title: Spatial and temporal characteristics of the 2009 A/H1N1 influenza pandemic in Peru.
abstract: BACKGROUND
  Highly refined surveillance data on the 2009 A/H1N1 influenza pandemic are crucial to quantify the spatial and temporal characteristics of the pandemic. There is little information about the spatial-temporal dynamics of pandemic influenza in South America. Here we provide a quantitative description of the age-specific morbidity pandemic patterns across administrative areas of Peru.
  
  
  METHODS
  We used daily cases of influenza-like-illness, tests for A/H1N1 influenza virus infections, and laboratory-confirmed A/H1N1 influenza cases reported to the epidemiological surveillance system of Peru's Ministry of Health from May 1 to December 31, 2009. We analyzed the geographic spread of the pandemic waves and their association with the winter school vacation period, demographic factors, and absolute humidity. We also estimated the reproduction number and quantified the association between the winter school vacation period and the age distribution of cases.
  
  
  RESULTS
  The national pandemic curve revealed a bimodal winter pandemic wave, with the first peak limited to school age children in the Lima metropolitan area, and the second peak more geographically widespread. The reproduction number was estimated at 1.6-2.2 for the Lima metropolitan area and 1.3-1.5 in the rest of Peru. We found a significant association between the timing of the school vacation period and changes in the age distribution of cases, while earlier pandemic onset was correlated with large population size. By contrast there was no association between pandemic dynamics and absolute humidity.
  
  
  CONCLUSIONS
  Our results indicate substantial spatial variation in pandemic patterns across Peru, with two pandemic waves of varying timing and impact by age and region. Moreover, the Peru data suggest a hierarchical transmission pattern of pandemic influenza A/H1N1 driven by large population centers. The higher reproduction number of the first pandemic wave could be explained by high contact rates among school-age children, the age group most affected during this early wave.
authors.affiliation: Mathematical, Computational & Modeling Sciences Center, School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, United States of America. gchowell@asu.edu
authors.forename: Gerardo
authors.initials: G
authors.lastname: Chowell
authors.forename: Cécile
authors.initials: C
authors.lastname: Viboud
authors.forename: Cesar V
authors.initials: CV
authors.lastname: Munayco
authors.forename: Jorge
authors.initials: J
authors.lastname: Gómez
authors.forename: Lone
authors.initials: L
authors.lastname: Simonsen
authors.forename: Mark A
authors.initials: MA
authors.lastname: Miller
authors.forename: James
authors.initials: J
authors.lastname: Tamerius
authors.forename: Victor
authors.initials: V
authors.lastname: Fiestas
authors.forename: Eric S
authors.initials: ES
authors.lastname: Halsey
authors.forename: Victor A
authors.initials: VA
authors.lastname: Laguna-Torres
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D053118:Influenza A Virus, H1N1 Subtype / Q000235:genetics / Q000472:pathogenicity*; D007251:Influenza, Human / Q000453:epidemiology* / Q000821:virology*; D008875:Middle Aged; D058873:Pandemics; D010568:Peru / Q000453:epidemiology; D012574:Schools; D012621:Seasons; D055815:Young Adult