Climatic Factors and Influenza Transmission, Spain, 2010-2015

Convolutional LSTM-LSTM model for predicting the daily number of influenza patients in South Korea using satellite images

OBJECTIVES

Influenza affects a considerable proportion of the global population each year, and meteorological conditions may have a significant impact on its transmission. In this study, we aimed to develop a prediction model for the number of influenza patients at the national level using satellite images and provide a basis for predicting influenza through satellite image data.

STUDY DESIGN

We developed an influenza incidence prediction model using satellite images and influenza patient data.

METHODS

We collected satellite images and daily influenza patient data from July 2014 to June 2019 and developed a convolutional long short-term memory (LSTM)-LSTM neural network model. The model with the lowest average of mean absolute error (MAE) was selected.

RESULTS

The final model showed a high correlation between the predicted and actual number of influenza patients, with an average MAE of 5.9010 per million population. The model performed best with a 2-week time sequence.

CONCLUSIONS

We developed a national-level prediction model using satellite images to predict influenza incidence. The model offers the advantage of nationwide analysis. These results may reduce the burden of influenza by enabling timely public health interventions.

Geographical characteristics and influencing factors of the influenza epidemic in Hubei, China, from 2009 to 2019

Influenza is an acute respiratory infectious disease that commonly affects people and has an important impact on public health. Based on influenza incidence data from 103 counties in Hubei Province from 2009 to 2019, this study used time series analysis and geospatial analysis to analyze the spatial and temporal distribution characteristics of the influenza epidemic and its influencing factors. The results reveal significant spatial-temporal clustering of the influenza epidemic in Hubei Province. Influenza mainly occurs in winter and spring of each year (from December to March of the next year), with the highest incidence rate observed in 2019 and an overall upward trend in recent years. There were significant spatial and urban-rural differences in influenza prevalence in Hubei Province, with the eastern region being more seriously affected than the central and western regions, and the urban regions more seriously affected than the rural region. Hubei's influenza epidemic showed an obvious spatial agglomeration distribution from 2009 to 2019, with the strongest clustering in winter. The hot spot areas of interannual variation in influenza were mainly distributed in eastern and western Hubei, and the cold spot areas were distributed in north-central Hubei. In addition, the cold hot spot areas of influenza epidemics varied from season to season. The seasonal changes in influenza prevalence in Hubei Province are mainly governed by meteorological factors, such as temperature, sunshine, precipitation, humidity, and wind speed. Low temperature, less rain, less sunshine, low wind speed and humid weather will increase the risk of contracting influenza; the interannual changes and spatial differentiation of influenza are mainly influenced by socioeconomic factors, such as road density, number of health technicians per 1,000 population, urbanization rate and population density. The strength of influenza's influencing factors in Hubei Province exhibits significant spatial variation, but in general, the formation of spatial variation of influenza in Hubei Province is still the result of the joint action of socioeconomic factors and natural meteorological factors. Understanding the temporal and spatial distribution characteristics of influenza in Hubei Province and its influencing factors can provide a reasonable decision-making basis for influenza prevention and control and public health development in Hubei Province and can also effectively improve the scientific understanding of the public with respect to influenza and other respiratory infectious diseases to reduce the influenza incidence, which also has reference significance for the prevention and control of influenza and other respiratory infectious diseases in other countries or regions.

Latitude and longitude as drivers of COVID-19 waves' behavior in Europe: A time-space perspective of the pandemic

BACKGROUND

Social restrictions and vaccination seem to have shaped the pandemic development in Europe, but the influence of geographical position is still debated. This study aims to verify whether the pandemic spread through Europe following a particular direction, during the period between the start of the pandemic and November 2021. The existence of a spatial gradient for epidemic intensity is also hypothesized.

METHODS

Daily COVID-19 epidemiological data were extracted from Our World in Data COVID-19 database, which also included vaccination and non-pharmacological interventions data. Latitude and longitude of each country's centroid were used as geographic variables. Epidemic periods were delimited from epidemic surge data. Multivariable linear and Cox's regression models were performed for each epidemic period to test if geographical variables influenced surge dates. Generalized additive models (GAM) were used to test the spatial gradient hypothesis with three epidemic intensity measures.

RESULTS

Linear models suggest a possible west-east shift in the first epidemic period and features a significant association of NPIs with epidemic surge delay. Neither latitude nor longitude had significant associations with epidemic surge timing in both second and third periods. Latitude displays strong negative associations with all epidemic intensity measures in GAM models. Vaccination was also negatively associated with intensity.

CONCLUSIONS

A longitudinal spread of the pandemic in Europe seems plausible, particularly concerning the first wave. However, a recurrent trend was not observed. Southern Europe countries may have experienced increased transmissibility and incidence, despite climatic conditions apparently unfavourable to the virus.

Impact of meteorological and demographic factors on the influenza epidemic in Japan: a large observational database study

Factors affecting the start date of the influenza epidemic season and total number of infected persons per 1,000,000 population in 47 prefectures of Japan were evaluated. This retrospective observational study (September 2014-August 2019; N = 472,740-883,804) evaluated data from a Japanese health insurance claims database. Single and multiple regression analyses evaluated the time to start of the epidemic or total infected persons per 1,000,000 population with time to absolute humidity (AH) or number of days with AH (≤ 5.5, ≤ 6.0, ≤ 6.5, and ≤ 7.0), total visitors (first epidemic month or per day), and total population. For the 2014/15, 2015/16, and 2016/17 seasons, a weak-to-moderate positive correlation (R2: 0.042-0.417) was observed between time to start of the epidemic and time to first day with AH below the cutoff values. Except in the 2016/17 season (R2: 0.089), a moderate correlation was reported between time to start of the epidemic and the total population (R2: 0.212-0.401). For all seasons, multiple regression analysis showed negative R2 for time to start of the epidemic and total visitors and population density (positive for time to AH ≤ 7.0). The earlier the climate becomes suitable for virus transmission and the higher the human mobility (more visitors and higher population density), the earlier the epidemic season tends to begin.

Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces

Urban dwellers are exposed to communicable diseases, such as influenza, in various urban spaces. Current disease models are able to predict health outcomes at the individual scale but are mostly validated at coarse scales due to the lack of fine-scaled ground truth data. Further, a large number of transmission-driving factors have been considered in these models. Because of the lack of individual-scaled validations, the effectiveness of factors at their intended scale is not substantiated. These gaps significantly undermine the efficacy of the models in assessing the vulnerability of individuals, communities, and urban society. The objectives of this study are twofold. First, we aim to model and, most importantly, validate influenza-like illness (ILI) symptoms at the individual scale based on four sets of transmission-driving factors pertinent to home-work space, service space, ambient environment, and demographics. The effort is supported by an ensemble approach. For the second objective, we investigate the effectiveness of the factor sets through an impact analysis. The validation accuracy reaches 73.2-95.1%. The validation substantiates the effectiveness of factors pertinent to urban spaces and unveils the underlying mechanism that connects urban spaces and population health. With more fine-scaled health data becoming available, the findings of this study may see increasing value in informing policies that improve population health and urban livability.

Viral respiratory infections in a rapidly changing climate: the need to prepare for the next pandemic

Viral respiratory infections (VRIs) cause seasonal epidemics and pandemics, with their transmission influenced by climate conditions. Despite the risks posed by novel VRIs, the relationships between climate change and VRIs remain poorly understood. In this review, we synthesized existing literature to explore the connections between changes in meteorological conditions, extreme weather events, long-term climate warming, and seasonal outbreaks, epidemics, and pandemics of VRIs from an interdisciplinary perspective. We proposed a comprehensive conceptual framework highlighting the potential biological, socioeconomic, and ecological mechanisms underlying the impact of climate change on VRIs. Our findings suggested that climate change increases the risk of VRI emergence and transmission by affecting the biology of viruses, host susceptibility, human behavior, and environmental conditions of both society and ecosystems. Further interdisciplinary research is needed to address the dual challenge of climate change and pandemics.

Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh

The ongoing COVID-19 contagious disease caused by SARS-CoV-2 has disrupted global public health, businesses, and economies due to widespread infection, with 676.41 million confirmed cases and 6.77 million deaths in 231 countries as of February 07, 2023. To control the rapid spread of SARS-CoV-2, it is crucial to determine the potential determinants such as meteorological factors and their roles. This study examines how COVID-19 cases and deaths changed over time while assessing meteorological characteristics that could impact these disparities from the onset of the pandemic. We used data spanning two years across all eight administrative divisions, this is the first of its kind--showing a connection between meteorological conditions, vaccination, and COVID-19 incidences in Bangladesh. We further employed several techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Integrated Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet). We further analyzed the effects of COVID-19 vaccination on daily cases and deaths. Data on COVID-19 cases collected include eight administrative divisions of Bangladesh spanning March 8, 2020, to January 31, 2023, from available online servers. The meteorological data include rainfall (mm), relative humidity (%), average temperature (°C), surface pressure (kPa), dew point (°C), and maximum wind speed (m/s). The observed wind speed and surface pressure show a significant negative impact on COVID-19 cases (-0.89, 95% confidence interval (CI): 1.62 to -0.21) and (-1.31, 95%CI: 2.32 to -0.29), respectively. Similarly, the observed wind speed and surface pressure show a significant negative impact on COVID-19 deaths (-0.87, 95% CI: 1.54 to -0.21) and (-3.11, 95%CI: 4.44 to -1.25), respectively. The impact of meteorological factors is almost similar when vaccination information is included in the model. However, the impact of vaccination in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to -0.38 and for deaths: 1.55, 95%CI: 2.88 to -0.43). Accordingly, vaccination effectively reduces the number of new COVID-19 cases and fatalities in Bangladesh. Thus, these results could assist future researchers and policymakers in the assessment of pandemics, by making thorough efforts that account for COVID-19 vaccinations and meteorological conditions.

Agreement in extreme precipitation exposure assessment is modified by race and social vulnerability

Epidemiologic investigations of extreme precipitation events (EPEs) often rely on observations from the nearest weather station to represent individuals' exposures, and due to structural factors that determine the siting of weather stations, levels of measurement error and misclassification bias may differ by race, class, and other measures of social vulnerability. Gridded climate datasets provide higher spatial resolution that may improve measurement error and misclassification bias. However, similarities in the ability to identify EPEs among these types of datasets have not been explored. In this study, we characterize the overall and temporal patterns of agreement among three commonly used meteorological data sources in their identification of EPEs in all census tracts and counties in the conterminous United States over the 1991-2020 U.S. Climate Normals period and evaluate the association between sociodemographic characteristics with agreement in EPE identification. Daily precipitation measurements from weather stations in the Global Historical Climatology Network (GHCN) and gridded precipitation estimates from the Parameter-elevation Relationships on Independent Slopes Model (PRISM) and the North American Land Data Assimilation System (NLDAS) were compared in their ability to identify EPEs defined as the top 1% of precipitation events or daily precipitation >1 inch. Agreement among these datasets is fair to moderate from 1991 to 2020. There are spatial and temporal differences in the levels of agreement between ground stations and gridded climate datasets in their detection of EPEs in the United States from 1991 to 2020. Spatial variation in agreement is most strongly related to a location's proximity to the nearest ground station, with areas furthest from a ground station demonstrating the lowest levels of agreement. These areas have lower socioeconomic status, a higher proportion of Native American population, and higher social vulnerability index scores. The addition of ground stations in these areas may increase agreement, and future studies intending to use these or similar data sources should be aware of the limitations, biases, and potential for differential misclassification of exposure to EPEs. Most importantly, vulnerable populations should be engaged to determine their priorities for enhanced surveillance of climate-based threats so that community-identified needs are met by any future improvements in data quality.

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.

The Impact of Cold Ambient Temperature in the Pattern of Influenza Virus Infection

Background

Prior literature suggests that cold temperature strongly influences the immune function of animals and human behaviors, which may allow for the transmission of respiratory viral infections. However, information on the impact of cold stimuli, especially the impact of temporal change in the ambient temperature on influenza virus transmission, is limited.

Methods

A susceptible-infected-recovered-susceptible model was applied to evaluate the effect of temperature change on influenza virus transmission.

Results

The mean temperature of the prior week was positively associated with the number of newly diagnosed cases (0.107 [95% Bayesian credible interval {BCI}, .106-.109]), whereas the mean difference in the temperature of the prior week was negatively associated (-0.835 [95% BCI, -.840 to -.830]). The product of the mean temperature and mean difference in the temperature of the previous week were also negatively associated with the number of newly diagnosed cases (-0.192 [95% BCI, -.197 to -.187]).

Conclusions

The mean temperature and the mean difference in temperature affected the number of newly diagnosed influenza cases differently. Our data suggest that high ambient temperature and a drop in the temperature and their interaction increase the risk of infection. Therefore, the highest risk of infection is attributable to a steep fall in temperature in a relatively warm environment.

Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm

BACKGROUND

Influenza epidemics pose a threat to human health. It has been reported that meteorological factors (MFs) are associated with influenza. This study aimed to explore the similarities and differences between the influences of more comprehensive MFs on influenza in cities with different economic, geographical and climatic characteristics in Fujian Province. Then, the information was used to predict the daily number of cases of influenza in various cities based on MFs to provide bases for early warning systems and outbreak prevention.

METHOD

Distributed lag nonlinear models (DLNMs) were used to analyse the influence of MFs on influenza in different regions of Fujian Province from 2010 to 2021. Long short-term memory (LSTM) was used to train and model daily cases of influenza in 2010-2018, 2010-2019, and 2010-2020 based on meteorological daily values. Daily cases of influenza in 2019, 2020 and 2021 were predicted. The root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to quantify the accuracy of model predictions.

RESULTS

The cumulative effect of low and high values of air pressure (PRS), air temperature (TEM), air temperature difference (TEMD) and sunshine duration (SSD) on the risk of influenza was obvious. Low (< 979 hPa), medium (983 to 987 hPa) and high (> 112 hPa) PRS were associated with a higher risk of influenza in women, children aged 0 to 12 years, and rural populations. Low (< 9 °C) and high (> 23 °C) TEM were risk factors for influenza in four cities. Wind speed (WIN) had a more significant effect on the risk of influenza in the ≥ 60-year-old group. Low (< 40%) and high (> 80%) relative humidity (RHU) in Fuzhou and Xiamen had a significant effect on influenza. When PRS was between 1005-1015 hPa, RHU > 60%, PRE was low, TEM was between 10-20 °C, and WIN was low, the interaction between different MFs and influenza was most obvious. The RMSE, MAE, MAPE, and SMAPE evaluation indices of the predictions in 2019, 2020 and 2021 were low, and the prediction accuracy was high.

CONCLUSION

All eight MFs studied had an impact on influenza in four cities, but there were similarities and differences. The LSTM model, combined with these eight MFs, was highly accurate in predicting the daily cases of influenza. These MFs and prediction models could be incorporated into the influenza early warning and prediction system of each city and used as a reference to formulate prevention strategies for relevant departments.

How do El Niño Southern Oscillation (ENSO) and local meteorological factors affect the incidence of seasonal influenza in New York state

Background

Research is lacking in examining how multiple climate factors affect the incidence of seasonal influenza. We investigated the associations between El Niño Southern Oscillation (ENSO), meteorological factors, and influenza incidence in New York State, United States.

Method

We collected emergency department visit data for influenza from the New York State Department of Health. ENSO index was obtained from the National Oceanic and Atmospheric Administration. Meteorological factors, Google Flu Search Index (GFI), and Influenza-like illness (ILI) data in New York State were also collected. Wavelet analysis was used to quantitatively estimate the coherence and phase difference of ENSO, temperature, precipitation, relative humidity, and absolute humidity with emergency department visits of influenza in New York State. Generalized additive models (GAM) were employed to examine the exposure-response relationships between ENSO, weather, and influenza. GFI and ILI data were used to simulate synchronous influenza visits.

Results

The influenza epidemic in New York State had multiple periodic and was primarily on the 1-year scale. The incidence of influenza closely followed the low ENSO index by an average of two months, and the lag period of ENSO on influenza was shorter during 2015-2018. Low temperature in the previous 2 weeks and low absolute humidity in the prior week were positively associated with influenza incidence in New York State. We found an l-shaped association between ENSO index and influenza, a parabolic relationship between temperature in the previous two weeks and influenza, and a linear negative association between absolute humidity in the previous week and influenza. The simulation models including GFI and ILI had higher accuracy for influenza visit estimation.

Conclusions

Low ENSO index, low temperature, and low absolute humidity may drive the influenza epidemics in New York State. The findings can help us deepen the understanding of the climate-influenza association, and help to develop an influenza forecasting model.

Air Pollution-Related Respiratory Diseases and Associated Environmental Factors in Chiang Mai, Thailand, in 2011–2020

The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet’s ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate conditions, with two respiratory infections, influenza and pneumonia, in Chiang Mai, Thailand, which has been considered the most polluted city on Earth during the hot season. We used a seasonal-trend decomposition procedure based on loess regression (STL) and a seasonal cycle subseries (SCS) plot to determine the seasonality of the two diseases. In addition, multivariable negative binomial regression (NBR) models were used to assess the association between the diseases and environmental variables (temperature, precipitation, relative humidity, PM2.5, and PM10). The data revealed that influenza had a clear seasonal pattern during the cold months of January and February, whereas the incidence of pneumonia showed a weak seasonal pattern. In terms of forecasting, the preceding month’s PM2.5 and temperature (lag1) had a significant association with influenza incidence, while the previous month’s temperature and relative humidity influenced pneumonia. Using air pollutants as an indication of respiratory disease, our models indicated that PM2.5 lag1 was correlated with the incidence of influenza, but not pneumonia. However, there was a linear association between PM10 and both diseases. This research will help in allocating clinical and public health resources in response to potential environmental changes and forecasting the future dynamics of influenza and pneumonia in the region due to air pollution.

Concentrations of nitrogen compounds are related to severe rhinovirus infection in infants. A time-series analysis from the reference area of a pediatric university hospital in Barcelona

BACKGROUND

There is scarce information focused on the effect of weather conditions and air pollution on specific acute viral respiratory infections, such as rhinovirus (RV), with a wide clinical spectrum of severity.

OBJECTIVE

The aim of this study was to analyze the association between episodes of severe respiratory tract infection by RV and air pollutant concentrations (NO_x and SO₂ ) in the reference area of a pediatric university hospital.

METHODS

An analysis of temporal series of daily values of NO_x and SO₂ , weather variables, circulating pollen and mold spores, and daily number of admissions in the pediatric intensive care unit (PICU) with severe respiratory RV infection (RVi) in children between 6 months and 18 years was performed. Lagged variables for 0-5 days were considered. The study spanned from 2010 to 2018. Patients with comorbidities were excluded.

RESULTS

One hundred and fifty patients were admitted to the PICU. Median age was 19 months old (interquartile range [IQR]: 11-47). No relationship between RV-PICU admissions and temperature, relative humidity, cumulative rainfall, or wind speed was found. Several logistic regression models with one pollutant and two pollutants were constructed but the best model was that which included average daily NO_x concentrations. Average daily NO_x concentrations were related with the presence of PICU admissions 3 days later (odds ratio per IQR-unit increase: 1.64, 95% confidence interval: 1.20-2.25)).

CONCLUSIONS

This study has shown a positive correlation between NO_x concentrations at Lag 3 and children's PICU admissions with severe RV respiratory infection. Air pollutant data should be taken into consideration when we try to understand the severity of RVis.

Effects and Interaction of Meteorological Parameters on Influenza Incidence During 2010-2019 in Lanzhou, China

Background

Influenza is a seasonal infectious disease, and meteorological parameters critically influence the incidence of influenza. However, the meteorological parameters linked to influenza occurrence in semi-arid areas are not studied in detail. This study aimed to clarify the impact of meteorological parameters on influenza incidence during 2010-2019 in Lanzhou. The results are expected to facilitate the optimization of influenza-related public health policies by the local healthcare departments.

Methods

Descriptive data related to influenza incidence and meteorology during 2010-2019 in Lanzhou were analyzed. The exposure-response relationship between the risk of influenza occurrence and meteorological parameters was explored according to the distributed lag no-linear model (DLNM) with Poisson distribution. The response surface model and stratified model were used to estimate the interactive effect between relative humidity (RH) and other meteorological parameters on influenza incidence.

Results

A total of 6701 cases of influenza were reported during 2010-2019. DLNM results showed that the risk of influenza would gradually increase as the weekly mean average ambient temperature (AT), RH, and absolute humidity (AH) decrease at lag 3 weeks when they were lower than 12.16°C, 51.38%, and 5.24 g/m³, respectively. The low Tem (at 5th percentile, P₅) had the greatest effect on influenza incidence; the greatest estimated relative risk (RR) was 4.54 (95%CI: 3.19-6.46) at cumulative lag 2 weeks. The largest estimates of RRs for low RH (P₅) and AH (P₅) were 4.81 (95%CI: 3.82-6.05) and 4.17 (95%CI: 3.30-5.28) at cumulative lag 3 weeks, respectively. An increase in AT by 1°C led to an estimates of percent change (95%CI) of 3.12% (-4.75% to -1.46%) decrease in the weekly influenza case counts in a low RH environment. In addition, RH showed significant interaction with AT and AP on influenza incidence but not with wind speed.

Conclusion

This study indicated that low AT, low humidity (RH and AH), and high air pressure (AP) increased the risk of influenza. Moreover, the interactive effect of low RH with low AT and high AP can aggravate the incidence of influenza.

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.

Extreme Precipitation Events and Infectious Disease Risk: A Scoping Review and Framework for Infectious Respiratory Viruses

Extreme precipitation events (EPE) change the natural and built environments and alter human behavior in ways that facilitate infectious disease transmission. EPEs are expected with high confidence to increase in frequency and are thus of great public health importance. This scoping review seeks to summarize the mechanisms and severity of impacts of EPEs on infectious diseases, to provide a conceptual framework for the influence of EPEs on infectious respiratory diseases, and to define areas of future study currently lacking in this field. The effects of EPEs are well-studied with respect to enteric, vector-borne, and allergic illness where they are shown to moderately increase risk of illness, but not well-understood in relation to infectious respiratory illness. We propose a framework for a similar influence of EPEs on infectious respiratory viruses through several plausible pathways: decreased UV radiation, increased ambient relative humidity, and changes to human behavior (increased time indoors and use of heating and cooling systems). However, limited work has evaluated meteorologic risk factors for infectious respiratory diseases. Future research is needed to evaluate the effects of EPEs on infectious respiratory diseases using individual-level case surveillance, fine spatial scales, and lag periods suited to the incubation periods of the disease under study, as well as a full characterization of susceptible, vulnerable, and sensitive population characteristics.

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.

Impact of meteorological parameters on COVID-19 transmission in Bangladesh: a spatiotemporal approach

It has been more than 10 months since the first COVID-19 case was reported in Wuhan, China, still menacing the world with a possible second wave. This study aimed to analyze how meteorological variables can affect the spread of local COVID-19 transmission in Bangladesh. Nine spatial units were considered from a meteorological standpoint to characterize COVID-19 transmission in Bangladesh. The daily COVID-19 incidence and meteorological variable (e.g., mean temperature, relative humidity, precipitation, and wind speed) data from April 5 to September 20, 2020, were collected. The Spearman rank correlation, heat maps, and multivariate quasi-Poisson regression were employed to understand their association. The effect of meteorological variables on COVID-19 transmission was modeled using a lag period of 10 days. Results showed that mean temperature, relative humidity, and wind speed are substantially associated with an increased risk of COVID-19. On the other hand, daily precipitation is significantly associated with a decreased risk of COVID-19 incidence. The relative risks (RR) of mean temperature for daily COVID-19 incidences were 1.222 (95% confidence interval [CI], 1.214-1.232). For wind speed, the RR was 1.087 (95% CI, 1.083-1.090). For relative humidity, the RR was 1.027 (95% CI, 1.025-1.029). Overall, this study found the profound effect of meteorological parameters on COVID-19 incidence across selected nine areas in Bangladesh. This study is probably the first study to explore the impact of region-specific meteorological conditions on COVID-19 incidence in Bangladesh. Moreover, adjustments on the areal-aggregated and regional levels were made for three confounding factors, including lockdown, population density, and potential seasonal effects. The study's findings suggest that SARS-CoV-2 can be transmitted in high temperatures and humidity conditions, which contradicts many other countries' prior studies. The research outcomes will provide implications for future control and prevention measures in Bangladesh and other countries with similar climate conditions and population density.

Transmissibility of influenza during the 21st-century epidemics, Spain, influenza seasons 2001/02 to 2017/18

BackgroundUnderstanding influenza seasonality is necessary for determining policies for influenza control.AimWe characterised transmissibility during seasonal influenza epidemics, including one influenza pandemic, in Spain during the 21th century by using the moving epidemic method (MEM) to calculate intensity levels and estimate differences across seasons and age groups.MethodsWe applied the MEM to Spanish Influenza Sentinel Surveillance System data from influenza seasons 2001/02 to 2017/18. A modified version of Goldstein's proxy was used as an epidemiological-virological parameter. We calculated the average starting week and peak, the length of the epidemic period and the length from the starting week to the peak of the epidemic, by age group and according to seasonal virus circulation.ResultsIndividuals under 15 years of age presented higher transmissibility, especially in the 2009 influenza A(H1N1) pandemic. Seasons with dominance/co-dominance of influenza A(H3N2) virus presented high intensities in older adults. The 2004/05 influenza season showed the highest influenza-intensity level for all age groups. In 12 seasons, the epidemic started between week 50 and week 3. Epidemics started earlier in individuals under 15 years of age (-1.8 weeks; 95% confidence interval (CI):-2.8 to -0.7) than in those over 64 years when influenza B virus circulated as dominant/co-dominant. The average time from start to peak was 4.3 weeks (95% CI: 3.6-5.0) and the average epidemic length was 8.7 weeks (95% CI: 7.9-9.6).ConclusionsThese findings provide evidence for intensity differences across seasons and age groups, and can be used guide public health actions to diminish influenza-related morbidity and mortality.

Impacts of transportation and meteorological factors on the transmission of COVID-19

The ongoing pandemic of 2019 novel coronavirus disease (COVID-19) is challenging global public health response system. We aim to identify the risk factors for the transmission of COVID-19 using data on mainland China. We estimated attack rate (AR) at county level. Logistic regression was used to explore the role of transportation in the nationwide spread. Generalized additive model and stratified linear mixed-effects model were developed to identify the effects of multiple meteorological factors on local transmission. The ARs in affected counties ranged from 0.6 to 9750.4 per million persons, with a median of 8.8. The counties being intersected by railways, freeways, national highways or having airports had significantly higher risk for COVID-19 with adjusted odds ratios (ORs) of 1.40 (p = 0.001), 2.07 (p < 0.001), 1.31 (p = 0.04), and 1.70 (p < 0.001), respectively. The higher AR of COVID-19 was significantly associated with lower average temperature, moderate cumulative precipitation and higher wind speed. Significant pairwise interactions were found among above three meteorological factors with higher risk of COVID-19 under low temperature and moderate precipitation. Warm areas can also be in higher risk of the disease with the increasing wind speed. In conclusion, transportation and meteorological factors may play important roles in the transmission of COVID-19 in mainland China, and could be integrated in consideration by public health alarm systems to better prevent the disease.

Modeling analysis revealed the distinct global transmission patterns of influenza A viruses and their influencing factors

Influenza A virus has caused huge damage to human health and poultry production worldwide, but its global transmission patterns and influencing factors remain unclear. Here, by using the Nearest Genetic Distance Approach with genetic sequences data, we reconstructed the global transmission patterns of 4 most common subtypes of influenza A virus (H1N1, H3N2, H5N1, and H7N9) and analyzed associations of transmission velocity of these influenza viruses with environmental factors. We found that the transmission patterns of influenza viruses and their associations with environmental factors were closely related to their host properties. H1N1 and H3N2, which are mainly held by humans, are transmitted between regions at high velocity and over long distances, which may be due to human transportation via airplane; while H5N1 and H7N9, which are mainly carried by animals, are transmitted locally at short distances and at low velocity, which may be facilitated by poultry transportation via railways or high ways. H1N1 and H3N2 spread faster in cold seasons, while H5N1 spread faster in both cold and warm seasons, and H7N9 spread faster in wet seasons. H1N1, H3N2, and H5N1 spread faster in places with both high and low human densities. Our study provided novel insights into the global transmission patterns, processes, and management strategies for influenza under accelerated global change.

Could Environment Affect the Mutation of H1N1 Influenza Virus?

H1N1 subtype influenza A viruses are the most common type of influenza A virus to infect humans. The two major outbreaks of the virus in 1918 and 2009 had a great impact both on human health and social development. Though data on their complete genome sequences have recently been obtained, the evolution and mutation of A/H1N1 viruses remain unknown to this day. Among many drivers, the impact of environmental factors on mutation is a novel hypothesis worth studying. Here, a geographically disaggregated method was used to explore the relationship between environmental factors and mutation of A/H1N1 viruses from 2000-2019. All of the 11,721 geo-located cases were examined and the data was analysed of six environmental elements according to the time and location (latitude and longitude) of those cases. The main mutation value was obtained by comparing the sequence of the influenza virus strain with the earliest reported sequence. It was found that environmental factors systematically affect the mutation of A/H1N1 viruses. Minimum temperature displayed a nonlinear, rising association with mutation, with a maximum ~15 °C. The effects of precipitation and social development index (nighttime light) were more complex, while population density was linearly and positively correlated with mutation of A/H1N1 viruses. Our results provide novel insight into understanding the complex relationships between mutation of A/H1N1 viruses and environmental factors.

Evaluating the impact of the weather conditions on the influenza propagation

Background

Predicting the details of how an epidemic evolves is highly valuable as health institutions need to better plan towards limiting the infection propagation effects and optimizing their prediction and response capabilities. Simulation is a cost- and time-effective way of predicting the evolution of the infection as the joint influence of many different factors: interaction patterns, personal characteristics, travel patterns, meteorological conditions, previous vaccination, etc. The work presented in this paper extends EpiGraph, our influenza epidemic simulator, by introducing a meteorological model as a modular component that interacts with the rest of EpiGraph’s modules to refine our previous simulation results. Our goal is to estimate the effects of changes in temperature and relative humidity on the patterns of epidemic influenza based on data provided by the Spanish Influenza Sentinel Surveillance System (SISSS) and the Spanish Meteorological Agency (AEMET).

Methods

Our meteorological model is based on the regression model developed by AB and JS, and it is tuned with influenza surveillance data obtained from SISSS. After pre-processing this data to clean it and reconstruct missing samples, we obtain new values for the reproduction number of each urban region in Spain, every 10 minutes during 2011. We simulate the propagation of the influenza by setting the date of the epidemic onset and the initial influenza-illness rates for each urban region.

Results

We show that the simulation results have the same propagation shape as the weekly influenza rates as recorded by SISSS. We perform experiments for a realistic scenario based on actual meteorological data from 2010-2011, and for synthetic values assumed under simplified predicted climate change conditions. Results show that a diminishing relative humidity of 10% produces an increment of about 1.6% in the final infection rate. The effect of temperature changes on the infection spread is also noticeable, with a decrease of 1.1% per extra degree.Conclusions: Using a tool like ours could help predict the shape of developing epidemics and its peaks, and would permit to quickly run scenarios to determine the evolution of the epidemic under different conditions. We make EpiGraph source code and epidemic data publicly available.

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.

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.

Low Temperature and Low UV Indexes Correlated with Peaks of Influenza Virus Activity in Northern Europe during 2010⁻2018

With the increasing pace of global warming, it is important to understand the role of meteorological factors in influenza virus (IV) epidemics. In this study, we investigated the impact of temperature, UV index, humidity, wind speed, atmospheric pressure, and precipitation on IV activity in Norway, Sweden, Finland, Estonia, Latvia and Lithuania during 2010–2018. Both correlation and machine learning analyses revealed that low temperature and UV indexes were the most predictive meteorological factors for IV epidemics in Northern Europe. Our in vitro experiments confirmed that low temperature and UV radiation preserved IV infectivity. Associations between these meteorological factors and IV activity could improve surveillance and promote development of accurate predictive models for future influenza outbreaks in the region.

Influenza-associated neurological complications during 2014-2017 in Taiwan

INTRODUCTION

Seasonal influenza-associated neurological complications had high mortality and morbidity rates in recent studies. We reported influenza-associated encephalitis/encephalopathy in children during 2014-2017 in Taiwan, focusing on neurological presentations, neuroimaging correlations, and critical care managements.

MATERIALS/SUBJECTS

During January 1st 2014 to June 30th 2017, pediatric patients reported to the Taiwan Centers for Disease Control surveillance system for severe complicated influenza infections in the hospital were retrospectively reviewed. Children with influenza-associated encephalitis/encephalopathy were inspected for clinical presentations, laboratory data, neuroimaging studies, treatment modalities, and neurological outcomes.

RESULTS

Ten children with median age 5.9 years were enrolled for analysis. Influenza-associated encephalitis/encephalopathy appeared in the spring and summer, with a delayed peak comparing with the occurrence of pneumonia and septic shock. The neurological symptoms developed rapidly within median 1 day after the first fever episode. All patients had consciousness disturbance. Seven patients (70%) had seizures at initial presentation, and six of them had status epilepticus. Anti-viral treatments were applied in all patients, with median door-to-drug time 0.9 h for oseltamivir and 6.0 h for peramivir. Multi-modality treatments also included steroid pulse therapy, immunoglobulin treatment, and target temperature management, with 85.2% of the major treatments administered within 12 h after admission. Nine of the ten patients recovered without neurological sequelae. Only one patient had epilepsy requiring long-term anticonvulsants and concomitant cognitive decline.

CONCLUSIONS

In highly prevalent area, influenza-associated encephalitis/encephalopathy should be considered irrespective of seasons. Our study suggested the effects of timely surveillance and multi-modality treatments in influenza-associated encephalitis/encephalopathy.

Forecasting influenza epidemics by integrating internet search queries and traditional surveillance data with the support vector machine regression model in Liaoning, from 2011 to 2015

Background

Influenza epidemics pose significant social and economic challenges in China. Internet search query data have been identified as a valuable source for the detection of emerging influenza epidemics. However, the selection of the search queries and the adoption of prediction methods are crucial challenges when it comes to improving predictions. The purpose of this study was to explore the application of the Support Vector Machine (SVM) regression model in merging search engine query data and traditional influenza data.

Methods

The official monthly reported number of influenza cases in Liaoning province in China was acquired from the China National Scientific Data Center for Public Health from January 2011 to December 2015. Based on Baidu Index, a publicly available search engine database, search queries potentially related to influenza over the corresponding period were identified. An SVM regression model was built to be used for predictions, and the choice of three parameters (C, γ, ε) in the SVM regression model was determined by leave-one-out cross-validation (LOOCV) during the model construction process. The model’s performance was evaluated by the evaluation metrics including Root Mean Square Error, Root Mean Square Percentage Error and Mean Absolute Percentage Error.

Results

In total, 17 search queries related to influenza were generated through the initial query selection approach and were adopted to construct the SVM regression model, including nine queries in the same month, three queries at a lag of one month, one query at a lag of two months and four queries at a lag of three months. The SVM model performed well when with the parameters (C = 2, γ = 0.005, ɛ = 0.0001), based on the ensemble data integrating the influenza surveillance data and Baidu search query data.

Conclusions

The results demonstrated the feasibility of using internet search engine query data as the complementary data source for influenza surveillance and the efficiency of SVM regression model in tracking the influenza epidemics in Liaoning.