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A maximum curvature method for estimating epidemic onset of seasonal influenza in Japan.
Cai, Jun; Zhang, Bing; Xu, Bo; Chan, Karen Kie Yan; Chowell, Gerardo; Tian, Huaiyu; Xu, Bing.
Affiliation
  • Cai J; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Zhang B; Joint Center for Global Change Studies, Beijing, 100875, China.
  • Xu B; School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, 518107, China.
  • Chan KKY; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Chowell G; Joint Center for Global Change Studies, Beijing, 100875, China.
  • Tian H; Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Xu B; Joint Center for Global Change Studies, Beijing, 100875, China.
BMC Infect Dis ; 19(1): 181, 2019 Feb 20.
Article in En | MEDLINE | ID: mdl-30786869
BACKGROUND: Detecting the onset of influenza epidemic is important for epidemiological surveillance and for investigating the factors driving spatiotemporal transmission patterns. Most approaches define the epidemic onset based on thresholds, which use subjective criteria and are specific to individual surveillance systems. METHODS: We applied the empirical threshold method (ETM), together with two non-thresholding methods, including the maximum curvature method (MCM) that we proposed and the segmented regression method (SRM), to determine onsets of influenza epidemics in each prefecture of Japan, using sentinel surveillance data of influenza-like illness (ILI) from 2012/2013 through 2017/2018. Performance of the MCM and SRM was evaluated, in terms of epidemic onset, end, and duration, with those derived from the ETM using the nationwide epidemic onset indicator of 1.0 ILI case per sentinel per week. RESULTS: The MCM and SRM yielded complete estimates for each of Japan's 47 prefectures. In contrast, ETM estimates for Kagoshima during 2012/2013 and for Okinawa during all six influenza seasons, except 2013/2014, were invalid. The MCM showed better agreement in all estimates with the ETM than the SRM (R2 = 0.82, p < 0.001 vs. R2 = 0.34, p < 0.001 for epidemic onset; R2 = 0.18, p < 0.001 vs. R2 = 0.05, p < 0.001 for epidemic end; R2 = 0.28, p < 0.001 vs. R2 < 0.01, p = 0.35 for epidemic duration). Prefecture-specific thresholds for epidemic onset and end were established using the MCM. CONCLUSIONS: The Japanese national epidemic onset threshold is not applicable to all prefectures, particularly Okinawa. The MCM could be used to establish prefecture-specific epidemic thresholds that faithfully characterize influenza activity, serving as useful complements to the influenza surveillance system in Japan.
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Full text: 1 Database: MEDLINE Main subject: Seasons / Models, Statistical / Influenza, Human Type of study: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Country/Region as subject: Asia Language: En Journal: BMC Infect Dis Journal subject: DOENCAS TRANSMISSIVEIS Year: 2019 Type: Article Affiliation country: China

Full text: 1 Database: MEDLINE Main subject: Seasons / Models, Statistical / Influenza, Human Type of study: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Country/Region as subject: Asia Language: En Journal: BMC Infect Dis Journal subject: DOENCAS TRANSMISSIVEIS Year: 2019 Type: Article Affiliation country: China