{"id":"c57d01ac-e98b-4b8c-90e7-0a0bdbde6e7e","authors":[{"author":{"id":"033b72dc-7936-4fcb-aff8-f09ed8996359","openalex_id":"pmc:author:aboutalib_fa","orcid":null,"display_name":"Aboutalib FA","works_count":1,"cited_by_count":0,"h_index":0,"last_institution":null,"country_code":null},"position":"middle"},{"author":{"id":"3542542f-d69a-4c3c-a2ea-ec1ec676f9c5","openalex_id":"pmc:author:kaka_bk","orcid":null,"display_name":"Kaka BK","works_count":1,"cited_by_count":0,"h_index":0,"last_institution":null,"country_code":null},"position":"middle"},{"author":{"id":"54654078-0366-4e43-9ba7-732f2de2329c","openalex_id":"pmc:author:jambou_r","orcid":null,"display_name":"Jambou R","works_count":2,"cited_by_count":0,"h_index":0,"last_institution":null,"country_code":null},"position":"last"},{"author":{"id":"57f7ab23-8859-49da-91b1-d121747c311f","openalex_id":"pmc:author:moumouni_k","orcid":null,"display_name":"Moumouni K","works_count":1,"cited_by_count":0,"h_index":0,"last_institution":null,"country_code":null},"position":"middle"},{"author":{"id":"5969cc8d-d9ab-4de4-8bb5-2008233c4942","openalex_id":"pmc:author:sidikou_ba","orcid":null,"display_name":"Sidikou BA","works_count":1,"cited_by_count":0,"h_index":0,"last_institution":null,"country_code":null},"position":"middle"},{"author":{"id":"9b861c35-b398-4bc2-adf7-eb5b38c17aee","openalex_id":"pmc:author:issaka_b","orcid":null,"display_name":"Issaka B","works_count":1,"cited_by_count":0,"h_index":0,"last_institution":null,"country_code":null},"position":"middle"},{"author":{"id":"a0ec101f-998c-44a8-a5a9-b00730259da0","openalex_id":"pmc:author:lagare_a","orcid":null,"display_name":"Lagare A","works_count":1,"cited_by_count":0,"h_index":0,"last_institution":null,"country_code":null},"position":"first"},{"author":{"id":"ce59f7ca-881d-48fe-b01f-ead378dc0ec5","openalex_id":"pmc:author:perthame_e","orcid":null,"display_name":"Perthame E","works_count":1,"cited_by_count":0,"h_index":0,"last_institution":null,"country_code":null},"position":"middle"},{"author":{"id":"cf690f17-b620-4458-958b-0d8ec22d77e1","openalex_id":"pmc:author:testa_j","orcid":null,"display_name":"Testa J","works_count":1,"cited_by_count":0,"h_index":0,"last_institution":null,"country_code":null},"position":"middle"},{"author":{"id":"ec73c108-8794-4526-85ae-253ecb003497","openalex_id":"pmc:author:lazoumar_rh","orcid":null,"display_name":"Lazoumar RH","works_count":1,"cited_by_count":0,"h_index":0,"last_institution":null,"country_code":null},"position":"middle"}],"concepts":[],"publisher_name":null,"publisher_website_url":null,"publisher_info":null,"bib_extra":[],"journal_info":{"id":"0e464aa9-c23c-4a0e-8cfd-8673072d12f3","slug":"plos-one-19326203","sjr_quartile":"Q1","sjr_score":0.803,"sjr_year":2025,"sjr_history":{"2024":{"score":0.803,"quartile":"Q1","categories":["Multidisciplinary (Q1)"]},"2025":{"score":0.726,"quartile":"Q1","categories":["Multidisciplinary (Q1)"]}},"jif":2.6,"jif_quartile":"Q2","jif_year":2025,"doaj_seal":false,"is_curated":false,"ulke":"US","url":"https://journals.plos.org/plosone/","h_index":596},"raw_data":{"abstract_tr":"Grip bulaşımı ile iklim arasındaki ilişki, özellikle salgınların meydana gelişi ve hastalık şiddeti üzerinde birçok halk sağlığı etkisi yaratmaktadır. Sıcaklık, rüzgar ve nem gibi çevresel faktörler, özellikle bu iklim değişikliği döneminde bulaşımı etkileyebilir. Bu çalışma, Nijer'de grip bulaşımı üzerindeki iklim faktörlerinin etkisini çözmek için istatistiksel modelleme kullanmayı amaçlamaktadır. Solunum Hastalıkları Referans Merkezi (CERMES), on iki yıl boyunca sekiz nöbetçi noktada akut solunum hastalığı olan hastalardan örnekler topladı. Her örnekte solunum virüsü tespit edilmesi moleküler yaklaşımlarla gerçekleştirildi. Meteorolojik parametreler haftalık olarak Niamey'deki Ulusal Meteoroloji İstasyonu'nda kaydedildi. İklim ve virolojik veriler yılların haftaları boyunca çizildi. Mevsimden bağımsız olarak homojen iklim koşullarına sahip haftalık kümeleri belirlemek için çok değişkenli bir yaklaşım kullanıldı. Tahmin değişkenlerinin etkisi, genel ekleme modelleme (GAM) kullanılarak belirlendi. Bu çalışma sırasında 9836 şüpheli grip vakası PCR testi yapıldı; bunların 982'si (%9,98) grip A veya B için pozitif olarak doğrulandı. 631 (%64,25) grip A/B pozitif vakaları düşük sıcaklık dönemlerinde (Aralık-Şubat) tespit edildi. Kümeleme analizi kullanılarak, grip için en elverişli koşulların kuru, soğuk ve rüzgarlı hava düzenleriyle birlikte gerçekleştiği altı farklı dönem tanımlanabilir. Ancak daha önemli olan, klinik vakaların tespitinden önceki haftalarda baskın olan durumlardır. Son GAM modeli, grip vakalarındaki değişkenliğin %77'sini oluşturuyor ve bu da salgının klinik tespitten haftalar önce dispanserlerde rüzgar ve minimum sıcaklık göstergeleri kullanılarak öngörülebileceğini gösteriyor. Kümeleme ve GAM modelleri, etkisini analiz etmek için verimli ve basit bir yaklaşım olarak değerlendirilebilir.","title_en":"Climatic factors driving influenza transmission in Sahelian area: A twelve-year retrospective study in Niger (2010-2021).","abstract_source":"crossref"},"openalex_id":"pmc:12061145","doi":"10.1371/journal.pone.0322288","title":"Climatic factors driving influenza transmission in Sahelian area: A twelve-year retrospective study in Niger (2010-2021).","publication_year":2025,"type":"article","cited_by_count":3,"is_open_access":true,"pdf_url":null,"abstract":"The relationship between influenza transmission and climate has many public health implications, particularly on the occurrence of epidemics and disease severity. Environmental factors such as temperature, wind and humidity can influence transmission, particularly in this time of climate change. This study aims to use statistical modelling to decipher the impact of climate factors on influenza transmission in Niger. The reference center of respiratory disease (CERMES) collected samples from patients with acute respiratory illness in eight sentinel sites over a period of twelve years. Detection of respiratory virus was conducted on each sample using molecular approaches. Meteorological parameters were recorded on a weekly basis at the National Meteorological Station in Niamey. Climatic and virological data were plotted over the weeks of the years. A multivariate approach was used to identify clusters of weeks with homogeneous climatic conditions, independent of the season. The impact of the predictor variables was determined using generalized additive modelling (GAM). During this study, 9836 suspected influenza cases were PCR tested, of which 982 (9.98%) were confirmed positive for either influenza A or B. 631 (64.25%) of the influenza A/B positive cases were detected during the low temperature periods (December to February). Using clustering analysis, six distinct periods can be identified, with the most favorable conditions for influenza occurring in conjunction with dry, cold and windy weather patterns. Of greater importance, however, are the conditions that predominate in the weeks preceding the detection of clinical cases. The final GAM model accounts for 77% of the variability in the occurrence of influenza cases, indicating that the epidemic can be anticipated weeks before clinical detection in dispensaries using wind and minimum temperature as indicators. Clustering and GAM models can be considered as an efficient and simple approach to analyze the impact of climatic conditions on the transmission of infectious diseases.","source_name":"PLoS One","source_issn":null,"volume":null,"issue":null,"first_page":null,"last_page":null,"language":"en","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12061145/","is_relevant":true,"thesis_level":null,"title_tr":"Sahel bölgesinde grip bulaşmasını tetikleyen iklim faktörleri: Nijer'de on iki yıllık retrospektif bir çalışma (2010-2021).","license_code":"CC-BY","license_url":null,"doi_status":"unknown","doi_last_checked":null,"merged_at":null,"lens_id":"064-821-875-036-859","patent_cited_by_count":null,"oa_colour":"Gold","created_at":"2026-05-13T14:57:21.225895+03:00","updated_at":"2026-05-15T12:21:03.568507+03:00","publisher":null,"merged_into":null}