scholarly journals People’s Concerns With the Prediction of COVID-19 in Bangladesh: Application of Autoregressive Integrated Moving Average Model

2021 ◽  
Vol 9 (2) ◽  
pp. 84-93
Author(s):  
Md. Ismail Hossain ◽  
Ahmed Abdus Saleh Saleheen ◽  
Iqramul Haq ◽  
Maliha Afroj Zinnia ◽  
Md. Rifat Hasan ◽  
...  
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Binary Logistic Regression ◽  
Vital Role ◽  
Health Concern ◽  
Binary Logistic Regression Model ◽  
Autoregressive Integrated Moving Average ◽  
Aged People ◽  
Materials Used ◽  
The Impact

Introduction: The coronavirus disease 2019 (COVID-19) has become a public health concern, and behavioral adjustments will minimize its spread worldwide by 80%. The main purpose of this research was to examine the factors associated with concerns about COVID-19 and the future direction of the COVID-19 scenario of Bangladesh. Methods: The binary logistic regression model was performed to assess the impact of COVID-19 concern in Bangladesh. Based on data obtained through online surveys in November 2020 and to predict the next 40 days daily confirmed and deaths of COVID-19 in Bangladesh by applying the Autoregressive Integrated Moving Average (ARIMA) model. Results: The study enrolled 400 respondents, with 253 (63.2%) were male, and 147 (36.8%) were female. The mean age of respondents was 25.13 ± 5.74 years old. Almost 70% of them were found to be concerned about the COVID-19 pandemic. The result showed that respondents’ education level, knowledge regarding COVID-19 transmits, households with aged people, seasonal flu and HD/respiratory problems, and materials used while sneezing/coughing significantly influenced COVID-19 concerns. The analysis predicted that confirmed cases would gradually decrease for the ARIMA model while death cases will be constant for the next 40 days in Bangladesh. Conclusion: The current study suggested that knowledge about COVID-19 spread and education played a vital role in the decline of COVID-19 concerned. A particular program should focus on creating an awareness of the disadvantages of concerns about the COVID-19 pandemic by augmenting knowledge about COVID-19 spread, enhancing Education in Bangladesh.

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 The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

2019 ◽  
Vol 4 (3) ◽  
pp. 58
Author(s):  
Lu Qin ◽  
Kyle Shanks ◽  
Glenn Allen Phillips ◽  
Daphne Bernard
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Moving Average ◽  
Arima Model ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Enrollment Forecasting ◽  
Moving Average Model ◽  
The Impact ◽  
Practical Impact

The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

scholarly journals Policies to Reduce Antibiotic Consumption: The Impact in the Basque Country

Antibiotics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 423
Author(s):  
Paula Rojas ◽  
Fernando Antoñanzas
Keyword(s):  
Bacterial Resistance ◽  
Moving Average ◽  
Basque Country ◽  
Arima Model ◽  
Antibiotic Consumption ◽  
Line Treatment ◽  
Autoregressive Integrated Moving Average ◽  
Drug Packaging ◽  
One Year ◽  
The Impact

In 2013, a change in copayment rate was introduced in the Basque Country (one year later than in the other regions in Spain), and improvements were made to drug packaging. In 2014, a National Program Against Bacterial Resistance (Spanish abbreviation: PRAN) was approved. The aim of this study is to analyze the impact of change to the copayment rate, the adjustment of drug packaging, and the approval of PRAN on the consumption of antibiotics. Raw monthly data on the consumption of antibiotics (costs, packages, and daily defined doses per thousand people (DID)) were collected from January 2009 to December 2018 in the Basque Country. Counterfactual and intervention analysis (Autoregressive integrated moving average (ARIMA) model) was performed for the total series, disaggregated by group of antibiotics (2019 WHO Access, Watch, and Reserve (AWaRe) Classification) and active substances with the highest cost per prescription (cefditoren and moxifloxacin), the lowest cost per prescription (doxycycline and cloxacillin), and the most prescribed active ingredients (amoxicillin, azithromycin, and levofloxacin). Introduction of copayment led to a ‘stockpiling effect’ one month before its implementation, equal to 8% in the three consumption series analyzed. Only the adjustment of drug packaging significantly reduced the number of packages dispensed (−12.19%). PRAN approval reduced consumption by 0.779 DID (−4.51%), representing a significant decrease for both ’access’ and ’watch’ group antibiotics. Despite the delay in implementing changes to copayment, there was a ‘stockpiling effect’. With the adjustment of packaging, fewer packs were prescribed but with a higher drug load and price. PRAN approval reduced both the consumption of ’access group antibiotics’ (first-line treatment) and ’watch group antibiotics’ (second-line treatment).

scholarly journals Assessing the Recovery Rate of Inbound Tourist Arrivals Amid COVID-19: Evidence from the Maldives

2021 ◽  
Vol 7 (6) ◽  
pp. 7-15
Author(s):  
Ahmed Rabeeu ◽  
Chen Shouming ◽  
Md Abid Hasan ◽  
Disney Leite Ramos ◽  
Abdul Basit Abdul Rahim
Keyword(s):  
Recovery Rate ◽  
Moving Average ◽  
Vital Role ◽  
Tourism Industry ◽  
Policy Implications ◽  
Autoregressive Integrated Moving Average ◽  
Tourism Management ◽  
The Face ◽  
The Government ◽  
The Impact

The present study examines the impact of COVID-19 on Maldivian tourism, highlighting the loss of tourists and tourism earnings for the period 2020Q1 to 2021Q2 and analyses the recovery rate of inbound tourists’ arrivals post border re-opening (i.e., 2020Q3 – 2021Q2). Seasonal Autoregressive Integrated Moving Average (SARIMA) model was employed to generate monthly forecasts for 2020 and 2021. The results indicate an estimated loss of 1.9 million tourists between 2020Q1 and 2021Q2. A massive drop in tourist arrivals caused an estimated loss of USD 3.5 billion in tourism earnings by June 2021. Results further indicate that with an average monthly recovery rate of 3%, inbound arrivals have recovered 34% of forecasted levels and 40% of 2019 levels by June 2021. The measures implemented by the government of Maldives played a vital role in the recovery of inbound tourism. However, the rebound of tourists has not reached the desired levels except for the arrivals from Russia. Therefore, additional strategies must be implemented for the quick revival of the Maldivian tourism industry. This study expands and enriches tourism management knowledge in the face of a massive crisis highlighting important managerial and policy implications for reviving the tourism industry of the Maldives.

scholarly journals Estimating the impact of non-pharmaceutical interventions against COVID-19 on mumps incidence in Sichuan, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wenqiang Zhang ◽  
Rongsheng Luan
Keyword(s):  
Public Health ◽  
Respiratory Virus ◽  
Moving Average ◽  
Arima Model ◽  
Potential Reduction ◽  
Autoregressive Integrated Moving Average ◽  
Health Measures ◽  
Virus Incidence ◽  
The Impact ◽  
Pharmaceutical Interventions

Abstract Background A series of social and public health measures have been implemented to contain coronavirus disease 2019 (COVID-19) in China. We examined the impact of non-pharmaceutical interventions against COVID-19 on mumps incidence as an agent to determine the potential reduction in other respiratory virus incidence. Methods We modelled mumps incidence per month in Sichuan using a seasonal autoregressive integrated moving average (ARIMA) model, based on the reported number of mumps cases per month from 2017 to 2020. Results The epidemic peak of mumps in 2020 is lower than in the preceding years. Whenever compared with the projected cases or the average from corresponding periods in the preceding years (2017–2019), the reported cases in 2020 markedly declined (P < 0.001). From January to December, the number of mumps cases was estimated to decrease by 36.3% (33.9–38.8%), 34.3% (31.1–37.8%), 68.9% (66.1–71.6%), 76.0% (73.9–77.9%), 67.0% (65.0–69.0%), 59.6% (57.6–61.6%), 61.1% (58.8–63.3%), 49.2% (46.4–52.1%), 24.4% (22.1–26.8%), 30.0% (27.5–32.6%), 42.1% (39.6–44.7%), 63.5% (61.2–65.8%), respectively. The total number of mumps cases in 2020 was estimated to decrease by 53.6% (52.9–54.3%). Conclusion Our study shows that non-pharmaceutical interventions against COVID-19 have had an effective impact on mumps incidence in Sichuan, China.

scholarly journals DAMPAK PERJANJIAN PERDAGANGAN INDONESIA - JEPANG (IJEPA) TERHADAP KINERJA PERDAGANGAN BILATERAL

2015 ◽  
Vol 9 (2) ◽  
pp. 129-151
Author(s):  
Septika Tri Ardiyanti
Keyword(s):  
Bilateral Trade ◽  
Moving Average ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Monitoring Scheme ◽  
Economic Partnership Agreement ◽  
The Difference ◽  
Economic Partnership ◽  
Partnership Agreement ◽  
The Impact

Studi ini bertujuan untuk mengkaji dampak perjanjian Indonesia – Japan Economic Partnership Agreement (IJEPA) terhadap perdagangan bilateral Indonesia-Jepang dari sisi ekspor maupun impor, dengan menggunakan data bulanan Januari 1990 sampai dengan Juni 2014. Studi ini menggunakan pendekatan counterfactual dengan melakukan ekstrapolasi terhadap data perdagangan tanpa FTA (basis ekstrapolasi Jan 1990-Juni 2008) dan kemudian membandingkannya dengan data perdagangan aktual pada saat IJEPA telah diimplementasikan (Juli 2008-Juni 2014). Ekstrapolasi dilakukan dengan menggunakan metode Autoregressive Integrated Moving Average (ARIMA), sementara untuk menguji perbedaan antara kedua pengamatan data aktual dengan data ekstrapolasi digunakan uji t -berpasangan. Hasil analisis menunjukkan bahwa IJEPA secara signifikan mampu meningkatkan nilai ekspor non migas Indonesia ke Jepang, namun tidak memiliki dampak signifikan terhadap peningkatan nilai impor non migas Indonesia dari Jepang. Dengan demikian, Indonesia terbukti mendapatkan keuntungan dari sisi perdagangan karena mampu meningkatkan nilai ekspornya ke Jepang. Oleh karena itu, kerjasama yang intensif antara kedua negara harus terus ditingkatkan untuk mengoptimalkan perjanjian tersebut. Pemerintah dapat mengusulkan adanya bilateral monitoring scheme kepada pemerintah Jepang dalam rangka meningkatkan pemanfaatan IJEPA. This study aims at examining the impact of Indonesia-Japan Economic Partnership Agreement (IJEPA) towards bilateral trade between Indonesia and Japan, using monthly data from January 1990 to June 2014. This research used a counterfactual approach by constructing extrapolated trade values with pre-FTA data (extrapolation based on January 1990-June 2008), then comparing those extrapolated data with the actual trade data in the period after the implementation of IJEPA (July 2008-June 2014). The extrapolation was done using Autoregressive Integrated Moving Average (ARIMA) model, while paired t-test was used to examine the difference between the actual data and the extrapolated data. The results show that IJEPA can significantly increase the value of Indonesia’s non-oil exports to Japan, but it has no significant impact on the value of Indonesia’s non-oil imports from Japan. It is proven that Indonesia gets benefits from IJEPA in terms of foreign trade since it can increase its export value to Japan. Therefore, intensive cooperation between Indonesia and Japan should be improved by proposing a bilateral monitoring scheme to the Japanese government in order to improve the functions of IJEPA.

scholarly journals Reduced Dengue Incidence During COVID-19 Restrictions in Sri Lanka From March 2020 to April 2021

2021 ◽  
Author(s):  
Sinnathamby Noble Surendran ◽  
Ratnarajah Nagulan ◽  
Kokila Sivabalakrishnan ◽  
Sivasingham Arthiyan ◽  
Annathurai Tharshan ◽  
...  
Keyword(s):  
Public Health ◽  
Sri Lanka ◽  
Moving Average ◽  
Health Concern ◽  
Tropical Islands ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
The Public ◽  
Health Measures ◽  
The Impact

Abstract BackgroundDengue is a major public health concern in Sri Lanka. COVID-19 in Sri Lanka was first detected in January 2020, and has continued to be prevalent in the country since that time. The impact of public health measures imposed to restrict COVID-19 transmission on the incidence of dengue throughout the island and particularly its northern Jaffna district in the period March 2020 to April 2021 was determined.MethodsThe incidence of dengue and COVID-19, rainfall and the public health measures implemented to contain COVID-19 transmission for each district in Sri Lanka were obtained from Government sources. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to predict the dengue incidence expected in March 2020 to April 2021, based on pre-pandemic data and this was compared with the actual reported incidence of dengue during the period of COVID-19 restrictions. Ovitrap collections of Aedes larvae were also carried out in the Gurunagar ward of Jaffna city in the Jaffna district during the 2020 and 2021 lockdown and the findings compared with data from 2019.ResultsThe reported number of dengue cases for the whole country from March 2020 to April 2021 was significantly lower than the numbers of dengue cases predicted from the five years immediately preceding the COVID-19 pandemic (2015-2019). Decreased numbers of dengue cases were reported compared to predicted numbers of cases in all 25 administrative districts in the country including the Jaffna district. Aedes larval numbers collected from ovitraps in the Gurunagar ward in Jaffna city during the COVID-19 lockdown period were decreased, with significantly lower proportions of Ae. aegypti than Ae. albopictus, compared with 2019. ConclusionPublic health measures that restricted movement of people, closed schools, universities and offices in order to contain COVID-19 transmission unexpectedly led to a marked reduction in the incidence of dengue in Sri Lanka, in contrast to Singapore. The differences between the two tropical islands have significant implications for the epidemiology of dengue.

Stochastic modeling of plasma mode forecasting in tokamak

2011 ◽  
Vol 78 (2) ◽  
pp. 99-104 ◽  
Author(s):  
SH. SAADAT ◽  
M. SALEM ◽  
M. GHORANNEVISS ◽  
P. KHORSHID
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Vital Role ◽  
Mode Number ◽  
Tokamak Plasma ◽  
Model Parameters ◽  
Autoregressive Integrated Moving Average ◽  
Plasma Mode ◽  
Mhd Instabilities ◽  
Interesting Study

AbstractThe structure of magnetohydrodynamic (MHD) modes has always been an interesting study in tokamaks. The mode number of tokamak plasma is the most important parameter, which plays a vital role in MHD instabilities. If it could be predicted, then the time of exerting external fields, such as feedback fields and Resonance Helical Field, could be obtained. Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average are useful models to predict stochastic processes. In this paper, we suggest using ARIMA model to forecast mode number. The ARIMA model shows correct mode number (m = 4) about 0.5 ms in IR-T1 tokamak and equations of Mirnov coil fluctuations are obtained. It is found that the recursive estimates of the ARIMA model parameters change as the plasma mode changes. A discriminator function has been proposed to determine plasma mode based on the recursive estimates of model parameters.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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