Associations between seasonal influenza and meteorological parameters in Costa Rica, Honduras and Nicaragua.

Seasonal influenza affects a considerable proportion of the global population each year. We assessed the association between subnational influenza activity and temperature, specific humidity and rainfall in three Central America countries, i.e. Costa Rica, Honduras and Nicaragua. Using virologic data from each country's national influenza centre, rainfall from the Tropical Rainfall Measuring Mission and air temperature and specific humidity data from the Global Land Data Assimilation System, we applied logistic regression methods for each of the five sub-national locations studied. Influenza activity was represented by the weekly proportion of respiratory specimens that tested positive for influenza. The models were adjusted for the potentially confounding co-circulating respiratory viruses, seasonality and previous weeks' influenza activity. We found that influenza activity was proportionally associated (P<0.05) with specific humidity in all locations [odds ratio (OR) 1.21-1.56 per g/kg], while associations with temperature (OR 0.69-0.81 per °C) and rainfall (OR 1.01-1.06 per mm/day) were location-dependent. Among the meteorological parameters, specific humidity had the highest contribution (~3-15%) to the model in all but one location. As model validation, we estimated influenza activity for periods, in which the data was not used in training the models. The correlation coefficients between the estimates and the observed were ≤0.1 in 2 locations and between 0.6-0.86 in three others. In conclusion, our study revealed a proportional association between influenza activity and specific humidity in selected areas from the three Central America countries.

Associations between Meteorological Parameters and Influenza Activity in Berlin (Germany), Ljubljana (Slovenia), Castile and León (Spain) and Israeli Districts.

BACKGROUND: Studies in the literature have indicated that the timing of seasonal influenza epidemic varies across latitude, suggesting the involvement of meteorological and environmental conditions in the transmission of influenza. In this study, we investigated the link between meteorological parameters and influenza activity in 9 sub-national areas with temperate and subtropical climates: Berlin (Germany), Ljubljana (Slovenia), Castile and León (Spain) and all 6 districts in Israel. METHODS: We estimated weekly influenza-associated influenza-like-illness (ILI) or Acute Respiratory Infection (ARI) incidence to represent influenza activity using data from each country's sentinel surveillance during 2000-2011 (Spain) and 2006-2011 (all others). Meteorological data was obtained from ground stations, satellite and assimilated data. Two generalized additive models (GAM) were developed, with one using specific humidity as a covariate and another using minimum temperature. Precipitation and solar radiation were included as additional covariates in both models. The models were adjusted for previous weeks' influenza activity, and were trained separately for each study location. RESULTS: Influenza activity was inversely associated (p<0.05) with specific humidity in all locations. Minimum temperature was inversely associated with influenza in all 3 temperate locations, but not in all subtropical locations. Inverse associations between influenza and solar radiation were found in most locations. Associations with precipitation were location-dependent and inconclusive. We used the models to estimate influenza activity a week ahead for the 2010/2011 period which was not used in training the models. With exception of Ljubljana and Israel's Haifa District, the models could closely follow the observed data especially during the start and the end of epidemic period. In these locations, correlation coefficients between the observed and estimated ranged between 0.55 to 0.91and the model-estimated influenza peaks were within 3 weeks from the observations. CONCLUSION: Our study demonstrated the significant link between specific humidity and influenza activity across temperate and subtropical climates, and that inclusion of meteorological parameters in the surveillance system may further our understanding of influenza transmission patterns.

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.

Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters.

BACKGROUND: Influenza transmission is often associated with climatic factors. As the epidemic pattern varies geographically, the roles of climatic factors may not be unique. Previous in vivo studies revealed the direct effect of winter-like humidity on air-borne influenza transmission that dominates in regions with temperate climate, while influenza in the tropics is more effectively transmitted through direct contact. METHODOLOGY/PRINCIPAL FINDINGS: Using time series model, we analyzed the role of climatic factors on the epidemiology of influenza transmission in two regions characterized by warm climate: Hong Kong (China) and Maricopa County (Arizona, USA). These two regions have comparable temperature but distinctly different rainfall. Specifically we employed Autoregressive Integrated Moving Average (ARIMA) model along with climatic parameters as measured from ground stations and NASA satellites. Our studies showed that including the climatic variables as input series result in models with better performance than the univariate model where the influenza cases depend only on its past values and error signal. The best model for Hong Kong influenza was obtained when Land Surface Temperature (LST), rainfall and relative humidity were included as input series. Meanwhile for Maricopa County we found that including either maximum atmospheric pressure or mean air temperature gave the most improvement in the model performances. CONCLUSIONS/SIGNIFICANCE: Our results showed that including the environmental variables generally increases the prediction capability. Therefore, for countries without advanced influenza surveillance systems, environmental variables can be used for estimating influenza transmission at present and in the near future.