Understanding the Early Evolution of COVID-19 Disease Spread Using Mathematical Model and Machine Learning Approaches

Understanding the Early Evolution of COVID-19 Disease Spread Using Mathematical Model and Machine Learning Approaches

Samuel Sowole

Contact

Oladimeji Samuel Sowole

Contact

Abdullahi Adinoyi Ibrahim

Contact

Daouda Sangare

Contact

Ismaila Omeiza Ibrahim

Contact

Francis I. Ibukun

Contact

Baze University

Understanding the Early Evolution of COVID-19 Disease Spread Using Mathematical Model and Machine Learning Approaches

Article Fingerprint

ReserarchID

R3K11

Understanding the Early Evolution of COVID-19 Disease Spread Using Mathematical Model and Machine Learning Approaches Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

In response to the global COVID-19 pandemic, this work aims to understand the early time evolution and the spread of the disease outbreak with a data driven approach. To this effect, we applied Susceptible-Infective-Recovered/Removed (SIR) epidemiological model on the disease. Additionally, we used the Machine Learning linear regression model on the historical COVID-19 data to predict the earlier stage of the disease. The evolution of the disease spread with the Mathematical SIR model and Machine Learning regression model for time series forecasting of the COVID-19 data without, and with lags and trends, was able to capture the early spread of the disease. Consequently, we suggest that if using a more advanced epidemiological model, and sophisticated machine learning regression models on the COVID-19 data, we can understand, as well as predict the long time evolution of the disease spread.

References

27 Cites in Article

Reference Format

(2020). Everything You should Know about the Coronavirus outbreak.
Adnan Muhammad,Suliman Shereena,Abeer Khana,Nadia Kazmic,Rabeea Bashira,Siddiquea (2020). COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses.
Phạm Võ,Chí Đinh,Ngọc Nguyễn,Đăng Lý,Vạn Trang,Thị Phạm,Thị Hoàng (2020). 1. Coronavirus disease (COVID-19) – World Health Organization. Accessed March 3, 2023. https://www.who.int/emergencies/diseases/novel-coronavirus-2019 2. Mathieu E, Ritchie H, Rodés-Guirao L, et al. Coronavirus Pandemic (COVID-19). Our World in Data. Publ.
(2020). Transmission Mode of COVID-19.
(2020). The SIR model and the Foundations of Public Health.
(2020). Johns Hopkins COVID-19 Datasets.
S Sowole,D Sangare,A Ibrahim,I Paul (2019). On the Existence, Uniqueness, Stability of Solution and Numerical Simulations of a Mathematical Model for Measles Disease.
Min Shrestha,Guna Bhatta (2018). Selecting appropriate methodological framework for time series data analysis.
Maciej Kowalik,Piotr Trzonkowski,Magdalena Łasińska-Kowara,Andrzej Mital,Tomasz Smiatacz,Miłosz Jaguszewski (2020). COVID-19 — Toward a comprehensive understanding of the disease.
E Soares (2020). Unknown Title.
M Abbas Khan (2020). Decoding the structure of RNA-dependent RNA polymerase (RdRp), understanding the ancestral relationship and dispersion pattern of 2019 Wuhan Coronavirus, PREPRINT (Version 1) available at Researchgate.
Mayla Borba,Fernando Val,Vanderson Sampaio,Marcia Alexandre,Gisely Melo,Marcelo Brito,Maria Mourão,José Brito-Sousa,Djane Baía-Da-Silva,Marcus Guerra,Ludhmila Hajjar,Rosemary Pinto,Antonio Balieiro,Antônio Pacheco,James Santos,Felipe Naveca,Mariana Xavier,André Siqueira,Alexandre Schwarzbold,Júlio Croda,Maurício Nogueira,Gustavo Romero,Quique Bassat,Cor Fontes,Bernardino Albuquerque,Cláudio-Tadeu Daniel-Ribeiro,Wuelton Monteiro,Marcus Lacerda (2020). Effect of High vs Low Doses of Chloroquine Diphosphate as Adjunctive Therapy for Patients Hospitalized With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection.
Alex Zhavoronkov,Vladimir Aladinskiy,Alexander Zhebrak,Bogdan Zagribelnyy,Victor Terentiev,Dmitry Bezrukov,Daniil Polykovskiy,Rim Shayakhmetov,Andrey Filimonov,Philipp Orekhov,Yilin Yan,Olga Popova,Quentin Vanhaelen,Alex Aliper,Yan Ivanenkov (2020). Potential COVID-2019 3C-like Protease Inhibitors Designed Using Generative Deep Learning Approaches.
Fotios Petropoulos,Spyros Makridakis (2020). Forecasting the novel coronavirus COVID-19.
(2020). WHO Coronavirus Disease (COVID-19) Dashboard.
M Pal,G Berhanu,C Desalegn,V Kandi (2020). Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2): An update.
Su Park (2020). Epidemiology, virology, and clinical features of severe acute respiratory syndrome -coronavirus-2 (SARS-CoV-2; Coronavirus Disease-19).
Catharine Paules,Hilary Marston,Anthony Fauci (2020). Coronavirus Infections—More Than Just the Common Cold.
Yan-Rong Guo,Qing-Dong Cao,Zhong-Si Hong,Yuan-Yang Tan,Shou-Deng Chen,Hong-Jun Jin,Kai-Sen Tan,De-Yun Wang,Yan Yan (2020). The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak – an update on the status.
Marco Cascella,Raffaela Di Napoli,Domenico Carbone,Gaia Cuomo,Sabrina Bimonte,Bertrand Ellenga Mbolla,Maria Muzio (2020). Chemotherapy-related cognitive impairment: mechanisms, clinical features and research perspectives.
Firas Rabi,Mazhar Al Zoubi,Ghena Kasasbeh,Dunia Salameh,Amjad Al-Nasser (2020). SARS-CoV-2 and Coronavirus Disease 2019: What We Know So Far.
D Cennimo,M Bronze (2020). Coronavirus disease 2019 (COVID-19) treatment & management.
Nadia Jebril (2020). World Health Organization Declared a Pandemic Public Health Menace: A Systematic Review of the Coronavirus Disease 2019 “COVID-19”.
R Assessment (2020). Updated rapid risk assessment from ECDC on coronavirus disease 2019 (COVID-19) pandemic: increased transmission in the EU/EEA and the UK.
Adam Kucharski,Timothy Russell,Charlie Diamond,Yang Liu,John Edmunds,Sebastian Funk,Rosalind Eggo,Fiona Sun,Mark Jit,James Munday,Nicholas Davies,Amy Gimma,Kevin Van Zandvoort,Hamish Gibbs,Joel Hellewell,Christopher Jarvis,Sam Clifford,Billy Quilty,Nikos Bosse,Sam Abbott,Petra Klepac,Stefan Flasche (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study.
(2020). Overview of public health and social measures in the context of COVID-19: in-terim guidance.
Adnan Muhammad,Suliman Shereena,Abeer Khana,Nadia Kazmic,Rabeea Bashira,Siddiquea (2020). COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses.

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Samuel Sowole. 2020. \u201cUnderstanding the Early Evolution of COVID-19 Disease Spread Using Mathematical Model and Machine Learning Approaches\u201d. Global Journal of Science Frontier Research - F: Mathematics & Decision GJSFR-F Volume 20 (GJSFR Volume 20 Issue F5).

More Citation Formats

Select Citation Style:

Download Citation

GJSFR Volume 20 Issue F5

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

Keywords

Not Found

Classification

GJSFR-F Classification MSC 2010: 93A30

Submission ReceivedJune 21, 2020
Peer Review Double Blind
Handling Editor
Accepted July 13, 2020
Published July 28, 2020

Version of record

v1.2

Issue date

August 22, 2020

Language

English

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Views: 2260

Total Downloads: 1181

2026 Trends

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]