Improving prediction of future Mycoplasma pneumoniae epidemics using data-driven transmission models.

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Bibliographic Details
Title: Improving prediction of future Mycoplasma pneumoniae epidemics using data-driven transmission models.
Authors: Zhang XS; Modelling Division, Analysis and Intelligence Assessment Chief Data Officer Group, UK Health Security Agency, London, UK., Sidorov S; Division of Infectious Diseases and Hospital Epidemiology, Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich 8008, Switzerland., Dalrymple U; Modelling Division, Analysis and Intelligence Assessment Chief Data Officer Group, UK Health Security Agency, London, UK., Emborg HD; Department of Infectious Disease Epidemiology and Prevention, Statens Serum Institut, Copenhagen, Denmark., Uldum SA; Department of Bacteria, Parasites and Fungi, Statens Serum Institut, Copenhagen, Denmark., Beeton ML; Microbiology and Infection Research Group, Department of Biomedical Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom., Meyer Sauteur PM; Division of Infectious Diseases and Hospital Epidemiology, Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich 8008, Switzerland. Electronic address: patrick.meyersauteur@kispi.uzh.ch.
Corporate Authors: European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Study Group for Mycoplasma and Chlamydia Infections (ESGMAC) and the ESGMAC Mycoplasma pneumoniae Surveillance (MAPS) Study Group
Source: The Lancet. Microbe [Lancet Microbe] 2026 Jul; Vol. 7 (7), pp. 101429. Date of Electronic Publication: 2026 May 26.
Publication Type: Letter
Journal Info: Publisher: Elsevier Ltd Country of Publication: England NLM ID: 101769019 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2666-5247 (Electronic) Linking ISSN: 26665247 NLM ISO Abbreviation: Lancet Microbe Subsets: MEDLINE; In Process
Database: MEDLINE Ultimate
Description
ISSN:2666-5247
DOI:10.1016/j.lanmic.2026.101429