scholarly journals Forecasting of COVID-19 Cases and Deaths Using ARIMA Models

2020 ◽  
Author(s):  
Lutfi Bayyurt ◽  
Burcu Bayyurt
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Health Crisis ◽  
Public Health Crisis ◽  
New Public Health ◽  
New Public ◽  
Science Community ◽  
Auto Regressive ◽  
Global Threat ◽  
And Control

ABSTRACTAfter the outbreak of severe acute respiratory syndrome (SARS-2002/2003) and middle east respiratory syndrome (MERS-2012/2014) in the world, new public health crisis, called new coronavirus disease (COVID-19), started in China in December 2019 and has spread all over countries. COVID-19 coronavirus has been global threat of the disease and infected humans rapidly. Control of the pandemi is urgently essential, and science community have continued to research treatment agents. Support therapy and intensive care units in hospitals are also efective to overcome of COVID-19. Statistic forecasting models could aid to healthcare system in preventation of COVID-19. This study aimed to compose of forecasting model that could be practical to predict the spread of COVID-19 in Italy, Spain and Turkey. For this purpose, we performed Auto Regressive Integrated Moving Average (ARIMA) model on the European Centre for Disease Prevention and Control COVID-19 data to predict the number of cases and deaths in COVID-19. According to the our results, while number of cases in Italy and Spain is expected to decrease as of July, in Turkey is expected to decline as of September. The number of deaths in Italy and Spain is expected to be the lowest in July. In Turkey, this number is expected to reach the highest in July. In addition, it is thought that if studies in which the sensitivity and validity of this method are tested with more cases, they will contribute to researchers working in this field.

Molecular characterization of SARS-CoV-2

2020 ◽  
Vol 20 ◽  
Author(s):  
Miribane Dërmaku-Sopjani ◽  
Mentor Sopjani
Keyword(s):  
Public Health ◽  
Drug Targets ◽  
Virus Genome ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Therapeutic Drugs ◽  
New Public Health ◽  
New Public ◽  
And Function

Abstract:: The coronavirus disease 2019 (COVID-19) is currently a new public health crisis threatening the world. This pandemic disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus has been reported to be originated in bats and by yet unknown intermediary animals were transmitted to humans in China 2019. The SARSCoV- 2 spreads faster than its two ancestors the SARS-CoV and Middle East respiratory syndrome coronavirus (MERSCoV) but has reduced fatality. At present, the SARS-CoV-2 has caused about a 1.16 million of deaths with more than 43.4 million confirmed cases worldwide, resulting in a serious threat to public health globally with yet uncertain impact. The disease is transmitted by inhalation or direct contact with an infected person. The incubation period ranges from 1 to 14 days. COVID-19 is accompanied by various symptoms, including cough, fatigue. In most people the disease is mild, but in some other people, such as in elderly and people with chronic diseases, it may progress from pneumonia to a multi-organ dysfunction. Many people are reported asymptomatic. The virus genome is sequenced, but new variants are reported. Numerous biochemical aspects of its structure and function are revealed. To date, no clinically approved vaccines and/or specific therapeutic drugs are available to prevent or treat the COVID-19. However, there are reported intensive researches on the SARSCoV- 2 to potentially identify vaccines and/or drug targets, which may help to overcome the disease. In this review, we discuss recent advances in understanding the molecular structure of SARS-CoV-2 and its biochemical characteristics.

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.

scholarly journals Emerging Human Coronaviruses: Molecular Biology and Vaccine Challenges

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Jawhar Gharbi
Keyword(s):  
Respiratory Pathogen ◽  
The Novel ◽  
Emerging Pathogens ◽  
Health Crisis ◽  
Public Health Crisis ◽  
New Public Health ◽  
New Public ◽  
Potential Vaccine ◽  
Human Coronaviruses ◽  
Tract Infections

COVID-19 is a new public health crisis caused by the novel respiratory pathogen SARS-CoV-2. It is one of the most significant pandemic events in recent history. The SARS-CoV-2 Beta corona virus was transmitted to humans in the end of 2019 by unknown intermediary host from bats in Wuhan, Hubei province (China). It marked the third major coronavirus source of disaster in the 21stcentury.The three last severe respiratory tract infections caused by the SARS-CoV-1, MERS-CoV and SARS-CoV-2 caused high human mortality. Viral genomic sequencing and investigations and the development of advanced vaccine strategies are expected to give us more information on these emerging pathogens and controlling them in the future. The aim of this review is to summarize updated information regarding these emerging human coronaviruses to understand their molecular and structural biology, transmissions and potential vaccine approaches actually developed against the SARS-CoV-2.

scholarly journals Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models

2020 ◽  
Author(s):  
Tania Dehesh ◽  
H.A. Mardani-Fard ◽  
Paria Dehesh
Keyword(s):  
South Korea ◽  
Prediction Models ◽  
Moving Average ◽  
Arima Model ◽  
Mainland China ◽  
Johns Hopkins University ◽  
Auto Regressive ◽  
Novel Coronavirus ◽  
Global Threat ◽  
Near Future

AbstractBackgroundThe epidemic of a novel coronavirus illness (COVID-19) becomes as a global threat. The aim of this study is first to find the best prediction models for daily confirmed cases in countries with high number of confirmed cases in the world and second to predict confirmed cases with these models in order to have more readiness in healthcare systems.MethodsThis study was conducted based on daily confirmed cases of COVID-19 that were collected from the official website of Johns Hopkins University from January 22th, 2020 to March 1th, 2020. Auto Regressive Integrated Moving Average (ARIMA) model was used to predict the trend of confirmed cases. Stata version 12 were used.ResultsParameters used for ARIMA were (2,1,0) for Mainland China, ARIMA (2,2,2) for Italy, ARIMA(1,0,0) for South Korea, ARIMA (2,3,0) for Iran, and ARIMA(3,1,0) for Thailand. Mainland China and Thailand had almost a stable trend. The trend of South Korea was decreasing and will become stable in near future. Iran and Italy had unstable trends.ConclusionsMainland China and Thailand were successful in haltering COVID-19 epidemic. Investigating their protocol in this control like quarantine should be in the first line of other countries’ program

scholarly journals Brief Analysis of the ARIMA model on the COVID-19 in Italy

2020 ◽  
Author(s):  
Guorong Ding ◽  
Xinru Li ◽  
Fan Jiao ◽  
Yang Shen
Keyword(s):  
Inflection Point ◽  
Moving Average ◽  
Arima Model ◽  
Personal Hygiene ◽  
Cumulative Number ◽  
Newly Diagnosed ◽  
Analysis Model ◽  
Auto Regressive ◽  
Global Threat ◽  
The Right

AbstractCoronavirus disease 2019 (COVID-19) has been considered as a global threat infectious disease, and various mathematical models are being used to conduct multiple studies to analyze and predict the evolution of this epidemic. We statistically analyze the epidemic data from February 24 to March 30, 2020 in Italy, and proposes a simple time series analysis model based on the Auto Regressive Integrated Moving Average (ARIMA). The cumulative number of newly diagnosed and newly diagnosed patients in Italy is preprocessed and can be used to predict the spread of the Italian COVID-19 epidemic. The conclusion is that an inflection point is expected to occur in Italy in early April, and some reliable points are put forward for the inflection point of the epidemic: strengthen regional isolation and protection, do a good job of personal hygiene, and quickly treat the team leaders existing medical forces. It is hoped that the “City Closure” decree issued by the Italian government will go in the right direction, because this is the only way to curb the epidemic.

scholarly journals Maternal and Neonatal Complications, Outcomes and Possibility of Vertical Transmission in Iranian Women with COVID-19

2021 ◽  
Vol 24 (9) ◽  
pp. 713-721
Author(s):  
Zohreh Heidary ◽  
Omid Kohandel ◽  
Hanieh Fathi ◽  
Majid Zaki-Dizaji ◽  
Marjan Ghaemi ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Clinical Manifestations ◽  
Perinatal Outcomes ◽  
Health Crisis ◽  
Public Health Crisis ◽  
New Public Health ◽  
New Public ◽  
Maternal And Neonatal Outcomes ◽  
The Common ◽  
First Time

Background: The emergence and fast spread of coronavirus disease 2019 (COVID-19) threatens the world as a new public health crisis. Little is known about its effects during pregnancy. This study aimed to investigate the clinical manifestations of COVID-19 on maternal and neonatal outcomes. Methods: In this systematic review, PubMed, Scopus, Web of Science, and Google Scholar databases were searched focusing on pregnancy and perinatal outcomes of COVID-19. Results: The initial search yielded 1236 articles, from which finally 21 unique studies, involving 151 pregnant women and 17 neonates, met the criteria. Mean ± SD age of included mothers and mean ± SD gestational age at admission were 30.6 ± 6.2 years and 30.8 ± 8.9 weeks, respectively. The common symptoms were fever, cough, fatigue, dyspnea and myalgia. The mortality rates of pregnant women and neonates were 28 out of 151 (18.5%) and 4 out of 17 (23.5%), respectively. Most of the neonates were preterm at the time of delivery. Three neonates had positive RT-PCR test on the first day after birth and three others on day two. On the average, neonate’s PCR became positive on day 4 for the first time. Conclusion: Early diagnosis of COVID-19 is crucial due to the possibility of the prenatal complications. Strict prevention strategies may reduce the risk of mother to infant transmission.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

scholarly journals Growth, instability and forecast of marine products export from India

2016 ◽  
Vol 63 (4) ◽  
Author(s):  
Apu Das ◽  
Nalini Ranjan Kumar ◽  
Prathvi Rani
Keyword(s):  
Moving Average ◽  
Export Performance ◽  
Arima Model ◽  
South East Asia ◽  
Arima Models ◽  
Policy Makers ◽  
Increasing Trend ◽  
Marine Products ◽  
Auto Regressive ◽  
Instability Indices

This paper analysed growth and instability in export of marine products from India with an attempt to forecast the total export quantity of marine products from the country. The compound growth rates and instability indices of marine products export from India were estimated for major importing countries viz., Japan, USA, European Union, South-east Asia and Middle East; as more than 80% of the marine products export from India destines to these markets. The study revealed high compound growth rate and low instability in case of selected countries. The study also revealed that India’s marine products export concentrated mainly to those countries, which were falling in less desirable or least desirable category which has affected export performance of the country. Forecast of India’s marine products export was done by fitting univariate Auto Regressive Integrated Moving Average (ARIMA) models. ARIMA (1, 1, 0) was found suitable for modelling marine products export from India. The results of ARIMA model indicated increasing trend in export of Indian marine products. This calls for serious attention by policy makers to identify competitive and stable market destinations for marine products export which could help in harnessing the potential of marine products export from India.

Modelling and control of greenhouse system using neural networks

2016 ◽  
Vol 40 (3) ◽  
pp. 918-929 ◽  
Author(s):  
A Manonmani ◽  
T Thyagarajan ◽  
M Elango ◽  
S Sutha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Moving Average ◽  
Growth Conditions ◽  
Operating Conditions ◽  
Adverse Weather ◽  
Control Schemes ◽  
Non Linear ◽  
Auto Regressive ◽  
And Control

A greenhouse system (GHS) is a closed structure that facilitates modified growth conditions to crops and provides protection from pests, diseases and adverse weather. However, a GHS exhibits non-linearity due to the interaction between the biological subsystem and the physical subsystem. Non-linear systems are difficult to control, particularly when their characteristics change with time. These systems are best handled with methods of computation intelligence, such as artificial neural networks (ANNs) and fuzzy systems. In the present work, the approximation capability of a neural network is used to model and control sufficient growth conditions of a GHS. An optimal neural network-based non-linear auto regressive with exogenous input (NARX) time series model is developed for a GHS. Based on the NARX model, two intelligent control schemes, namely a neural predictive controller (NPC) and non-linear auto regressive moving average (NARMA-L2) controller are proposed to achieve the desired growth conditions such as humidity and temperature for a better yield. Finally, closed-loop performances of the above two control schemes for servo and regulatory operations are analysed for various operating conditions using performance indices.

Sign in / Sign up

Export Citation Format

Share Document