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The code for construction of the data along with plots and Tables can be found in Supplementary Materials Code/Code for S figures and Tables. \n<li>The description of how this data is constructed in available in the Supplementary Materials of the above paper\n</ul>\n</ul>\nTo run the code with synthetic mobility data: \n<ul>\n<li>run inference.m, which uses the following functions:\n<li>SEIRHS.m - runs the stochastic version of the epidemiology model and an RK4 integration\n<li>transf.m - calculates the time-varying transition function of contacts multiplied by the susceptibility parameter\n<li>waning - calculates the waning of protection parameters in the second compartment set\n<li>initialize - initializes the parameters and the unobserved states\n<li>checkbound_ini - checks initial bounds on unobserved states (for example, S cannot be negative or larger than population)\n<li>checkbound - checks bounds on unobserved states - after updating (for example, S cannot be negative or larger than population)\n<li>Inflation.m - performs variance inflation of posterior updates to prevent the filter from collapsing due to lack of variance \n<li>liki.m  - calculates the marginal posterior (quasi-)likelihood of the model based on the ensemble adjustment Kalman filter\n<li>the dataset movNSperc.mat (generated for Figures S11 and S19) contains percentage change in National train mobility compared to pre-pandemic levels and is used by the function synthetic_mobility.m (note that instead, Google mobility data could be used, but priors on parameters theta would likely need adjustment)\n<li>the dataset CBSprov.mat contains in column 4 the number of people living in province i and working in j, and is downloaded from CBS (Central Bureau  Statistics)\n  https://www.cbs.nl/nl-nl/cijfers/detail/83628NED#shortTableDescription   \n<li>use year 2018, first published in 2020. \n</ul>\n<br>\nMain Code/ Output files \n<br><br>\n<ul>\n<li>contains output.m, main output in the paper\n<li>output_synth.m, output with synthetic train mobility data\n</ul>\n\n<br>\nMain Code/ Code for Main Text Figures \n<br><br>\n<ul>\n<li>contains code for reproducing Figures in the main text\n</ul>Supplementary Materials Code\n\n<br><br>\n<ul>\n<li> contains two folders. \n<li>The folder \"Code for S Figures and Tables\" contains code for constructing all the data used in the main code, as well as code for reproducing\n  the Tables and Figures in the Supplementary Materials of the paper, labeled \"S\"\n<li>The folder \"Code for A Figures and Tables\" contains code for reproducing the figures and tables in the Supplementary Materials labeled \"A\"\n</ul>\n<br>\nMain Text Figures\n<br><br>\n<ul>\n<li>contains all the figures in the main text of the paper\n</ul>\n\nSupplementary Figures\n<ul>\n<li>contains all the figures in the Supplement\n</ul>\nUniverse: Covering the entire Dutch population<br><br>\nCountry / Nation: the Netherlands."},"dsDescriptionDate":{"typeName":"dsDescriptionDate","multiple":false,"typeClass":"primitive","value":"2023-11-23"}}]},{"typeName":"subject","multiple":true,"typeClass":"controlledVocabulary","value":["Medicine, Health and Life Sciences","Social Sciences"]},{"typeName":"keyword","multiple":true,"typeClass":"compound","value":[{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Epidemiology"},"keywordVocabulary":{"typeName":"keywordVocabulary","multiple":false,"typeClass":"primitive","value":"Epidemiology Glossary"},"keywordVocabularyURI":{"typeName":"keywordVocabularyURI","multiple":false,"typeClass":"primitive","value":"https://www.cdc.gov/reproductive-health/glossary/?CDC_AAref_Val=https://www.cdc.gov/reproductivehealth/data_stats/glossary.html"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Children"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"COVID-19"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Differential susceptibility"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Transportation"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Mobility"}},{"keywordValue":{"typeName":"keywordValue","multiple":false,"typeClass":"primitive","value":"Meta-population model"}}]},{"typeName":"publication","multiple":true,"typeClass":"compound","value":[{"publicationCitation":{"typeName":"publicationCitation","multiple":false,"typeClass":"primitive","value":"Boldea, O., Alipoor, A. , Pei S., Shaman, J. and  G. Rozhnova (2024).  Age-specific transmission dynamics of SARS-CoV-2 during the first two years of the pandemic. Proceeding of the National Academy of Sciences Nexus 3:2, February 2024, pgae024."},"publicationIDType":{"typeName":"publicationIDType","multiple":false,"typeClass":"controlledVocabulary","value":"doi"},"publicationIDNumber":{"typeName":"publicationIDNumber","multiple":false,"typeClass":"primitive","value":"10.1093/pnasnexus/pgae024"},"publicationURL":{"typeName":"publicationURL","multiple":false,"typeClass":"primitive","value":"https://doi.org/10.1093/pnasnexus/pgae024"}}]},{"typeName":"notesText","multiple":false,"typeClass":"primitive","value":"The files are large due to two output files that are large.\n<br>\n<a href=\"https://github.com/oboldea/COVID_age_regions\">https://github.com/oboldea/COVID_age_regions</a><br>\n\nThe datasets used as input in this study are listed in Table 1 of the related publication. All input datasets except for regional train mobility data are in this dataset. Requests for regional train mobility data to the Dutch Railways (Nederlandse Spoorwegen—NS)."},{"typeName":"producer","multiple":true,"typeClass":"compound","value":[{"producerName":{"typeName":"producerName","multiple":false,"typeClass":"primitive","value":"Tilburg University"}}]},{"typeName":"productionDate","multiple":false,"typeClass":"primitive","value":"2023-11-01"},{"typeName":"distributor","multiple":true,"typeClass":"compound","value":[{"distributorName":{"typeName":"distributorName","multiple":false,"typeClass":"primitive","value":"DataverseNL"}}]},{"typeName":"depositor","multiple":false,"typeClass":"primitive","value":"Boldea, Otilia"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2024-05-30"},{"typeName":"dateOfCollection","multiple":true,"typeClass":"compound","value":[{"dateOfCollectionStart":{"typeName":"dateOfCollectionStart","multiple":false,"typeClass":"primitive","value":"2020-02-01"},"dateOfCollectionEnd":{"typeName":"dateOfCollectionEnd","multiple":false,"typeClass":"primitive","value":"2022-01-31"}}]},{"typeName":"kindOfData","multiple":true,"typeClass":"primitive","value":["Daily longitudinal data","Time series data"]},{"typeName":"accessToSources","multiple":false,"typeClass":"primitive","value":"Data files: The number of files is too large to fill this in as it includes both data creation, modelling and outputs of the paper and the supplement. Readme files are available in each subfolder. \n<br><br>\nStructure data package: Data is structured in the following subfolders: Main code (for results, figures and tables in the paper), Main Text Figures, Supplementary Figures, Supplementary Materials code, and an overall readme file explaining the structure further."}]},"dansDataVaultMetadata":{"displayName":"Data Vault Metadata","name":"dansDataVaultMetadata","fields":[{"typeName":"dansDataversePid","multiple":false,"typeClass":"primitive","value":"doi:10.34894/J5UHNL"},{"typeName":"dansDataversePidVersion","multiple":false,"typeClass":"primitive","value":"1.1"},{"typeName":"dansBagId","multiple":false,"typeClass":"primitive","value":"urn:uuid:22c7305c-9400-4fef-8c5e-3f2b0b54595d"},{"typeName":"dansNbn","multiple":false,"typeClass":"primitive","value":"urn:nbn:nl:ui:13-2b53c9ba-ad4d-4626-97ca-8cff1e26e345"}]}},"files":[{"label":"april.mat","restricted":false,"directoryLabel":"Supplementary Materials Code/Code for S Figures and 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