Open Access Research article

The influence of climatic conditions on the transmission dynamics of the 2009 A/H1N1 influenza pandemic in Chile

Gerardo Chowell12*, Sherry Towers13, Cécile Viboud2, Rodrigo Fuentes4, Viviana Sotomayor4, Lone Simonsen25, Mark A Miller2, Mauricio Lima6, Claudia Villarroel7, Monica Chiu4, Jose E Villarroel4 and Andrea Olea4

Author Affiliations

1 Mathematical, Computational & Modeling Sciences Center, School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA

2 Division of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA

3 Department of Mathematics, Purdue University, West Lafayette, IN, USA

4 Department of Epidemiology, Ministerio de Salud, Santiago, Chile

5 Department of Global Health, School of Public Health and Health Services, George Washington University, Washington DC, USA

6 Center for Advanced Studies in Ecology and Biodiversity, Pontificia Universidad Católica de Chile, Santiago, Chile

7 Applied Meteorology, Dirección Meteorológica de Chile, Santiago, Chile

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BMC Infectious Diseases 2012, 12:298  doi:10.1186/1471-2334-12-298

Published: 13 November 2012



The role of demographic factors, climatic conditions, school cycles, and connectivity patterns in shaping the spatio-temporal dynamics of pandemic influenza is not clearly understood. Here we analyzed the spatial, age and temporal evolution of the 2009 A/H1N1 influenza pandemic in Chile, a southern hemisphere country covering a long and narrow strip comprising latitudes 17°S to 56°S.


We analyzed the dissemination patterns of the 2009 A/H1N1 pandemic across 15 regions of Chile based on daily hospitalizations for severe acute respiratory disease and laboratory confirmed A/H1N1 influenza infection from 01-May to 31-December, 2009. We explored the association between timing of pandemic onset and peak pandemic activity and several geographical and demographic indicators, school vacations, climatic factors, and international passengers. We also estimated the reproduction number (R) based on the growth rate of the exponential pandemic phase by date of symptoms onset, estimated using maximum likelihood methods.


While earlier pandemic onset was associated with larger population size, there was no association with connectivity, demographic, school or climatic factors. In contrast, there was a latitudinal gradient in peak pandemic timing, representing a 16-39-day lag in disease activity from the southern regions relative to the northernmost region (P < 0.001). Geographical differences in latitude of Chilean regions, maximum temperature and specific humidity explained 68.5% of the variability in peak timing (P = 0.01). In addition, there was a decreasing gradient in reproduction number from south to north Chile (P < 0.0001). The regional mean R estimates were 1.6-2.0, 1.3-1.5, and 1.2-1.3 for southern, central and northern regions, respectively, which were not affected by the winter vacation period.


There was a lag in the period of most intense 2009 pandemic influenza activity following a South to North traveling pattern across regions of Chile, significantly associated with geographical differences in minimum temperature and specific humidity. The latitudinal gradient in timing of pandemic activity was accompanied by a gradient in reproduction number (P < 0.0001). Intensified surveillance strategies in colder and drier southern regions could lead to earlier detection of pandemic influenza viruses and improved control outcomes.

A/H1N1 influenza pandemic; Acute respiratory infection; Influenza-like-illness; Reproduction number; Spatial heterogeneity; School cycles; Climatological variables, Specific humidity; Temperature; Chile