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

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.

scholarly journals A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 141
Author(s):  
Jacob Hale ◽  
Suzanna Long
Keyword(s):  
Time Series ◽  
Electricity Generation ◽  
Sustainability Assessment ◽  
Moving Average ◽  
Time Series Prediction ◽  
State Level ◽  
Autoregressive Integrated Moving Average ◽  
Energy Transitions ◽  
Univariate Time Series ◽  
Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

scholarly journals A statistical study of COVID-19 pandemic in Egypt

2021 ◽  
Vol 54 (1) ◽  
pp. 233-244
Author(s):  
Taha Radwan
Keyword(s):  
Linear Relationship ◽  
Statistical Study ◽  
Moving Average ◽  
Arima Model ◽  
Decision Makers ◽  
Accurate Data ◽  
Autoregressive Integrated Moving Average ◽  
The World ◽  
The Future

Abstract The spread of the COVID-19 started in Wuhan on December 31, 2019, and a powerful outbreak of the disease occurred there. According to the latest data, more than 165 million cases of COVID-19 infection have been detected in the world (last update May 19, 2021). In this paper, we propose a statistical study of COVID-19 pandemic in Egypt. This study will help us to understand and study the evolution of this pandemic. Moreover, documenting of accurate data and taken policies in Egypt can help other countries to deal with this epidemic, and it will also be useful in the event that other similar viruses emerge in the future. We will apply a widely used model in order to predict the number of COVID-19 cases in the coming period, which is the autoregressive integrated moving average (ARIMA) model. This model depicts the present behaviour of variables through linear relationship with their past values. The expected results will enable us to provide appropriate advice to decision-makers in Egypt on how to deal with this epidemic.

scholarly journals Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250149
Author(s):  
Fuad A. Awwad ◽  
Moataz A. Mohamoud ◽  
Mohamed R. Abonazel
Keyword(s):  
Saudi Arabia ◽  
Moving Average ◽  
Arima Model ◽  
Daily Basis ◽  
World Health ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

scholarly journals Autoregressive Integrated Moving Average (ARIMA) Sebagai Model Peramalan Kasus Demam Berdarah Dengue

2020 ◽  
Vol 10 (2) ◽  
pp. 76-80
Author(s):  
Roro Kushartanti ◽  
Maulina Latifah
Keyword(s):  
Community Health ◽  
Health Center ◽  
Moving Average ◽  
Arima Model ◽  
Community Health Center ◽  
Percentage Error ◽  
Dengue Case ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method

ARIMA is a forecasting method time series that does not require a specific data pattern. This study aims to analyze the forecasting of Semarang City DHF cases specifically in the Rowosari Community Health Center. The study used monthly data on DHF cases in the Rowosari Community Health Center in 2016, 2017, and 2019 as many as 36 dengue case data. The best ARIMA model for forecasting is a model that meets the requirements for parameter significance, white noise and has the MAPE (Mean Absolute Percentage Error Smallest) value. The results of the analysis show that the best model for predicting the number of dengue cases in the Rowosari Public Health Center Semarang is the ARIMA model (1,0,0) with a MAPE value of 43.98% and a significance coefficient of 0.353, meaning that this model is suitable and feasible to be used as a forecasting model. DHF cases in the Rowosari Community Health Center in Semarang City.

scholarly journals Seawater intrusion vulnerability evaluation and prediction: a case study of Qeshm Island, Iran

2020 ◽  
Author(s):  
Amin Zeynolabedin ◽  
Reza Ghiassi ◽  
Moharram Dolatshahi Pirooz
Keyword(s):  
Seawater Intrusion ◽  
Moving Average ◽  
Arima Model ◽  
Chloride Ion ◽  
Absolute Error ◽  
Ion Concentration ◽  
Qeshm Island ◽  
Autoregressive Integrated Moving Average ◽  
Dangerous Condition

Abstract Seawater intrusion is one of the most serious issues to threaten coastal aquifers. Tourian aquifer, which is selected as the case study, is located in Qeshm Island, Persian Gulf. In this study, first the vulnerability of the region to seawater intrusion is assessed using chloride ion concentration value, then by using the autoregressive integrated moving average (ARIMA) model, the vulnerability of the region is predicted for 14 wells in 2018. The results show that the Tourian aquifer experiences moderate vulnerability and the area affected by seawater intrusion is wide and is in danger of expanding. It is also found that 0.95 km2 of the region is in a state of high vulnerability with Cl concentration being in a dangerous condition. The prediction model shows that ARIMA (2,1,1) is the best model with mean absolute error of 13.3 mg/L and Nash–Sutcliffe value of 0.81. For fitted and predicted data, mean square error is evaluated as 235.3 and 264.3, respectively. The prediction results show that vulnerability is increasing through the years.

scholarly journals Rainfall Forecast of Merauke Using Autoregressive Integrated Moving Average Model

2018 ◽  
Vol 73 ◽  
pp. 12010 ◽  
Author(s):  
Yenni P. Pasaribu ◽  
Hariani Fitrianti ◽  
Dessy Rizki Suryani
Keyword(s):  
Global Climate ◽  
Human Life ◽  
Moving Average ◽  
Arima Model ◽  
Secondary Data ◽  
Crop Failure ◽  
Maximum Rainfall ◽  
Autoregressive Integrated Moving Average ◽  
Agriculture Sector ◽  
Rainfall Forecast

Climate is an important element for human life, one of them is to agriculture sector. Global climate change leads to increased frequency and extreme climatic intensity such as storms, floods, and droughts. Rainfall is climate factor that causes the failure of harvest in Merauke. Therefore, rainfall forecast information is very useful in anticipating the occurrence of extreme events that can lead to crop failure. The purpose of this research is to model rainfall using autoregressive integrated moving average (ARIMA) model. The ARIMA model can be used to predict future events using a set of past data, including predicting rainfall. This research was conducted by collecting secondary data from Agency of Meteorology, Climatology, and Geophysics (BMKG) from 2005 until 2017, then the data was analyzed using R.3.4.2. software. The analysis result showed that ARIMA model (2.0,2) as the right model to predict rainfall in Merauke. The result of forecasting based on ARIMA model (2.0,2) for one period ahead is 179 mm of average rainfall, 46 mm of minimum rainfall, and 295 mm of maximum rainfall. Thus it can be concluded that the intensity of rainfall in Merauke has decreased and there was a seasonal shift from the previous period.

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