The Role of Temperature and Humidity on Seasonal Influenza in Tropical Areas: Guatemala, El Salvador and Panama, 2008-2013

Background: The role of meteorological factors on influenza transmission in the tropics is less defined than in the temperate regions. We assessed the association between influenza activity and temperature, specific humidity and rainfall in 6 study areas that included 11 departments or provinces within 3 tropical Central American countries: Guatemala, El Salvador and Panama. Method/ Findings: Logistic regression was used to model the weekly proportion of laboratory-confirmed influenza positive samples during 2008 to 2013 (excluding pandemic year 2009). Meteorological data was obtained from the Tropical Rainfall Measuring Mission satellite and the Global Land Data Assimilation System. We found that specific humidity was positively associated with influenza activity in El Salvador (Odds Ratio (OR) and 95% Confidence Interval of 1.18 (1.07-1.31) and 1.32 (1.08-1.63)) and Panama (OR = 1.44 (1.08-1.93) and 1.97 (1.34-2.93)), but negatively associated with influenza activity in Guatemala (OR = 0.72 (0.6-0.86) and 0.79 (0.69-0.91)). Temperature was negatively associated with influenza in El Salvador's west-central departments (OR = 0.80 (0.7-0.91)) whilst rainfall was positively associated with influenza in Guatemala's central departments (OR = 1.05 (1.01-1.09)) and Panama province (OR = 1.10 (1.05-1.14)). In 4 out of the 6 locations, specific humidity had the highest contribution to the model as compared to temperature and rainfall. The model performed best in estimating 2013 influenza activity in Panama and west-central El Salvador departments (correlation coefficients: 0.5-0.9). Conclusions/Significance: The findings highlighted the association between influenza activity and specific humidity in these 3 tropical countries. Positive association with humidity was found in El Salvador and Panama. Negative association was found in the more subtropical Guatemala, similar to temperate regions. Of all the study locations, Guatemala had annual mean temperature and specific humidity that were lower than the others

Meteorological parameters, influenza positive proportion and regression output for the study areas.

<p>In the last row, black curves are the observed data; grey shades indicate the 95% confidence interval; red curves are modeled results; and blue curves are the prospectively estimated influenza activity using actual meteorological data and regression models trained with influenza data from previous years. OR is the odds ratio from the regression for the meteorological parameters, and CI is the associated 95% Confidence Interval.</p

Study areas.

<p>Departments or provinces included in the study. Adjacent departments in Guatemala and El Salvador were combined in the analysis: Western departments in Guatemala (1,2), Central departments in Guatemala (3,4) and West-central departments in El Salvador (5–8).</p

Multivariable analysis of meteorological factors associated with influenza positivity.

<p>Bold font indicates a statistically significant variable (<i>p-value</i><0.05). RMSE is the Root Mean Squared Error and Corr. Coeff is the correlation coefficient between the observation and estimated influenza positive proportion in 2013.</p><p>The models were adjusted for: potentially confounding variables (RSV, parainfluenza and adeno viruses), previous weeks' influenza positivity, seasonality and other possible nonlinear relationships (modeled as a polynomial function, up to degree of 3, of the week number).</p

Change in influenza positive proportion when the indicated meteorological variable was increased by 1 standard deviation (temperature 2.74°C, specific humidity 2.61 g/kg, rainfall 6.48 mm/day).

<p>Change in influenza positive proportion when the indicated meteorological variable was increased by 1 standard deviation (temperature 2.74°C, specific humidity 2.61 g/kg, rainfall 6.48 mm/day).</p

Percent change in model deviance.

<p>Change in deviance between the full model (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100659#pone-0100659-t002" target="_blank">Table 2</a>) and the model with the indicated meteorological variable removed.</p