{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T04:50:57Z","timestamp":1747198257572,"version":"3.40.5"},"reference-count":23,"publisher":"Walter de Gruyter GmbH","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Analyzing COVID-19 data presents a challenge in Bayesian computations of the Poisson process because the experimental conditions are not under control. This lack of homogeneity can lead to inconsistent model parameters, which violates the assumptions of Bayesian inference.\nIn this paper, we study the multiple change-point detection problem from this viewpoint for a non-homogeneous sample path of the Poisson process as the response variable. The rate parameters are linked to some explanatory using a generalized linear model. The number of change-points is considered to be unknown as well as their locations. We introduce a Bayesian paradigm to estimate the number and location of change-points. We also present an adaptive RJMCMC algorithm to generate pseudo-random samples from the posterior distributions.\nWe apply the proposed model to analyze the COVID-19 infection curves from different countries and identify patterns of cases. We also assess the efficacy of interventions, such as vaccination and public health emergency responses, implemented by different countries. The results of the analysis provide valuable insights into the spread of COVID-19 and the effectiveness of interventions.\nThe proposed model can be used to inform public health decision-making and help to improve the management of the pandemic.<\/jats:p>","DOI":"10.1515\/mcma-2024-2020","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T12:52:01Z","timestamp":1731588721000},"page":"449-465","source":"Crossref","is-referenced-by-count":0,"title":["Bayesian analysis of the COVID-19 pandemic using a Poisson process with change-points"],"prefix":"10.1515","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1626-7693","authenticated-orcid":false,"given":"Masoud","family":"Majidizadeh","sequence":"first","affiliation":[{"name":"Department of Statistics , Faculty of Mathematical Sciences , Shahid Beheshti University , Evin , Tehran , Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"2024112721095517258_j_mcma-2024-2020_ref_001","doi-asserted-by":"crossref","unstructured":"A.  Al-Dousari, A.  Ellahi and I.  Hussain,\nUse of non-homogeneous Poisson process for the analysis of new cases, deaths, and recoveries of Covid-19 patients: A case study of Kuwait,\nJ. King Saud University-Sci. 33 (2021), no. 8, Article ID 101614.","DOI":"10.1016\/j.jksus.2021.101614"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_002","doi-asserted-by":"crossref","unstructured":"I. V.  Basawa and B. L. S.  Prakasa Rao,\nAsymptotic inference for stochastic processes,\nStochastic Process. Appl. 10 (1980), no. 3, 221\u2013254.","DOI":"10.1016\/0304-4149(80)90010-1"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_003","doi-asserted-by":"crossref","unstructured":"L.  Bauwens, G.  Koop, D.  Korobilis and J. V. K.  Rombouts,\nThe contribution of structural break models to forecasting macroeconomic series,\nJ. Appl. Econometrics 30 (2015), no. 4, 596\u2013620.","DOI":"10.1002\/jae.2387"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_004","doi-asserted-by":"crossref","unstructured":"\u00c1.  Berihuete, M.  S\u00e1nchez-S\u00e1nchez and A.  Su\u00e1rez-Llorens,\nA Bayesian model of Covid-19 cases based on the Gompertz curve,\nMathematics 9 (2021), 10.3390\/math9030228.","DOI":"10.3390\/math9030228"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_005","doi-asserted-by":"crossref","unstructured":"B. P.  Carlin, A. E.  Gelfand and A. F.  Smith,\nHierarchical Bayesian analysis of changepoint problems,\nJ. Roy. Statist. Soc. Ser. C 41 (1992), no. 2, 389\u2013405.","DOI":"10.2307\/2347570"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_006","doi-asserted-by":"crossref","unstructured":"S. S.  Coughlin, A.  Yi\u00c7\u00a7iter and H.  Xu,\nEarly detection of change patterns in Covid-19 incidence and the implementation of public health policies: A multi-national study,\nPublic Health Practice (2021), 10.1016\/j.puhip.2020.100064.","DOI":"10.1016\/j.puhip.2020.100064"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_007","doi-asserted-by":"crossref","unstructured":"D. R.  Cox and P. A. W.  Lewis,\nThe Statistical Analysis of Series of Events,\nJohn Wiley & Sons, New York, 1966.","DOI":"10.1007\/978-94-011-7801-3"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_008","doi-asserted-by":"crossref","unstructured":"J.  Dehning, J.  Zierenberg and F. P.  Spitzner,\nInferring change points in the spread of Covid-19 reveals the effectiveness of interventions,\nScience 369 (2020), 10.1126\/science.abb9789.","DOI":"10.1126\/science.abb9789"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_009","doi-asserted-by":"crossref","unstructured":"P. J.  Green,\nReversible jump Markov chain Monte Carlo computation and Bayesian model determination,\nBiometrika 82 (1995), no. 4, 711\u2013732.","DOI":"10.1093\/biomet\/82.4.711"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_010","doi-asserted-by":"crossref","unstructured":"E.  Guidotti,\nA worldwide epidemiological database for Covid-19 at fine-grained spatial resolution,\nSci. Data 9 (2022), 10.1038\/s41597-022-01245-1.","DOI":"10.1038\/s41597-022-01245-1"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_011","doi-asserted-by":"crossref","unstructured":"N.  Guler Dincer, S.  Demir and M. O.  Yal\u00e7in,\nForecasting Covid19 reliability of the countries by using non-homogeneous Poisson process models,\nNew Generation Comput. 40 (2022), no. 4, 1143\u20131164.","DOI":"10.1007\/s00354-022-00183-1"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_012","doi-asserted-by":"crossref","unstructured":"F.  Jiang, Z.  Zhao and X.  Shao,\nModelling the COVID-19 infection trajectory: A piecewise linear quantile trend model,\nJ. R. Stat. Soc. Ser. B. Stat. Methodol. 84 (2022), no. 5, 1589\u20131607.","DOI":"10.1111\/rssb.12453"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_013","doi-asserted-by":"crossref","unstructured":"F.  Jiang, Z.  Zhao and X.  Shao,\nTime series analysis of COVID-19 infection curve: A change-point perspective,\nJ. Econometrics 232 (2023), no. 1, 1\u201317.","DOI":"10.1016\/j.jeconom.2020.07.039"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_014","doi-asserted-by":"crossref","unstructured":"H.  K\u00fcchenhoff, F.  G\u00fcnther and M.  H\u00f6hle,\nAnalysis of the early Covid-19 epidemic curve in germany by regression models with change points,\nEpidemiology & Infection 149 (2021), 10.1017\/S0950268821000558.","DOI":"10.1017\/S0950268821000558"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_015","doi-asserted-by":"crossref","unstructured":"T.  Leonard,\nDensity estimation, stochastic processes and prior information,\nJ. Roy. Statist. Soc. Ser. B 40 (1978), no. 2, 113\u2013146.","DOI":"10.1111\/j.2517-6161.1978.tb01655.x"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_016","doi-asserted-by":"crossref","unstructured":"R.  Mbuvha and T.  Marwala,\nBayesian inference of Covid-19 spreading rates in South Africa,\nPloS one 15 (2020), 10.1371\/journal.pone.0237126.","DOI":"10.1101\/2020.04.28.20083873"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_017","doi-asserted-by":"crossref","unstructured":"M. H.  Pesaran and A.  Timmermann,\nMarket timing and return prediction under model instability,\nJ. Empirical Finance 9 (2002), no. 5, 495\u2013510.","DOI":"10.1016\/S0927-5398(02)00007-5"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_018","unstructured":"A. E.  Raftery,\nChange point and change curve modeling in stochastic processes and spatial statistics,\nJ. Appl. Stat. Sci. 1 (1994), no. 4, 403\u2013423."},{"key":"2024112721095517258_j_mcma-2024-2020_ref_019","doi-asserted-by":"crossref","unstructured":"A. E.  Raftery and V. E.  Akman,\nBayesian analysis of a Poisson process with a change-point,\nBiometrika 73 (1986), no. 1, 85\u201389.","DOI":"10.1093\/biomet\/73.1.85"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_020","doi-asserted-by":"crossref","unstructured":"Pavan Kumar S T, B.  Lahiri and R.  Alvarado,\nMultiple change point estimation of trends in Covid-19 infections and deaths in India as compared with WHO regions,\nSpat. Stat. 49 (2022), Article ID 100538.","DOI":"10.1016\/j.spasta.2021.100538"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_021","doi-asserted-by":"crossref","unstructured":"W. R.  West and T. R.  Ogden,\nContinuous-time estimation of a change-point in a Poisson process,\nJ. Stat. Comput. Simul. 56 (1997), no. 4, 293\u2013302.","DOI":"10.1080\/00949659708811795"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_022","doi-asserted-by":"crossref","unstructured":"K.  Yamanishi, L.  Xu and R.  Yuki,\nChange sign detection with differential MDL change statistics and its applications to Covid-19 pandemic analysis,\nScientific Rep. 11 (2021), 10.1038\/s41598-021-98781-4.","DOI":"10.1038\/s41598-021-98781-4"},{"key":"2024112721095517258_j_mcma-2024-2020_ref_023","doi-asserted-by":"crossref","unstructured":"T. Y.  Yang and L.  Kuo,\nBayesian binary segmentation procedure for a Poisson process with multiple changepoints,\nJ. Comput. Graph. Statist. 10 (2001), no. 4, 772\u2013785.","DOI":"10.1198\/106186001317243449"}],"container-title":["Monte Carlo Methods and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2024-2020\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2024-2020\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T21:10:43Z","timestamp":1732741843000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/mcma-2024-2020\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"references-count":23,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,11,14]]},"published-print":{"date-parts":[[2024,12,1]]}},"alternative-id":["10.1515\/mcma-2024-2020"],"URL":"https:\/\/doi.org\/10.1515\/mcma-2024-2020","relation":{},"ISSN":["0929-9629","1569-3961"],"issn-type":[{"type":"print","value":"0929-9629"},{"type":"electronic","value":"1569-3961"}],"subject":[],"published":{"date-parts":[[2024,11,14]]}}}