Effects of weather variability on infectious gastroenteritis

2009 ◽  
Vol 138 (2) ◽  
pp. 236-243 ◽  
Author(s):  
D. ONOZUKA ◽  
M. HASHIZUME ◽  
A. HAGIHARA
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Gastrointestinal Disease ◽  
Confounding Factors ◽  
Weather Variability ◽  
Average Temperature ◽  
Series Analysis ◽  
Infectious Gastroenteritis

SUMMARYAlthough multiple combinations of weather variability may contribute to an increased incidence of infectious gastrointestinal disease, few studies have investigated the association between weather variability and cases of infectious gastroenteritis. We acquired data for infectious gastroenteritis cases and weather variability in Fukuoka, Japan, from 1999 to 2007 and used time-series analysis to assess the effects of weather variability on infectious gastroenteritis cases, adjusting for confounding factors. In total, 422 176 infectious gastroenteritis cases were reported during the 9-year study period. The weekly number of infectious gastroenteritis cases increased by 7·7% (95% CI 4·6–10·8) for every 1°C increase in the average temperature and by 2·3% (95% CI 1·4–3·1) for every 1% decrease in relative humidity. From 1999 to 2007, infectious gastroenteritis cases increased significantly with increased average temperature and decreased relative humidity in Fukuoka, Japan.

scholarly journals Association between weather factors and dengue in Dhaka: a time-series analysis

2021 ◽  
Vol 39 (1) ◽  
pp. 43-49
Author(s):  
Shafia Shaheen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Incidence Rate ◽  
Negative Binomial ◽  
Weather Data ◽  
Negative Binomial Regression Model ◽  
Weather Factors ◽  
Average Temperature ◽  
Series Analysis ◽  
Average Maximum

Background: There was an epidemic of dengue fever that happened  in Bangladesh  in the year of 2019. Temperature of this country has been raising which leads to changing in rainfall pattern. This study was aimed to investigate the relationship of weather factors and dengue incidence in Dhaka. Methods: A time series analysis was carried out by using 10 years weather data as average , maximum and minimum monthly temperature, average monthly humidity and average and cumulative monthly rainfall. Reported number of dengue cases was extracted from January 2009 to July 2019. Firstly, dengue incidence rate was  calculated. Correlation analysis and negative binomial regression model was developed. Results: Dengue incidence rate had sharp upward trend. Dengue incidence and mean, maximum and minimum average temperature showed statistically significant negative correlation at 3 months' lag. Highest incidence Rate Ratio (IRR) of dengue was found at minimum average temperature at 0 and I-month lag. Average humidity showed positive and significant correlation with dengue incidence at 0-month lag. Average and cumulative rainfall also showed negative and significant correlation only at 3-months lag period. Conclusion: Weather variability influences dengue incidence and the association between the weather factors are non­ linear and not consistent. So the study findings should be evaluated area basis with other local factors to develop early warning for dengue epidemic prediction. JOPSOM 2020; 39(1): 43-49

scholarly journals Forecasting Malaria Cases Using Climatic Factors in Delhi, India: A Time Series Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Varun Kumar ◽  
Abha Mangal ◽  
Sanjeet Panesar ◽  
Geeta Yadav ◽  
Richa Talwar ◽  
...  
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Climatic Factors ◽  
Series Data ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Training Centre ◽  
Series Analysis

Background. Malaria still remains a public health problem in developing countries and changing environmental and climatic factors pose the biggest challenge in fighting against the scourge of malaria. Therefore, the study was designed to forecast malaria cases using climatic factors as predictors in Delhi, India. Methods. The total number of monthly cases of malaria slide positives occurring from January 2006 to December 2013 was taken from the register maintained at the malaria clinic at Rural Health Training Centre (RHTC), Najafgarh, Delhi. Climatic data of monthly mean rainfall, relative humidity, and mean maximum temperature were taken from Regional Meteorological Centre, Delhi. Expert modeler of SPSS ver. 21 was used for analyzing the time series data. Results. Autoregressive integrated moving average, ARIMA (0,1,1) (0,1,0)12, was the best fit model and it could explain 72.5% variability in the time series data. Rainfall (P value = 0.004) and relative humidity (P value = 0.001) were found to be significant predictors for malaria transmission in the study area. Seasonal adjusted factor (SAF) for malaria cases shows peak during the months of August and September. Conclusion. ARIMA models of time series analysis is a simple and reliable tool for producing reliable forecasts for malaria in Delhi, India.

Weather Forecasting in Coastal Districts of Odisha and Andhra Pradesh by Using Time Series Analysis

2018 ◽  
Vol 6 (8) ◽  
pp. 85
Author(s):  
Naresh Patnaik ◽  
F Baliarsingh
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Time Series Analysis ◽  
Andhra Pradesh ◽  
Maximum Temperature ◽  
Climatic Parameters ◽  
Series Analysis ◽  
Annual Maximum Temperature

Climate change in world is always one of the most important topics in Water Resources. Now the issue is so predominant that it is gradually restricting out social life, peace and harmony. Climate change is a change in the statistical distribution of weather pattern of an area, when such changes occur for a long period of time. Weather is the state of atmosphere at a particular place and time. Climate is the long term statistical expression of short term weather. This study presents a comprehensive assessment of the future climate pattern/weather prediction by taking different climatic parameters such as temperature, precipitation, solar radiation, wind speed and relative humidity by using time series analysis. The study area of research work covers the coastal districts of Odisha and some parts of Andhra Pradesh. The climatic parameters are collected over last 20 years (1993-2013) from the selected 10 stations and the prediction is made using Time Series Analysis (ARIMA Model). The annual maximum temperature, solar radiation of all districts indicates a statistically significant increase in trend, whereas in the case of wind speed and relative humidity indicates significant deceasing trend. The annual rain fall shows an increasing trend of 2.69 mm/year in all station except Srikakulam, Khordha, Jagatsinghpur and Balasore which shows a decreasing trend of 1.94, 1.29, 0.56 and 1.18 mm/year respectively. As a whole the annual maximum temperature and solar radiation shows an increase trend of 0.16 ⁰C and 0.073 MJ/m² per year respectively. Further the wind speed and relative humidity of all stations indicates a decreasing trend of 0.056 m/s and 0.003(Units in fraction) per year respectively.

scholarly journals Time Series Analysis of Climate and Air Pollution Factors Associated with Atmospheric Nitrogen Dioxide Concentration in Japan

2020 ◽  
Vol 17 (24) ◽  
pp. 9507
Author(s):  
Takeshi Miyama ◽  
Hiroshi Matsui ◽  
Kenichi Azuma ◽  
Chika Minejima ◽  
Yasuyuki Itano ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Nitrogen Dioxide ◽  
Rural Areas ◽  
Air Pollutant ◽  
Climate Conditions ◽  
Series Analysis ◽  
Urban Rural

Nitrogen dioxide (NO2) is an air pollutant discharged from combustion of human activities. Nitrous acid (HONO), measured as NO2, is thought to impact respiratory function more than NO2. HONO and NO2 have an equilibrium relationship, and their reaction is affected by climate conditions. This study was conducted to discuss the extent of HONO contained in NO2, depending on the level of urbanization. Whether climate conditions that promote HONO production enhanced the level of NO2 measured was investigated using time series analysis. Climate and outdoor air pollution data measured in April 2009–March 2017 in urban (Tokyo, Osaka, and Aichi) and rural (Yamanashi) areas in Japan were used for the analysis. Air temperature had a trend of negative associations with NO2, which might indicate the decomposition of HONO in the equilibrium between HONO and NO2. The associations of relative humidity with NO2 did not have consistent trends by prefecture: humidity only in Yamanashi was positively associated with NO2. In high relative humidity conditions, the equilibrium goes towards HONO production, which was observed in Yamanashi, suggesting the proportion of HONO in NO2 might be low/high in urban/rural areas.

Chaoticity analysis of the current through pure, hydrogenated and hydrophobically modified PEG-Si thin films under varying relative humidity

Open Physics ◽  
2009 ◽  
Vol 7 (3) ◽  
Author(s):  
Kaan Atak ◽  
Ozgur Aybar ◽  
Gokhan Şahin ◽  
Avadis Hacınlıyan ◽  
Yani Skarlatos
Keyword(s):  
Thin Films ◽  
Time Series ◽  
Polyethylene Glycol ◽  
Lyapunov Exponent ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Maximal Lyapunov Exponent ◽  
Series Analysis ◽  
Hydrophobically Modified ◽  
Irregular Behavior

AbstractPolyethylene Glycol has an irregular current characteristic under constant voltage and slowly varying relative humidity. The current through a thin film of Gamma-isocyanatopropyltriethoxysilane added Polyethylene glycol (PEG-Si), its hydrogenated and hydrophobically modified forms, as a function of increasing relative humidity at equal time steps is analyzed for chaoticity. We suggest that the irregular behavior of current through PEG-Si thin films as a function of increasing relative humidity could best be analyzed for chaoticity using both time series analysis and detrended uctuation analysis; the relative humidity is kept as a slowly varying parameter. The presence of more then one regime is suggested by the calculation of the maximal Lyapunov exponents. Furthermore, the maximal Lyapunov exponent in each of the regimes was positive, thus confirming the presence of low dimensional chaos. DFA also confirms the presence of at least two different regimes, in agreement with the behavior of the maximal Lyapunov exponent in the time series analysis. We also suggest that the irregular behavior of the current through PEG-Si can be reduced by hydrogenating and hydrophobically modifying PEG-Si and the improvement in stability can be confirmed by our study.

scholarly journals Weather variability and transmissibility of COVID-19: a time series analysis based on effective reproductive number

2021 ◽  
pp. 1-10
Author(s):  
Xiaohan Si ◽  
Hilary Bambrick ◽  
Yuzhou Zhang ◽  
Jian Cheng ◽  
Hannah McClymont ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Reproductive Number ◽  
Weather Variability ◽  
Series Analysis
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