Simplifying COVID-19 Data Analytics for Efficient Pandemic Management: A Novel Approach

Article Sidebar

15-May-5269.pdf
Published Apr 30, 2024
DOI: https://doi.org/10.18103/mra.v12i4.5269

Downloads

Download data is not yet available.

Submit your own article

Register as an author to reserve your spot in the next issue of the Medical Research Archives.

Author Registration

Join the Society

The European Society of Medicine is more than a professional association. We are a community. Our members work in countries across the globe, yet are united by a common goal: to promote health and health equity, around the world.

Join Europe’s leading medical society and discover the many advantages of membership, including free article publication.

Membership

Main Article Content

H. M. Morales-Fajardo, MSc
School of Engineering, Universidad Autónoma del Estado de México, México
J. Rodríguez-Arce, PhD
School of Engineering, Universidad Autónoma del Estado de México, México;  School of Medicine, Universidad Autónoma del Estado de México, México;  Tecnologico de Monterrey, School of Engineering and Sciences, México
B. E. Ruvalcaba-Ramos, PhD
Institute of Neurosciences, CUCBA, Universidad de Guadalajara, México
S. Montes de Oca, PhD
Tecnologico de Monterrey, School of Engineering and Sciences, México

Abstract

This study presents a streamlined approach to pandemic management by simplifying COVID-19 data analytics. It focuses on the significant role of mobility patterns in forecasting case trajectories. Utilizing open mobility data from Google and Apple, a novel predictive model is proposed that aids health authorities in scenario projection and case monitoring. This model facilitates informed decision-making with minimal economic impact during future outbreaks.


Key findings highlight the profound link between mobility changes and COVID-19 case trends, emphasizing the necessity of integrating mobility data into predictive models. The model employing linear and polynomial regression analyses and incorporating the effective reproduction number, Rt, and the influence mobility changes have on population forecasts can be extended up to 90 days.


The study acknowledges limitations, particularly the reliance on mobility data that does not fully encompass all variables affecting virus transmission. Moreover, it explores the mental health implications of mobility restrictions, suggesting a broader impact of pandemic management strategies.


The proposed model is a practical tool for managing pandemics through mobility data analysis, underscoring the need for comprehensive studies on the broader effects of mobility changes to guide public health policies.

Keywords: COVID-19, Mobility Data, Predictive Modeling, Data Analytics, Public Health Policy

Article Details

How to Cite
MORALES-FAJARDO, H. M. et al. Simplifying COVID-19 Data Analytics for Efficient Pandemic Management: A Novel Approach. Medical Research Archives, [S.l.], v. 12, n. 4, apr. 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/5269>. Date accessed: 03 july 2024. doi: https://doi.org/10.18103/mra.v12i4.5269.
ABNT APA BibTeX CBE EndNote - EndNote format (Macintosh & Windows) MLA ProCite - RIS format (Macintosh & Windows) RefWorks Reference Manager - RIS format (Windows only) Turabian
Issue
Vol 12 No 4 (2024): April issue, Vol.12, Issue 4
Section
Research Articles

The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.

 

References

1. Mitchell Edith P. Corona Virus: Global Pandemic Causing World-Wide Shutdown Journal of the National Medical Association. 2020,112:113–114.
2. Li Qun, Guan Xuhua, Wu Peng, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus Infected Pneumonia New England Journal of Medicine. 2020,382:1199–1207.
3. Hu Zhiliang, Song Ci, Xu Chuanjun, et al. Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing, China Science China Life Sciences. 2020,63:706–711.
4. Team Novel Coronavirus Pneumonia Emergency Response Epidemiology. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China Zhonghua Liu Xing Bing Xue Za Zhi.2020,41:145–151.
5. WHO coronavirus disease (COVID-19) dashboard Online 2020. Accessed 30 June 2020.
6. Lai Chih-Cheng, Shih Tzu-Ping, Ko Wen-Chien, Tang Hung-Jen, Hsueh Po-Ren. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges International Journal of Antimicrobial Agents. 2020,55:105924.
7. Bedford Juliet, Enria Delia, Giesecke Johan, et al. COVID-19: towards controlling of a pandemic The Lancet. 2020,395:1015–1018.
8. Warren Michael S., Skillman Samuel W. Mobility Changes in Response to COVID-19 2020.
9. Kissler Stephen M., Tedijanto Christine, Goldstein Edward, Grad Yonatan H., Lipsitch Marc. Projecting the transmission dynamics of SARS- CoV-2 through the postpandemic period Science. 2020,368:860–868.
10. Kissler Stephen M, Tedijanto Christine, Lipsitch Marc, Grad Yonatan.Social distancing strategies for curbing the COVID-19 epidemic medRxiv.2020.
11. Lopez Leonardo, Rodo Xavier. The end of social confinement and COVID-19 re-emergence risk Nature Human Behaviour. 2020,4:746–755.
12. Lopez Leonardo R, Rodo Xavier. A modified SEIR model to predict the COVID-19 outbreak in Spain and Italy: simulating control scenarios and multi-scale epidemics medRxiv. 2020.
13. Li Lixiang, Yang Zihang, Dang Zhongkai, et al. Propagation analysis and prediction of the COVID-19 Infectious Disease Modelling. 2020,5:282–292.
14. Petropoulos Fotios, Makridakis Spyros. Forecasting the novel coronavirus COVID-19 PLOS ONE. 2020,15:e0231236.
15. Oliver Nuria, Lepri Bruno, Sterly Harald, et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle Science Advances. 2020,6:eabc0764.
16. Moulaei Khadijeh, Shanbehzadeh Mostafa, Mohammadi-Taghiabad Zahra, Kazemi-Arpanahi Hadi. Comparing machine learning algorithms for predicting COVID-19 mortality BMC Medical Informatics and Decision Making. 2022,22:2.
17. Rambhatla Sirisha, Zeighami Sepanta, Shahabi Kameron, Shahabi Cyrus, Liu Yan. Toward Accurate Spatiotemporal COVID-19 Risk Scores Using High-Resolution Real-World Mobility Data ACM Trans. Spatial Algorithms Syst. 2022,8.
18. Google COVID-19 Community Mobility Reports Online 2020. Accessed 30 June 2020.
19. Apple’s Mobility Trends Report Online 2020. Accessed 30 June 2020. Delamater Paul L., Street Erica J., Leslie Timothy F., Yang Y. Tony,
20. Jacobsen Kathryn H. Complexity of the Basic Reproduction Number (R0) Emerging Infectious Diseases. 2019,25:1–4.
21. Mahase Elisabeth. Covid-19: What is the R number? The BMJ. 2020.
22. Hartfield Matthew, Alizon Samuel. Introducing the Outbreak Threshold in Epidemiology PLoS Pathogens. 2013,9:e1003277.
23. Ridenhour Benjamin, Kowalik Jessica M., Shay David K. Unraveling R0: Considerations for Public Health Applications American Journal of Public Health. 2014,104:e32–e41.
24. Bar-On Yinon M, Flamholz Avi, Phillips Rob, Milo Ron. SARS-CoV-2 (COVID-19) by the numbers eLife. 2020,9.
25. Distante Cosimo, Pereira Igor Gadelha, Goncalves Luiz Marcos Garcia, Piscitelli Prisco, Miani Alessandro. Forecasting Covid-19 Outbreak Progression in Italian Regions: A model based on neural network training from Chinese data medRxiv. 2020.
26. Considerations for quarantine of individuals in the context of containment for coronavirus disease (COVID-19) Online 2020. Accessed 30 June 2020.
27. Qualls Noreen, Levitt Alexandra, Kanade Neha, et al. Community Mitigation Guidelines to Prevent Pandemic Influenza - United States, 2017 MMWR Recommendations and Reports. 2017,66:1–34.
28. Updated Preparedness and Response Framework for Influenza Pandemics Online 2014. Accessed 30 June 2020.
29. Reed Carrie, Biggerstaff Matthew, Finelli Lyn, et al. Novel Framework for Assessing Epidemiologic Effects of Influenza Epidemics and Pandemics Emerging Infectious Diseases. 2013,19:85–91.
30. Interim pre-pandemic planning guidance: community strategy for pandemic influenza mitigation in the United States-early, targeted, layered use of nonpharmaceutical interventions Online 2007. Accessed 30 June 2020.
31. Roser Max, Ritchie Hannah, Ortiz-Ospina Esteban, Hasell Joe. Coronavirus Pandemic (COVID-19) Our World in Data. 2020. https://ourworldindata.org/coronavirus.
32. Gloster A. T., Lamnisos D., Lubenko J., et al. Impact of COVID-19 pandemic on mental health: An international study PLOS ONE.2020,15:e0244809.
33. Brooks S. K., Webster R. K., Smith L. E., et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence The Lancet.2020,395:912-920.
34. Santomauro D. F., Mantilla-Herrera A. M., Shadid J., et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic The Lancet.2021,398:1700-1712.
35. Xiong J., Lipsitz O., Nasri F., et al. Impact of COVID-19 pandemic on mental health in the general population: A systematic review Journal of Affective Disorders. 2020,277:55-64.