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World J Clin Cases. Oct 16, 2023; 11(29): 6974-6983
Published online Oct 16, 2023. doi: 10.12998/wjcc.v11.i29.6974
Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic
Latchezar Tomov, Lyubomir Chervenkov, Dimitrina Georgieva Miteva, Hristiana Batselova, Tsvetelina Velikova
Latchezar Tomov, Department of Informatics, New Bulgarian University, Sofia 1618, Bulgaria
Lyubomir Chervenkov, Department of Diagnostic Imaging, Medical University Plovdiv, Plovdiv 4000, Bulgaria
Dimitrina Georgieva Miteva, Department of Genetics, Faculty of Biology, Sofia University "St. Kliment Ohridski", Sofia 1164, Bulgaria
Hristiana Batselova, Department of Epidemiology and Disaster Medicine, Medical University, University Hospital "St George", Plovdiv 4000, Bulgaria
Tsvetelina Velikova, Department of Medical Faculty, Sofia University, St. Kliment Ohridski, Sofia 1407, Bulgaria
Author contributions: Tomov L and Velikova T contributed to the conceptualization; Chervenkov L and Miteva DG performed the resources and literature review; Tomov L wrote the original draft preparation; Velikova T, Chervenkov L and Miteva DG wrote the review and editing; Velikova T performed the supervision; All authors revised and approved the final version of the manuscript.
Supported by European Union-NextGenerationEU, Through the National Recovery and Resilience Plan of the Republic of Bulgaria, No. BG-RRP-2.004-0008-C01.
Conflict-of-interest statement: The authors declare no conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Latchezar Tomov, PhD, Academic Research, Department of Informatics, New Bulgarian University, Montevideo 21 Str, Sofia 1618, Bulgaria. luchesart@gmail.com
Received: April 26, 2023
Peer-review started: April 26, 2023
First decision: July 27, 2023
Revised: August 12, 2023
Accepted: September 4, 2023
Article in press: September 4, 2023
Published online: October 16, 2023
Processing time: 170 Days and 0.7 Hours
Abstract

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc.). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Keywords: Time series analysis; Epidemiology; COVID-19; Pandemic; Auto-regressive integrated moving average; Excess mortality; Seroprevalence

Core tip: Time-series analysis allows us to do easily and, in less time, precise short-term forecasting in novel pandemics by estimating directly from data. These models do not need extensive knowledge of pandemic mechanisms and interactions between peoples, societal structures, and pathogens. Its secondary but equally important role is distinguishing factors contributing to the spread or slowing it down. Of course, the time series analysis approach cannot give a forecast for an end of a pandemic, nor the precise moment of its peak, but it is invaluable for fast response based on sound statistical methodology.