Percorrer por autor "Lytras, Theodore"
A mostrar 1 - 3 de 3
Resultados por página
Opções de ordenação
- Excess all-cause and influenza-attributable mortality in Europe, December 2016 to February 2017Publication . Vestergaard, Lasse S; Nielsen, Jens; Krause, Tyra G; Espenhain, Laura; Tersago, Katrien; Bustos Sierra, Natalia; Denissov, Gleb; Innos, Kaire; Virtanen, Mikko J; Fouillet, Anne; Lytras, Theodore; Paldy, Anna; Bobvos, Janos; Domegan, Lisa; O’Donnell, Joan; Scortichini, Matteo; de Martino, Annamaria; England, Kathleen; Calleja, Neville; van Asten, Liselotte; Teirlinck, Anne C; Tønnessen, Ragnhild; White, Richard A; Silva, Susana Pereira; Rodrigues, Ana Paula; Larrauri, Amparo; Leon, Inmaculada; Farah, Ahmed; Junker, Christoph; Sinnathamby, Mary; Pebody, Richard G; Reynolds, Arlene; Bishop, Jennifer; Gross, Diane; Adlhoch, Cornelia; Penttinen, Pasi; Mølbak, KåreSince December 2016, excess all-cause mortality was observed in many European countries, especially among people aged ≥ 65 years. We estimated all-cause and influenza-attributable mortality in 19 European countries/regions. Excess mortality was primarily explained by circulation of influenza virus A(H3N2). Cold weather snaps contributed in some countries. The pattern was similar to the last major influenza A(H3N2) season in 2014/15 in Europe, although starting earlier in line with the early influenza season start.
- Excess all-cause mortality during the COVID-19 pandemic in Europe – preliminary pooled estimates from the EuroMOMO network, March to April 2020Publication . Vestergaard, Lasse S.; Nielsen, Jens; Richter, Lukas; Schmid, Daniela; Bustos, Natalia; Braeye, Toon; Denissov, Gleb; Veideman, Tatjana; Luomala, Oskari; Möttönen, Teemu; Fouillet, Anne; Caserio-Schönemann, Céline; an der Heiden, Matthias; Uphoff, Helmut; Lytras, Theodore; Gkolfinopoulou, Kassiani; Paldy, Anna; Domegan, Lisa; O'Donnell, Joan; de’ Donato, Francesca; Noccioli, Fiammetta; Hoffmann, Patrick; Velez, Telma; England, Kathleen; van Asten, Liselotte; White, Richard A.; Tønnessen, Ragnhild; Silva, Susana Pereira; Rodrigues, Ana Paula; Larrauri, Amparo; Delgado-Sanz, Concepción; Farah, Ahmed; Galanis, Ilias; Junker, Christoph; Perisa, Damir; Sinnathamby, Mary; Andrews, Nick; O'Doherty, Mark; Marquess, Diogo F.P.; Kennedy, Sharon; Olsen, Sonja J.; Pebody, Richard; Krause, Tyra G.; Mølbak, KåreA remarkable excess mortality has coincided with the COVID-19 pandemic in Europe. We present preliminary pooled estimates of all-cause mortality for 24 European countries/federal states participating in the European monitoring of excess mortality for public health action (EuroMOMO) network, for the period March–April 2020. Excess mortality particularly affected ≥ 65 year olds (91% of all excess deaths), but also 45–64 (8%) and 15–44 year olds (1%). No excess mortality was observed in 0–14 year olds.
- FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detectionPublication . Lytras, Theodore; Gkolfinopoulou, Kassiani; Bonovas, Stefanos; Nunes, BaltazarTimely detection of the seasonal influenza epidemic is important for public health action. We introduce FluHMM, a simple but flexible Bayesian algorithm to detect and monitor the seasonal epidemic on sentinel surveillance data. No comparable historical data are required for its use. FluHMM segments a typical influenza surveillance season into five distinct phases with clear interpretation (pre-epidemic, epidemic growth, epidemic plateau, epidemic decline and post-epidemic) and provides the posterior probability of being at each phase for every week in the period under surveillance, given the available data. An alert can be raised when the probability that the epidemic has started exceeds a given threshold. An accompanying R package facilitates the application of this method in public health practice. We apply FluHMM on 12 seasons of sentinel surveillance data from Greece, and show that it achieves very good sensitivity, timeliness and perfect specificity, thereby demonstrating its usefulness. We further discuss advantages and limitations of the method, providing suggestions on how to apply it and highlighting potential future extensions such as with integrating multiple surveillance data streams.