Associations of meteorological factors and dynamics of scrub typhus incidence in South Korea: A nationwide time-series study.

Scrub typhus, also known as Tsutsugamushi disease, is a climate-sensitive vector-borne disease that poses a growing public health threat. However, studies on the association between scrub typhus epidemics and meteorological factors in South Korea need to be complemented. Therefore, we aimed to analyze the association among ambient temperature, precipitation, and the incidence of scrub typhus in South Korea. First, we obtained data on the weekly number of scrub typhus cases and concurrent meteorological variables at the city-county level (Si-Gun) in South Korea between 2001 and 2019. Subsequently, a two-stage meta-regression analysis was conducted. In the first stage, we conducted time-series regression analyses using a distributed lag nonlinear model (DLNM) to investigate the association between temperature, precipitation, and scrub typhus incidence at each location. In the second stage, we employed a multivariate meta-regression model to combine the association estimates from all municipalities, considering regional indicators, such as mite species distribution, Normalized Difference Vegetation Index (NDVI), and urban-rural classification. Weekly mean temperature and weekly total precipitation exhibited a reversed U-shaped nonlinear association with the incidence of scrub typhus. The overall cumulative association with scrub typhus incidence peaked at 18.7 C° (with RRs of 9.73, 95% CI: 5.54-17.10) of ambient temperature (reference 9.7 C°) and 162.0 mm (with RRs of 1.87, 95% CI: 1.02-3.83) of precipitation (reference 2.8 mm), respectively. These findings suggest that meteorological factors contribute to scrub typhus epidemics by interacting with vectors, reservoir hosts, and human behaviors. This information serves as a reference for future public health policies and epidemiological research aimed at controlling scrub typhus infections.

Integrated analysis of remote sensing with meteorological and health data for allergic rhinitis forecasting in Tianjin.

Long time series of vegetation monitoring can be carried out by remote sensing data, the level of urban greening is objectively described, and the spatial characteristics of plant pollen are indirectly understood. Pollen is the main allergen in patients with seasonal allergic rhinitis. Meteorological factors affect the release and diffusion of pollen. Therefore, studying of the complex relationship between meteorological factors and allergic rhinitis is essential for effective prevention and treatment of the disease. In this study, we leverage remote sensing data for a comprehensive decade-long analysis of urban greening in Tianjin, which exhibits an annual increase in vegetative cover of 0.51 per annum, focusing on its impact on allergic rhinitis through changes in pollen distribution. Utilizing high-resolution imagery, we quantify changes in urban Fractional Vegetation Coverage (FVC) and its correlation with pollen types and allergic rhinitis cases. Our analysis reveals a significant correlation between FVC trends and pollen concentrations, with a surprising value of 0.71, highlighting the influence of urban greenery on allergenic pollen levels. We establish a robust connection between the seasonal patterns of pollen outbreaks and allergic rhinitis consultations, with a noticeable increase in consultations during high pollen seasons. our findings indicate a higher allergenic potential of herbaceous compared to woody vegetation. This nuanced understanding underscores the importance of pollen sensitivity, alongside concentration, in driving allergic rhinitis incidents. Utilizing a Generalized Linear Model, significant features influencing the number of visits for allergic rhinitis (P < 0.05) were identified. Both GLM and LSTM models were employed to forecast the visitation volumes for rhinitis during the spring and summer-autumn of 2022. Upon validation, it was found that the R² values between the simulated and actual values for both GLM and LSTM models surpassed the 95% confidence threshold. Moreover, the R² values for the summer-autumn seasons (GLM: 0.56, LSTM: 0.72) were higher than those for spring (GLM: 0.22, LSTM: 0.47). Comparing the errors between the simulated and actual values of GLM and LSTM models, LSTM exhibited higher simulation precision in both spring and summer-autumn seasons, demonstrating superior simulation performance. Overall, our study pioneers the integration of remote sensing with meteorological and health data for allergic rhinitis forecasting. This integrative approach provides valuable insights for public health planning, particularly in urban settings, and lays the groundwork for advanced, location-specific allergenic pollen forecasting and mitigation strategies.

Interaction between air pollutants and meteorological factors on pulmonary tuberculosis in northwest China: A case study of eight districts in Urumqi.

Meteorological factors and air pollutants are associated with the spread of pulmonary tuberculosis (PTB), but few studies have examined the effects of their interactions on PTB. Therefore, this study investigated the impact of meteorological factors and air pollutants and their interactions on the risk of PTB in Urumqi, a city with a high prevalence of PTB and a high level of air pollution. The number of new PTB cases in eight districts of Urumqi from 2014 to 2019 was collected, along with data on meteorological factors and air pollutants for the same period. A generalized additive model was applied to explore the effects of meteorological factors and air pollutants and their interactions on the risk of PTB incidence. Segmented linear regression was used to estimate the nonlinear characteristics of the impact of meteorological factors on PTB. During 2014-2019, a total of 14,402 new cases of PTB were reported in eight districts, with March to May being the months of high PTB incidence. The exposure-response curves for temperature (Temp), relative humidity (RH), wind speed (WS), air pressure (AP), and diurnal temperature difference (DTR) were generally inverted "U" shaped, with the corresponding threshold values of - 5.411 °C, 52.118%, 3.513 m/s, 1021.625 hPa, and 8.161 °C, respectively. The effects of air pollutants on PTB were linear and lagged. All air pollutants were positively associated with PTB, except for O3, which was not associated with PTB, and the ER values for the effects on PTB were as follows: 0.931 (0.255, 1.612) for PM2.5, 1.028 (0.301, 1.760) for PM10, 5.061 (0.387, 9.952) for SO2, 2.830 (0.512, 5.200) for NO2, and 5.789 (1.508, 10.251) for CO. Meteorological factors and air pollutants have an interactive effect on PTB. The risk of PTB incidence was higher when in high Temp-high air pollutant, high RH-high air pollutant, high WS-high air pollutant, lowAP-high air pollutant, and high DTR-high air pollutant. In conclusion, both meteorological and pollutant factors had an influence on PTB, and the influence on PTB may have an interaction.

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.

Comprehensive source identification of heavy metals in atmospheric particulate matter in a megacity: A case study of Hangzhou.

Megacities face significant pollution challenges, particularly the elevated levels of heavy metals (HMs) in particulate matter (PM). Despite the advent of interdisciplinary and advanced methods for HM source analysis, integrating and applying these approaches to identify HM sources in PM remains a hurdle. This study employs a year-long daily sampling dataset for PM1 and PM1-10 to examine the patterns of HM concentrations under hazy, clean, and rainy conditions in Hangzhou City, aiming to pinpoint the primary sources of HMs in PM. Contrary to other HMs that remained within acceptable limits, the annual average concentrations of Cd and Ni were found to be 20.6 ± 13.6 and 46.9 ± 34.8 ng/m³, respectively, surpassing the World Health Organization's limits by 4.1 and 1.9 times. Remarkably, Cd levels decreased on hazy days, whereas Ni levels were observed to rise on rainy days. Using principal component analysis (PCA), enrichment factor (EF), and backward trajectory analysis, Fe, Mn, Cu, and Zn were determined to be primarily derived from traffic emissions, and there was an interaction between remote migration and local emissions in haze weather. Isotope analysis reveals that Pb concentrations in the Hangzhou region were primarily influenced by emissions from unleaded gasoline, coal combustion, and municipal solid waste incineration, with additional impact from long-range transport; it also highlights nuanced differences between PM1 and PM1-10. Pb isotope and PCA analyses indicate that Ni primarily stemmed from waste incineration emissions. This explanation accounts for the observed higher Ni concentrations on rainy days. Backward trajectory cluster analysis revealed that southern airflows were the primary source of high Cd concentrations on clean days in Hangzhou City. This study employs a multifaceted approach and cross-validation to successfully delineate the sources of HMs in Hangzhou's PM. It offers a methodology for the precise and reliable analysis of complex HM sources in megacity PM.

Urbanization and weather dynamics co-dominated the spatial-temporal variation in pCO2 and CO2 fluxes in small montanic rivers draining diverse landscapes.

Rivers have been widely reported as important CO2 emitters to the atmosphere. Rapid urbanization has a profound impact on the carbon biogeochemical cycle of rivers, leading to enhanced riverine CO2 evasions. However, it is still unclear whether the spatial-temporal patterns of CO2 emissions in the rivers draining diverse landscapes dominated by urbanization were stable, especially in mountainous areas. This study carried out a two-year investigation of water environmental hydrochemistry in three small mountainous rivers draining urban, suburban and rural landscapes in southwestern China, and CO2 partial pressure (pCO2) and fluxes (fCO2) in surface water were measured using headspace equilibrium method and classical thin boundary layer model. The average pCO2 and fCO2 in the highly urbanized river were of 4783.6 µatm and 700.0 mmol m-2 d-1, conspicuously higher than those in the rural river (1525.9 µatm and 123.2 mmol m-2 d-1), and the suburban river presented a moderate level (3114.2 µatm and 261.2 mmol m-2 d-1). It provided even clearer evidence that watershed urbanization could remarkably enhance riverine CO2 emissions. More importantly, the three rivers presented different longitudinal variations in pCO2, implying diversified spatial patterns of riverine CO2 emissions as a result of urbanization. The urban land can explain 49.6-69.1% of the total spatial variation in pCO2 at the reach scale, indicating that urban land distribution indirectly dominated the longitudinal pattern of riverine pCO2 and fCO2. pCO2 and fCO2 in the three rivers showed similar temporal variability with higher warm-rainy seasons and lower dry seasons, which are significantly controlled by weather dynamics, including monthly temperature and precipitation, but seem to be impervious to watershed urbanization. High temperature-stimulated microorganisms metabolism and riched-CO2 runoff input lead much higher pCO2 in warm-rainy seasons. However, it showed more sensitivity of pCO2 to monthly weather dynamics in urbanized rivers than that in rural rivers, and warm-rainy seasons showed hot moments of CO2 evasion for urban rivers. TOC, DOC, TN, pH and DO were the main controls on pCO2 in the urban and suburban rivers, while only pH and DO were connected with pCO2 in the rural rivers. This indicated differential controls and regulatory processes of pCO2 in the rivers draining diverse landscapes. Furthermore, it suggested that pCO2 calculated by the pH-total alkalinity method would obviously overestimate pCO2 in urban polluted rivers due to the inevitable influence of non-carbonate alkalinity, and thus, a relatively conservative headspace method should be recommended. We highlighted that urbanization and weather dynamics co-dominated the multiformity and uncertainty in spatial-temporal patterns of riverine CO2 evasions, which should be considered when modeling CO2 dynamics in urbanized rivers.

Predictions of PM2.5 using air pollutants and meteorological factors with COVID-19 cases in Malaysia and Indonesia: a comparative study using feature selection and robust regression.

The study examines the relationship between air quality, meteorological factors, and COVID-19 cases in Cheras, Kuala Lumpur, and Kelapa Gading, North Jakarta. Analyzing data from 2020 and 2021, the research found notable correlations: COVID-19 cases in Cheras were positively associated with relative humidity (RH) and carbon monoxide (CO) but negatively with ozone (O3) and RH in different years. In Kelapa Gading, COVID-19 cases were positively correlated with pollutants like sulfur dioxide (SO2) and CO, while ambient temperature (AT) showed a negative correlation. The enforcement of social restrictions notably reduced air pollution, affecting COVID-19 spread. Predictive models for PM2.5 levels using robust regression techniques showed strong performance in Kuala Lumpur (R² > 0.9) but exhibited overfitting tendencies in Jakarta, suggesting the need for a longer study period for more accurate results.

ELoran Propagation Delay Prediction Model Based on a BP Neural Network for a Complex Meteorological Environment.

The core of eLoran ground-based timing navigation systems is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for a complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on a Back-Propagation neural network (BPNN) for a complex meteorological environment, which realizes the function of directly mapping propagation delay fluctuation through meteorological factors. First, the theoretical influence of meteorological factors on each component of propagation delay is analyzed based on calculation parameters. Then, through the correlation analysis of the measured data, the complex relationship between the seven main meteorological factors and the propagation delay, as well as their regional differences, are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with that of the existing linear model and simple neural network model.

Machine Learning-Aided Causal Inference Framework for Environmental Data Analysis: A COVID-19 Case Study.

Links between environmental conditions (e.g., meteorological factors and air quality) and COVID-19 severity have been reported worldwide. However, the existing frameworks of data analysis are insufficient or inefficient to investigate the potential causality behind the associations involving multidimensional factors and complicated interrelationships. Thus, a causal inference framework equipped with the structural causal model aided by machine learning methods was proposed and applied to examine the potential causal relationships between COVID-19 severity and 10 environmental factors (NO2, O3, PM2.5, PM10, SO2, CO, average air temperature, atmospheric pressure, relative humidity, and wind speed) in 166 Chinese cities. The cities were grouped into three clusters based on the socio-economic features. Time-series data from these cities in each cluster were analyzed in different pandemic phases. The robustness check refuted most potential causal relationships' estimations (89 out of 90). Only one potential relationship about air temperature passed the final test with a causal effect of 0.041 under a specific cluster-phase condition. The results indicate that the environmental factors are unlikely to cause noticeable aggravation of the COVID-19 pandemic. This study also demonstrated the high value and potential of the proposed method in investigating causal problems with observational data in environmental or other fields.

Meteorological factors, governmental responses and COVID-19: Evidence from four European countries.

With the global lockdown, meteorological factors are highly discussed for COVID-19 transmission. In this study, national-specific and region-specific data sets from Germany, Italy, Spain and the United Kingdom were used to explore the effect of temperature, absolute humidity and diurnal temperature range (DTR) on COVID-19 transmission. From February 1st to November 1st, a 7-day COVID-19 case doubling time (Td), meteorological factors with cumulative 14-day-lagged, government response index and other factors were fitted in the distributed lag nonlinear models. The overall relative risk (RR) of the 10th and the 25th percentiles temperature compared to the median were 0.0074 (95% CI: 0.0023, 0.0237) and 0.1220 (95% CI: 0.0667, 0.2232), respectively. The pooled RR of lower (10th, 25th) and extremely high (90th) absolute humidity were 0.3266 (95% CI: 0.1379, 0.7734), 0.6018 (95% CI: 0.4693, 0.7718) and 0.3438 (95% CI: 0.2254, 0.5242), respectively. While the DTR did not have a significant effect on Td. The total cumulative effect of temperature (10th) and absolute humidity (10th, 90th) on Td increased with the change of lag days. Similarly, a decline in temperature and absolute humidity at cumulative 14-day-lagged corresponded to the lower RR on Td in pooled region-specific effects. In summary, the government responses are important factors in alleviating the spread of COVID-19. After controlling that, our results indicate that both the cold and the dry environment also likely facilitate the COVID-19 transmission.

Meteorological conditions are heterogeneous factors for COVID-19 risk in China.

Whether meteorological factors influence COVID-19 transmission is an issue of major public health concern, but available evidence remains unclear and limited for several reasons, including the use of report date which can lag date of symptom onset by a considerable period. We aimed to generate reliable and robust evidence of this relationship based on date of onset of symptoms. We evaluated important meteorological factors associated with daily COVID-19 counts and effective reproduction number (Rt) in China using a two-stage approach with overdispersed generalized additive models and random-effects meta-analysis. Spatial heterogeneity and stratified analyses by sex and age groups were quantified and potential effect modification was analyzed. Nationwide, there was no evidence that temperature and relative humidity affected COVID-19 incidence and Rt. However, there were heterogeneous impacts on COVID-19 risk across different regions. Importantly, there was a negative association between relative humidity and COVID-19 incidence in Central China: a 1% increase in relative humidity was associated with a 3.92% (95% CI, 1.98%-5.82%) decrease in daily counts. Older population appeared to be more sensitive to meteorological conditions, but there was no obvious difference between sexes. Linear relationships were found between meteorological variables and COVID-19 incidence. Sensitivity analysis confirmed the robustness of the association and the results based on report date were biased. Meteorological factors play heterogenous roles on COVID-19 transmission, increasing the possibility of seasonality and suggesting the epidemic is far from over. Considering potential climatic associations, we should maintain, not ease, current control measures and surveillance.

A Fault Diagnosis Method Considering Meteorological Factors for Transmission Networks Based on P Systems.

Bad meteorological conditions may reduce the reliability of power communication equipment, which can increase the distortion possibility of fault information in the communication process, hence raising its uncertainty and incompleteness. To address the issue, this paper proposes a fault diagnosis method for transmission networks considering meteorological factors. Firstly, a spiking neural P system considering a meteorological living environment and its matrix reasoning algorithm are designed. Secondly, based on the topology structure of the target power transmission network and the action logic of its protection devices, a diagnosis model based on the spiking neural P system considering the meteorological living environment is built for each suspicious fault transmission line. Following this, the action messages of protection devices and corresponding temporal order information are used to obtain initial pulse values of input neurons of the diagnosis model, which are then modified with the gray fuzzy theory. Finally, the matrix reasoning algorithm of each model is executed in a parallel manner to obtain diagnosis results. Experiment results achieved out on IEEE 39-bus system show the feasibility and effectiveness of the proposed method.

Strong link between coronavirus count and bad air: a case study of India.

The present study aims to highlight the contrast relationship between COVID-19 (Coronavirus Disease-2019) infections and air pollutants for the Indian region. The COVID-19 data (cumulative, confirmed cases and deaths), air pollutants (PM10, PM2.5, NO2 and SO2) and meteorological data (temperature and relative humidity) were collected from January 2020 to August 2020 for all 28 states and the union territory of India during the pandemic. Now, to understand the relationship between air pollutant concentration, meteorological factor, and COVID-19 cases, the nonparametric Spearman's and Kendall's rank correlation were used. The COVID-19 shows a favourable temperature (0.55-0.79) and humidity (0.14-0.52) over the Indian region. The PM2.5 and PM10 gave a strong and negative correlation with COVID-19 cases in the range of 0.64-0.98. Similarly, the NO2 shows a strong and negative correlation in the range of 0.64-0.98. Before the lockdown, the concentration of pollution parameters is high due to the shallow boundary layer height. But after lockdown, the overall reduction was reported up to 33.67% in air quality index (AQI). The background metrological parameters showed a crucial role in the variation of pollutant parameters (SO2, NO2, PM10 and PM2.5) and the COVID-19 infection with the economic aspects. The European Centre for Medium-Range Weather Forecasts derived monthly average wind speed was also plotted. It can see that January and February of 2020 show the least variation of air mass in the range of 1-2 m/s. The highest wind speed was reported during July and August 2020. India's western and southern parts experienced an air mass in the range of 4-8 m/s. The precipitation/wet deposition of atmospheric aerosols further improves the AQI over India. According to a study, the impact of relative humidity among all other metrological parameters is positively correlated with Cases and death. Outcomes of the proposed work had the aim of supporting national and state governance for healthcare policymakers.

Modeling the Effects of Meteorological Factors and Unreported Cases on Seasonal Influenza Outbreaks in Gansu Province, China.

Influenza usually breaks out seasonally in temperate regions, especially in winter, infection rates and mortality rates of influenza increase significantly, which means that dry air and cold temperatures accelerate the spread of influenza viruses. However, the meteorological factors that lead to seasonal influenza outbreaks and how these meteorological factors play a decisive role in influenza transmission remain unclear. During the epidemic of infectious diseases, the neglect of unreported cases leads to an underestimation of infection rates and basic reproduction number. In this paper, we propose a new non-autonomous periodic differential equation model with meteorological factors including unreported cases. First, the basic reproduction number is obtained and the global asymptotic stability of the disease-free periodic solution is proved. Furthermore, the existence of periodic solutions and the uniformly persistence of the model are demonstrated. Second, the best-fit parameter values in our model are identified by the MCMC algorithm on the basis of the influenza data in Gansu province, China. We also estimate that the basic reproduction number is 1.2288 (95% CI:(1.2287, 1.2289)). Then, to determine the key parameters of the model, uncertainty and sensitivity analysis are explored. Finally, our results show that influenza is more likely to spread in low temperature, low humidity and low precipitation environments. Temperature is a more important factor than relative humidity and precipitation during the influenza epidemic. In addition, our results also show that there are far more unreported cases than reported cases.

The role of maternal methylation in the association between prenatal meteorological conditions and neonatal H19/H19-DMR methylation.

Meteorological conditions during pregnancy can affect birth outcome, which has been linked to the H19/H19-differentially methylated region (DMR). However, the detailed mechanisms underlying this association are unclear. This was investigated in the present study to provide epidemiological evidence for elucidating the pathogenesis of adverse birth outcomes. A total of 550 mother-newborn pairs were recruited in Zhengzhou, China from January 2010 to January 2012. Meteorological data including temperature (T), relative humidity (RH), and sunshine duration (SSD) were obtained from the China Meteorological Data Sharing Service System. Bisulfite sequencing PCR was performed to determine the methylation levels of H19/H19-DMR using genomic DNA extracted from maternal peripheral and umbilical cord blood. The results showed that H19-DMR methylation status in cord blood was positively associated with that in maternal blood. Neonatal H19-DMR methylation was negatively associated with T and RH during the first trimester and positively associated with these variables during the third trimester. There was a positive correlation between neonatal H19-DMR methylation and SSD during the second trimester and a negative correlation during the third trimester. Similar associations were observed between maternal H19-DMR methylation and prenatal meteorological conditions. We also observed significant interaction effects of maternal H19/H19-DMR methylation and most prenatal meteorological factors on neonatal methylation, and found that changes in the methylation status of maternal H19-DMR were responsible for the effects of prenatal meteorological conditions on neonatal methylation. In summary, neonatal H19-DMR methylation was significantly associated with prenatal meteorological conditions, which was modified and mediated by maternal H19-DMR methylation changes. These findings provide insights into the relationship between meteorological factors during pregnancy and adverse birth outcomes or disease susceptibility in offspring, and can serve as a reference for environmental policy-making.

[Variation characteristics and meteorological influencing factors of PM_(2. 5)mass concentration in Xi'an City from 2015 to 2018].

OBJECTIVE: To master the variation characteristics of PM_(2. 5) mass concentration in Lianhu district and Yanta district of Xi'an City and its relationship with meteorological conditions. METHODS: From 2015 to 2018, according to the environmental monitoring data of six main urban areas in Xi'an City in 2012, including NO_2, SO_2, PM_(10), PM_(2. 5), CO and O_3, air samples were collected in the relatively heavy polluted Lianhu district and the relatively light Yanta district of Xi'an City. The mass concentration test of PM_(2. 5) was carried out in accordance with the Ministry of Environmental Protection's "Determination of atmospheric articles PM_(10) and PM_(2. 5) in ambient air by gravimetric method "(HJ 618-2011). According to the Ambient air quality standards(GB 3095-2012), the average daily secondary concentration limit(75 µg/m~3) was used for statistical analysis and evaluation according to different annual, regional and seasonal test result. Meteorological data of Xi'an City were collected, including daily average temperature, daily average pressure, daily average relative humidity, daily average wind speed, daily precipitation, maximum temperature and minimum temperature, and the relationship between PM_(2. 5)concentration and meteorological factors was analyzed. RESULTS: A total of 660 air samples were collected and qualified, the median concentration of PM_(2. 5) was 71 µg/m~3. 356 air samples were qualified, and the pass rate was 53. 94%. The sample pass rate for each year from high to low was 2017>2018>2016>2015(P<0. 001), The average level of PM_(2. 5) mass concentration from high to low was 2015>2016>2017>2018(P<0. 001). There was no significant difference in the sample pass rate and PM_(2. 5)mass concentration between Lianhu district and Yanta district(P>0. 05). The qualified rate of samples from high to low in different seasons was summer>spring> autumn>winter(P<0. 05). The average concentration of PM_(2. 5) in different seasons from high to low was winter>autumn>spring>summer(P<0. 001). Mean temperature, mean air pressure, average wind speed, average relative humidity, precipitation and lowest temperature were significantly correlated with PM_(2. 5) mass concentration(P<0. 001). The determination coefficients of multiple regression analysis of meteorological factors in Lianhu and Yanta regions were 0. 390 and 0. 373, respectively. CONCLUSION: The air quality in Lianhu district and Yanta district of Xi'an City had improved year by year, and the pollution of PM_(2. 5) in autumn and winter was more serious. Meteorological conditions affected the concentration level of PM_(2. 5) in the atmosphere.

Influenza activity prediction using meteorological factors in a warm temperate to subtropical transitional zone, Eastern China.

Influenza activity is subject to environmental factors. Accurate forecasting of influenza epidemics would permit timely and effective implementation of public health interventions, but it remains challenging. In this study, we aimed to develop random forest (RF) regression models including meterological factors to predict seasonal influenza activity in Jiangsu provine, China. Coefficient of determination (R2) and mean absolute percentage error (MAPE) were employed to evaluate the models' performance. Three RF models with optimum parameters were constructed to predict influenza like illness (ILI) activity, influenza A and B (Flu-A and Flu-B) positive rates in Jiangsu. The models for Flu-B and ILI presented excellent performance with MAPEs <10%. The predicted values of the Flu-A model also matched the real trend very well, although its MAPE reached to 19.49% in the test set. The lagged dependent variables were vital predictors in each model. Seasonality was more pronounced in the models for ILI and Flu-A. The modification effects of the meteorological factors and their lagged terms on the prediction accuracy differed across the three models, while temperature always played an important role. Notably, atmospheric pressure made a major contribution to ILI and Flu-B forecasting. In brief, RF models performed well in influenza activity prediction. Impacts of meteorological factors on the predictive models for influenza activity are type-specific.

Effects of meteorological factor and air pollution on sudden sensorineural hearing loss using the health claims data in Busan, Republic of Korea.

PURPOSE: Specific meteorological factors, including air pollution in the form of particulate matter (PM), affect the development of otologic disease and have adverse effects on the cardiovascular and respiratory systems. This study investigated relationships between the development of sudden sensorineural hearing loss(SSNHL) and meteorological factor with air pollution including PM. MATERIALS AND METHODS: The daily patient number in 2015 admitted to the hospital with SSNHL were extracted from the Health Insurance Review and Assessment Service Bigdata in Busan. The meteorological factors and air pollution data of Busan area were obtained from meteorological stations in Busan. The relationship between the number of hospitalizations and the climatic factors was checked. RESULTS: SSNHL patient group showed more common in women, and the highest rates were observed in patients in their 50s. The daily mean patient numbers were 2.27. The number of SSNHL patients in spring was statistically significantly higher than that in summer. The mean daily PM10 and PM2.5 concentrations were 48.0 and 29.4 µg/m3, respectively. The mean wind speed, maximum wind speed and daily atmospheric pressure range was weakly positively associated with SSNHL patient number. There were weak negative correlations between maximum PM2.5 and SSNHL admissions. The mean temperature and wind chill index showed non-significantly negative relationships with SSNHL admissions. CONCLUSIONS: In Busan area, statistically significant weak relationships were detected between the daily numbers of patients admitted to the hospital with SSNHL and meteorological data, including PM level. Further investigation of these associations is required.

Surveillance on the endemic of Zika virus infection by meteorological factors in Colombia: a population-based spatial and temporal study.

BACKGROUND: Zika virus (ZIKV) infection is a pandemic and a public health emergency. It is transmitted by mosquitoes, primarily the Aedes genus. In light of no treatment currently, it is crucial to develop effective vector control programs to prevent the spread of ZIKV infection earlier when observing possible risk factors, such as weather conditions enhancing mosquito breeding and surviving. METHODS: This study collected daily meteorological measurements and weekly ZIKV infectious cases among 32 departments of Colombia from January 2015-December 2016. This study applied the distributed lag nonlinear model to estimate the association between the number of ZIKA virus infection and meteorological measurements, controlling for spatial and temporal variations. We examined at most three meteorological factors with 20 lags in weeks in the model. RESULTS: Average humidity, total rainfall, and maximum temperature were more predictable of ZIKV infection outbreaks than other meteorological factors. Our models can detect significantly lagged effects of average humidity, total rainfall, and maximum temperature on outbreaks up to 15, 14, and 20 weeks, respectively. The spatial analysis identified 12 departments with a significant threat of ZIKV, and eight of those high-risk departments were located between the Equator and 6°N. The outbreak prediction also performed well in identified high-risk departments. CONCLUSION: Our results demonstrate that meteorological factors could be used for predicting ZIKV epidemics. Building an early warning surveillance system is important for preventing ZIKV infection, particularly in endemic areas.

Impact of meteorological factors on mumps and potential effect modifiers: An analysis of 10 cities in Guangxi, Southern China.

BACKGROUND: In the current context of global climate change, understanding the impact of climate on respiratory infectious diseases such as mumps and the potential modified factors is crucial, especially in developing countries. However, research on the climate-related incidence of mumps is rare, inconsistent and mainly limited to a single city or region. METHODS: Daily mumps cases and meteorological variables of 10 cities in Guangxi, Southern China were collected for 2005-2017. Two-stage analyses were performed to assess the relationship between meteorological factors and mumps incidence during two time-periods: 2005-2012 and 2013-2017, separately. First, a Poisson regression model that allows over-dispersion was used to estimate the city-specific climate-related morbidity after controlling for temporal trends, day of week, and national statutory holidays. Then, we used a multivariate meta-analytical model to pool the city-specific effect estimates and conducted subgroup analyses. Multivariate meta-regression was applied to detect potential effect modifiers. RESULTS: Non-linear relationships were observed among mean temperature, wind speed, and mumps incidence in 2005-2012. The impact of high temperature on mumps incidence was short and rapid, whereas the impact of low temperature was long and slow. The total cumulative relative risk (RR) associated with hot temperature was 1.18 [95% Confidence Interval (CI): 0.93, 1.48], which was calculated by comparing the incidence of mumps above the 90th percentile of temperature with its incidence at the median temperature at lag of 0-30 days. Meanwhile, the RR associated with cold temperature was calculated to be 1.50 (95% CI: 1.08, 2.10) by comparing the incidence of mumps below the 10th percentile of temperature with its incidence at the median temperature. Similarly, the RRs associated with windless and windy conditions for the total population were 1.23 (95% CI: 1.04, 1.46) and 0.83 (95% CI: 0.67, 1.02), respectively. Effects based on extreme temperature and wind speed conditions were more prominent in males than in females. Compared with children and adults, adolescents (5-14 years old) were more sensitive to extreme weather conditions. Geographical latitude, Population density, GDP per capita, Number of health institutions, Highly educated population and Inoculation rate were considered the most likely associated modifiers. In addition, the correlation between meteorological factors and the incidence of mumps and modification of socioeconomic factors after 2013 showed similar curves compared with results in 2005-2012, but the cumulative effect was not statistically significant. CONCLUSIONS: Meteorological factors, such as temperature and wind speed, exert a significant impact on the incidence of mumps. The relationship varies depending on gender and age. Socioeconomic factors such as vaccination, GDP, geographical latitude, etc. may substantially affect the weather-related mumps incidence.