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Environmental Predictors of Seasonal Influenza Epidemics across Temperate and Tropical Climates
James D. Tamerius ,
* E-mail: jt2684@columbia.edu
Affiliations Environmental Health Sciences, Columbia University, New York, New York, United States of America, Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
Jeffrey Shaman,
Wladmir J. Alonso,
Affiliation Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
Kimberly Bloom-Feshbach,
Affiliation Mount Sinai School of Medicine, New York, New York, United States of America
Christopher K. Uejio,
Affiliation Department of Geography, Florida State University, Tallahassee, Florida, United States of America
Andrew Comrie,
Affiliation School of Geography and Development, University of Arizona, Tucson, Arizona, United States of America
James D. Tamerius,
https://doi.org/10.1371/journal.ppat.1003194
James D. Tamerius Jeffrey Shaman ... Cécile Viboud
21 Nov 2013: Tamerius JD, Shaman J, Alonso WJ, Bloom-Feshbach K, Uejio CK, et al. (2013) Correction: Environmental Predictors of Seasonal Influenza Epidemics across Temperate and Tropical Climates. PLOS Pathogens 9(11): 10.1371/annotation/df689228-603f-4a40-bfbf-a38b13f88147. https://doi.org/10.1371/annotation/df689228-603f-4a40-bfbf-a38b13f88147 View correction
Human influenza infections exhibit a strong seasonal cycle in temperate regions. Recent laboratory and epidemiological evidence suggests that low specific humidity conditions facilitate the airborne survival and transmission of the influenza virus in temperate regions, resulting in annual winter epidemics. However, this relationship is unlikely to account for the epidemiology of influenza in tropical and subtropical regions where epidemics often occur during the rainy season or transmit year-round without a well-defined season. We assessed the role of specific humidity and other local climatic variables on influenza virus seasonality by modeling epidemiological and climatic information from 78 study sites sampled globally. We substantiated that there are two types of environmental conditions associated with seasonal influenza epidemics: "cold-dry" and "humid-rainy". For sites where monthly average specific humidity or temperature decreases below thresholds of approximately 11–12 g/kg and 18–21°C during the year, influenza activity peaks during the cold-dry season (i.e., winter) when specific humidity and temperature are at minimal levels. For sites where specific humidity and temperature do not decrease below these thresholds, seasonal influenza activity is more likely to peak in months when average precipitation totals are maximal and greater than 150 mm per month. These findings provide a simple climate-based model rooted in empirical data that accounts for the diversity of seasonal influenza patterns observed across temperate, subtropical and tropical climates.
Human influenza infections have a pronounced seasonal cycle in temperate regions. Recent laboratory and epidemiological evidence suggests that low humidity conditions in the winter may increase virus survival and enable the virus to transmit efficiently between hosts. However, seasonal influenza activity in some tropical locations occurs during the rainy season, whereas other tropical locations do not experience a well-defined influenza season. The primary goal of this study was to identify the relationship between the timing of seasonal influenza epidemics and climate variability across the globe. We show the importance of thresholds in humidity, temperature and precipitation that affect the epidemiology, and potentially the transmission route, of influenza. A better understanding of the environmental, demographic and behavioral drivers of influenza seasonality is important for optimizing intervention strategies, especially in low and middle-latitude regions.
Citation: Tamerius JD, Shaman J, Alonso WJ, Bloom-Feshbach K, Uejio CK, Comrie A, et al. (2013) Environmental Predictors of Seasonal Influenza Epidemics across Temperate and Tropical Climates. PLoS Pathog 9(3): e1003194. https://doi.org/10.1371/journal.ppat.1003194
Editor: Steven Riley, Imperial College London, United Kingdom
Received: August 28, 2012; Accepted: December 26, 2012; Published: March 7, 2013
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: JDT acknowledges support from the Earth Institute Postdoctoral Fellowship program at Columbia University. Support was also provided by the in-house Influenza Research Program of the Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, which is funded by the International Influenza Unit, Office of Global Affairs, Department of Health and Human Services, the Research and Policy for Infectious Disease Dynamics (RAPIDD) program of the Science and Technology Directorate, Department of Homeland Security, and the National Institutes of Health Models of Infectious Disease Agent Study program (MIDAS) through cooperative agreement 1U54GM088558. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Influenza exerts a significant health burden on human populations across temperate, subtropical and tropical regions [1]. The striking seasonal pattern that characterizes influenza in temperate populations has long suggested a causal link between seasonal fluctuations in climatic and social factors and influenza transmission [2]–[4]. Temperate regions of the northern and southern hemispheres are characterized by highly synchronized annual influenza epidemics during their respective winter months [5], [6]. In contrast, influenza seasonal characteristics are more diverse in tropical and subtropical regions; some sites experience annual epidemics coinciding with the local rainy season [7]–[11], whereas others are characterized by semi-annual epidemics or year-round influenza activity without well-defined influenza seasons [7], [12], [13].
Recent epidemiological studies indicate that low levels of specific humidity are associated with the onset of pandemic and epidemic influenza in the US [14], [15], consistent with laboratory experiments and animal models suggesting that low specific humidity favors virus survival and aerosol transmission [16]–[17]. There are several alternative explanations for the winter seasonal transmission of influenza in temperate regions, including the inhibition of host immune function due to decreased exposure to solar radiation [18], [19], and the inhibition of mucociliary clearance by the inhalation of cold-dry air [20]. Person-to-person contact rates may also strengthen in the winter due to increased indoor crowding, modulated by school terms [21]. There are few biological explanations for the association between precipitations and influenza activity reported in some tropical and subtropical regions, although rainy conditions may also favor indoor crowding [4].
Although it is common for epidemiological studies to examine relationships between seasonal influenza activity and climatic factors for individual sites, few studies have assessed the consistency of these relationships across a broad range of temperate, subtropical and tropical sites. A recent study evidenced a link between influenza and temperature based on aggregate country-level data, but did not characterize the geographical and climatic boundaries that define regions experiencing different influenza seasonality patterns [22]. Here we investigate both relative and absolute associations between climatic factors and the timing of seasonal influenza epidemics for 78 individual sites sampled globally [23]. We develop models that predict the month of peak influenza activity for each study site as a function of climatic variables and identify climatic thresholds accounting for the diversity of influenza seasonality patterns observed globally.
Influenza epidemiological data.
We used a recently developed global database that provides information on the month of maximum influenza activity ("influenza peaks") for 78 sites worldwide, of which 39% were located in the tropics. A detailed description of the database is provided in the Supplement and [23], and is briefly summarized below.
The data were compiled based on a systematic literature review of published influenza and respiratory virus surveillance studies, reporting weekly or monthly laboratory-confirmed influenza cases for a period of 12 consecutive months or more, augmented with electronic data from regional or national influenza surveillance schemes. Studies focused on the 2009 A/H1N1 pandemic virus were excluded to restrict the analysis to seasonal patterns of inter-pandemic influenza.
We identified 85 studies matching our inclusion criteria, encompassing 78 sites in 40 countries sampled during the period 1975–2008, with median study duration of 2 years (Figure 1). A majority of study sites (76%) represented a specific city rather than a state, province, or region. The 24% of study sites representing regional level data encompassed areas that were relatively small and homogeneous with respect to climate, including countries such as Italy and the Republic of Korea; states within large countries such as Michigan, USA or Victoria, Australia; climatologically-homogeneous regions in Peru; provinces within Thailand; multiple cities in northern Argentina or in Taiwan; and a subtropical island in Japan.
Figure 1. Map of 78 study sites included in this study.
The site symbols indicate whether a location has annual or semi-annual influenza activity, and symbol size is proportional to the duration of the epidemiological studies used to determine the month of peak activity for each location.
https://doi.org/10.1371/journal.ppat.1003194.g001
For sites with multiple years of data, the peak influenza month of each year was determined and the average month of peak influenza was calculated. Because some sites were characterized by semi-annual influenza activity, influenza peaks separated by four months or greater were considered distinct influenza seasons (see Supplement for more information). Indeed, 17 sites (22% of the dataset) concentrated in East and South-East Asia, and equatorial regions of Central and South America, were characterized by two distinct influenza peaks within the year (Figure 1). Of these 17 sites with semi-annual influenza peaks, 15 had a primary peak (present in all study years) and a secondary peak (present in a subset of years), while primary and secondary influenza peaks were not distinguishable in two sites. We report results from analyses performed on the superset of sites consisting of all primary and secondary influenza peaks (n = 96), as well as the subset restricted to primary influenza peaks (n = 76).
In addition to the epidemiological influenza database gleaned from the literature and encompassing 78 sites, we collected laboratory-confirmed influenza epidemiological data for 9 countries from FluNet, the WHO global influenza surveillance effort [24], to ensure proper model validation with an independent disease dataset. The group of 9 countries (Spain, Tunisia, Senegal, Philippines, Vietnam, Colombia, Paraguay, South Africa, and Argentina) was selected because they were latitudinally diverse, with a heavy focus on subtropical and tropical regions, each country was relatively small geographically, and provided several years of data. We also favored countries that were not represented in the original 78-location database.
Climate data.
For the 78 influenza sites, we compiled average monthly temperature (°C), relative humidity (%) and precipitation (mm) data from the CRU/Oxford/IWMI 10′ latitude/longitude gridded dataset (CRU CL 2.0) [25]. This dataset was selected because of its global coverage, high spatial resolution, and monthly temporal resolution (equal to the temporal resolution of the epidemiological dataset). We then calculated the average monthly specific humidity (g/kg)—a measure of absolute humidity—from relative humidity, temperature and surface pressure (estimated from elevation) [14].
Because the CRU climate dataset consists of monthly averages for the arbitrary multiyear period of 1960–1991 and does not necessarily represent the local monthly conditions in the years sampled for influenza activity, we also considered a more recent meteorological dataset from the NCEP/NCAR Global Reanalysis (GR) project [26]. Unlike the CRU dataset, the GR dataset provides comprehensive time series data for 1948-present, which enables calculation of average monthly meteorological conditions for the appropriate time periods at all sites. The major drawback of the GR dataset is its coarser spatial scale (2°×2° latitude/longitude), which can obscure local variability in weather and climate.
To evaluate potential biases associated with a mismatched time period (CRU dataset) and a coarse spatial resolution (GR dataset), we performed a comparative analysis of both datasets (Figure S1 in Text S1). We established that the root-mean-square (RMS) error introduced by the mismatched temporal period was 3–5 times smaller than the RMS error introduced by a coarse spatial resolution. Thus, herein we report the results of analyses based on temperature, specific humidity, relative humidity and precipitation data from the more spatially-resolved CRU dataset. We also use the solar radiation variable from the GR dataset as the CRU dataset does not have appropriate solar radiation information (Supplement). Finally, we obtained climate data from the CRU and GR datasets for the most populous city in each of the 9 countries selected from FluNet for additional model validation.
Statistical Analyses of Influenza and Climate
Exploratory rank analyses.
We conducted exploratory analyses based on a non-parametric rank order approach to assess the relative association between influenza peaks and seasonal climate variation. Specifically, we ranked the monthly values of temperature, solar radiation, specific humidity, and precipitation for each site in ascending order. We calculated the mean of the ranks corresponding to the peak influenza month(s) for each site. Assuming that there is no relative relationship between climatic factors and timing of influenza peaks, we would expect mean ranks of 6.5 ("null value"). We also performed the rank order analysis across latitude by employing a window spanning 10° of latitude and sliding it across 5–50°N/S latitude at 2.5° intervals. We calculated the mean rank for each climate variable corresponding to influenza peaks for all the sites within each interval. To test for significance we generated a null distribution (p = 0.05) by bootstrapping randomly generated distributions for each latitudinal interval. We evaluated lag relationships of up to 4 months between climate factors and influenza peaks.
Univariate and multivariate regression.
To assess the absolute relationship between influenza seasonality and climate, we developed univariate and multivariate logistic regression models. Because exploratory analyses revealed a bimodal relationship between influenza peaks and some of the environmental predictors we employed second-degree polynomial functions for climate predictors. We also added interaction terms describing the deviation of each monthly predictor from its annual average. The dependent variable was a vector of months indicating the presence or absence of an influenza peak for each site and month.
As a sensitivity analysis, we considered mixed effects logistic models with independent intercepts (exchangeable covariance structure) to control for the repeated measurements in each site and different disease periodicities (i.e., annual versus semi-annual influenza activity). We found this did not have a significant effect on the modeled relationships and therefore report the results from classical logistic regression for simplicity.
We used the "all possible subsets" multivariate regression approach and retained models in which all predictors were significant (p = 0.05). In conjunction with a jackknife leave-one-out method we assessed model fit with a "peak prediction metric" that was defined by calculating the difference between the observed month of peak influenza activity versus the month with the highest predicted probability of a peak for each site. The cumulative proportion of peaks predicted within +/−1 month of the observed peaks were compared to the upper 97.5%, 99% and 99.9% thresholds of the cumulative proportion of influenza peaks randomly distributed within +/−1 months of the observed peaks (10,000 runs). In addition to using the peak prediction metric to quantify model performances across all sites, we compared model performances between sites in high latitudes (poleward of 25°N/S), middle latitudes (12.5–25°N/S) and low latitudes (equatorward of 12.5°N/S). We evaluated lag relationships of up to 4 months between the predictors and influenza peaks.
For additional model validation, we confronted the predictions of the selected multivariate climate model against the seasonal distribution of influenza viruses in independent sites selected from the FluNet database.
Defining influenza geographical and climatic boundaries.
The rank order and logistic regression analyses indicated that the relationship between seasonal climate and influenza peaks was not consistent globally. Low temperatures, solar radiation and specific humidity corresponded to epidemics in high latitudes, whereas high levels of precipitation, specific humidity and relative humidity corresponded to epidemics in low latitudes. To try to synthesize these results, we explored whether the seasonal range of climatic factors in a site was predictive of the environmental conditions during the local influenza season. Specifically, we constructed a binary dependent variable by classifying each influenza peak as either 'cold-dry' or 'humid-rainy' based on whether the influenza peak corresponded to a month with a specific humidity rank less than or equal to 6 or greater than or equal to 7, respectively. Specific humidity was chosen to classify the peaks because the rank-relationship between influenza peaks in the low latitudes was the opposite of the relationship observed in high latitudes, and because specific humidity was significantly correlated with other relevant climate predictors, including temperature (Pearson rho = 0.87, p<0.0001) and precipitation (Pearson rho = 0.62, p<0.0001). We then used the site-specific annual minimum and maximum of each environmental predictor to generate a conditional probability function through logistic regression. We defined the value at which the function equaled 0.50 to be the threshold between cold-dry and humid-rainy influenza locations. We used a jackknife leave-one-out method to assess the accuracy of the logistic model in predicting the climate conditions corresponding to each influenza peak. Finally, for additional model validation, we confronted the predicted probability function against observed influenza activity patterns in the independent study sites selected from FluNet.
Influenza Peaks and Climate: Rank Analysis
Across the 78 sites, influenza peaks generally coincided with months of lower temperature, lower solar radiation and lower specific humidity than expected under the null hypothesis (mean rank = 4.3 [95% CI: 3.7, 5.0] for temperature, 4.5 [95% CI: 3.9, 5.1] for solar radiation and 4.8 [95% CI: 4.0, 5.6] for specific humidity). In contrast, relative humidity and precipitation did not significantly deviate from the null value (mean rank not significantly different than 6.5). The association between influenza peaks, temperature, solar radiation, relative humidity and specific humidity was most significant when the influenza peaks lagged 1-month behind the environmental predictors. We obtained similar results when the analysis was restricted to primary influenza peaks.
A similar analysis performed with a sliding geographical window revealed that the association between influenza peaks and climatic variables varied with latitude (Figure 2). The strongest association was found at high latitudes poleward of 25°N/S, with influenza peaks preferentially occurring in months with the lowest temperature, solar radiation and specific humidity. Influenza peaks occurred in the months with the highest levels of relative humidity and lowest levels of precipitation poleward of approximately 40°N/S. Primary influenza peaks equatorward of 10°N/S corresponded to the months with the highest annual levels of specific humidity and precipitation (p<0.05); whereas there was no association with temperature, solar radiation and relative humidity. In middle latitudes ranging between 12.5–25°N/S, there was no significant association between influenza peaks and climatic variables.
Figure 2. Influenza peaks and climate by latitude.
The mean monthly rank of each climate variable corresponding to the month of peak influenza for each 10° latitudinal band. Solar radiation, temperature and specific humidity are lagged by 1 month. The background interval corresponds to the 95% null distribution. (A) displays the results for both primary and secondary influenza peaks; whereas (B) shows the results for primary influenza peaks only. Influenza peaks corresponded to months characterized by low ranks of temperature, solar radiation, and specific humidity in high latitudes. Primary influenza peaks corresponded to months with high ranks of humidity (both relative and specific) and precipitation in low latitudes.
Univariate and Multivariate Models for Influenza Peaks
Temperature and specific humidity were the best individual predictors of influenza peaks. The model fits improved slightly when influenza peaks lagged 1-month behind these predictors, and accurately predicted 56–66% of the peaks in the global datasets, with highest accuracy at latitudes poleward of 25°N/S (Tables 1 and 2). The modeled relationship between specific humidity and all influenza peaks was U-shaped, with lowest probability of an influenza peak at 12 g/kg of specific humidity and increasing probabilities at lower and higher values (Figure 3). The analysis restricted to primary peaks revealed a similar relationship, with a minimum influenza probability at 11 g/kg. Unlike specific humidity the modeled relationship between temperature and influenza peaks was monotonic, with the greatest probability of a peak corresponding to low temperatures. Although the specific humidity and temperature models were the best predictors of the timing of influenza peaks across all sites, they were not significant predictors of influenza peaks equatorward of 25°N/S.
Figure 3. Influenza peaks, specific humidity and precipitation.
(A) Estimated U-shaped relationship between the likelihood of an influenza peak and average monthly specific humidity across all sites, based on logistic regression (Table 1). The left side of the curve is strongly correlated with the relationship between specific humidity and influenza survival and transmission observed in laboratory studies [17], [32], [33]. However, the mechanism that causes the pattern on the right side of the curve is not readily explained. (B) The relationship between average monthly specific humidity and precipitation across all sites. Influenza peaks clustered in months associated with low specific humidity and high precipitation conditions. This suggests that precipitation may explain the occurrence of humid-rainy influenza peaks and may be responsible for the right hand side of the U-shaped curve between specific humidity and influenza (A).
Table 1. Environmental models for all influenza peaks, by latitudinal interval.
https://doi.org/10.1371/journal.ppat.1003194.t001
Table 2. Environmental models for primary influenza peaks.
There was a strong inverse relationship between solar radiation and the probability of an influenza peak, especially when influenza peaks were lagged by 1-month. The solar radiation model outperformed the temperature and specific humidity models based on AIC, but it was not as strong a predictor of the timing of the influenza peaks (Tables 1 and 2).
Precipitation was a weak predictor of influenza peaks overall, but it was a strong predictor of influenza peaks equatorward of 12.5°N/S, particularly for primary influenza peaks (p<0.01) (Tables 1 and 2). Unlike the other climate variables, precipitation-based models performed slightly better when no lag was considered between precipitation and influenza activity.
Relative humidity was a strong predictor of influenza peaks globally, particularly when a 1-month lag was applied to the influenza peaks. There was a positive association between relative humidity and influenza peaks in high and low latitudes, but this model was a poor predictor of influenza peaks in middle latitudes. Further, the relative humidity model was not as strong as the specific humidity, solar radiation and temperature models (Tables 1 and 2).
Overall, the multivariate models most predictive of influenza peak timing included combinations of temperature and precipitation (all peaks, Table 1), and temperature and specific humidity (primary peaks, Table 2). These models accurately predicted peak influenza months in 78% and 89% of the 9 independent sites selected from the FluNet database, respectively (Figure 4). Further, the models predicted a nearly uniform probability of influenza peaks every month of the year in equatorial Colombia, a location that experiences minimal seasonal climatic fluctuation (Figure 4). Taken together, this analysis exploring the shape of the relationship between climatic variables and influenza highlights the covariability between specific humidity and temperature, and the significant predictive power of these variables at high latitudes. In contrast, precipitation and relative humidity were predictive of influenza peaks at low latitudes. Interaction terms describing monthly deviations from the annual average of each environmental predictor marginally improved some models but did not affect the main conclusions (Tables S1 and S2 in Text S1).
Figure 4. Influenza seasonal distribution for 9 sites selected from an independent epidemiological dataset and climate model outputs.
(A,C,E,G,I,K,M,O,Q) Box plots indicate the proportion of influenza cases occurring in each month of the year for 9 countries with multiyear data selected from FluNet. Results of the best-fit climate models for all and primary peaks (Tables 1 and 2) are displayed for comparison. Specific humidity and temperature were advanced one month to account for the one month lag between influenza peaks and these variables. Although the models were designed to estimate the timing of peak influenza activity, they also provide estimates of the seasonal distribution of influenza virus circulation. (B,D,F,H,J,L,N,P,R) The right column displays the monthly precipitation, temperature and specific humidity for each location. Dotted lines indicate the climatic thresholds for each variable. In general, when temperature or specific humidity drops below their respective thresholds, or precipitation surpasses its threshold, there is an increase in influenza activity.
Geographic and Climatic Boundaries Predictive of Influenza Peaks
To further characterize the distribution of influenza peaks globally and identify the geographical and climatic boundaries defining influenza seasonality patterns, we categorized sites based on whether influenza epidemics occurred in months with low (cold-dry season) or high (humid-rainy season) levels of specific humidity relative to the local climatology (Figure 5). We found that the annual minimum level of specific humidity in a site was predictive of the seasonal characteristics of influenza activity locally. Sites characterized by cold-dry influenza peaks generally experienced annual minimum specific humidity values less than 12 g/kg when all influenza peaks were considered, and approximately 11 g/kg when the analysis was restricted to primary influenza peaks (Figure 5). The minimum specific humidity models were statistically significant, and classified 75% of the 96 total influenza peaks correctly (p<0.001, Figure 5C), and 82% of the 76 primary peaks correctly (p<0.001). Annual minimum temperature was a slightly better predictor of the type of influenza peak characterizing a site, correctly classifying 77% and 87% of all peaks and primary peaks, respectively (p<0.001). Sites characterized by cold-dry influenza peaks generally had annual minimum temperature values less than 21°C when all influenza peaks were considered, and approximately 18°C when the analysis was restricted to primary influenza peaks. The temperature and specific humidity models differentiated between the 6 cold-dry and 3 humid-rainy influenza peaks available in the independent FluNet sites with 100% and 78% accuracy, respectively.
Figure 5. Climatic thresholds predictive of influenza seasonal characteristics.
(A) density plot showing the specific humidity in absolute terms (x-axis) and relative terms (y-axis) during influenza peaks across all sites. The plot shows that a vast majority of influenza peaks occurred in "cold-dry" conditions when specific humidity was lower than 8 g/kg and ranks were less than 4, or during "humid-rainy" conditions when specific humidity was greater than 14 g/kg and ranks were greater than 9. (B) a line plot showing the average annual range of specific humidity (y-axis) for each location (x-axis). Sites are ordered based on minimum specific humidity. The black dots indicate the specific humidity during the month of the primary peak and circles indicate specific humidity during secondary peaks. Together, the plots suggest that sites with the lowest annual minimum specific humidity have influenza peaks when specific humidity is at locally-minimal levels. (C) a map displaying the predictions of a logistic regression indicating the probability of an influenza peak during the cold-dry season, versus the humid-rainy season, based on annual minimum specific humidity. The markers indicate the 78 study sites with influenza peaks classified as cold-dry (circles) and humid-rainy (squares). (D) same as (C) but the model is based on annual minimum temperature.
Annual minimum solar radiation was a significant predictor of the type of influenza peaks, comparable to temperature and specific humidity, correctly classifying 71% and 79%of all peaks and primary peaks, respectively (p<0.001). Annual minimum relative humidity and annual monthly maximum precipitation were also significant predictors, but the models were significantly weaker than the other models. It should be noted that 12 sites had both a cold-dry and humid-rainy influenza peak, assuring that one peak would be classified incorrectly when all influenza peaks were considered.
Taken together, this analysis indicates that thresholds in specific humidity and temperature, and perhaps solar radiation, are associated with the timing of the influenza season and the occurrence of influenza activity in the dry-cold or humid-rainy months of the year. The specific humidity and temperature models were then used to predict the expected seasonal characteristics of influenza globally (Figure 5C–D). Both models suggest that seasonal influenza activity coincides with the humid-rainy season in large areas of Central and South America, and Southern Asia; while predictions were more uncertain in middle latitudes and there were inconsistencies between the two models for parts of Central Africa. In particular, the model driven by minimum temperature predicted the occurrence of humid-rainy influenza peaks in most of Central Africa, while the model driven by minimum specific humidity predicted a more restricted zone of humid-rainy peaks concentrated on the Western coast of this region. These discrepancies can be explained by a combination of warm year-round temperatures in this area, with low specific humidity values in parts of the year.
We explored the association between influenza seasonality and climate in a representative sample of 78 global sites, spanning an absolute latitudinal range between 1° and 60°. Our analyses revealed two distinct types of climatic conditions associated with influenza seasons observed globally: "cold-dry" and "humid-rainy". In general, sites that experienced low levels of specific humidity and temperature (less than 11–12 g/kg and 18–21°C) for at least one month during the year were characterized by seasonal influenza activity during the months with minimal levels of specific humidity and temperature. In contrast, sites that maintained high levels of specific humidity and temperature were generally characterized by influenza epidemics during the most humid and rainy months of the year. The predictions of our climate-based models compared favorably to influenza epidemiological information collected independently of the dataset used for the model-building exercise.
The bimodal nature of the relationship, in both relative and absolute terms, between specific humidity and influenza peaks, and its strong relationship to other climate variables such as temperature and precipitation, makes specific humidity a useful gauge of the environmental favorability of influenza activity across all latitudes (Figure 5). However, although the specific humidity models were significant predictors of influenza peaks globally, this was primarily due to their performance in high latitudes. In low latitudes, precipitation was a stronger predictor of the timing of influenza activity, with peaks typically occurring in months with average precipitation greater than 150 mm (Figure 3).
Overall, although precipitation was strongly associated with influenza peaks in low latitudes, the timing of influenza peaks in this region was more difficult to predict than in high-latitude sites. Several sites in this region were not characterized by well-defined influenza season; rather, influenza activity was present year-round likely due to the limited seasonal environmental variation that characterizes much of the region. For example, equatorial sites such as Iquitos, Peru, and Singapore—where influenza seasonality is weak [27], [28]— experience limited fluctuations in precipitation, with monthly averages constrained to a narrow range of 150–300 mm year-round. In contrast, middle and low-latitude sites such as Fortaleza, Brazil and Yangon, Myanmar —which are noted for their well-defined influenza seasons [9], [11]—are characterized by large amplitude range in average monthly precipitation from 25 mm in the dry season to over 300 mm and 600 mm in the rainy season, respectively.
Model performances were particularly poor in a number of middle latitudes sites. Predicting influenza peaks in these sites may be complicated by large seasonal swings in climate that characterize the region, generating both cold-dry and humid-rainy seasons that are equally favorable for seasonal influenza activity, such as in Senegal (Figure 4). For these sites other factors might play a critical role in determining the timing of influenza activity, including population mixing (i.e., travel) with regions that do experience well-defined influenza seasons [29], [30], or the seasonal phasing with school cycles [21]. Moreover, the presence of both cold-dry and humid-rainy seasons could explain the occurrence of semi-annual influenza epidemics in some of these middle-latitude sites. For example, Hong Kong has a primary influenza peak in winter when average monthly specific humidity and temperature are less than 8 g/kg and 17°C, and a secondary influenza peak in summer when average monthly precipitation is near 400 mm.
Temperature was a strong predictor of influenza seasonality in high latitudes, suggesting that cold temperatures may drive seasonal epidemics in these regions. However, previous analyses of laboratory experiments have indicated that specific humidity is a more parsimonious predictor of virus survival and transmission than temperature [17]. Furthermore, individuals in temperate regions spend a vast majority of their time indoors where temperature is managed and does not correlate well with outdoor temperatures. Yet, temperature may affect the timing of influenza epidemics through mechanisms independent of virus survival; for example, low outdoor temperatures may promote indoor crowding, thereby increasing person-to-person contact rates [2]–[4]. It is also possible that even limited exposure to cold outdoor temperatures can have long-lasting physiological effects on hosts that make them more susceptible to infection or affect viral shedding [16]. Additional experimental and observational work is needed to disentangle the contribution of specific humidity and temperature on influenza seasonality; epidemiological information from Central Africa would be particularly useful in this respect as our climate-based predictive models disagreed in this region.
The findings that both cold-dry and humid-rainy conditions are associated with influenza peaks could be used to support the hypothesis that two distinct mechanisms account for influenza seasonality in temperate and tropical climates, perhaps due to changes in the dominant mode of transmission [31]. For example, specific humidity may drive the timing of influenza epidemics in high latitudes by increasing virus survival and enabling aerosol transmission; whereas direct transmission or transmission by fomites may dominate in low-latitude sites where rainy conditions favor indoor crowding. Middle latitudes may be a transition zone where influenza seasons are driven by low specific humidity or high levels of precipitation depending on local climate. Another intriguing possibility is that the relationship between specific humidity and virus survival underlies influenza transmission across all latitudes. For example, a few experimental studies have indicated a U-shaped relationship between relative humidity and influenza virus survival, suggesting a similar relationship for specific humidity given that experiments were held at constant temperature [32]–[34]. Other laboratory studies, however, have indicated that virus survival and transmission increase monotonically as specific humidity decreases [17], [35], [36]. Further, the hypothesis that specific humidity drives influenza transmission globally is inconsistent with the low predictive power of this climatic variable in middle and low-latitude sites in our study.
Relative humidity was a strong predictor of influenza peaks in high and low latitudes, but a poor predictor in middle-latitude. In high-latitude regions, relative humidity can vary significantly between indoor and outdoor environments, and it is typically minimal indoors during the winter when building air is heated. Our analysis relied on outdoor humidity and hence we cannot rule out that winter influenza epidemics in high latitudes could be related to low indoor relative humidity and associated changes to host physiology, such as reduced mucociliary clearance [20]. In low latitudes it is possible that relative humidity is confounding precipitation in our analysis. Disentangling these two factors will require more highly-resolved epidemiological data from equatorial regions, and further experimental and observational studies.
Solar radiation was also a significant predictor of influenza peaks in high latitudes suggesting that it may also underlie influenza seasonality in these regions, perhaps through variation in vitamin D intake [18]. However, solar radiation was not as strong a predictor of influenza peaks as were specific humidity and temperature. This corroborates recent studies indicating that specific humidity is a stronger predictor of seasonal influenza activity than solar radiation and vitamin D variability in the U.S. [14], [37]. Still, the potential seasonal forcing of solar radiation on influenza transmission warrants further experimental and observational investigation.
The power of this study was rooted in the large number of spatially diverse sites used to develop the epidemiological and climatic databases and associated models. However, the challenge of describing seasonal influenza activity consistently across a variety of data sources required a crude epidemiological measure, such as the average month of peak influenza activity. This measure of influenza activity has several key drawbacks. Foremost, all months with the exception of the peak influenza months were considered equal, whether they had substantial influenza activity or not. Second, the month of peak influenza activity may not be contemporaneous with the month in which transmission is under the most environmentally favorable conditions, since non-environmental factors such as viral seeding, population susceptibility, and person-to-person contact rates likely play a role in the timing of influenza epidemics [15], [21]. In this respect, it is reassuring that a 1-month lag maximized the association between influenza peak and most of the climatic variables, which is broadly consistent with the time scale of the ascending phase of a local epidemic. Third, we could not assess putative geographical variation in the transmission potential or intensity of influenza epidemics. For example, we may expect locations that have the most favorable environmental conditions to experience the greatest influenza annual attack rates and reproduction numbers, holding all other relevant variables equal. A further limitation relates to between-year variability in influenza timing and the limited temporal sampling of our dataset, which may have resulted in imprecise estimates of the average influenza peak in some sites, especially sites that had only one year of influenza data. However, sensitivity analyses limited to multi-year studies revealed similar relationships between climate predictors and influenza peaks, confirming the robustness of our results. Finally, we were unable to check whether between-year fluctuations in climatic variables may result in departures from average influenza seasonal characteristics in specific years. This question could be an interesting area for future research with more temporally refined epidemiological datasets.
A number of follow-up studies could help refine our understanding of the small and large-scale processes underlying influenza seasonality. For example, experimental infections in humans under controlled temperature and humidity conditions could determine which environmentally-mediated mechanisms are most important for human-to-human transmission. However, there are several ethical and methodological hurdles to overcome in such studies [38]. Seasonal fluctuation in contact rates could be monitored by wireless sensor technology, which has recently proved successful in estimating dynamic contact patterns in schools and at conferences [39]–[40]. On a broader spatial scale, determining regional differences in influenza transmission dynamics and attack rates would be most informative. A recent study has suggested that the reproduction number of seasonal epidemics was lower on average in Brazil than in temperate countries, which could be mediated by environmental factors [41]. Modeling of long-term influenza time series data could help assess the transmission impact of seasonal fluctuations in population mixing in different regions, such as those associated with school cycles [21] and transportation networks [29], [30], [42]. For example, epidemiological evidence indicates that influenza circulation was weakly seasonal in Iceland prior to the 1930s, presumably because of low connectivity with other populations, and epidemics only became fully synchronized with those in Europe and the USA following a dramatic increase in international travel in the 1990s [43]. Hence, efforts to collate multiyear influenza epidemiological information retrospectively and prospectively in various regions of the globe, especially from middle and low latitude regions, will be of tremendous help to further elucidate the environmental and population drivers of seasonality.
In conclusion, our study broadens our understanding of the relationships between seasonal influenza epidemics and environmental factors and provides a synthesis of epidemiological and climatic characteristics across temperate, subtropical and tropical regions. We have highlighted the importance of thresholds in specific humidity, temperature and precipitation that are associated with the epidemiology (and potentially the modes of transmission) of influenza. The results of this study could help improve existing influenza transmission models by providing a more accurate estimate of the environmental forcing on transmission processes, particularly in low and middle latitudes [14], [44]. Further, our models could be used to predict the seasonal timing of influenza activity in locations with little or no observational data on influenza activity, and help target surveillance efforts and optimize the timing of seasonal vaccine delivery, [45]. More broadly, we hope that our work will generate interest in testing the association between climatic patterns and infectious disease across a wide range of diseases and latitudes, particularly for respiratory and enteric pathogens that display marked seasonality [2], [46]. A better understanding of the environmental, demographic and social drivers of infectious disease seasonality is key for improving transmission models and optimizing interventions [47].
Text S1.
Additional information regarding data and results. Included is a more detailed description of the influenza database and the algorithm developed to define each "peak". We also provide a comparison of CRU and GR climate datasets, and provide results from additional predictive models of peak influenza timing.
https://doi.org/10.1371/journal.ppat.1003194.s001
(DOC)
This research was conducted in the context of the MISMS Study, an ongoing international collaborative effort to understand influenza epidemiological and evolutionary patterns, led by the Fogarty International Center, National Institutes of Health (http://www.origem.info/misms/index.php).
Conceived and designed the experiments: JDT CV JS WJA AC KBF. Performed the experiments: JDT. Analyzed the data: JDT CV JS. Contributed reagents/materials/analysis tools: JDT KBF CV JS CKU. Wrote the paper: JDT CV JS WJA AC.
1. Simonsen L (1999) The global impact of influenza on morbidity and mortality. Vaccine 17: S3–10.
2. Lipsitch M, Viboud C (2009) Influenza seasonality: lifting the fog. Proc Nat Acad Sci 106: 3645–3646.
3. Lofgren E, Fefferman NH, Naumov YN, Gorski J, Naumova EN (2007) Influenza seasonality: underlying causes and modeling theories. J Virol 81: 5429–5436.
4. Tamerius JD, Nelson MI, Zhou SZ, Viboud C, Miller MA, et al. (2011) Global influenza seasonality: reconciling patterns across temperate and tropical regions. Environ Health Persp 119: 439–445.
5. Finkelman BS, Viboud C, Koelle K, Ferrari MJ, Bharti N, et al. (2007) Global patterns in seasonal activity of influenza A/H3N2, A/H1N1, and B from 1997 to 2005: viral coexistence and latitudinal gradients. PLoS ONE 2: e1296.
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9. Dapat C, Saito R, Kyaw Y, Naito M, Hasegawa G, et al. (2009) Epidemiology of human influenza A and B viruses in Myanmar from 2005 to 2007. Intervirology 52: 310–320.
10. Dosseh A, Ndiaye K, Spiegel A, Sagna M, Mathiot C (2000) Epidemiological and virological influenza survey in Dakar, Senegal: 1996–1998. Am J Trop Med Hyg 62: 639–643.
11. Moura FE, Perdigão , Siqueira MM (2009) Seasonality of influenza in the tropics: a distinct pattern in northeastern Brazil. Am J Trop Med Hyg 81: 180–183.
12. Chiu SS, Chan KH, Chen H, Young BW, Lim W, et al. (2009) Virologically confirmed population-based burden of hospitalization caused by influenza A and B among children in Hong Kong. Clin Infect Dis 49(7): 1016–1021 .
13. Lee VJ, Yap J, Ong JBS, Chan KP, Lin RTP, et al. (2009) Influenza excess mortality from 1950–2000 in tropical Singapore. PLoS ONE 4(12): e8096 .
14. Shaman J, Pitzer VE, Viboud C, Grenfell BT, Lipsitch M (2010) Absolute humidity and the seasonal onset of influenza in the continental United States. PLoS Biol 8: e1000316.
15. Shaman J, Goldstein E, Lipsitch M (2011) Absolute humidity and pandemic versus epidemic influenza. Am J Epidemiol 173(2): 127–135 .
16. Lowen AC, Mubareka S, Steel J, Palese P (2007) Influenza virus transmission is dependent on relative humidity and temperature. PLoS Pathog 3: 1470–1476.
17. Shaman J, Kohn M (2009) Absolute humidity modulates influenza survival, transmission, and seasonality. Proc Nat Acad Sci 106: 3243–3248.
18. Cannell JJ, Vieth R, Umhau JC, Holick MF, Grant WB, et al. (2006) Epidemic influenza and vitamin D. Epidemiol Infect 134: 1129–1140.
19. Dowell SF (2001) Seasonal variation in host susceptibility and cycles of certain infectious diseases. Emerg Infect Dis 7: 369–374.
20. Salah B, Dinh Xuan AT, Fouilladieu JL, Lockhart A, Regnard J (1988) Nasal mucociliary transport in healthy subjects is slower when breathing dry air. Eur Respir J 1: 852–855.
21. Cauchemez S, Valleron AJ, Boëlle PY, Flahault A, Ferguson NM (2008) Estimating the impact of school closure on influenza transmission from Sentinel data. Nature 452: 750–754.
22. Azziz-Baumgartner E, Dao C, Nasreen S, Bhuiyan MU, Mah-E-Muneer S, et al. (2012) Seasonality, timing, and climate drivers of influenza activity worldwide. J Infect Dis 206: 838–46 .
23. Bloom-Feshbach K, Alonso WJ, Charu V, Tamerius J, Simonsen L (2012) Latitudinal variation in seasonal activity of influenza and respiratory syncytial virus (RSV): a global comparative review. Plos One In Press.
24. World Health Organization. (2012) FluNet. Available: http://www.who.int/flunet. Accessed Oct 12, 2011.
25. New M, Lister D, Hulme M, Makin I (2002) A high-resolution data set of surface climate over global land areas. Clim Res 21: 1–25.
26. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, et al. (1996) The NCEP/NCAR 40-year reanalysis project. B Am Meteorol Soc 77: 437–472.
27. Laguna-Torres AV, Gómez J, Ocaña V, Aguilar P, Saldarriaga T, et al. (2009) Influenza-like illness sentinel surveillance in Peru. PLoS ONE 4: e6118.
28. Chow A, Ma S, Ling AE, Chew SK (2006) Influenza-associated deaths in tropical Singapore. Emerg Infect Dis 12: 114–121.
29. Colizza V, Barrat A, Barthelemy M, Valleron A (2007) Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions. PLoS Medicine 4(1): e13 .
30. Kenah E, Chao DL, Matrajt L, Halloran ME, Longini IM Jr (2011) The Global Transmission and Control of Influenza. PLoS ONE 6(5): e19515 .
31. Lowen AC, Steel J, Mubareka S, Palese P (2008) High temperature (30°C) blocks aerosol but not contact transmission of influenza virus. J Virol 78: 5650–5652.
32. Shechmeister I (1950) Studies on the experimental epidemiology of respiratory infections: III. certain aspects of the behavior of type A influenza virus as an air-borne cloud. J Infect Dis 87: 128–132.
33. Schaffer F, Soergel M, Straube D (1976) Survival of airborne influenza virus: effects of propagating host, relative humidity, and composition of spray fluids. Arch Virol 51: 263–278.
34. Yang W, Elankumaran S, Marr LC (2012) Relationship between humidity and influenza A viability in droplets and implications for influenza's seasonality. PLoS ONE 7(10): e46789.
35. McDevitt J, Rudnick S, First M, Spengler J (2010) Role of absolute humidity in the inactivation of influenza viruses on stainless steel surfaces at elevated temperatures. Appl Environ Microb 76: 3943–3947.
36. Harper G (1961) Airborne micro-organisms: survival tests with four viruses. J Hyg - Cambridge 59: 479–486.
37. Shaman J, Jeon CY, Giovannucci E, Lipsitch M (2011) Shortcomings of vitamin D-based model simulations of seasonal influenza. PLoS ONE 6: e20743.
38. Killingley B, Enstone J, Booy R, Hayward A (2011) Potential role of human challenge studies for investigation of influenza transmission. Lancet Infect Dis 11: 70142–6.
39. Salathé M, Kazandjieva M, Lee JW, Levis P, Feldman MW, et al. (2010) A high-resolution human contact network for infectious disease transmission. Proc Natl Acad Sci U S A 107: 22020–5.
40. Stehlé J, Voirin N, Barrat A, Cattuto C, Colizza V, et al. (2011) Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees. BMC Med 9: 87.
41. Chowell G, Viboud C, Simonsen L, Miller M, Alonso W (2010) The reproduction number of seasonal influenza epidemics in Brazil, 1996–2006. Proc R Soc B 277(1689): 1857–1866.
42. Viboud C, Bjørnstad ON, Smith DL, Simonsen L, Miller MA, et al. (2006) Synchrony, waves, and spatial hierarchies in the spread of influenza. Science 312(5772): 447–451.
43. Weinberger DM, Krause TG, Mølbak K, Cliff A, Briem H, et al. (2012) Influenza epidemics in iceland over 9 decades: changes in timing and synchrony with the United States and Europe. Am J Epidemiol 176(7): 649–655.
44. Truscott J, Fraser C, Cauchemez S, Meeyai A, Hinsley W, et al. (2012) Essential epidemiological mechanisms underpinning the transmission dynamics of seasonal influenza. J R Soc Interface 9(67): 304–312.
45. de Mello WA, de Paiva TM, Ishida MA, Benega MA, Santos MC, et al. (2009) The dilemma of influenza vaccine recommendations when applied to the tropics: the Brazilian case examined under alternative scenarios. PLoS ONE 4(4): e5095.
46. Stensballe LG, Devasundaram JK, Simoes EA (2003) Respiratory syncytial virus epidemics: the ups and downs of a seasonal virus. Pediatr Infect Dis J 22: S21–32.
47. Pitzer VE, Viboud C, Lopman BA, Patel MM, Parashar UD, et al. (2011) Influence of birth rates and transmission rates on the global seasonality of rotavirus incidence. J R Soc Interface 8(64): 1584–1593.
Related PLOS Articles
has CORRECTION
Correction: Environmental Predictors of Seasonal Influenza Epidemics across Temperate and Tropical Climates
Is the Subject Area "Influenza" applicable to this article?
Is the Subject Area "Humidity" applicable to this article?
Is the Subject Area "Seasons" applicable to this article?
Is the Subject Area "Latitude" applicable to this article?
Is the Subject Area "Meteorology" applicable to this article?
Is the Subject Area "Solar radiation" applicable to this article?
Influenza A virus
Is the Subject Area "Influenza A virus" applicable to this article?
Influenza viruses
Is the Subject Area "Influenza viruses" applicable to this article?


2019 Thame Town Award Winners. Picture: Peter Lambert.
Unsung heroes were celebrated at Thame's annual Town Awards ceremony.
The 11th Thame Town Awards took place at The Barns Centre on Thursday, March 14.
The Mayor of Thame, Cllr Ann Midwinter, welcomed this year's winners and their guests, members of the Town Awards panel, councillors and Town Council staff.
Individuals who nominated the winners were also invited to attend the awards ceremony and the Mayor thanked everyone who had made a nomination this year. The panel had a difficult time deciding the six winners from so many outstanding nominations.
Before the Town Awards were presented, the Mayor invited the members of the Thame Remembers Project steering group to give a presentation on their extensive efforts in marking the centenary of World War One in Thame. The Mayor presented the group with the Mayor's Special Award in recognition of their efforts.
The first award of the evening went to Alice Hopkins; a former Barley Hill pupil who is now attaining outstanding sporting achievements at a national level. She recently made her debut representing Great Britain in the pentathlon, where she achieved several personal bests and contributed to the team winning gold. She is also first in the UK and 6th in Europe for the Under-23 long jump. She was described as quiet and unassuming, but with determination to go the whole way.
Jesse is the lead organiser of the ever-growing and popular Thame Tractor Run; an event which has helped bring the local farming community together while raising thousands of pounds for local charities and causes. Over 230 vehicles, many festively decorated, took part in the second Tractor Run in December which attracted support from the Mayors of Oxford and Thame, local media and hundreds of local residents who came out to line the route and support the event. The two tractor runs have raised over £20,000 for Cancer Research and Thames Valley Air Ambulance. His dedication to help others and constant drive to get things done were described as admirable.
Curtis is a postman in Thame who delivers a professional, helpful and excellent service. His customers described him as the best postman in town! He delivers Thame's post with a spring in his step, no matter what the weather and is always smiling bringing joy to all he meets. His politeness was described as a credit to young people.
This new category aims to showcase the wonderful businesses we have in our town and the work they are doing to support their employees and the wider community. The 2019 award went to Windles Group; a family-owned and community-focused business who do so much to support Thame from printing lantern templates for the primary schools at Christmas to offering work experience placements for Lord Williams's School pupils and being an active member of the local business forum. Their purpose-built building enables them to be innovative, environmentally friendly and efficient. They place great emphasis on working together and looking after their staff, customers, suppliers and the community of Thame.
Yvonne is the lead organiser of the annual Thame Arts and Literature Festival. She was presented with the Creative Arts Achievement for her hard work and dedication to this successful event which is in its tenth year this October. The festival has evolved year-on-year to become a vibrant and wide-ranging event that attracts internationally renowned speaks and a diverse audience to our town each year.
Jean has volunteered with the Thame Society for the Visually Impaired group for over 40 years, where she organises meetings and supports group members in a range of activities. She goes above and beyond in this role by visiting and telephoning those who cannot come to the group to see if they are ok, and provides emotional support with homemade soups and cakes. She has a "can do" attitude and is an active member and leader of several local fundraising groups, community forums and charitable activities.
The Mayor congratulated all of the winners and encouraged everyone to make a nomination in the 2020 awards. Guests enjoyed a buffet and photographs.


Wonder what to do with your partner on Valentine's Day? Check out our list of activities that your partner might like according to their star sign!
With Valentine's Day just around the corner, you might be losing hairs over what to do with your significant other. Fret not, for here we have compiled a list of activities that your significant other may like based on their horoscope!
Symbolised by the ram, Aries-born people are generally active, independent with leadership qualities. They can't stand being in one place for too long and enjoy thrill-seeking activities such as outdoor activities and games.
Born under the sign of Taurus, these people appreciate the arts and enjoy activities that allow them to unwind and relax.
Gemini individuals are bright yet fickle, and their personalities can be seen through their hobbies. They do not have fixed hobbies, but they do enjoy fun and interactive activities.
Cancers are usually homebodies, and their personality is most likely to influence their hobbies and interests. Things like cooking and gardening will appeal to them and should they leave the comfort of their houses, they will appreciate things like museum-hopping.
Suggested Valentine's Day activity: Netflix & Chill, Museum-hopping.
The most active and energetic sign among the twelve, Leo-born people have plenty of energy inside of them. They also love to socialise and meet new people, so going to a place full of fun and noise will definitely appeal to them.
Suggested Valentine's Day activity: Visiting a theme park.
Analytical and reserved, Virgos prefer intellectual activities to physical ones. Mind-stimulating hobbies such as reading and learning new languages will pique their interests greatly.
Libras are known to be sociable and lovers of the fine arts. An artistic sign, their interests lie in musicals, operas, and fine dining.
People born in the sign of Scorpio are fixed, determined and passionate. The term 'giving up' does not exist in their dictionary and they will do whatever it takes to reach their goal. For this Valentine's, why not have them chill out a little?
Free-spirited, adventurous and enthusiastic, those born in the sign of Sagittarius love nature and outdoor activities. A Valentine's day date full of sports will be perfect for them!
Suggested Valentine's Day activity: Camping, trekking, kayaking.
Known as the workaholic sign, Capricorns would opt for a practical gift rather than a whimsical one. Their interests lie in useful activities like gardening or even cooking!
Independent, intellectual and idealistic, Aquarians like new technology and quirky gadgets. However, they also enjoy activities which stimulate the mind.
Pisces-born people are known to be tender, compassionate and dreamy. Romantics at heart, nothing can please a Pisces more than a sincere and thoughtful activity.
Oktoberfest is back in Singapore for it's 5th year and this year promises to be bigger and better in terms of food, beer and fun!


The new future hotel in China will boast of an exciting array of technological experimentation.
The future has arrived in china and it looks greater and bigger than your imagination.
Alibaba, the e-commerce giant has shown what it means to be the richest conglomerate in the world by opening a futuristic hotel in Hongzhou, China and the world cannot stop looking in awe.
Recently opened Flyzoo Hotel in Hangzhou has created a stir on the internet.
The hotel welcomes guests with a face-recognition module as part of the check-in process. The hotel rooms will actually enbale its guests to control features like lights, television and curtains using ALibaba's voice activated digital assistant.
The hotel also has robot waiters, deployed at the restaurants which will feed you dishes and beverages. The robots are programmed to also assist you with directions and answer your queries should you have any.
The entire operation is handled by an Al management system called Tmall Genie while the robots have been developed by Alibaba's A.I Labs.
According to company officials, extensive testing was done to ensure smooth operations and one that is fully protected from vulnerabilities and cyber attacks. Some though are questioning the morality of the move considering that it is a sign robots are replacing humans.


When a cylinder is picked in the wrong direction or picking requires the plug to be rotated and then flipped, a plug spinner is a highly efficient, highly effective tool to have in your collection. They're also very helpful when a right-handed or left-handed lock's plug or tumblers are worn, or the lock is positioned in an awkward angle. At LockPickShop, we carry a variety of plug spinners that can make your job easier.
Our selection of high quality plug spinners will help you when you run into difficulty with locks that have been picked in the wrong direction. At LockPickShop, we carry Dino Gun plug spinners, the Peterson 2-tip plug spinner set and even automotive tips that are ideal for vehicle locks. We've included several styles in our catalog because we want you to be able to find the tools that best suit your needs.


text:
Q: In Javascript is there a way to stop an exception from ending a .some() or .forEach() loop? Environment: Node.js, axios package
Question: In Javascript is there a way to stop an exception from ending a .some() or forEach() loop?
Background: I'm using a some() loop to process an Array.  On the first match a return true statement stops the loop.  Inside of the loop is an async .get() request.  Certain responses including error responses indicate a match.  Unfortunately when an exception is thrown it stops the some() loop.
Example:
const axios = require('axios');

let startURL = 'example.com';

let httpsWwwURL = `https://www.${ startURL }`;
let httpsURL = `https://${ startURL }`;
let httpWwwURL = `http://www.${ startURL }`;
let httpURL = `http://${ startURL }`;

let URLArray = [httpsWwwURL, httpsURL, httpWwwURL, httpURL];

let axiosResult = false;
let okResult = new RegExp('OK', 'i');

URLArray.some(async function(element) {

    await axios.get(element)
        .then(function(response) {
            if(String(response.status).includes('2')) axiosResult = true;
            if(response.statusText.match(okResult)) axiosResult = true;
        })
        .catch(function(error) {
            if(error.response !== undefined) axiosResult = true;
        })
        .finally(async function() {
            if(axiosResult === true) {
                console.log('hooray it worked');
            }
        });
    if(axiosResult === true) return true;

});

A: Your code isn't ending early because of an exception, it's ending because .some doesn't work with async functions.
It's going to call your async function for the first element of the array, and then your async function will return a promise. Promises are truthy, so the .some is immediately done executing and will not move on to the second element of the array, even if the promise later resolves to false.
You will not be able to use .some, .forEach, etc if your operations are asynchronous. Instead, you might try a for ... of loop.
(async () {
  let URLArray = [httpsWwwURL, httpsURL, httpWwwURL, httpURL];

  let axiosResult = false;
  let okResult = new RegExp('OK', 'i');

  for (const element of URLArray) {
    try {
      const response = await axios.get(element);
      if(String(response.status).includes('2')) axiosResult = true;
      if(response.statusText.match(okResult)) axiosResult = true;
    } catch (error) {
      if(error.response !== undefined) axiosResult = true;
    }

    if (axiosResult) {
      console.log('hooray, it worked');
      break;
    }
  }
})();

redpajama_set_name: RedPajamaStackExchange


A "Football Hall of Fame" should be created by FIFA.
Why is Pelé still the greatest of all time?
Basketball Hall of Fame, American Football Hall of Fame; there are several examples of similar institutions in the sport world. Thus, it is astonishing that there is no "FIFA Football Hall of Fame". Because of that, I've decided to suggest its creation and explain an easy way that it could work and specially talk about its inaugural inductees, voting panel and criteria of electability.
3 from each World Cup winning country: Argentina, Brazil, England, France, Germany, Italy, Spain, and Uruguay.
1 from each World Cup runners-up country: Czech Republic (taking turns each year with Slovakia, since there is no more Czechoslovakia), Hungary, Netherlands, and Sweden.
Every time there is a new World Cup Champion or runners up, its representatives would be added. Journalists would be only replaced in specific cases: death, exclusion from the journalist's association (following due process), definitive criminal conviction, or health condition that harms their mental capacities.
1 from each Continental Confederation (CONMEBOL, UEFA, CONCACAF, CAF, AFC e OFC).). The Confederation would draw the first country (excluding those above — champions and runners up) to represent it in the election (also trough a journalist chosen by the nation's association of journalists) and from them, all the countries would annually take turns in alphabetical order. Those 34 journalists would have the task to choose the first 20 members of the Hall of Fame, necessarily divided in the aforementioned way. If there is a tie, the older player gets elected. Evidently, some legends would be locks to get in, like Pele, Maradona, Cruyff, Di Stefano, Beckenbauer and Garrincha. Those 20 legends would be gathered at the event that grants the FIFA Ballon d'Or to best player of the year (something like what the NBA did in the 1997 All Star Game, when the 50 greatest players of the league were honored).
Definitive rules of following years.
With the initial inductees set, it becomes possible to establish the rules to be followed the following years. Each member of the aforementioned Committee would freely vote for 10 male players, 2 female players and 2 coaches. Those with the most votes, in that same quantity, would be part of a pre-list to the final choice (the more advance age would still be the tie breaker in all groups). Important to say that if in three straight or five alternate years, a male player gets in the top five, or a female player or coach gets the top spot, they would automatically enter the Hall of Fame, without the need of approval in the voting panel.
From the list of 10 male players, 2 female players and 2 coaches, the voting panel would elect: 5 male players, 1 female player and 1 coach, who would have the honor to be inductees of the Hall of Fame and to be honored during the FIFA Ballon d'Or annual award event.
Example for an easier understanding (pre list with 10 male players and votes).
Players A B, C, G E J, would join the Hall.
I consider this system to be fair and hope, for honoring the great idols of the sport, a FIFA Hall of Fames gets created.
Although everything looks great indeed (certainly worth a visit), I don't think it is really an oficcial FIFA Hall of Fame. "FIFA" does not seem to call them by that title. They should!
It is weird indeed. I just might visit it in the future. Thanks.


text:
Office steht für:

 Office-Paket, Bürosoftware im Allgemeinen
 Office (Hongkong-Film), Comedy-Musical (2015)
 Office (südkoreanischer Film), südkoreanischer Thriller von Hong Won-chan (2015)

The Office steht für:

 The Office, britische Comedyserie von Ricky Gervais und Stephen Merchant (2001–2003)
 The Office (US-amerikanische Fernsehserie), US-amerikanische Adaption der gleichnamigen britischen Comedyserie (2005–2013)

Siehe auch:
 Officer

redpajama_set_name: RedPajamaWikipedia


text:
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MP3 Great Aunt Ida - Our Fall
Indie Folk Pop
11 MP3 Songs
FOLK: Folk Pop, POP: Piano
Formed in 2003, Great Aunt Ida is a Vancouver-based pop band led by multi-instrumentalist and composer Ida Nilsen. Best known as a member of experimental rock quintet the Beans, alt-roots Radiogram, and pop supergroup the Gay, Nilsen has also performed and recorded with many well known Vancouver artists including Veda Hille, The Buttless Chaps, Jerk with a Bomb, P:ano and Toronto based The Violet Archers (Tim Vesely [Rheostatics] solo project). Many also remember Ida for her role as co-proprietor and booking agent for the much missed downtown music and art venue the (sugar refinery).
A long awaited vehicle for Nilsen's talents as a singer and songwriter, Great Aunt Ida also includes Barry Mirochnick drums (Veda Hille's Skilled and Devoted Band, Martin Tielli's Operation Infinate Justice Band), Annie Wilkinson bass (Thermos, Knock Down Ginger, Beekeepers), and JP Carter trumpet (Tony Wilson Quintet, the Inhabitants, Fond of Tigers). Extolled for their contributions to some of the most innovative and acclaimed bands to surface amid Vancouver's independent music scene, Great Aunt Ida's talented co-conspirators provide the perfect foil for Nilsen's pretty voice, instantly memorable melodies and elegant piano. Together, they have performed at the Railway and Anza Clubs, the Western Front, and Richards on Richards, with the Weakerthans, the Constantines, Veda Hille, Ford Pier, David P. Smith and others in Vancouver, ...and during fall/winter 2004, they recorded their debut album Our Fall with Colin Stewart (Black Mountain, p:ano, Destroyer) at The Hive Studios with guest appearances from Jon Anderson (Jonathan Inc./Radiogram) and Tim Vesely (Rheostatics).
More akin to a song cycle than a concept album, Our Fall plumbs a darkly enchanting vestibule of dreams, memories, and tales within tales. Hauntingly beautiful, melancholic yet never cloying, Great Aunt Ida takes stock of the debts and promises of a world unfolding endlessly upon itself. Revealing just enough of her secrets to seduce, Nilsen explores fragility, longing and relief in a sotto voce which reminds us just how much more, less can be.
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ICYMI: May Podcast Roundup
Missed any of our podcasts last month? Or just can't get enough of them? Say no more – we got you!
Here, we've rounded up every podcast from last month for at-your-fingertips easy access and your listening pleasure.
How to Navigate A Loss of Revenue
The market always evolves. While the pandemic has created additional financial pressure for most businesses, it also rendered some business models obsolete or struggling to hold on while serving a shrinking market.
In this episode, we'll talk about how to navigate a significant loss in revenue so your business can survive and soon return to the place where it thrives.
What Franchising Can Teach You About Scaling Tom DuFore
Joining us today is Tom DuFore, the CEO of Big Sky Franchise Team, where he is responsible for the company strategy, vision, and growth. He also supports and consults with Big Sky Franchise Team's clients on their expansion and franchise efforts.
Almost all successful business owners will eventually ask the question: "Should I franchise?"
But it's not just a matter of opening another business. People who franchise the right way have certain principles and operating procedures in place that make scaling a business as smooth as possible.
This episode will help business owners understand how a franchising strategy might work for them – as well as when it's the right time to make the move.
Every Top Leader's Secret Weapon with Jason Pond
Joining us is Jason Pond, CEO at Grizzly Broadband, to share what he thinks is every top leader's secret weapon – the executive assistant.
He'll share what life was like before he got help from an assistant – and what it's like now – to help our community of listeners discover the power of an executive assistant and exactly how it can elevate the capacity and effectiveness of the leader personally and professionally.
What to Do If You Hate Working from Home
After years of mastering productivity and leadership in a shared office environment, many successful leaders have been forced to work and lead from home and some have decided they personally miss the office and hate working from home.
In this episode, we'll acknowledge that working from home isn't for everyone and help diagnose why someone may not like it so they can customize a personal work environment that enables them to thrive, even if the organization is remote.
Small Business Leadership Entrepreneur
ICYMI: December Podcast Roundup
Missed any of our podcasts in December? We've rounded up every podcast for at-your-fingertips easy access and listening pleasure.
ICYMI: January Podcast Roundup
Missed any of our podcasts in January? Here, we've rounded up every podcast for at-your-fingertips easy access and listening pleasure.
ICYMI: April Podcast Roundup
Missed any of our podcasts in April? We've rounded up every podcast for at-your-fingertips easy access and listening pleasure.


Every team needs a leader. Someone that can take charge and direct the process, support the staff, captains and players, and allow the team and players to learn how to work hard, discipline themselves and get better as athletes, teammates and people. In this way, an effective leader needs to be able to lead, follow and get out of the way.
These words have been attributed to several powerful leaders through the ages and now they are in the public domain. They were used basically as a demand to step up and lead, join in the effort and do what you can to help the cause but don't get in our way.
Here's how they relate to LeadFollowGED.
Lead: To lead is to take charge of the situation, to communicate effectively and to give excellent direction and feedback.
Follow: To follow is to support the process and operate at acceptance, to be positive at all times and actively participate in the solution.
Get out of the way: To get out of the way is to allow the staff, captains and players to make their own decisions within reason and to be responsible for resolving issues and building a strong and cooperative team system.
Grateful, Excited and Devoted enhances the ability to lead, follow and get out of the way.
Gratitude– Provides the awareness you need to lead: to know and feel the "why" behind your intentions and the expectations you have for your staff, captains and players and be strong and confident as you give excellent direction and feedback.
To lead in this way, you need to build trust: be consistent, genuine and honest.
Excitement–The energy you need to "follow:" to have an abundance of fire and passion for coaching. The fire is the intensity that will allow you to learn how to settle down and get through the fear and anger related to the upsets, disappointments and issues.
The passion will keep you coming back year after year, season after season and keep working on being a better leader and building a culture that promotes hard work, discipline and togetherness.
To lead in this way, you need to be open, accepting and able to express your own truth.
The power of conviction that allows you to get out of the way: to trust your staff, captains and players to balance the results that don't measure up to the expectations. To reflect on the situation, learn and grow from their efforts and make really good decisions in moving forward.
To lead in this way you need to have the courage and confidence to inspire others to work harder than they ever thought they could, to sacrifice their own gains for the team and enjoy the process, and to love and accept each other.
Peter Balding, Legendary Volleyball Player, Coach and Teacher speaks to the challenge of leading today's youth.
1. The important directions and strategy needed to be "in solution" (make adjustments) during moments of intense competition.
Bill Curry, Former NFL player, Big Time College Coach and All–American College Player speaks to the coach that had the biggest impact on him.
That was it! Coach John Robert did help me with my feet but most of all he 'lit my fire' and my spirit was back. I'm grateful to Coach John Robert for giving me the direction, support and belief that I could play. His act of leadership and acceptance changed my career. I did play for 12 more years and football has been the foundation of my life for the last 60 years!


Ottawa City Council has approved a rezoning application for private and commercial development on two islands at the Chaudière Falls, about a kilometre upstream from the Parliament buildings on the Ottawa River. The decision, made at a council meeting on Oct. 8, came despite numerous objections from citizens as well as a formal request for further dialogue from Kitigan Zibi Anishinabeg Chief Gilbert W. Whiteduck.
The land in question is unceded Algonquin territory, and the area at the falls is considered a central sacred site of the Algonquin and all Anishnaabe peoples. Samuel de Champlain's writings documented a tobacco ceremony conducted at this specific site during the first European visits to the area in the early 1600s.
— Read the full article here on Ricochet.
For this article, I did a number of interviews.
I've posted two of them below, both are edited versions ie not the complete interviews.
See below the interviews for some relevant documents / links.
Topics include the sacredness of the site to the Algonquin peoples, the consultation process for the developments, and a response to the community opposition to the development/ rezoning plans.
Topics include the consultation process and the City of Ottawa's need for "due diligence" – then "profound disappointment" with the rezoning going ahead despite a request he'd sent for dialogue first, a response to a statement from the Mayor's office, considerations around Aboriginal title to the land, the Kitigan Zibi letter putting the federal and provincial governments on notice, some history of the area and the relationship between First Nations and European settlers, and the significance of the Chaudière area to the Algonquin and particularily Kitigan Zibi.


The D.C. U.S. Attorney's Office wants a second shot at prosecuting ex-pitching star Roger Clemens for lying to Congress about using performance enhancing drugs, saying it inadvertently goofed last month in the first trial when it played a video to the jury on the second day.
A mistrial was declared as a result of the error. The government filed a motion Friday asking for a second trial, and requesting that the judge reject Clemens' effort to dismiss the charges.
"On the second day of what was expected to be a 4-to-6 week trial, the government mistakenly played a video clip that included evidence this Court had ruled inadmissible, " the government stated in the motion written by Assistant U.S. Attorney David B. Goodhand. "At defendant's request, this Court therefore declared a mistrial. Now, defendant seeks to gain an unwarranted windfall from this inadvertent error. Defendant seeks to bar a retrial, arguing the government did not make a mistake, but rather engaged in deliberate misconduct intended to provoke his mistrial request.
U.S. District Judge Reggie Walton last month declared the mistrial after the prosecution introduced the video which contained evidence Walton had barred. After declaring the mistrial, he said he was going to have to make a determination whether to grant the government a second chance or dismiss the charges.
Posted: 8/19/11 at 4:29 PM under News Story.


Demolition workers face dangers on the job every day, but they may face a silent killer and not even know it. Buildings that were constructed prior to 1980 were likely constructed with asbestos or asbestos-based products. Known for being fire-resistant and extremely strong, asbestos was used in millions of construction products before it was banned in 1979. Unfortunately, thousands of people had been exposed to the dangerous fibers of asbestos at that point, and demolition workers may still be in danger today.
If you or someone you know has suffered from asbestos exposure, it is important to explore your legal rights as a victim of exposure. Contact the experienced and dedicated mesothelioma attorneys of Williams Hart today at 800-781-3955 to learn about your legal options.
Unfortunately, many people were wholly unaware of the dangers of exposure to asbestos, as companies neglected to tell people of its deadliness. As a result, many people have suffered debilitating illnesses and death, causing trauma for both themselves and their loved ones.
If you or someone you know worked in demolition or currently works in demolition, it is important to understand all of the risks associated with this work. If you have been exposed to asbestos, contact the experienced mesothelioma attorneys of Williams Hart today by calling 800-781-3955.


लिफ्ट कत्तिको सुरक्षित छन् त ?
Kantipur TV HD, the most popular TV channel in Nepal, brings Nepal to its audiences. Its programmes provide in-depth analyses about the issues of the day and reflect the people's voice. Its shows explore the multidimensional impacts of important events, by framing them in formats that range from news bulletins, interviews and features to satires, comedies and social drama. Kantipur TV HD captures the many colours our lives are imbued with.
Kantipur Television Network Pvt. Ltd.
© 2017 Kantipur Digital Corp.


Wow.. Well it's safe to say the last month has been all go here at Ecigclick towers! I truly appreciate your patience with the delay in getting the results out this year. In addition to all that has happened, this year was the biggest yet so it took that little bit more time to go through the votes.
But.. We are finally there and all the results of the 6th Annual Ecigclick Vape Awards are in!
Again, a big thank you to everyone that took the time to vote in what is fast becoming one of the most popular 'vaper voted' vape awards online! I'm sure you will agree it's important to recognise the hard work those in the vape industry put in each year.
With that being said, congratulations to all the brands, products and personalities that reached the final poll stage. Reaching this stage is a great achievement on it's own despite the position finished so well done again, keep up the good work far into 2019 and beyond!
For those that entered the giveaway please check the link at the bottom of this post, winners have been drawn. A big thank you to this years sponsor – Vapour.UK – for supplying the prizes.
We have new, updated badges available to all finalists. Please get in touch with me – admin (at) ecigclick.co.uk – to get your personalised badge!
One vote per person – Any attempt to vote numerous times will result in all votes being removed.
No prizes/vouchers/incentives for votes. If you are a company/FB page etc and want to offer an incentive then you can use the official Ecigclick Awards giveaway. Nothing else. Any nominee found offering incentives will be removed without notice and where applicable replaced with a nominee in the same category that received the next highest number of votes.
Voting is available between the dates given on this website. Although we reserve the right to change these dates.
Ecigclick reserves the right to disqualify votes if it has reasonable grounds to suspect that fraudulent, excessive or suspicious patterns of voting have occurred, or if it considers there has been any attempt to unfairly influence the voting, or promote, or not discourage fraudulent voting. Participants are required to encourage their supporters to vote in the way specified by these terms & conditions. This may include for example, votes from spambots, programmes designed to specifically vote continuously for one project, or from devices where it is evident that cookies have intentionally been disabled or other attempts have been made to try to vote in a way that is not in line with these terms & conditions.
The outcome of the vote or poll will be revealed by Ecigclick on social media (Facebook and Twitter) within a week of the vote or poll having finished. The result will also officially be announced by email newsletter.
In the event of any fault, mistake, misunderstanding or dispute concerning the outcome of a vote or poll, or the operation of any part of the web based voting system, the decision of Ecigclick shall be final.
Once the votes have been counted, the decision of Ecigclick is final.
Thought about disqualification in the past but it won't work for a number of reasons John.
Riot squad and Dinner Lady at the top? I wonder how much they had to pay to get the win?
Strange comment George, both great brands with a lot of love in the community. Out of interest who would you have expected to see at the top ?
Thanks so much Jonny, hope all is well with your little family 😉 I'm over the moon with my 3rd place for best Blogger, wasn't expecting that at all!
Nice one Paul, all great here thanks! Hope all's good with you!
You're too kind Jonny 😉 thank you!
Congrats Paul , fully deserved !
Oh nice to see that you are just doing the banners, how long will that be.. will we see the results this lovely Sunday?
Haha 🙂 hope you are doing well, baby and mum.
Looking forward to seeing the results! 🙂 Hope you are well Jonny and the new born and mummy!
Hey Anne, all doing superb here after a little adjustment period 🙂 Not long now on the results, keep an eye on FB and Twitter, will be announcing there when ready!
Can't wait!!! Approx when we looking today or tomorrow? Any date for the results?
Do we have any date?
Not far off now John.
Hey John, thanks for the love!
I cant give an exact date unfortunately. I've been going through everything for the last week and continue to work on it. Lot of data to go through to ensure everything is accurate. Not an area that I'm cutting corners on.
Make sure you follow our twitter/FB pages. Will be announced on there the minute it goes live.
Congratulations E Cig Click for hosting and creating such a wonder platform for the industry, nice to see some of the cream of industry getting nominated like to name few Aspire, evolution, IVG, Twelve Monkies, Dinner Lady, Nasty Etc.
Any idea what day youll think you will have the results up?
Well done to the and ecigclick and industry especially to Evolution, Vapearround, IVG, Dinner Lady, Twelve Monkies for coming together and hosting this wonderful vape event. Feeling proud of the industry we all belong to.
Well done to all the industry leaders, shops and reviewers for making the final polls. Delighted to be nominated again and its wonderful to see Northern Ireland represented again this year.
Whilst these polls may not be fully representative of the whole community its awesome for the cream of the crop to be recognised.
IVG and riot squad are definitely hacking the votes!!
The results are checked before final confirmation Jo!
How can you have a Best Of and note have Five Pawns!?
The above results are taken from a a nomination stage. Five Pawns didn't get enough nominations unfortunately.
can't answer all categories honestly as I don't know much about pods mtl ect.
The vaping Bogan Lmfao !!
Approved vapers has helped me get off and stay off cigs.. the support of the community is amazing and they need recognising for everything they do !!
Nice to see most of the people voting for Mooch. He well deserves it.
Approved Vapers is the best facebook group in my opinion. Being part of the community has changed my life in just 5 months. It has helped me find lots of helpful advice and made many new friends.
The final polls are the result of nominations by vapers.
vape hard or go ohm best fb group, most friendly group, most active group! everybody needs to vote on this group!
Approved Vapers has really helped me a lot with me just getting of the ciggies and moving to vaping. All the admins were helpful when I joined. They thoroughly deserve to win.
Martin thank you for them kind words about Approved Vapers.
888 Lincoln all the way. Titanic vapour is not bad for a small local shop but nothing compared to the real ones. They only sell own juices that are not very nice at all.
Titanic has over 100 brands of eliquid from a wide range of companies dude.
Not true David, visit their website and you will see the ranges of juice they have available.
Bit annoying when people are trying to vote but your polls are crashing so they cant vote.
APPROVED VAPERS – Best facebook group!
Can't comment on most. Only what I have had and tried.
Can't wait to see overall outcome.
a little sad not to reveal the results for the best UK reviewer. mine said it will show up when closing the voting, while the International one shows the results right away.
Thanks for the opportunity to vote for who I suppose..
It would be great if more vapers are aware.
Flavour of the dragons by far….
Moreish Puff all the way!
I don't know enough about all the products to vote. Ecigclick is a great site to get information. I wish I knew more about all the manufacturers but I don't. I hope next year I can vote as I'd know more.
No problem Shawn, it's all good to just vote in the categories you have experience with.