Effect of Weather Variability on Seasonal Influenza Among Different Age Groups in Queensland, Australia: A Bayesian Spatiotemporal Analysis

Transmission dynamics of seasonal influenza in a remote island population

Seasonal influenza outbreaks remain an important public health concern, causing large numbers of hospitalizations and deaths among high-risk groups. Understanding the dynamics of individual transmission is crucial to design effective control measures and ultimately reduce the burden caused by influenza outbreaks. In this study, we analyzed surveillance data from Kamigoto Island, Japan, a semi-isolated island population, to identify the drivers of influenza transmission during outbreaks. We used rapid influenza diagnostic test (RDT)-confirmed surveillance data from Kamigoto island, Japan and estimated age-specific influenza relative illness ratios (RIRs) over eight epidemic seasons (2010/11 to 2017/18). We reconstructed the probabilistic transmission trees (i.e., a network of who-infected-whom) using Bayesian inference with Markov-chain Monte Carlo method and then performed a negative binomial regression on the inferred transmission trees to identify the factors associated with onwards transmission risk. Pre-school and school-aged children were most at risk of getting infected with influenza, with RIRs values consistently above one. The maximal RIR values were 5.99 (95% CI 5.23, 6.78) in the 7-12 aged-group and 5.68 (95%CI 4.59, 6.99) in the 4-6 aged-group in 2011/12. The transmission tree reconstruction suggested that the number of imported cases were consistently higher in the most populated and busy districts (Tainoura-go and Arikawa-go) ranged from 10-20 to 30-36 imported cases per season. The number of secondary cases generated by each case were also higher in these districts, which had the highest individual reproduction number (R_eff: 1.2-1.7) across the seasons. Across all inferred transmission trees, the regression analysis showed that cases reported in districts with lower local vaccination coverage (incidence rate ratio IRR = 1.45 (95% CI 1.02, 2.05)) or higher number of inhabitants (IRR = 2.00 (95% CI 1.89, 2.12)) caused more secondary transmissions. Being younger than 18 years old (IRR = 1.38 (95%CI 1.21, 1.57) among 4-6 years old and 1.45 (95% CI 1.33, 1.59) 7-12 years old) and infection with influenza type A (type B IRR = 0.83 (95% CI 0.77, 0.90)) were also associated with higher numbers of onwards transmissions. However, conditional on being infected, we did not find any association between individual vaccination status and onwards transmissibility. Our study showed the importance of focusing public health efforts on achieving high vaccine coverage throughout the island, especially in more populated districts. The strong association between local vaccine coverage (including neighboring regions), and the risk of transmission indicate the importance of achieving homogeneously high vaccine coverage. The individual vaccine status may not prevent onwards transmission, though it may reduce the severity of infection.

Stronger susceptibilities to air pollutants of influenza A than B were identified in subtropical Shenzhen, China

Air pollution was indicated to be a key factor contributing to the aggressive spread of influenza viruses, whereas uncertainty still exists regarding to whether distinctions exist between influenza subtypes. Our study quantified the impact of five air pollutants on influenza subtype outbreaks in Shenzhen, China, a densely populated and highly urbanized megacity. Daily influenza outbreak data of laboratory-confirmed positive cases were obtained from the Shenzhen CDC, from May 1, 2013 to Dec 31, 2015. Concentrations of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), particulate matters ≤2.5 μm (PM_2.5), particulate matters ≤10 μm (PM₁₀), and ozone (O₃), were retrieved from the 18 national monitoring stations. The generalized additive model (GAM) and distributed lag non-linear model (DLNM) were used to calculate the concentration-response relationships between environmental inducers and outbreak epidemics, respectively for influenza A (Flu-A) and B (Flu-B). There were 1687 positive specimens were confirmed during the study period. The cold season was restricted from Nov. 4th to Apr. 20th, covering all seasons other than the long-lasting summer. Relatively heavy fine particle matter (PM_2.5) and NO₂ pollution was observed in cold months, with mean concentrations of 46.06 μg/m³ and 40.03 μg/m³, respectively. Time-series analysis indicated that high concentrations of NO₂, PM_2.5, PM₁₀, and O₃ were associated with more influenza outbreaks at short lag periods (0-5 d). Although more Flu-B (679 cases) epidemics occurred than Flu-A (382 cases) in the cold season, Flu-A generally showed higher susceptibility to air pollutants. A 10 μg/m³ increment in concentrations of PM_2.5, PM₁₀, and O₃ at lag 04, was associated with a 2.103 (95%CI: 1.528-2.893), 1.618 (95%CI: 1.311-1.996), and 1.569 (95%CI: 1.214-2.028) of the relative risk (RR) of Flu-A, respectively. A 5 μg/m³ increase in NO₂ was associated with higher risk of Flu-A at lag 03 (RR = 1.646, 95%CI: 1.295-2.092) and of Flu-B at lag 04 (RR = 1.319, 95%CI: 1.095-1.588). Nevertheless, barely significant effect of particulate matters (PM_2.5, PM₁₀) on Flu-B and SO₂ on both subtypes was detected. Further, the effect estimates of NO₂ increased for both subtypes when coexisting with other pollutants. This study provides evidence that declining concentrations of main pollutants including NO₂, O₃, and particulate matters, could substantially decrease influenza risk in subtropical Shenzhen, especially for influenza A.

Geospatial epidemiology of hospitalized patients with a positive influenza assay: A nationwide study in Iran, 2016-2018

INTRODUCTION

Seasonal influenza is a significant public health challenge worldwide. This study aimed to investigate the epidemiological characteristics and spatial patterns of severe hospitalized influenza cases confirmed by polymerase chain reaction (PCR) in Iran.

METHODS

Data were obtained from Iran's Ministry of Health and Medical Education and included all hospitalized lab-confirmed influenza cases from January 1, 2016, to December 30, 2018 (n = 9146). The Getis-Ord Gi* and Local Moran's I statistics were used to explore the hotspot areas and spatial cluster/outlier patterns of influenza. We also built a multivariable logistic regression model to identify covariates associated with patients' mortality.

RESULTS

Cumulative incidence and mortality rate were estimated at 11.44 and 0.49 (per 100,000), respectively, and case fatality rate was estimated at 4.35%. The patients' median age was 40 (interquartile range: 22-63), and 55.5% (n = 5073) were female. The hotspot and cluster analyses revealed high-risk areas in northern parts of Iran, especially in cold, humid, and densely populated areas. Moreover, influenza hotspots were more common during the colder months of the year, especially in high-elevated regions. Mortality was significantly associated with older age (adjusted odds ratio [aOR]: 1.01, 95% confidence interval [CI]: 1.01-1.02), infection with virus type-A (aOR: 1.64, 95% CI: 1.27-2.15), male sex (aOR: 1.77, 95% CI: 1.44-2.18), cardiovascular disease (aOR: 1.71, 95% CI: 1.33-2.20), chronic obstructive pulmonary disease (aOR: 1.82, 95% CI: 1.40-2.34), malignancy (aOR: 4.77, 95% CI: 2.87-7.62), and grade-II obesity (aOR: 2.11, 95% CI: 1.09-3.74).

CONCLUSIONS

We characterized the spatial and epidemiological heterogeneities of severe hospitalized influenza cases confirmed by PCR in Iran. Detecting influenza hotspot clusters could inform prioritization and geographic specificity of influenza prevention, testing, and mitigation resource management, including vaccination planning in Iran.

Respiratory virus detection during the COVID-19 pandemic in Queensland, Australia

OBJECTIVE

To determine if non-pharmaceutical interventions (NPIs) impacted on respiratory virus detections in Queensland, Australia, during the COVID-19 pandemic year of 2020.

METHODS

We analysed weekly counts of influenza, human metapneumovirus, parainfluenza, respiratory syncytial virus, rhinovirus, and adenovirus available from a Queensland laboratory network for the year 2020. These were compared with averaged counts from 2015 to 2019.

RESULTS

Overall, 686,199 tests were performed. The timing of NPI implementation was associated with a sharp and sustained decline in influenza, where during the typical annual influenza season (weeks 23-40) no cases were detected from 163,296 tests compared with an average of 26.1% (11,844/45,396) of tests positive in 2015-2019. Similar results were observed for human metapneumovirus and parainfluenza. Respiratory syncytial virus detections also declined but increased in weeks 48-52 (5.6%; 562/10,078) to exceed the 2015-2019 average (2.9%; 150/5,018). Rhinovirus detections increased after schools reopened, peaking in weeks 23-27 (57.4%; 36,228/63,115), exceeding the 2017-2019 detections during that period (21.9%; 8,365/38,072).

CONCLUSIONS

NPIs implemented to control COVID-19 were associated with altered frequency and proportions of respiratory virus detections. Implications for public health: NPIs derived from influenza pandemic plans were associated with profound decreases in influenza detections during 2020.

Influenza A and B outbreaks differed in their associations with climate conditions in Shenzhen, China

Under the variant climate conditions in the transitional regions between tropics and subtropics, the impacts of climate factors on influenza subtypes have rarely been evaluated. With the available influenza A (Flu-A) and influenza B (Flu-B) outbreak data in Shenzhen, China, which is an excellent example of a transitional marine climate, the associations of multiple climate variables with these outbreaks were explored in this study. Daily laboratory-confirmed influenza virus and climate data were collected from 2009 to 2015. Potential impacts of daily mean/maximum/minimum temperatures (T/T_max/T_min), relative humidity (RH), wind velocity (V), and diurnal temperature range (DTR) were analyzed using the distributed lag nonlinear model (DLNM) and generalized additive model (GAM). Under its local climate partitions, Flu-A mainly prevailed in summer months (May to June), and a second peak appeared in early winter (December to January). Flu-B outbreaks usually occurred in transitional seasons, especially in autumn. Although low temperature caused an instant increase in both Flu-A and Flu-B risks, its effect could persist for up to 10 days for Flu-B and peak at 17 C (relative risk (RR) = 14.16, 95% CI: 7.46-26.88). For both subtypes, moderate-high temperature (28 C) had a significant but delayed effect on influenza, especially for Flu-A (RR = 26.20, 95% CI: 13.22-51.20). The Flu-A virus was sensitive to RH higher than 76%, while higher Flu-B risks were observed at both low (< 65%) and high (> 83%) humidity. Flu-A was active for a short term after exposure to large DTR (e.g., DTR = 10 C, RR = 12.45, 95% CI: 6.50-23.87), whereas Flu-B mainly circulated under stable temperatures. Although the overall wind speed in Shenzhen was low, moderate wind (2-3 m/s) was found to favor the outbreaks of both subtypes. This study revealed the thresholds of various climatic variables promoting influenza outbreaks, as well as the distinctions between the flu subtypes. These data can be helpful in predicting seasonal influenza outbreaks and minimizing the impacts, based on integrated forecast systems coupled with short-term climate models.

The contrasting relationships of relative humidity with influenza A and B in a humid subtropical region

Influenza is an acute respiratory disease that seriously threatens public health. The occurrence of influenza has been proved to be related to a variety of meteorological factors. However, less attention has been paid to the effect of relative humidity (RH) on different types of influenza, especially in subtropical regions. Daily data on laboratory-confirmed influenza cases, weather variables, and air pollutants in Hefei covering the 2014-2019 period were collected. The seasonality and trend of daily influenza cases were explored by the time series seasonal decomposition method. Generalized linear model was fitted in conjunction with distributed lag nonlinear model to quantify the associations of RH with influenza A and influenza B. Subgroup analyses were conducted by sex, age (0-4, 5-17, and ≥18 years), and season (cold and warm seasons). A total of 5238 influenza cases including 2847 influenza A cases and 2391 influenza B cases were recorded. The epidemic of influenza presented a distinct seasonal pattern, and the number of daily influenza cases increased steadily since 2016. High RH was related to an increased risk of influenza A (maximum RR = 1.683, 95%CI: 1.365-2.076), especially among males, females, and school-age children. Low RH was associated with an increased risk of influenza B (maximum RR = 1.252, 95%CI: 1.169-1.340). The contrasting relationships of RH with influenza A and B remained significant in cold seasons. High RH and low RH were significantly associated with the increased risk of influenza A and B, respectively. The findings of our study may provide clues for proposing new effective interventions.

Impact of biometeorological conditions and air pollution on influenza-like illnesses incidence in Warsaw

In order to assess the influence of atmospheric conditions and particulate matter (PM) on the seasonally varying incidence of influenza-like illnesses (ILI) in the capital of Poland-Warsaw, we analysed time series of ILI reported for the about 1.75 million residents in total and for different age groups in 288 approximately weekly periods, covering 6 years 2013-2018. Using Poisson regression, we predicted ILI by the Universal Thermal Climate Index (UTCI) as biometeorological indicator, and by PM2.5 and PM10, respectively, as air quality measures accounting for lagged effects spanning up to 3 weeks. Excess ILI incidence after adjusting for seasonal and annual trends was calculated by fitting generalized additive models. ILI morbidity increased with rising PM concentrations, for both PM2.5 and PM10, and with cooler atmospheric conditions as indicated by decreasing UTCI. While the PM effect focused on the actual reporting period, the atmospheric influence exhibited a more evenly distributed lagged effect pattern over the considered 3-week period. Though ILI incidence adjusted for population size significantly declined with age, age did not significantly modify the effect sizes of both PM and UTCI. These findings contribute to better understanding environmental conditionings of influenza seasonality in a temperate climate. This will be beneficial to forecasting future dynamics of ILI and to planning clinical and public health resources under climate change scenarios.

Extreme weather conditions and dengue outbreak in Guangdong, China: Spatial heterogeneity based on climate variability

BACKGROUND

Previous studies have shown associations between local weather factors and dengue incidence in tropical and subtropical regions. However, spatial variability in those associations remains unclear and evidence is scarce regarding the effects of weather extremes.

OBJECTIVES

We examined spatial variability in the effects of various weather conditions on the unprecedented dengue outbreak in Guangdong province of China in 2014 and explored how city characteristics modify weather-related risk.

METHODS

A Bayesian spatial conditional autoregressive model was used to examine the overall and city-specific associations of dengue incidence with weather conditions including (1) average temperature, temperature variation, and average rainfall; and (2) weather extremes including numbers of days of extremely high temperature and high rainfall (both used 95th percentile as the cut-off). This model was run for cumulative dengue cases during five months from July to November (accounting for 99.8% of all dengue cases). A further analysis based on spatial variability was used to validate the modification effects by economic, demographic and environmental factors.

RESULTS

We found a positive association of dengue incidence with average temperature in seven cities (relative risk (RR) range: 1.032 to 1.153), a positive association with average rainfall in seven cities (RR range: 1.237 to 1.974), and a negative association with temperature variation in four cities (RR range: 0.315 to 0.593). There was an overall positive association of dengue incidence with extremely high temperature (RR:1.054, 95% credible interval (CI): 1.016 to 1.094), without evidence of variation across cities, and an overall positive association of dengue with extremely high rainfall (RR:1.505, 95% CI: 1.096 to 2.080), with seven regions having stronger associations (RR range: 1.237 to 1.418). Greater effects of weather conditions appeared to occur in cities with higher economic level, lower green space coverage and lower elevation.

CONCLUSIONS

Spatially varied effects of weather conditions on dengue outbreaks necessitate area-specific dengue prevention and control measures. Extremes of temperature and rainfall have strong and positive associations with dengue outbreaks.

Intraday Variation Mapping of Population Age Structure via Urban-Functional-Region-Based Scaling

The spatial distribution of the population is uneven for various reasons, such as urban-rural differences and geographical conditions differences. As the basic element of the natural structure of the population, the age structure composition of populations also varies considerably across the world. Obtaining accurate and spatiotemporal population age structure maps is crucial for calculating population size at risk, analyzing populations mobility patterns, or calculating health and development indicators. During the past decades, many population maps in the form of administrative units and grids have been produced. However, these population maps are limited by the lack of information on the change of population distribution within a day and the age structure of the population. Urban functional regions (UFRs) are closely related to population mobility patterns, which can provide information about population variation intraday. Focusing on the area within the Beijing Fifth Ring Road, the political and economic center of Beijing, we showed how to use the temporal scaling factors obtained by analyzing the population survey sampling data and population dasymetric maps in different categories of UFRs to realize the intraday variation mapping of elderly individuals and children. The population dasymetric maps were generated on the basis of covariates related to population. In this article, 50 covariates were calculated from remote sensing data and geospatial data. However, not all covariates are associate with population distribution. In order to improve the accuracy of dasymetric maps and reduce the cost of mapping, it is necessary to select the optimal subset for the dasymetric model of elderly and children. The random forest recursive feature elimination (RF-RFE) algorithm was introduced to obtain the optimal subset of different age groups of people and generate the population dasymetric model in this article, as well as to screen out the optimal subset with 38 covariates and 26 covariates for the dasymetric models of the elderly and children, respectively. An accurate UFR identification method combining point of interest (POI) data and OpenStreetMap (OSM) road network data is also introduced in this article. The overall accuracy of the identification results of UFRs was 70.97%, which is quite accurate. The intraday variation maps of population age structure on weekdays and weekends were made within the Beijing Fifth Ring Road. Accuracy evaluation based on sampling data found that the overall accuracy was relatively high—R2 for each time period was higher than 0.5 and root mean square error (RMSE) was less than 0.05. On weekdays in particular, R2 for each time period was higher than 0.61 and RMSE was less than 0.02.

The association between the seasonality of pediatric pandemic influenza virus outbreak and ambient meteorological factors in Shanghai

BACKGROUND AND OBJECTIVES

The number of pediatric patients diagnosed with influenza types A and B is increasing annually, especially in temperate regions such as Shanghai (China). The onset of pandemic influenza viruses might be attributed to various ambient meteorological factors including temperature, relative humidity (Rh), and PM₁ concentrations, etc. The study aims to explore the correlation between the seasonality of pandemic influenza and these factors.

METHODS

We recruited pediatric patients aged from 0 to 18 years who were diagnosed with influenza A or B from July 1st, 2017 to June 30th, 2019 in Shanghai Children's Medical Centre (SCMC). Ambient meteorological data were collected from the Shanghai Meteorological Service (SMS) over the same period. The correlation of influenza outbreak and meteorological factors were analyzed through preliminary Pearson's r correlation test and subsequent time-series Poisson regression analysis using the distributed lag non-linear model (DLNM).

RESULTS

Pearson's r test showed a statistically significant correlation between the weekly number of influenza A outpatients and ambient meteorological factors including weekly mean, maximum, minimum temperature and barometric pressure (P < 0.001), and PM₁ (P < 0.01). While the weekly number of influenza B outpatients was statistically significantly correlated with weekly mean, maximum and minimum temperature (P < 0.001), barometric pressure and PM₁ (P < 0.01), and minimum Rh (P < 0.05). Mean temperature and PM₁ were demonstrated to be the statistically significant variables in the DLNM with influenza A and B outpatients through time-series Poisson regression analysis. A U-shaped curve relationship was noted between the mean temperature and influenza A cases (below 15 °C and above 20 °C), and the risks increased for influenza B with mean temperature below 10 °C. PM₁ posed a risk after a concentration of 23 ppm for both influenza A and B. High PM₁, low and the high temperature had significant effects upon the number of influenza A cases, whereas low temperature and high PM₁ had significant effects upon the number of influenza B cases.

CONCLUSION

This study indicated that mean temperature and PM₁ were the primary factors that were continually associated with the seasonality of pediatric pandemic influenza A and B and the recurrence in the transmission and spread of influenza viruses.

Association of weather variability with resurging pertussis infections among different age groups: A non-linear approach

Pertussis has resurged in many countries over recent years, especially among adolescents and adults. This study assessed the effect of weather variability on resurging pertussis among different age groups in Jinan, China. Data on weekly pertussis notifications by age group and weather factors (mean temperature (MeanT), mean temperature standard deviation within a week (MeanT SD), diurnal temperature range (DTR) and relative humidity (RH)) were collected between 2013 and 2017. Distributed lag non-linear models (DLNMs) and regression tree models were used to examine the non-linear association between weather variability and pertussis infections. The 2-weeks cumulative relative risk (RR) of pertussis infections was 4.46 (95% confidence interval (CI): 2.33-9.51) in 0-4 age group, 6.25 (95% CI: 1.38-22.76) in 5-9 age group and 10.11 (95% CI: 2.83-39.07) in 10+ age group when MeanT was at 30.0 °C. MeanT SD (RR range in the three age groups: 2.82-5.83), DTR (RR range: 6.33-11.56) and RH (RR range: 2.02-7.43) also exert significant influence, with the highest risks at 10+ age group. Regression tree models showed the interactive effects of weather variability. The mean pertussis infections increased by over 1.7-fold in 0-4 years group when MeanT ≥14 °C, RH ≥57% and DTR ≥10 °C; by over 2.3-fold in 5-9 years group when MeanT ≥20 °C and MeanT SD ≥3 °C; by 2.0-fold in 10+ years group when MeanT ≥0.7 °C, DTR ≥8.3 °C and RH ≥74%. The study found significantly different associations between weather variability and pertussis infections by age group, and appeared to be stronger in 10+ years group. Continuing climate change, together with other risk factors such as low antibody levels among adolescents and adults, may facilitate pertussis resurgence. This supports previous suggestions of carefully reconsidering current vaccination programme to effectively curb the resurgence of pertussis.

The delayed effect of wildfire season particulate matter on subsequent influenza season in a mountain west region of the USA

Particularly in rural settings, there has been little research regarding the health impacts of fine particulate matter (PM2.5) during the wildfire season smoke exposure period on respiratory diseases, such as influenza, and their associated outbreaks months later. We examined the delayed effects of PM2.5 concentrations for the short-lag (1-4 weeks prior) and the long-lag (during the prior wildfire season months) on the following winter influenza season in Montana, a mountainous state in the western United States. We created gridded maps of surface PM2.5 for the state of Montana from 2009 to 2018 using spatial regression models fit with station observations and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness data. We used a seasonal quasi-Poisson model with generalized estimating equations to estimate weekly, county-specific, influenza counts for Montana, associated with delayed PM2.5 concentration periods (short-lag and long-lag effects), adjusted for temperature and seasonal trend. We did not detect an acute, short-lag PM2.5 effect nor short-lag temperature effect on influenza in Montana. Higher daily average PM2.5 concentrations during the wildfire season was positively associated with increased influenza in the following winter influenza season (expected 16% or 22% increase in influenza rate per 1 μg/m3 increase in average daily summer PM2.5 based on two analyses, p = 0.04 or 0.008). This is one of the first observations of a relationship between PM2.5 during wildfire season and influenza months later.

Winter temperature and myocardial infarction in Brisbane, Australia: Spatial and temporal analyses

Myocardial infarction (MI) incidence often peaks in winter, but it remains unclear how winter temperature affects MI temporally and spatially. We examined the short-term effects of winter temperature on the risk of MI and explored spatial associations of winter MI hospitalizations with temperature and socioeconomic status (area-based index) in Brisbane, Australia. We used a distributed lag non-linear model to fit the association at the city level between population-weighted daily mean temperature and daily MI hospitalizations during 11 winters of 2005-2015. For each winter, a Bayesian spatial conditional autoregressive model was fitted to examine the associations at postal code level of MI hospitalisations with temperature and socioeconomic status measured as the Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD). Area-specific winter temperature was categorised into three levels: cold (<25th percentile of average winter temperature across postal areas), mild (25th-75th percentile) and warm (>75th percentile). This study included 4978 MI hospitalizations. At the city level, each 1 °C drop in temperature below a threshold of 15.6 °C was associated with a relative risk (RR) of 1.016 (95% confidence interval (CI): 1.008-1.024) for MI hospitalizations on the same day. Low temperature had a much delayed and transient effect on women but an immediate and longer-lasting effect on men. Winter MI incidence rate varied spatially in Brisbane, with a higher incidence rate in warmer areas (RR for mild areas: 1.214, 95%CI: 1.116-1.320; RR for warm areas: 1.251, 95%CI: 1.127-1.389; cold areas as the reference) and in areas with lower socioeconomic levels (RR: 0.900, 95%CI: 0.886-0.914 for each decile increase in IRSAD). This study provides compelling evidence that short-term winter temperature drops were associated with an elevated risk of MI in the subtropical region with a mild winter. Particular attention also needs to be paid to people living in relatively warm and socioeconomically disadvantaged communities in winter.

Epidemic features of seasonal influenza transmission among eight different climate zones in Gansu, China

BACKGROUNDS

Seasonal influenza remains epidemic globally with a substantial health burden. Understanding the transmission patterns and epidemic features of influenza may facilitate the improvement of preventive and control measures. This study aims to assess the epidemic features of influenza among different climate zones and identify high-risk zones across Gansu province, China.

METHODS

We collected weekly influenza cases at county-level between 1st January 2012 and 31st December 2016, as well as climate zones classification shapefile data from Köppen-Geiger climate map. We compared the epidemic features (Frequency index (α), Duration index (β) and Intensity index (γ)) of influenza among different climate zones. Spatial cluster analysis was used to examine the high-risk areas of transmission of influenza.

RESULTS

The distribution of cases existed significant differences among eight climate zones (F-test: 267.02, p < 0.05). The highest mean weekly incidence rate (per 100,000 population) was 0.59 in snow climate with dry winter and warm summer (Dwb). The primary (relative risk (RR): 3.61, p < 0.001) and secondary (RR: 2.45, p < 0.001) clusters were located in Dwb. The highest values of α, β and γ were 1.00, 261 and 154.38 in Dwb. The hot spots (high-high clusters) of the epidemic indices were detected in Dwb.

CONCLUSIONS

This study found the variability of epidemic features of influenza among eight climate zones. We highlight that Dwb was the high-risk zone where influenza clustered with the highest incidence rate and epidemic temporal indices. This provide further insight into potential improvement of preventive measures by climate zones to minimize the impact of epidemics.

Association of meteorological factors and atmospheric particulate matter with the incidence of pneumonia: an ecological study

OBJECTIVES

Inconsistent results have been found between pneumonia and meteorological factors. We aimed to identify principal meteorological factors associated with pneumonia, and to estimate the effect size and lag time.

METHODS

This was nationwide population-based study used a healthcare claims database merged with a weather database in eight metropolitan cities in Korea. We applied a stepwise approach using the Granger causality test and generalized additive model to elucidate the association between weekly pneumonia incidence (WPI) and meteorological factors/air pollutants (MFAP). Impulse response function was used to examine the time lag.

RESULTS

In total, 2 011 424 cases of pneumonia were identified from 2007 to 2017. Among MFAP, diurnal temperature range (DTR), humidity and particulate matter ≤2.5 μm in diameter (PM_2.5) showed statistically significant associations with WPI (p < 0.001 for all 3 MFAPs). The association of DTR and WPI showed an inverted U pattern for bacterial and unspecified pneumonia, whereas for viral pneumonia, WPI increased gradually in a more linear manner with DTR and no substantial decline. Humidity showed a consistent pattern in all three pneumonia categories. WPI steeply increased up to 10 to 20 μg/m³ of PM_2.5 but did not show a further increase in higher concentrations. On the basis of the result, we examined the effect of MFAP in different lag times up to 3 weeks.

CONCLUSIONS

DTR, humidity and PM_2.5 were identified as MFAP most closely associated with WPI. With the model, we were able to visualize the effect-time association of MFAP and WPI.

The complex associations of climate variability with seasonal influenza A and B virus transmission in subtropical Shanghai, China

Most previous studies focused on the association between climate variables and seasonal influenza activity in tropical or temperate zones, little is known about the associations in different influenza types in subtropical China. The study aimed to explore the associations of multiple climate variables with influenza A (Flu-A) and B virus (Flu-B) transmissions in Shanghai, China. Weekly influenza virus and climate data (mean temperature (MeanT), diurnal temperature range (DTR), relative humidity (RH) and wind velocity (Wv)) were collected between June 2012 and December 2018. Generalized linear models (GLMs), distributed lag non-linear models (DLNMs) and regression tree models were developed to assess such associations. MeanT exerted the peaking risk of Flu-A at 1.4 °C (2-weeks' cumulative relative risk (RR): 14.88, 95% confidence interval (CI): 8.67-23.31) and 25.8 °C (RR: 12.21, 95%CI: 6.64-19.83), Flu-B had the peak at 1.4 °C (RR: 26.44, 95%CI: 11.52-51.86). The highest RR of Flu-A was 23.05 (95%CI: 5.12-88.45) at DTR of 15.8 °C, that of Flu-B was 38.25 (95%CI: 15.82-87.61) at 3.2 °C. RH of 51.5% had the highest RR of Flu-A (9.98, 95%CI: 4.03-26.28) and Flu-B (4.63, 95%CI: 1.95-11.27). Wv of 3.5 m/s exerted the peaking RR of Flu-A (7.48, 95%CI: 2.73-30.04) and Flu-B (7.87, 95%CI: 5.53-11.91). DTR ≥ 12 °C and MeanT <22 °C were the key drivers for Flu-A and Flu-B, separately. The study found complex non-linear relationships between climate variability and different influenza types in Shanghai. We suggest the careful use of meteorological variables in influenza prediction in subtropical regions, considering such complex associations, which may facilitate government and health authorities to better minimize the impacts of seasonal influenza.

Using big data to predict pertussis infections in Jinan city, China: a time series analysis

This study aims to use big data (climate data, internet query data and school calendar patterns (SCP)) to improve pertussis surveillance and prediction, and develop an early warning model for pertussis epidemics. We collected weekly pertussis notifications, SCP, climate and internet search query data (Baidu index (BI)) in Jinan, China between 2013 and 2017. Time series decomposition and temporal risk assessment were used for examining the epidemic features in pertussis infections. A seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to predict pertussis occurrence using identified predictors. Our study demonstrates clear seasonal patterns in pertussis epidemics, and pertussis activity was most significantly associated with BI at 2-week lag (r_BI = 0.73, p < 0.05), temperature at 1-week lag (r_temp = 0.19, p < 0.05) and rainfall at 2-week lag (r_rainfall = 0.27, p < 0.05). No obvious relationship between pertussis peaks and school attendance was found in the study. Pertussis cases were more likely to be temporally concentrated throughout the epidemics during the study period. SARIMA models with 2-week-lagged BI and 1-week-lagged temperature had better predictive performance (β_{search query} = 0.06, p = 0.02; β_temp = 0.16, p = 0.03) with large correlation coefficients (r = 0.67, p < 0.01) and low root mean squared error (RMSE) value (r = 3.59). The regression tree model identified threshold values of potential predictors (search query, climate and SCP) for pertussis epidemics. Our results showed that internet query in conjunction with social and climatic data can predict pertussis epidemics, which is a foundation of using such data to develop early warning systems.

Effects of Absolute Humidity, Relative Humidity, Temperature, and Wind Speed on Influenza Activity in Toronto, Ontario, Canada

The occurrence of influenza in different climates has been shown to be associated with multiple meteorological factors. The incidence of influenza has been reported to increase during rainy seasons in tropical climates and during the dry, cold months of winter in temperate climates. This study was designed to explore the role of absolute humidity (AH), relative humidity (RH), temperature, and wind speed (WS) on influenza activity in the Toronto, ON, Canada, area. Environmental data obtained from four meteorological stations in the Toronto area over the period from 1 January 2010 to 31 December 2015 were linked to patient influenza data obtained for the same locality and period. Data were analyzed using correlation, negative binomial regressions with linear predictors, and splines to capture the nonlinear relationship between exposure and outcomes. Our study found a negative association of both AH and temperature with influenza A and B virus infections. The effect of RH on influenza A and B viruses was controversial. Temperature fluctuation was associated with increased numbers of influenza B virus infections. Influenza virus was less likely to be detected from community patients than from patients tested as part of an institutional outbreak investigation. This could be more indicative of nosocomial transmission rather than climactic factors. The nonlinear nature of the relationship of influenza A virus with temperature and of influenza B virus with AH, RH, and temperature could explain the complexity and variation between influenza A and B virus infections. Predicting influenza activity is important for the timing of implementation of disease prevention and control measures as well as for resource allocation.IMPORTANCE This study examined the relationship between environmental factors and the occurrence of influenza in general. Since the seasonality of influenza A and B viruses is different in most temperate climates, we also examined each influenza virus separately. This study reports a negative association of both absolute humidity and temperature with influenza A and B viruses and tries to understand the controversial effect of RH on influenza A and B viruses. This study reports a nonlinear relation between influenza A and B viruses with temperature and influenza B virus with absolute and relative humidity. The nonlinear nature of these relations could explain the complexity and difference in seasonality between influenza A and B viruses, with the latter predominating later in the season. Separating community-based specimens from those obtained during outbreaks was also a novel approach in this research. These findings provide a further understanding of influenza virus transmission in temperate climates.

Effects of air pollutants on occurrences of influenza-like illness and laboratory-confirmed influenza in Hefei, China

Accumulating evidence suggests that air pollution is a risk factor for adverse respiratory and cardiovascular health outcomes. However, the different impacts of exposure to air pollutants on influenza virus activity and influenza-like illness (ILI) have not been well documented in epidemiological studies. We examined the association between air pollutants of particular matters < 2.5 μm (PM_2.5), particular matters < 10 μm (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and influenza occurrences in Hefei, China, from December 2013 to December 2015 by generalized Poisson additive regression models. The result suggested that PM_2.5 and PM₁₀ had similar effects on clinical ILI and influenza incidence. PM₁₀ was negatively associated with clinical ILI (relative risk (RR) 0.980, 95% confidence interval (CI) 0.974-0.987), while PM_2.5 were positively associated with clinical ILI (RR 1.040; 95% CI 1.032-1.049). RRs for the laboratory-confirmed cases of influenza were 0.813 (95% CI, 0.755-0.875) for PM₁₀ and 1.216 (95% CI, 1.134-1.304) for PM_2.5. Nevertheless, the impacts of SO₂ and NO₂ on ILI and influenza were distinct. SO₂ had significant influence on laboratory-confirmed influenza and had no significant linear relationship with ILI. NO₂ was negatively correlated with influenza but had no obvious effect on clinical ILI cases. The present study contributes novel evidence on understanding of the effects of various air pollutants on influenza activities, and these findings can be useful and important for the development of influenza surveillance and early warning systems.

The burden of influenza-associated respiratory hospitalizations in Bhutan, 2015-2016

BACKGROUND

Influenza burden estimates help provide evidence to support influenza prevention and control programs. In this study, we estimated influenza-associated respiratory hospitalization rates in Bhutan, a country considering influenza vaccine introduction.

METHODS

Using real-time reverse transcription-polymerase chain reaction laboratory results from severe acute respiratory infection (SARI) surveillance, we estimated the proportion of respiratory hospitalizations attributable to influenza each month among patients aged <5, 5-49, and ≥50 years in six Bhutanese districts for 2015 and 2016. We divided the sum of the monthly influenza-attributed hospitalizations by the total of the six district populations to generate age-specific rates for each year.

RESULTS

In 2015, 10% of SARI patients tested positive for influenza (64/659) and 18% tested positive (129/736) in 2016. The incidence of influenza-associated hospitalizations among all age groups was 50/100 000 persons (95% confidence interval [CI]: 45-55) in 2015 and 118/100 000 persons (95% CI: 110-127) in 2016. The highest rates were among children <5 years: 182/100 000 (95% CI: 153-210) in 2015 and 532/100 000 (95% CI: 473-591) in 2016. The second highest influenza-associated hospitalization rates were among adults ≥50 years: 110/100 000 (95% CI: 91-130) in 2015 and 193/100 000 (95% CI: 165-221) in 2016.

CONCLUSIONS

Influenza viruses were associated with a substantial burden of severe illness requiring hospitalization especially among children and older adults. These findings can be used to understand the potential impact of seasonal influenza vaccination in these age groups.

Impact of weather factors on influenza hospitalization across different age groups in subtropical Hong Kong

Accumulating evidence demonstrates the significant influence of weather factors, especially temperature and humidity, on influenza seasonality. However, it is still unclear whether temperature variation within the same day, that is diurnal temperature range (DTR), is related to influenza seasonality. In addition, the different effects of weather factors on influenza seasonality across age groups have not been well documented in previous studies. Our study aims to explore the effects of DTR and humidity on influenza seasonality, and the differences in the association between weather factors and influenza seasonality among different age groups in Hong Kong, China. Generalized additive models were conducted to flexibly assess the impact of DTR, absolute humidity (vapor pressure, VP), and relative humidity on influenza seasonality in Hong Kong, China, from January 2012 to December 2016. Stratified analyses were performed to determine if the effects of weather factors differ across age groups (< 5, 5-9, 10-64, and > 64 years). The results suggested that DTR, absolute humidity, and relative humidity were significantly related to influenza seasonality in dry period (when VP is less than 20 mb), while no significant association was found in humid period (when VP is greater than 20 mb). The percentage changes of hospitalization rates due to influenza associated with per unit increase of weather factors in the very young children (age 0-4) and the elderly (age 65+) were higher than that in the adults (age 10-64). Diurnal temperature range is significantly associated with influenza seasonality in dry period, and the effects of weather factors differ across age groups in Hong Kong, China.

Estimation of sickness absenteeism among Italian healthcare workers during seasonal influenza epidemics

OBJECTIVES

To analyze absenteeism among healthcare workers (HCWs) at a large Italian hospital and to estimate the increase in absenteeism that occurred during seasonal flu periods.

DESIGN

Retrospective observational study.

METHODS

The absenteeism data were divided into three "epidemic periods," starting at week 42 of one year and terminating at week 17 of the following year (2010-2011, 2011-2012, 2012-2013), and three "non-epidemic periods," defined as week 18 to week 41 and used as baseline data. The excess of the absenteeism occurring among HCWs during periods of epidemic influenza in comparison with baseline was estimated. All data, obtained from Hospital's databases, were collected for each of the following six job categories: medical doctors, technical executives (i.e., pharmacists), nurses and allied health professionals (i.e., radiographers), other executives (i.e., engineers), nonmedical support staff, and administrative staff. The HCWs were classified by: in and no-contact; vaccinated and unvaccinated.

RESULTS

5,544, 5,369, and 5,291 workers in three years were studied. The average duration of absenteeism during the epidemic periods increased among all employees by +2.07 days/person (from 2.99 to 5.06), and the relative increase ranged from 64-94% among the different job categories. Workers not in contact with patients experienced a slightly greater increase in absenteeism (+2.28 days/person, from 2.73 to 5.01) than did employees in contact with patients (+2.04, from 3.04 to 5.08). The vaccination rate among HCWs was below 3%, however the higher excess of absenteeism rate among unvaccinated in comparison with vaccinated workers was observed during the epidemic periods (2.09 vs 1.45 days/person).

CONCLUSION

The influenza-related absenteeism during epidemic periods was quantified as totaling more than 11,000 days/year at the Italian hospital studied. This result confirms the economic impact of sick leave on healthcare systems and stresses on the necessity of encouraging HCWs to be immunized against influenza.