scholarly journals Meteorological Factors and Swine Erysipelas Transmission in Southern China

2020 ◽  
Vol 70 (1) ◽  
pp. 37-50
Author(s):  
Qin Hong-Yu ◽  
Xin Xiu ◽  
Sha Wanli ◽  
Wang Ben ◽  
Hu Xiansheng ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Meteorological Factors ◽  
Southern Oscillation ◽  
Southern China ◽  
Meteorological Variables ◽  
Maximum Temperature ◽  
Economic Losses ◽  
Lag Effects ◽  
Cross Correlation Analysis ◽  
Swine Erysipelas

AbstractSwine erysipelas (SE) is one of the best-known and most serious diseases that affect domestic pigs, which is caused by Erysipelothrix rhusiopathiae. It is endemic in Nanning and has been circulating for decades, causing considerable economic losses. The aim of this study was to investigate the effect of meteorological-related variations on the epidemiology of swine erysipelas in Nanning City, a subtropical city of China. Data on monthly counts of reported swine erysipelas and climate data in Nanning are provided by the authorities over the period from 2006 to 2015. Cross-correlation analysis was applied to identify the lag effects of meteorological variables. A zero-inflated negative binomial (ZINB) regression model was used to evaluate the independent contribution of meteorological factors to SE transmission. After controlling seasonality, autocorrelation and lag effects, the results of the model indicated that Southern Oscillation Index (SOI) has a positive effect on SE transmission. Moreover, there is a positive correlation between monthly mean maximum temperature and relative humidity at 0-1 month lag and the number of cases. Furthermore, there is a positive association between the number of SE incidences and precipitation, with a lagged effect of 2 months. In contrast, monthly mean wind velocity negatively correlated with SE of the current month. These findings indicate that meteorological variables may play a significant role in SE transmission in southern China. Finally, more public health actions should be taken to prevent and control the increase of SE disease with consideration of local weather variations.

scholarly journals Impact of Meteorological Factors and Southern Oscillation Index on Scrub Typhus Incidence in Guangzhou, Southern China, 2006–2018

2021 ◽  
Vol 8 ◽  
Author(s):  
Jianyun Lu ◽  
Yanhui Liu ◽  
Xiaowei Ma ◽  
Meixia Li ◽  
Zhicong Yang
Keyword(s):  
High Risk ◽  
Relative Humidity ◽  
Southern Oscillation Index ◽  
Scrub Typhus ◽  
Meteorological Factors ◽  
Southern Oscillation ◽  
Southern China ◽  
Meteorological Variables ◽  
Linear Relationships ◽  
Non Linear

Background: Scrub typhus was epidemic in the western Pacific Ocean area and East Asia, scrub typhus epidemic in densely populated areas in southern China. To better understand the association between meteorological variables, Southern Oscillation Index (SOI), and scrub typhus incidence in Guangzhou was benefit to the control and prevention.Methodology/Principal Findings: We collected weekly data for scrub typhus cases and meteorological variables in Guangzhou, and Southern Oscillation Index from 2006 to 2018, and used the distributed lag non-linear models to evaluate the relationships between meteorological variables, SOI and scrub typhus. The median value of each variable was set as the reference. The high-risk occupations were farmer (51.10%), house worker (17.51%), and retiree (6.29%). The non-linear relationships were observed with different lag weeks. For example, when the mean temperature was 27.7°C with1-week lag, the relative risk (RR) was highest as 1.08 (95% CI: 1.01–1.17). The risk was the highest when the relative humidity was 92.0% with 9-week lag, with the RR of 1.10 (95% CI: 1.02–1.19). For aggregate rainfall, the highest RR was 1.06 (95% CI: 1.03–1.11), when it was 83.0 mm with 4-week lag. When the SOI was 19 with 11-week lag, the highest RR was 1.06 (95% CI: 1.01–1.12). Most of the extreme effects of SOI and meteorological factors on scrub typical cases were statistically significant.Conclusion/Significance: The high-risk occupations of scrub typhus in Guangzhou were farmer, house worker, and retiree. Meteorological factors and SOI played an important role in scrub typhus occurrence in Guangzhou. Non-linear relationships were observed in almost all the variables in our study. Approximately, mean temperature, and relative humidity positively correlated to the incidence of scrub typhus, on the contrary to atmospheric pressure and weekly temperature range (WTR). Aggregate rainfall and wind velocity showed an inverse-U curve, whereas the SOI appeared the bimodal distribution. These findings can be helpful to facilitate the development of the early warning system to prevent the scrub typhus.

scholarly journals Effects of rainfall, temperature and topography on malaria incidence in elimination targeted district of Ethiopia

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Desalegn Dabaro ◽  
Zewdie Birhanu ◽  
Abiyot Negash ◽  
Dawit Hawaria ◽  
Delenasaw Yewhalaw
Keyword(s):  
Regression Model ◽  
Malaria Transmission ◽  
Malaria Elimination ◽  
Meteorological Factors ◽  
Malaria Incidence ◽  
Meteorological Variables ◽  
Maximum Temperature ◽  
Negative Binomial Regression Model ◽  
Regional State ◽  
Cross Correlation Analysis

Abstract Background Climate and environmental factors could be one of the primary factors that drive malaria transmission and it remains to challenge the malaria elimination efforts. Hence, this study was aimed to evaluate the effects of meteorological factors and topography on the incidence of malaria in the Boricha district in Sidama regional state of Ethiopia. Methods Malaria morbidity data recorded from 2010 to 2017 were obtained from all public health facilities of Boricha District in the Sidama regional state of Ethiopia. The monthly malaria cases, rainfall, and temperature (minimum, maximum, and average) were used to fit the ARIMA model to compute the malaria transmission dynamics and also to forecast future incidence. The effects of the meteorological variables and altitude were assessed with a negative binomial regression model using R version 4.0.0. Cross-correlation analysis was employed to compute the delayed effects of meteorological variables on malaria incidence. Results Temperature, rainfall, and elevation were the major determinants of malaria incidence in the study area. A regression model of previous monthly rainfall at lag 0 and Lag 2, monthly mean maximum temperature at lag 2 and Lag 3, and monthly mean minimum temperature at lag 3 were found as the best prediction model for monthly malaria incidence. Malaria cases at 1801–1900 m above sea level were 1.48 times more likely to occur than elevation ≥ 2000 m. Conclusions Meteorological factors and altitude were the major drivers of malaria incidence in the study area. Thus, evidence-based interventions tailored to each determinant are required to achieve the malaria elimination target of the country.

scholarly journals HAND-FOOT-AND-MOUTH DISEASE EPIDEMIOLOGICAL STATUS AND RELATIONSHIP WITH METEOROLOGICAL VARIABLES IN GUANGZHOU, SOUTHERN CHINA, 2008-2012

2014 ◽  
Vol 56 (6) ◽  
pp. 533-539 ◽  
Author(s):  
Tiegang Li ◽  
Zhicong Yang ◽  
Xiangyi Liu ◽  
Yan Kang ◽  
Ming Wang
Keyword(s):  
Negative Binomial ◽  
Southern China ◽  
Foot And Mouth Disease ◽  
Negative Binomial Regression ◽  
Incidence Rates ◽  
Mouth Disease ◽  
Meteorological Variables ◽  
High Morbidity ◽  
Weather Factors ◽  
Foot And Mouth

Hand-foot-and-mouth disease (HFMD) is becoming one of the extremely common airborne and contact transmission diseases in Guangzhou, southern China, leading public health authorities to be concerned about its increased incidence. In this study, it was used an ecological study plus the negative binomial regression to identify the epidemic status of HFMD and its relationship with meteorological variables. During 2008-2012, a total of 173,524 HFMD confirmed cases were reported, 12 cases of death, yielding a fatality rate of 0.69 per 10,000. The annual incidence rates from 2008 to 2012 were 60.56, 132.44, 311.40, 402.76, and 468.59 (per 100,000), respectively, showing a rapid increasing trend. Each 1 °C rise in temperature corresponded to an increase of 9.47% (95% CI 9.36% to 9.58%) in the weekly number of HFMD cases, while a one hPa rise in atmospheric pressure corresponded to a decrease in the number of cases by 7.53% (95% CI -7.60% to -7.45%). Similarly, each one percent rise in relative humidity corresponded to an increase of 1.48% or 3.3%, and a one meter per hour rise in wind speed corresponded to an increase of 2.18% or 4.57%, in the weekly number of HFMD cases, depending on the variables considered in the model. These findings revealed that epidemic status of HFMD in Guangzhou is characterized by high morbidity but low fatality. Weather factors had a significant influence on the incidence of HFMD.

scholarly journals Identifying the high-risk areas and associated meteorological factors of dengue transmission in Guangdong Province, China from 2005 to 2011

2013 ◽  
Vol 142 (3) ◽  
pp. 634-643 ◽  
Author(s):  
J. FAN ◽  
H. LIN ◽  
C. WANG ◽  
L. BAI ◽  
S. YANG ◽  
...  
Keyword(s):  
Distribution Pattern ◽  
Meteorological Factors ◽  
Southern Oscillation ◽  
Guangdong Province ◽  
River Delta ◽  
Maximum Temperature ◽  
Pearl River Delta Region ◽  
Delta Region ◽  
Dengue Transmission ◽  
The Pearl River

SUMMARYWe examined the spatial distribution pattern and meteorological drivers of dengue fever (DF) in Guangdong Province, China. Annual incidence of DF was calculated for each county between 2005 and 2011 and the geographical distribution pattern of DF was examined using Moran's I statistic and excess risk maps. A time-stratified case-crossover study was used to investigate the short-term relationship between DF and meteorological factors and the Southern Oscillation Index (SOI). High-epidemic DF areas were restricted to the Pearl River Delta region and the Han River Delta region, Moran's I of DF distribution was significant from 2005 to 2006 and from 2009 to 2011. Daily vapour pressure, mean and minimum temperatures were associated with increased DF risk. Maximum temperature and SOI were negatively associated with DF transmission. The risk of DF was non-randomly distributed in the counties in Guangdong Province. Meteorological factors could be important predictors of DF transmission.

scholarly journals Lag Effects of Ozone, PM2.5, and Meteorological Factors on COVID-19 New Cases at the Disease Epicenter in Queens, New York

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 357
Author(s):  
Atin Adhikari ◽  
Jingjing Yin
Keyword(s):  
New York ◽  
Environmental Factors ◽  
Air Pollutants ◽  
Negative Binomial ◽  
Meteorological Factors ◽  
Absolute Humidity ◽  
Negative Binomial Regression ◽  
Ambient Air ◽  
Reproductive Number ◽  
Lag Effects

The influences of environmental factors on COVID-19 may not be immediate and could be lagged for days to weeks. This study investigated the choice of lag days for calculating cumulative lag effects of ozone, PM2.5, and five meteorological factors (wind speed, temperature, relative humidity, absolute humidity, and cloud percentages) on COVID-19 new cases at the epicenter of Queens County, New York, before the governor’s executive order on wearing of masks in public places (1 March to 11 April 2020). Daily data for selected air pollutants and meteorological factors were collected from the US EPA Air Quality System, weather observation station of the NOAA National Centers for Environmental Information at John F. Kennedy Airport, and World Weather Online. Negative binomial regression models were applied, including the autocorrelations and trend of the time series, as well as the effective reproductive number as confounders. The effects of ozone, PM2.5, and five meteorological factors were significant on COVID-19 new cases with lag9-lag13 days. Incidence rate ratios (IRRs) were consistent for any lag day choice between lag0 and lag14 days and started fluctuating after lag15 days. Considering moving averages >14 days yielded less reliable variables for summarizing the cumulative lag effects of environmental factors on COVID-19 new cases and considering lag days from 9 to 13 would yield significant findings. Future studies should consider this approach of lag day checks concerning the modeling of COVID-19 progression in relation to meteorological factors and ambient air pollutants.

scholarly journals The Short-term Effects of Temperature on Infectious Diarrhea among Children under 5 Years Old in Jiangsu, China: A Time-series Study (2015–2019)

2021 ◽  
Vol 41 (2) ◽  
pp. 211-218
Author(s):  
Nan-nan Huang ◽  
Hao Zheng ◽  
Bin Li ◽  
Gao-qiang Fei ◽  
Zhen Ding ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Reference Value ◽  
Cold Weather ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Infectious Diarrhea ◽  
Lag Effects ◽  
Lag Effect ◽  
Children Under 5

SummaryThe association between meteorological factors and infectious diarrhea has been widely studied in many countries. However, investigation among children under 5 years old in Jiangsu, China remains quite limited. Data including infectious diarrhea cases among children under five years old and daily meteorological indexes in Jiangsu, China from 2015 to 2019 were collected. The lag-effects up to 21 days of daily maximum temperature (Tmax) on infectious diarrhea were explored using a quasi-Poisson regression with a distributed lag non-linear model (DLNM) approach. The cases number of infectious diarrhea was significantly associated with seasonal variation of meteorological factors, and the burden of disease mainly occurred among children aged 0–2 years old. Moreover, when the reference value was set at 16.7°C, Tmax had a significant lag-effect on cases of infectious diarrhea among children under 5 years old in Jiangsu Province, which was increased remarkably in cold weather with the highest risk at 8°C. The results of DLNM analysis implicated that the lag-effect of Tmax varied among the 13 cities in Jiangsu and had significant differences in 8 cities. The highest risk of Tmax was presented at 5 lag days in Huaian with a maximum RR of 1.18 (95% CI: 1.09, 1.29). Suzhou which had the highest number of diarrhea cases (15830 cases), had a maximum RR of 1.04 (95% CI:1.03, 1.05) on lag 15 days. Tmax is a considerable indicator to predict the epidemic of infectious diarrhea among 13 cities in Jiangsu, which reminds us that in cold seasons, more preventive strategies and measures should be done to prevent infectious diarrhea.

scholarly journals Characteristics and Drivers of Reference Evapotranspiration in Hilly Regions in Southern China

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1914 ◽  
Author(s):  
Liu ◽  
Liu ◽  
Chen ◽  
Labat ◽  
Li ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Reference Evapotranspiration ◽  
Southern Oscillation ◽  
Southern China ◽  
Sensitivity Coefficient ◽  
Meteorological Variables ◽  
The West ◽  
Average Temperature ◽  
Sunshine Hours ◽  
The Impact

This paper has adopted related meteorological data collected by 69 meteorological stations between 1951 and 2013 to analyze changes and drivers of reference evapotranspiration (ET0) in the hilly regions located in southern China. Results show that: (1) ET0 in southern China’s hilly regions reaches its maximum in summer and its minimum in winter, and that the annual ET0 shows an increasing trend. ET0 happened abrupt change due to the impact of abrupt meteorological variables changes, and the significant year of mutation were 1953, 1964 and 2008. Most abrupt changes of ET0 in meteorological stations occurred in the 1950s and 1960s. (2) The low value of ET0 was mainly captured in high-altitude areas. Spatially, the ET0 in the east was higher than that in the west. With the exception of a handful of stations, the trend coefficients of ET0 were all positive, exhibiting a gradual rise. Changes in ET0 in the east were much more sensitive than that in the west. Since ET0 was affected by the cyclical changes in relative humidity, short-period oscillations were observed in all these changes. (3) In general, the ET0 was negatively correlated with relative humidity, and positively correlated with temperature and sunshine percentage. ET0 is most sensitive to changes in average temperature, with a sensitivity coefficient of 1.136. ET0 showed positive sensitivity to average temperature and sunshine hours, which were notable in the northeastern, and uniform in the spatial. ET0 showed negatively sensitivity to relative humidity, and the absolute value of sensitivity coefficient in the northwestern is smaller. The highest contribution to ET0 is the average temperature (6.873%), and the total contribution of the four meteorological variables to the change of ET0 is 7.842%. The contribution of average temperature, relative humidity, and sunshine hours to ET0 is higher in the northern and eastern, northern, northern and eastern areas, respectively. Climate indexes (Western Pacific Index (WP), Southern Oscillation Index (SOI), Tropical Northern Atlantic Index (TNA), and El Niño-Southern Oscillation (ENSO)) were correlated with the ET0. In addition, the ET0 and altitude, as well as the latitude and longitude were also correlated with each other.

scholarly journals Evaluation of Drought Events in Various Climatic Conditions using Data-Driven Models and A Reliability-Based Probabilistic Model

2021 ◽  
Author(s):  
Ali Barzkar ◽  
Mohammad Najafzadeh ◽  
Farshad Homaei
Keyword(s):  
Gene Expression Programming ◽  
Limit State ◽  
Climatic Conditions ◽  
Meteorological Variables ◽  
Maximum Temperature ◽  
Economic Losses ◽  
State Function ◽  
Wide Range ◽  
Using Data ◽  
Definition Of

Abstract Due to a wide range of socio-economic losses caused by drought over the past decades, having a reliable insight of drought properties plays a key role in monitoring and forecasting the drought situations, and finally generating robust methodologies for adapting to various vulnerability of drought situations. The most important factor in causing drought is rainfall, but increasing or decreasing the temperature and consequently evapotranspiration can intensify or moderate the severity of drought events. Standardized Precipitation Evaporation Index (SPEI), as one of the most well-known indices in definition of drought situation, is applied based on potential precipitation, evapotranspiration, and the water balance. In this study, values of SPEI are formulated for various climates by three robust Artificial Intelligence (AI) models: Gene-Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS). Meteorological variables including maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tmean), relative humidity (RH), 24-hour rainfall (P24) and wind speed (U2) were used to perform the AI models. Dataset reported from four synoptic stations through Iran, dating back to a 58-year period beginning in 1957. Each AI technique was run for all the climatic situations: Temperate-Warm (T-W), Wet-Warm (W-W), Arid-Cold (A-C) and Arid-Warm (A-W). Results of AI models development indicated that M5 version of MT provided the most accurate SPEI prediction for all the climatic situations in comparison with GEP and MARS techniques. SPEI values for four climatic conditions were evaluated in the reliability-based probabilistic framework to take into account the influence of any uncertainty and randomness associated with meteorological variables. In this way, the Monte-Carlo scenario sampling approach has been used to assess the limit state function from the AI models-based-SPEI. Based on the reliability analysis for all the synoptic stations, as the probability of exceedance values declined to below 75%, drought situations varied from “Normal” to “Very Extreme Humidity”.

scholarly journals The correlation between three teleconnections and leptospirosis incidence in the Kandy District, Sri Lanka, 2004–2019

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
N. D. B. Ehelepola ◽  
Kusalika Ariyaratne ◽  
A. M. S. M. C. M. Aththanayake ◽  
Kamalanath Samarakoon ◽  
H. M. Arjuna Thilakarathna
Keyword(s):  
Time Series ◽  
Sri Lanka ◽  
Wavelet Analysis ◽  
Southern Oscillation ◽  
Population Data ◽  
Enso Modoki ◽  
Cross Correlation Analysis ◽  
Teleconnection Indices ◽  
Weather Parameters ◽  
Time Lagged

Abstract Background Leptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals excrete leptospires into the environment via their urine. Survival of leptospires in the environment until they enter into a person and several other factors that influence leptospirosis transmission are dependent upon local weather. Past studies show that rainfall and other weather parameters are correlated with the LI in the Kandy district, Sri Lanka. El Niño Southern Oscillation (ENSO), ENSO Modoki, and the Indian Ocean Dipole (IOD) are teleconnections known to be modulating rainfall in Sri Lanka. There is a severe dearth of published studies on the correlations between indices of these teleconnections and LI. Methods We acquired the counts of leptospirosis cases notified and midyear estimated population data of the Kandy district from 2004 to 2019, respectively, from weekly epidemiology reports of the Ministry of Health and Department of Census and Statistics of Sri Lanka. We estimated weekly and monthly LI of Kandy. We obtained weekly and monthly teleconnection indices data for the same period from the National Oceanic and Atmospheric Administration (NOAA) of the USA and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We performed wavelet time series analysis to determine correlations with lag periods between teleconnection indices and LI time series. Then, we did time-lagged detrended cross-correlation analysis (DCCA) to verify wavelet analysis results and to find the magnitudes of the correlations detected. Results Wavelet analysis displayed indices of ENSO, IOD, and ENSO Modoki were correlated with the LI of Kandy with 1.9–11.5-month lags. Indices of ENSO showed two correlation patterns with Kandy LI. Time-lagged DCCA results show all indices of the three teleconnections studied were significantly correlated with the LI of Kandy with 2–5-month lag periods. Conclusions Results of the two analysis methods generally agree indicating that ENSO and IOD modulate LI in Kandy by modulating local rainfall and probably other weather parameters. We recommend further studies about the ENSO Modoki and LI correlation in Sri Lanka. Monitoring for extreme teleconnection events and enhancing preventive measures during lag periods can blunt LI peaks that may follow.

Sign in / Sign up

Export Citation Format

Share Document