دورية أكاديمية
أعرض في EDS

Predictive modelling of Ross River virus using climate data in the Darling Downs.

التفاصيل البيبلوغرافية
العنوان: Predictive modelling of Ross River virus using climate data in the Darling Downs.
المؤلفون: Meadows J; School of Geography, Earth and Atmospheric Sciences, Faculty of Science, The University of Melbourne, 221 Bouverie St, Carlton, VIC 3053, Australia., McMichael C; School of Geography, Earth and Atmospheric Sciences, Faculty of Science, The University of Melbourne, 221 Bouverie St, Carlton, VIC 3053, Australia., Campbell PT; Department of Infectious Diseases, Melbourne Medical School, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia.; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia.
المصدر: Epidemiology and infection [Epidemiol Infect] 2023 Mar 14; Vol. 151, pp. e55. Date of Electronic Publication: 2023 Mar 14.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Cambridge University Press Country of Publication: England NLM ID: 8703737 Publication Model: Electronic Cited Medium: Internet ISSN: 1469-4409 (Electronic) Linking ISSN: 09502688 NLM ISO Abbreviation: Epidemiol Infect Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Cambridge Eng : Cambridge University Press
مواضيع طبية MeSH: Ross River virus* , Alphavirus Infections*/epidemiology, Animals ; Humans ; Mosquito Vectors ; Climate ; Australia/epidemiology
مستخلص: Ross River virus (RRV) is the most common mosquito-borne infection in Australia. RRV disease is characterised by joint pain and lethargy, placing a substantial burden on individual patients, the healthcare system and economy. This burden is compounded by a lack of effective treatment or vaccine for the disease. The complex RRV disease ecology cycle includes a number of reservoirs and vectors that inhabit a range of environments and climates across Australia. Climate is known to influence humans, animals and the environment and has previously been shown to be useful to RRV prediction models. We developed a negative binomial regression model to predict monthly RRV case numbers and outbreaks in the Darling Downs region of Queensland, Australia. Human RRV notifications and climate data for the period July 2001 - June 2014 were used for model training. Model predictions were tested using data for July 2014 - June 2019. The final model was moderately effective at predicting RRV case numbers (Pearson's r = 0.427) and RRV outbreaks (accuracy = 65%, sensitivity = 59%, specificity = 73%). Our findings show that readily available climate data can provide timely prediction of RRV outbreaks.
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فهرسة مساهمة: Keywords: Arboviruses; Ross River virus; climate; mosquito-borne disease; negative binomial regression
تواريخ الأحداث: Date Created: 20230314 Date Completed: 20230413 Latest Revision: 20230427
رمز التحديث: 20240513
مُعرف محوري في PubMed: PMC10126892
DOI: 10.1017/S0950268823000365
PMID: 36915217
قاعدة البيانات: MEDLINE
الوصف
تدمد:1469-4409
DOI:10.1017/S0950268823000365