Malaria Disease Distribution in Sudan Using Time Series ARIMA Model

2015 ◽  
Vol 4 (1) ◽  
pp. 7 ◽  
Author(s):  
Mohammed Ibrahim Musa
Keyword(s):  
African Country ◽  
Moving Average ◽  
Arima Model ◽  
Information Criteria ◽  
Climate Factors ◽  
Training Set ◽  
The World ◽  
Auto Regressive ◽  
Sub Saharan ◽  
The Relationship

<p>Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)<sup>12</sup>. The ARIMAX model showed that there is a significant variation between the states in Sudan.</p>

scholarly journals Malaria Disease Distribution in Sudan Using Time Series ARIMA Model

2015 ◽  
Vol 4 (1) ◽  
pp. 7
Author(s):  
Mohammed Ibrahim Musa
Keyword(s):  
African Country ◽  
Moving Average ◽  
Arima Model ◽  
Information Criteria ◽  
Climate Factors ◽  
Training Set ◽  
The World ◽  
Auto Regressive ◽  
Sub Saharan ◽  
The Relationship

<p>Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)<sup>12</sup>. The ARIMAX model showed that there is a significant variation between the states in Sudan.</p>

scholarly journals Detection of Tea Export Potentiality from Bangladesh in the World Market: An Autoregressive Integrated Moving Average (ARIMA) Approach

2020 ◽  
pp. 21-29
Author(s):  
A. U. Noman ◽  
S. Majumder ◽  
M. F. Imam ◽  
M. J. Hossain ◽  
F. Elahi ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
World Market ◽  
Information Criteria ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
The World

Export plays an important role in promoting economic growth and development. The study is conducted to make an efficient forecasting of tea export from Bangladesh for mitigating the risk of export in the world market. Forecasting has been done by fitting Box-Jenkins type autoregressive integrated moving average (ARIMA) model. The best ARIMA model is selected by comparing the criteria- coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Bayesian information criteria (BIC). Among the Box-Jenkins ARIMA type models for tea export the ARIMA (1,1,3) model is the most appropriate one for forecasting and the forecast values in thousand kilogram for the year 2017-18, 2018-19, 2019-20, 2020-21 and 2021-22, are 1096.48, 812.83, 1122.02, 776.25 and 794.33 with upper limit 1819.70, 1348.96, 1862.09, 1288.25, 1318.26 and lower limit 660.69, 489.78, 676.08, 467.74, 478.63, respectively. So, the result of this model may be helpful for the policymaker to make an export development plan for the country.

scholarly journals Time-Series Forecasting and Analysis of COVID-19 Outbreak in Highly Populated Countries

2022 ◽  
Vol 13 (2) ◽  
pp. 1-17
Author(s):  
Arunkumar P. M. ◽  
Lakshmana Kumar Ramasamy ◽  
Amala Jayanthi M.
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Research Area ◽  
Time Series Forecasting ◽  
Disease Spread ◽  
Time Period ◽  
The World ◽  
Auto Regressive ◽  
Important Research Area

A novel corona virus, COVID-19 is spreading across different countries in an alarming proportion and it has become a major threat to the existence of human community. With more than eight lakh death count within a very short span of seven months, this deadly virus has affected more than 24 million people across 213 countries and territories around the world. Time-series analysis, modeling and forecasting is an important research area that explores the hidden insights from larger set of time-bound data for arriving better decisions. In this work, data analysis on COVID-19 dataset is performed by comparing the top six populated countries in the world. The data used for the evaluation is taken for a time period from 22nd January 2020 to 23rd August 2020.A novel time-series forecasting approach based on Auto-regressive integrated moving average (ARIMA) model is also proposed. The results will help the researchers from medical and scientific community to gauge the trend of the disease spread and improvise containment strategies accordingly.

Forecasting Cryptocurrency Prices Using ARIMA and Neural Network: A Comparative Study

2020 ◽  
Author(s):  
Saurabh Kumar
Keyword(s):  
Neural Network ◽  
Time Horizon ◽  
Moving Average ◽  
Arima Model ◽  
Network Models ◽  
Sample Period ◽  
Neural Network Models ◽  
Time Horizons ◽  
The World ◽  
Auto Regressive

The prices of cryptocurrencies are very volatile and forecasting them is a challenging task for the researchers across the world. The present study examines the accuracy of forecasted returns of the two most popular cryptocurrencies (Bitcoin and Ethereum) for the sample period spanning from October 1, 2013, to November 30, 2018. Auto-regressive integrated moving average (ARIMA) and Neural Network models have been used to forecast the returns of the cryptocurrencies. The forecasting results for different time-horizons indicate that for a shorter time-horizon, ARIMA model is better for forecasting the returns of cryptocurrencies, whereas, for a longer time-horizon, Neural Network model is better for forecasting the returns of cryptocurrencies. These results have implications for traders, investors, regulators, policymakers and academia.

scholarly journals Relationship between Gross Domestic Product and Accident in China

2017 ◽  
Vol 3 (1) ◽  
pp. 19 ◽  
Author(s):  
Rujun Wang ◽  
Jinqiu Gong ◽  
Yu Wang ◽  
Haodong Chen ◽  
Sining Chen ◽  
...  
Keyword(s):  
Economic Development ◽  
Gross Domestic Product ◽  
Moving Average ◽  
Arima Model ◽  
Domestic Product ◽  
State Administration ◽  
Chemical Accident ◽  
Accident Data ◽  
Auto Regressive ◽  
The Relationship

The accident and death data from 2002 to 2015 were obtained from State Administration of Work Safety of China to investigate the relationship between gross domestic product (GDP) and accident. The statistical analysis shows that the accident, death and the death rate of per hundred million yuan present an exponential decreasing trend with the increase of national GDP. The chemical accident data in different provinces were further analyzed. It shows that the dangerous chemical accidents primarily distribute in the regions with better economic development, so more safety measures should be taken to prevent the accidents during economic development. In addition, the next three years of accidents were predicted based on auto-regressive integrated moving average (ARIMA) model. The results show that the following two years accidents predicted will be reduced by 4.3% and 6.5% than the last year.

Short term Forecast of the COVID-19 Epidemic in top 15 Affected Countries in the World using ARIMA Model with Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Pavan Kumar ◽  
Ranjit Sah ◽  
Alfonso J. Rodriguez-Morales ◽  
Himangshu Kalita Jr ◽  
Akshaya Srikanth Bhagavathula ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moving Average ◽  
Arima Model ◽  
The United States ◽  
Learning Approach ◽  
Average Model ◽  
Machine Learning Approach ◽  
The World ◽  
Moving Average Model ◽  
Auto Regressive

BACKGROUND The COVID-19 pendemic reached more than 200 countries, which was recognized during December-19 from CHINA and affected more than 28 lakh people on date April 26, 2020 (data source:Johns Hopkins Corona Virus Resource Center). OBJECTIVE We here predicted some trajectories of COVID-19 in the coming days (until July 2, 2020) using the most advanced Auto-Regressive Integrated Moving Average Model (ARIMA). METHODS Here we have used the Auto-Regressive Integrated Moving Average Model (ARIMA). Mathematical approaches are widely used to infer critical epidemiological transitions and parameters of COVID-19. Methods such as epidemic curve fitting, surveillance data during the early transmission R0, and other epidemic models are frequently applied to generate forecasts of COVID-19 pandemic across the world. RESULTS Our analysis predicted very frightening outcomes, which defines to worsen the conditions in Iran, entire Europe, especially Italy, Spain, and France. While South Korea, after the initial blast, has come to stability, the same goes for the COVID-19 origin country China with more positive recovery cases and confirm to remain stable. The United States of America (USA) is come as a surprise and going to become the epicenter for new cases during the mid-April 2020. CONCLUSIONS Based on our predictions, public health officials should tailor aggressive interventions to grasp the power exponential growth, and rapid infection control measures at hospital levels are urgently needed to curtail the COVID-19 pandemic. This study analyzed at global level and extracted data upon Machine Learning approach using Artificial intelligence techniques for top 10% or 20 countries.

scholarly journals Forecasting Area, Production and Yield of Onion in Bangladesh by Using ARIMA Model

2019 ◽  
pp. 1-12
Author(s):  
Farhana Arefeen Mila ◽  
Mst. Tania Parvin
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Information Criteria ◽  
Upward Trend ◽  
Average Model ◽  
Domestic Markets ◽  
Moving Average Model ◽  
Auto Regressive ◽  
The Government ◽  
Day By Day

In Bangladesh, onion is the widely used spices both for preparing food and curing diseases as it has medicinal values. As the demand for onion is increasing day by day, it is necessary to make actual projections of onion for undertaking some policies based on it. Therefore, the study investigates the future changes in the area, yield and production of onion in Bangladesh by using the most popular Box-Jenkins methodology. The auto regressive integrated moving average model has been used to understand the pattern of change over a period of 57 years (1961 to 2017) as well as to forecast the changes in the upcoming years. Some information criteria (such as AIC, AICc and BIC) was considered for selecting the best-fitted models of each variable. The forecasted results showed an upward trend for all the variables considered in this study. It implies that the area of onion will increase from 193932.6 hectares in 2018 to 265770.9 hectare in 2027. Again, the amount of onion production will increase from 2073.61 M tons to 3574.06 M tons and for onion yield, it will rise from 10343.17 Kg/ha to 12988.02 kg/ha from 2018 to 2027. These predictions may help the government balancing the demand with the supply and also regulating the price of onion in the domestic markets of Bangladesh.

scholarly journals Completion Of A Full Course Of Primary Schooling Among All Children Everywhere By 2015: A Case Of Sub-Saharan Africa

2013 ◽  
Vol 6 (1) ◽  
pp. 147
Author(s):  
Robert Wamala
Keyword(s):  
Primary Education ◽  
Moving Average ◽  
Arima Model ◽  
Sub Saharan Africa ◽  
Primary Schooling ◽  
Target Date ◽  
Auto Regressive ◽  
Development Goals ◽  
Sub Saharan ◽  
Education Indicators

Achieving the United Nations Millennium Development Goals (MDGs) remains a major challenge, particularly in developing countries. Specifically, achieving the target of completing a full course of primary schooling among all children, which is goal two, is a major challenge for Sub-Saharan Africa. Though literature consensually suggests that the goal will not be achieved by the 2015 target date, no estimates are provided to support these claims. This study seeks to envisage the situation in Sub-Saharan Africa by the target date using an Auto-Regressive Integrated Moving Average (ARIMA) model. The investigation is based on data sourced from the World Bank publication of education indicators for the period 19702010. The data, comprising 41 observations, represent the total number of new entrants in the last grade of primary education, regardless of age, expressed as a percentage of the total population of the theoretical entrance age to the last grade of primary education. Overall, an upward trend of completion estimates presented in the results shows that progress has been made in this regard. The success attained for the region following the adoption of the MDGs in 2000 demonstrates that the goal can be achieved. The sub-optimal predictions of the situation obtained in the results nevertheless indicate that the achievement certainly will not be realized by the 2015 target date.

scholarly journals ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries

Equilibrium ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 181-204
Author(s):  
Tadeusz Kufel
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Essential Element ◽  
Value Added ◽  
World Health ◽  
Global Pandemic ◽  
The World ◽  
Auto Regressive ◽  
Health Organization ◽  
Virus Distribution

Research background: On 11 March 2020, the Covid-19 epidemic was identified by the World Health Organization (WHO) as a global pandemic. The rapid increase in the scale of the epidemic has led to the introduction of non-pharmaceutical countermeasures. Forecast of the Covid-19 prevalence is an essential element in the actions undertaken by authorities. Purpose of the article: The article aims to assess the usefulness of the Auto-regressive Integrated Moving Average (ARIMA) model for predicting the dynamics of Covid-19 incidence at different stages of the epidemic, from the first phase of growth, to the maximum daily incidence, until the phase of the epidemic's extinction. Methods: ARIMA(p,d,q) models are used to predict the dynamics of virus distribution in many diseases. Model estimates, forecasts, and the accuracy of forecasts are presented in this paper. Findings & Value added: Using the ARIMA(1,2,0) model for forecasting the dynamics of Covid-19 cases in each stage of the epidemic is a way of evaluating the implemented non-pharmaceutical countermeasures on the dynamics of the epidemic.

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

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