Dynamic model of infectious diseases on the coronavirus disease 2019

Main Article Content

Bin Zhao*
Junjie Liu
Ke Wang
Zhiyong Deng
Jinming Cao

Abstract

Under the general trend of globalization, historically and newly discovered infectious diseases are seriously threatening people’s health and lives, including: Avian influenza H7N9, AIDS HIV, Influenza A H1N1, etc., a new type of corona that is currently spreading in many countries around the world Viral pneumonia (C0VID-19), there is currently no good therapeutic drug, which seriously affects human survival and development. The rapid spread of the new coronavirus in Hong Kong, while starting the epidemic prevention work, uses mathematical modeling methods to construct the propagation model, and then calculates the inflection point for better prevention and control of the spread of epidemic work. The spread of Hong Kong was analyzed, and the quantitative relationship between the growth rate of the number of new coronavirus infections and time was explored.
In order to find out and predict the impact on the spread of infectious diseases, we established a class of kinetic models, gave formulas for calculating numbers, analyzed inflection points and predicted the development trend of new coronaviruses, and used C0VID-19 as an example for numerical simulation .
Background: In December 2019, China’s first unexplained pneumonia patient was admitted to Wuhan Jinyintan Hospital, Hubei, China. Since then, COVID-19 has expanded rapidly in Wuhan, Hubei, China. Within a few months, COVID-19 soon It spread to 34 provincial administrative regions and neighboring countries across China, and Hubei Province immediately became the hardest hit by the new coronavirus.
New coronavirus pneumonia (COVID-19) As of April 17, 2020, the cumulative number of confirmed cases in China was 83,824, the cumulative number of deaths was 3352, the cumulative number of foreign diagnoses was 2,090000, and the cumulative deaths were 141,601. The outbreak and spread of COVID-19 have seriously affected people’s Good health has caused huge economic losses in our country. It is of far-reaching significance to explore effective prevention and control of infectious diseases and minimize the harm caused by infectious diseases.
In an emergency situation, we strive to establish an accurate infectious disease dynamic model to predict the development and spread of COVID-19, and make some effective short-term predictions on this basis. The construction of this model is relevant to all aspects of mainland China. It is helpful for the department to carry out the prevention and monitoring of the new coronavirus. It also strives for more time for the clinical trials of Chinese researchers and the research of vaccines against the virus to eliminate the new coronavirus as soon as possible.
Methods: Collect and compare and integrate the spread of COVID-19 in Hong Kong, record the spread of the virus in the population and the protest measures of relevant government departments, and establish a dynamic model of infectious diseases based on the original data changes.
Interpretation: In the early stage of the epidemic, due to inadequate anti-epidemic measures, the epidemic in Hong Kong quickly spread. However, with the gradual understanding of COVID-19, the epidemic began to be gradually controlled, and then the growth was blocked.

Downloads

Download data is not yet available.

Article Details

Zhao, B., Liu, J., Wang, K., Deng, Z., & Cao, J. (2020). Dynamic model of infectious diseases on the coronavirus disease 2019. Annals of Mathematics and Physics, 3(1), 018–022. https://doi.org/10.17352/amp.000013
Research Articles

Copyright (c) 2020 Zhao B, et al.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Licensing and protecting the author rights is the central aim and core of the publishing business. Peertechz dedicates itself in making it easier for people to share and build upon the work of others while maintaining consistency with the rules of copyright. Peertechz licensing terms are formulated to facilitate reuse of the manuscripts published in journals to take maximum advantage of Open Access publication and for the purpose of disseminating knowledge.

We support 'libre' open access, which defines Open Access in true terms as free of charge online access along with usage rights. The usage rights are granted through the use of specific Creative Commons license.

Peertechz accomplice with- [CC BY 4.0]

Explanation

'CC' stands for Creative Commons license. 'BY' symbolizes that users have provided attribution to the creator that the published manuscripts can be used or shared. This license allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to the author.

Please take in notification that Creative Commons user licenses are non-revocable. We recommend authors to check if their funding body requires a specific license.

With this license, the authors are allowed that after publishing with Peertechz, they can share their research by posting a free draft copy of their article to any repository or website.
'CC BY' license observance:

License Name

Permission to read and download

Permission to display in a repository

Permission to translate

Commercial uses of manuscript

CC BY 4.0

Yes

Yes

Yes

Yes

The authors please note that Creative Commons license is focused on making creative works available for discovery and reuse. Creative Commons licenses provide an alternative to standard copyrights, allowing authors to specify ways that their works can be used without having to grant permission for each individual request. Others who want to reserve all of their rights under copyright law should not use CC licenses.

National Health Commission of the People’s Republic of China. Link: https://bit.ly/3hdSQ87

National Health Commission of the People’s Republic of China. Link: https://bit.ly/2AVd6L4

Dye C, Gay N (2020) Modeling the SARS epidemic. Science 300: 1884-1885. Link: https://bit.ly/2YilTio

Riley S, Fraser C, Donnelly CA (2020) Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions. Science 300: 1961-1966. Link: https://bit.ly/3dREhFb

de Oliveira E M, Oliveira FLC (2020) Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy14: 776-788. Link: https://bit.ly/2MI2QIM

Chen P, Yuan H, Shu X (2020) Forecasting crime using the arima mode. 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE 18: 627-630. Link: https://bit.ly/2UtZxJz

Li X (2020) Comparison and analysis between holt exponential smoothing and brown exponential smoothing used for freight turnover forecasts. IEEE 15: 453-456. Link: https://bit.ly/2UsynCX

Hansun S (2016) A New Approach of Brown’s Double Exponential Smoothing Method in Time Series Analysis. Balkan Journal of Electrical and Computer Engineering 4: 75-78. Link: https://bit.ly/3fcZs54

Chadsuthi S, Modchang C, Lenbury Y (2020) Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time–series and ARIMAX analyses. Asian Pacific journal of tropical medicine 5: 75-78.

Ming W, Huang J, Zhang CJP (2020) Breaking down of healthcare system: Mathematical modelling for controlling the novel coronavirus (COVID-19) outbreak in Wuhan, China. bioRxiv 12: 627-630. Link: https://bit.ly/3dSX17s

Chowell G, Castillo-Chavez C, Fenimore PW (2020) Model parameters and outbreak control for SARS. Emerg Infect Dis 9: 75-8.

Luo H, Ye F, Sun T, Yue L, Peng S, et al. (2020) In vitro biochemical and thermodynamic characterization of nucleocapsid protein of SARS[J]. Biophysical chemistry 6: 76-78.

Colizza V, Barrat A, Barthélemy M (2020) Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study. BMC Med 8: 95-101.

Tsui W H K, Balli H O, Gower H (2020) Forecasting airport passenger traffic: the case of Hong Kong International Airport. 6: 75-78.

National Health Commission of the People’s Republic of China. Link: https://bit.ly/37sNulb

World Health Organization (WHO). Coronavirus. Link: https://bit.ly/2Yp5xUW

National Health Commission of the People’s Republic of China. Link: https://bit.ly/30t8sP2

Health Commission of Hubei Province. Link: https://bit.ly/3cPHeF0

Health Commission of Hubei Province. Link: https://bit.ly/3fm6J2z

National Health Commission of the People’s Republic of China. Link: https://bit.ly/2MLtaBQ

Health Commission of Hubei Province. Link: https://bit.ly/2XLKInI

Wu P, Hao X, Lau EHY, Wong YJ, Leung KSM, et al. (2020) Real-time tentative assessment of the epidemiological characteristics of novel coronavirus infections in Wuhan, China. Eurosurveillance 25: 2000044. Link: https://bit.ly/2XNbo7p

National Health Commission of the People’s Republic of China. Link: https://bit.ly/3hcgcer

National Health Commission of the People’s Republic of China. Link: https://bit.ly/37i98bu

Health Commission of Hubei Province. Link: https://bit.ly/2MOy9S4