li Yan, Lawley A. Mark., Siscovick S. David, Zhang D., Pagan A. Jose (2016) Agent Based Modeling of Chronic Diseases: A Narrative Reviews and _Future Research
Directions, Prev Chronic Dis. May.26;13:E69. DOI: 10.5888/pcd 13.150561.
Marchuk G. I. (2019) Mathematical Modeling of Immune Response in Infectious Diseases
(Mathematics and Its Applications), Kluwer Academic Publishers.
Ogibayashi S. and Shinagawa K. (2020) Model Structure of Agent-Based Artificial System
for Reproducing the Emergence of Bullying Phenomenon, In: Proceedings of the 2018
Conference of the Computational Social Science Society of the Americas, Springer,
pp229-250.
Ogibayashi S. and Takashima K. (2017) Influence of Inefficiency in Government
Expenditure on the Multiplier of Public Investment, Comput Econ 50:549-577,
DOI: 10.1007/210614-017-9671-y.
Ogibayashi S. and Takashima K. (2019) System Structure of Agent-Based Model
Responsible for Reproducing Business Cycles and the Effect of Tax Reduction on
GDP,” In: Journal on Policy and Complex Systems, Vol.5, No2, Fall, pp. 37-59.
Perez L. and Dragicevic S. (2009) An agent-based approach for modeling dynamics of
contagious disease spread, International Journal of Health Geographics, 8,50.
https://doi.org/10.1186/1476-072X-8-50.
Rockett, R. J., Arnott A., Lam C. Sadsad R. Timms V. Gray K., Eden J. S., Chang S., Gall
M. Draper J., Sim E., Bachmann L. N., Carter I, Basile K., Byun R., O’Sullivan, V.
M., Chen, C-A. S., Maddocks S., Sorrel C. T., Dwyoer E. D., Holmes C. E., Kok J.,
Prokopenko M., Sintchenko V. (2020) “Revealing COVID-19 transmission by SARS CoV-2 Genome
Sequencing and Agent Based Modeling,” Accessed August 18.
https://doi.org/10.1101/2020.04.19.048751.
Shu Teng. (2020) “Body Temperature and immunity,” Oriental Medical Care, Mar. 26.
Accessed August 18. https://orientalmedicalcare.com/2020/03/26/body-temperature and-Immunity.
Tuomisto T. J., Yrjola J., Kolehmainen M., Bonsdorff J., Pekkanen J., Tikkanen T. (2020)
“An agent-based epidemic model REINA for COVID-19 to identify destructive
policies,” Accessed August 18. https://doi.org/10.1101/2020.04.09.20047498.
Vynnycky E. and White, R. (2010) An Introduction to Infectious Disease Modelling,
Oxford University Press, New York.
Wilensky U. and Rand W. (2015) An Introduction to Agent-Based Modeling, The MIT
Press, London.
Worldmeter/coronavirus_updates (2020). Accessed August 18.
https://www.worldometers.info/coronavirus/country/japan/.

Category:

Publish with Nova Science Publishers

We publish over 800 titles annually by leading researchers from around the world. Submit a Book Proposal Now!