{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T23:50:53Z","timestamp":1788306653393,"version":"build-2803163510"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T00:00:00Z","timestamp":1578009600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T00:00:00Z","timestamp":1578009600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Infect Dis Poverty"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Background<\/jats:title>\n<jats:p>Understanding the global spatiotemporal pattern of seasonal influenza is essential for influenza control and prevention. Available data on the updated global spatiotemporal pattern of seasonal influenza are scarce. This study aimed to assess the spatiotemporal pattern of seasonal influenza after the 2009 influenza pandemic.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>Weekly influenza surveillance data in 86 countries from 2010 to 2017 were obtained from FluNet. First, the proportion of influenza A in total influenza viruses (P<jats:sub>A<\/jats:sub>) was calculated. Second, weekly numbers of influenza positive virus (A and B) were divided by the total number of samples processed to get weekly positive rates of influenza A (RW<jats:sub>A<\/jats:sub>) and influenza B (RW<jats:sub>B<\/jats:sub>). Third, the average positive rates of influenza A (R<jats:sub>A<\/jats:sub>) and influenza B (R<jats:sub>B<\/jats:sub>) for each country were calculated by averaging RW<jats:sub>A<\/jats:sub>, and RW<jats:sub>B<\/jats:sub> of 52\u2009weeks. A Kruskal-Wallis test was conducted to examine if the year-to-year change in P<jats:sub>A<\/jats:sub> in all countries were significant, and a universal kriging method with linear semivariogram model was used to extrapolate R<jats:sub>A<\/jats:sub> and R<jats:sub>B<\/jats:sub> in all countries.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>P<jats:sub>A<\/jats:sub> ranged from 0.43 in Zambia to 0.98 in Belarus, and P<jats:sub>A<\/jats:sub> in countries with higher income was greater than those countries with lower income. The spatial patterns of high R<jats:sub>B<\/jats:sub> were the highest in sub-Saharan Africa, Asia-Pacific region and South America. RW<jats:sub>A<\/jats:sub> peaked in early weeks in temperate countries, and the peak of RW<jats:sub>B<\/jats:sub> occurred a bit later. There were some temperate countries with non-distinct influenza seasonality (e.g., Mauritius and Maldives) and some tropical\/subtropical countries with distinct influenza seasonality (e.g., Chile and South Africa).<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Influenza seasonality is not predictable in some temperate countries, and it is distinct in Chile, Argentina and South Africa, implying that the optimal timing for influenza vaccination needs to be chosen with caution in these unpredictable countries.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s40249-019-0618-5","type":"journal-article","created":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T15:03:16Z","timestamp":1578063796000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Global dynamic spatiotemporal pattern of seasonal influenza since 2009 influenza pandemic"],"prefix":"10.1186","volume":"9","author":[{"given":"Zhi-Wei","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong-Jie","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Biao","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,1,3]]},"reference":[{"issue":"10127","key":"618_CR1","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1016\/S0140-6736(17)33293-2","volume":"391","author":"AD Iuliano","year":"2017","unstructured":"Iuliano AD, Roguski KM, Chang HH, Muscatello DJ, Palekar R, Tempia S, et al. 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