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Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

查看全文 作　　者：Latchezar [1]Tomov;Lyubomir [2]Chervenkov;Dimitrina Georgieva [3]Miteva;Hristiana [4]Batselova;[5]TsvetelinaVelikova 高影响力作者 机构地区：[1]Department of Informatics,New Bulgarian University,Sofia 1618,Bulgaria;[2]Department of Diagnostic Imaging,Medical University Plovdiv,Plovdiv 4000,Bulgaria;[3]Department of Genetics,Faculty of Biology,Sofia University'St.Kliment Ohridski',Sofia 1164,Bulgaria;[4]Department of Epidemiology and Disaster Medicine,Medical University,University Hospital'St George',Plovdiv 4000,Bulgaria;[5]Department of Medical Faculty,Sofia University,St.Kliment Ohridski,Sofia 1407,Bulgaria高影响力机构 出　　处：《World Journal of Clinical Cases》索引2023年第11卷第29期,共10页高影响力期刊 基　　金：Supported by European Union-NextGenerationEU,Through the National Recovery and Resilience Plan of the Republic of Bulgaria,No.BG-RRP-2.004-0008-C01. 摘　　要：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. 关 键 词：Time series analysis EPIDEMIOLOGY COVID-19 PANDEMIC Auto-regressive integrated moving average Excess mortality SEROPREVALENCE
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