{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T12:03:16Z","timestamp":1791288196298,"version":"4.3.0"},"reference-count":24,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T00:00:00Z","timestamp":1655078400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>This paper develops an individual\u2010based stochastic network SIR model for the empirical analysis of the Covid\u201019 pandemic. It derives moment conditions for the number of infected and active cases for single as well as multigroup epidemic models. These moment conditions are used to investigate the identification and estimation of the transmission rates. The paper then proposes a method that jointly estimates the transmission rate and the magnitude of under\u2010reporting of infected cases. Empirical evidence on six European countries matches the simulated outcomes once the under\u2010reporting of infected cases is addressed. It is estimated that the number of actual cases could be between 4 to 10 times higher than the reported numbers in October 2020 and declined to 2 to 3 times in April 2021. The calibrated models are used in the counterfactual analyses of the impact of social distancing and vaccination on the epidemic evolution and the timing of early interventions in the United Kingdom and Germany.<\/jats:p>","DOI":"10.1002\/jae.2904","type":"journal-article","created":{"date-parts":[[2022,5,12]],"date-time":"2022-05-12T10:00:44Z","timestamp":1652349644000},"page":"1204-1229","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Matching theory and evidence on Covid\u201019 using a stochastic network SIR model"],"prefix":"10.1002","volume":"37","author":[{"given":"M. Hashem","family":"Pesaran","sequence":"first","affiliation":[{"name":"University of Southern California  Los Angeles California USA"},{"name":"Trinity College, Cambridge  UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cynthia Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"Florida State University  Tallahassee Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,6,13]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"crossref","unstructured":"Chudik A. Pesaran M. H. &Rebucci A.(2021).COVID\u201019 Time\u2010varying reproduction numbers worldwide: An empirical analysis of mandatory and voluntary social distancing. (NBER working paper No. 28629): National Bureau of Economic Research.","DOI":"10.3386\/w28629"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bsheal.2020.03.004"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.3934\/mbe.2013.10.1475"},{"key":"e_1_2_12_5_1","unstructured":"Elliott S. &Gourieroux C.(2020).Uncertainty on the reproduction ratio in the SIR model. arXiv preprint: 2012.11542."},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/biostatistics\/4.4.621"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2458-14-147"},{"issue":"3","key":"e_1_2_12_8_1","first-page":"259","article-title":"Global stability of the endemic equilibrium of multigroup SIR epidemic models","volume":"14","author":"Guo H.","year":"2006","journal-title":"Canadian Applied Mathematics Quarterly"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2020.4130"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1137\/S0036144500371907"},{"key":"e_1_2_12_11_1","doi-asserted-by":"crossref","unstructured":"Jagodnik K. Ray F. Giorgi F. M. &Lachmann A.(2020).Correcting under\u2010reported COVID\u201019 case numbers: Estimating the true scale of the pandemic. medRxiv preprint doi:https:\/\/doi.org\/10.1101\/2020.03.14.20036178","DOI":"10.1101\/2020.03.14.20036178"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.abh3826"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1927.0118"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.abb3221"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.0050074"},{"issue":"1","key":"e_1_2_12_16_1","first-page":"11","article-title":"A survey of the individual\u2010based model applied in biomedical and epidemiology","volume":"1","author":"Nepomuceno E. G.","year":"2018","journal-title":"Journal of Biomedical Research and Reviews"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.15585\/mmwr.mm6950e2"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.15585\/mmwr.mm695152e1"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.15585\/mmwr.mm7009e4"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1002\/sdr.1673"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep31456"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1515\/9780691187655"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13104-020-05136-9"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-017-2699-8"},{"key":"e_1_2_12_25_1","doi-asserted-by":"crossref","unstructured":"Zhang J. Litvinova M. Liang Y. Wang Y. Wang W. Zhao S. Wu Q Merler S. Viboud C Vespignani A. 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