Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses

Influenza is a worldwide respiratory infectious disease that easily spreads from one person to another. Previous research has found that the influenza transmission process is often associated with climate variables. In this study, we used autocorrelation and partial autocorrelation plots to determine the appropriate autoregressive integrated moving average (ARIMA) model for influenza transmission in the central and southern regions of Thailand. The relationships between reported influenza cases and the climate data, such as the amount of rainfall, average temperature, average maximum relative humidity, average minimum relative humidity, and average relative humidity, were evaluated using cross-correlation function. Based on the available data of suspected influenza cases and climate variables, the most appropriate ARIMA(X) model for each region was obtained. We found that the average temperature correlated with influenza cases in both central and southern regions, but average minimum relative humidity played an important role only in the southern region. The ARIMAX model that includes the average temperature with a 4-month lag and the minimum relative humidity with a 2-month lag is the appropriate model for the central region, whereas including the minimum relative humidity with a 4-month lag results in the best model for the southern region.

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Using ERA-Interim Reanalysis output for creating datasets of energy-relevant climate variables

10.5194/essd-2016-67 ◽

2017 ◽

Cited By ~ 1

Author(s):

Philip D. Jones ◽

Colin Harpham ◽

Alberto Troccoli ◽

Benoit Gschwind ◽

Thierry Ranchin ◽

...

Keyword(s):

Climate Change ◽

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Climate Variables ◽

Climate Data ◽

Near Surface ◽

Bias Adjustment ◽

Dewpoint Temperature ◽

Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim Reanalysis is presented. A number of different bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting those calculated from ERA-Interim to those based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed and relative humidity, available at either 3 or 6 h timescales over the period 1979-2014. This dataset is available to anyone through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S), and can be accessed at present from (ftp://ecem.climate.copernicus.eu). The benefit of performing bias-adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against observations.

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The impact of climate change on the resin productivity of dammar tree (Agathis alba) in Inamosol subdistrict, West Seram district, Maluku-Indonesia

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/883/1/012079 ◽

2021 ◽

Vol 883 (1) ◽

pp. 012079

Author(s):

J M Matinahoru

Keyword(s):

Climate Change ◽

Relative Humidity ◽

Sampling Technique ◽

Climate Data ◽

Purposive Sampling ◽

Impact Of Climate Change ◽

Average Temperature ◽

Change Factors ◽

Temperature And Humidity ◽

The Impact

Abstract This research was aimed to determine the impact of climate change on the resin productivity of dammar tree. This research will be useful as data and information for farmers and government to maintain the resin of dammar tree to be optimal and sustainable in production. This research was conducted in Inamosol Sub-district, West Seram District, Maluku Indonesia, during September-October 2020. Village and farmer samples were determined by purposive sampling technique. The selected villages were Honitetu, Hukuanakota and Rambatu. Furthermore, from each village, It was ten farmers to select for interviews and filling the questionnaire. The results showed that the average resin production of farmers in 2019 was 904.2 kg/farmer, while in 2020 was 523.7 kg/farmer. This means that it occurred a decline in resin production in 2020 about 42.08 % for each farmer—the leading cause of the decreased production as climate change factors, namely rainfall, temperature and humidity. Based on climate data of West Seram District in 2019 indicated that rainfall has occurred during six months with an average temperature of 27 °C and relative humidity of 82 %. Meanwhile, in 2020 the rainfall occurs for nine months with an average temperature of 26.5 °C, and relative humidity of 85 %.

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Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

Earth System Science Data ◽

10.5194/essd-9-471-2017 ◽

2017 ◽

Vol 9 (2) ◽

pp. 471-495 ◽

Cited By ~ 19

Author(s):

Philip D. Jones ◽

Colin Harpham ◽

Alberto Troccoli ◽

Benoit Gschwind ◽

Thierry Ranchin ◽

...

Keyword(s):

Climate Change ◽

Wind Speed ◽

Relative Humidity ◽

Solar Radiation ◽

Climate Variables ◽

Climate Data ◽

Near Surface ◽

Bias Adjustment ◽

Dewpoint Temperature ◽

Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

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The influence of climate factors on COVID-19 transmission in Malaysia: An autoregressive integrated moving average (ARIMA) model

10.1101/2020.08.14.20175372 ◽

2020 ◽

Author(s):

Cho Naing ◽

Han Ni ◽

Htar Htar Aung ◽

Elaine Chan Wan Ling ◽

Joon Wah Mak

Keyword(s):

Moving Average ◽

Descriptive Analysis ◽

Arima Model ◽

State Level ◽

Climate Factors ◽

Single Digit ◽

Autoregressive Integrated Moving Average ◽

Explanatory Variables ◽

Average Temperature ◽

Climate Parameters

Background A unique concern pertaining to the spread of COVID-19 across countries is the asymmetry of risk and the irrational fear of a new pandemic and its possible serious consequences. This study aimed to perform a time series analysis on the association between climate factors and daily cases of COVID-19 in Malaysia up to 15 July 2020. The second objective was to predict daily new cases using a forecasting technique. To address within-country variations, the analysis was extended to the state level with Sarawak state as an example. Methodology/Principal Findings Datasets on the daily confirmed cases and climate variables in Malaysia and Sarawak state were obtained from publicly accessible official websites. A descriptive analysis was performed to characterize all the important variables over the study period. An autoregressive integrated moving average (ARIMA) model was introduced using daily cases as the dependent variable and climate parameters as the explanatory variables. For Malaysia, the findings suggest that, ceteris paribus, the number of COVID-19 cases decreased with increasing average temperature (p=0.003) or wind speed (p=0.029). However, none of the climate parameters showed a significant relationship with the number of COVID-19 cases in Sarawak state. Forecasts from the ARIMA models showed that new daily COVID-19 cases had already reached the outbreak level and a decreasing trend in both settings. Holding other parameters constant, a small number of new cases (approximately a single digit) is a probable second wave in Sarawak state, Conclusions/Significance The findings suggest that climate parameters and forecasts are helpful for reducing the uncertainty in the severity of future COVID-19 transmission. A highlight is that forecasts will be a useful tool for making decisions and taking the appropriate interventions to contain the spread of the virus in the community.

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Application of Support Vector Regression to the Prediction of the Long-Term Impacts of Climate Change on the Moisture Performance of Wood Frame and Massive Timber Walls

Buildings ◽

10.3390/buildings11050188 ◽

2021 ◽

Vol 11 (5) ◽

pp. 188

Author(s):

Naman Bansal ◽

Maurice Defo ◽

Michael A. Lacasse

Keyword(s):

Relative Humidity ◽

Full Range ◽

Support Vector ◽

Data Series ◽

Climate Variables ◽

Climate Data ◽

Moisture Performance ◽

Wood Frame ◽

Massive Timber

The objective of this study was to explore the potential of a machine learning algorithm, the Support Vector Machine Regression (SVR), to forecast long-term hygrothermal responses and the moisture performance of light wood frame and massive timber walls. Hygrothermal simulations were performed using a 31-year long series of climate data in three cities across Canada. Then, the first 5 years of the series were used in each case to train the model, which was then used to forecast the hygrothermal responses (temperature and relative humidity) and moisture performance indicator (mold growth index) for the remaining years of the series. The location of interest was the exterior layer of the OSB and cross-laminated timber in the case of the wood frame wall and massive timber wall, respectively. A sliding window approach was used to incorporate the dependence of the hygrothermal response on the past climatic conditions, which allowed SVR to capture time, implicitly. The variable selection was performed using the Least Absolute Shrinkage and Selection Operator, which revealed wind-driven rain, relative humidity, temperature, and direct radiation as the most contributing climate variables. The results show that SVR can be effectively used to forecast hygrothermal responses and moisture performance on a long climate data series for most of the cases studied. In some cases, discrepancies were observed due to the lack of capturing the full range of variability of climate variables during the first 5 years.

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Long-Term Prediction of Emergency Department Revenue and Visitor Volume Using Autoregressive Integrated Moving Average Model

Computational and Mathematical Methods in Medicine ◽

10.1155/2011/395690 ◽

2011 ◽

Vol 2011 ◽

pp. 1-7 ◽

Cited By ~ 13

Author(s):

Chieh-Fan Chen ◽

Wen-Hsien Ho ◽

Huei-Yin Chou ◽

Shu-Mei Yang ◽

I-Te Chen ◽

...

Keyword(s):

Relative Humidity ◽

Stock Market ◽

Minimum Temperature ◽

Moving Average ◽

Arima Model ◽

Maximum Temperature ◽

Stock Market Index ◽

Autoregressive Integrated Moving Average ◽

Market Index ◽

Independent Variable

This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.

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VARIABILIDADE TEMPORAL E MAPEAMENTO DOS DADOS CLIMÁTICOS DE BOTUCATU–SP

Irriga ◽

10.15809/irriga.2010v15n2p131 ◽

2010 ◽

Vol 15 (2) ◽

pp. 131-139 ◽

Cited By ~ 2

Author(s):

Anderson Antonio da Conceição Sartori ◽

Alessandra Fagioli da Silva ◽

Clovis Manoel Carvalho Ramos ◽

Célia Regina Lopes Zimback

Keyword(s):

Relative Humidity ◽

Air Temperature ◽

Temporal Variability ◽

Temporal Distribution ◽

Meteorological Station ◽

Climate Variables ◽

Climate Data ◽

Temporal Dependence ◽

Classical Statistics ◽

Made In

O trabalho teve objetivo estudar a variabilidade temporal da temperatura do ar, precipitação pluviométrica e umidade relativa do ar na cidade de Botucatu-SP, Brasil, utilizando técnicas geoestatísticas. Os dados de precipitação pluviométrica, temperatura do ar e umidade relativa do ar utilizados no presente estudo são provenientes da Estação Meteorológica da Fazenda Lageado, da Faculdade de Ciências Agronômicas-UNESP. As observações foram realizadas no período de 1988 a 2007, referem-se ao total mensal de precipitação pluvial expressa em altura de lâmina d'água (mm), médias mensais de temperatura em ºC e umidade relativa em %. Os dados foram avaliados por meio da estatística clássica e geoestatística. As variáveis climáticas tiveram sua dependência verificada por variogramas, apresentando dependência temporal maior que 76%. A série temporal de umidade relativa do ar foi a que apresentou maior alcance (8,67 meses) e, conseqüentemente, maior estabilidade climática. O conhecimento da distribuição temporal das variáveis climáticas é importante para o estudo e realização do zoneamento agroclimático, bem como para o dimensionamento do sistema de irrigação das culturas. UNITERMOS: geoestatística, mapeamento e krigagem SARTORI, A. A. C.; SILVA, A. F.; RAMOS, C. M. C; ZIMBACK, C. R. L. TEMPORAL VARIABILITY AND CLIMATE DATA MAPS OF BOTUCATU-SP 2 ABSTRACT The objective of this research to study the temporal variability of air temperature, rainfall and relative humidity at Botucatu-SP, Brazil, using geostatistics techniques. The data of rainfall, air temperature and relative humidity used in this study were obtamed from the Meteorological Station of the Agricultural Sciences College. The observations were made in the period from 1988 to 2007 and refer to the total monthly rainfall expressed in water depth (mm), average monthly of temperature in °C and relative humidity in %. The data were evaluated by means of classical statistics and geostatistics. The climatic variables were their dependence verified by variogramas, presenting temporal dependence greater than 76%. The temporal series of relative humidity presented the greatest value (8.67 months) and, consequently, more stability climate. Knowledge of the temporal distribution of climate variables is important for the study and realization of agroclimatic zoning and for design measurement of rrigation systems. KEYWORDS: geostatistics, mapping and kriging

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EL NINO RELATED CLIMATE AND DENGUE FEVER CASES IN JEDDAH, SAUDI ARABIA

FLORA AND FAUNA ◽

10.33451/florafauna.v25i1pp91-99 ◽

2019 ◽

Vol 25 (1) ◽

Author(s):

MASROOR ALI KHAN ◽

KHALID AL GHAMDI ◽

JAZEM A. MEHYOUB ◽

RAKHSHAN KHAN

Keyword(s):

Relative Humidity ◽

Dengue Fever ◽

El Niño ◽

Significant Relationship ◽

El Nino ◽

Climate Data ◽

Light Traps ◽

Mosquito Abundance ◽

Dengue Transmission ◽

The Relationship

The focus of this study is to find the relationship between El Nino and dengue fever cases in the study area.Mosquito density was recorded with the help of light traps and through aspirators collection. Climate data were obtained from National Meteorology and Environment centre. (Year wise El Nino and La Nina data are according to NOAA & Golden Gate Weather Services). Statistical methods were used to establish the correlation coefficient between different factors. A high significant relationship was observed between Relative Humidity and Dengue fever cases, but Aedes abundance had no significant relationship with either Relative humidity and Temperature. Our conclusion is that the El Nino does not affect the dengue transmission and Aedes mosquito abundance in this region, which is supported by earlier works.

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Model Statistika Prediksi Energi Surya Dengan Menggunakan Autoregresif Integrated Moving Average (ARIMA)

Jurnal MIPA ◽

10.35799/jmuo.8.3.2019.26193 ◽

2019 ◽

Vol 8 (3) ◽

pp. 181

Author(s):

Imriani Moroki ◽

Alfrets Septy Wauran

Keyword(s):

Renewable Energy ◽

Solar Energy ◽

Moving Average ◽

Arima Model ◽

Solar Irradiation ◽

Energy Sources ◽

Energy Potential ◽

Good Design ◽

Potential Sources ◽

The Common

Energi terbarukan adalah salah satu masalah energi paling terkenal saat ini. Ada beberapa sumber potensial energi terbarukan. Salah satu energi terbarukan yang umum dan sederhana adalah energi matahari. Masalah besar ketersediaan energi saat ini adalah terbatasnya sumber energi konvensional seperti bahan bakar. Ini semua sumber energi memiliki banyak masalah karena memiliki jumlah energi yang terbatas. Penting untuk membuat model dan analisis berdasarkan ketersediaan sumber energi. Energi matahari adalah energi terbarukan yang paling disukai di negara-negara khatulistiwa saat ini. Tergantung pada produksi energi surya di daerah tertentu untuk memiliki desain dan analisis energi matahari yang baik. Untuk memiliki analisis yang baik tentang itu, dalam makalah ini kami membuat model prediksi energi surya berdasarkan data iradiasi matahari. Kami membuat model energi surya dan angin dengan menggunakan Metode Autoregresif Integrated Moving Average (ARIMA). Model ini diimplementasikan oleh R Studio yang kuat dari statistik. Sebagai hasil akhir, kami mendapatkan model statistik solar yang dibandingkan dengan data aktualRenewable energy is one of the most fomous issues of energy today. There are some renewable energy potential sources. One of the common n simple renewable energy is solar energy. The big problem of the availability of energy today is the limeted sources of conventional enery like fuel. This all energy sources have a lot of problem because it has a limited number of energy. It is important to make a model and analysis based on the availability of the energy sources. Solar energy is the most prefered renewable energy in equator countries today. It depends on the production of solar energy in certain area to have a good design and analysis of the solar energy. To have a good analysis of it, in this paper we make a prediction model of solar energy based on the data of solar irradiation. We make the solar and wind enery model by using Autoregresif Integrated Moving Average (ARIMA) Method. This model is implemented by R Studio that is a powerfull of statistical. As the final result, we got the statistical model of solar comparing with the actual data

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