scholarly journals An exploratory Integrated Moving Average Time Series Model of the initial outbreak of COVID-19 in six (6) significantly impacted Countries

2020 ◽  
Author(s):  
Joseph Pascarella ◽  
Elaina Pascarella
Keyword(s):  
Mortality Rate ◽  
Rna Virus ◽  
Moving Average ◽  
Government Officials ◽  
Policy Makers ◽  
New Model ◽  
Public Health Officials ◽  
Preliminary Research ◽  
Novel Coronavirus ◽  
Peak Increase

The 2019 Novel Coronavirus SARS-CoV-2 (COVID-19) is a single-stranded RNA virus that has threatened the lives of humans all over the globe. Government officials, policy makers and public health officials have been scrambling and struggling to flatten the curve to decelerate the prevalence and spread of COVID-19 given the significant economic destruction of the spread of the virus. Most flatten the curve models are based on Compartmental Models. This preliminary research is based on six (6) selected countries significantly impacted by COVID-19 and endeavors to build a new model based on moving averages lagged at different time periods to better hone in on the time the COVID-19 begins to decelerate using the date of first reported case and date of first reported death. This new model, the Consistent Deceleration Model (CDM) is based on each individual countrys date of Peak Increase in Mortality Rate (PINC MR) and the Moving Average since the peak increase in mortality rate (MA POSTINC). The CDM can be utilized of one of many quantitative tools to determine the strength of the deceleration of an infectious outbreak.

scholarly journals When Second Best Might be the Best: Using Hospitalization Data to Monitor the Novel Coronavirus Pandemic

2020 ◽  
Author(s):  
Peter J Mallow ◽  
Michael Jones
Keyword(s):  
Moving Average ◽  
Hospital Utilization ◽  
High Rate ◽  
Ideal Solution ◽  
The Novel ◽  
Policy Makers ◽  
Hospital Administrators ◽  
Utilization Data ◽  
Novel Coronavirus ◽  
The Ideal

The novel coronavirus' high rate of asymptomatic transmission combined with a lack of testing kits call for a different approach to monitor its spread and severity. We proposed the use of hospitalizations and hospital utilization data to monitor the spread and severity. A proposed threshold of a declining 7-day moving average over a 14-day period, "7&14" was set to communicate when a wave of the novel coronavirus may have passed. The state of Ohio was chosen to illustrate this threshold. While not the ideal solution for monitoring the spread of the epidemic, the proposed approach is an easy to implement framework accounting for limitations of the data inherent in the current epidemic. Hospital administrators and policy makers may benefit from incorporating this approach into their decision making.

scholarly journals Biological Property of Novel Coronavirus SARS-CoV-2 (2019-nCoV)

2021 ◽  
Vol 9 (1) ◽  
pp. 16-22
Author(s):  
Emebet Mohammed Abdu ◽  
Abeba Haile Mariamenatu
Keyword(s):  
Mortality Rate ◽  
Rna Virus ◽  
Antiviral Drug ◽  
Biological Properties ◽  
Health Condition ◽  
World Health ◽  
Contact Tracing ◽  
E Proteins ◽  
Novel Coronavirus ◽  
Health Organization

Novel coronavirus (2019-nCoV) is a positive-sense RNA virus that possesses four genes that encode the spike (S), membrane (M), nucleocapsid (N), and envelope (E) proteins. The virus was originated in seafood market selling live animals and responsible for coronavirus disease 2019 (COVID-19). The initial case was traced to the city of Wuhan in the province of Hubei, China, reported as an emerging respiratory virus, the outbreak was reported to WHO on December 31, 2019, and soon after identified the causative pathogen as a beta coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Furthermore, It is a highly contagious virus that spreads swiftly outside of China in March and the World Health Organization had to declare COVID-19 pandemic on March 11, 2020, and as of August 15, 2020, more than 21 million confirmed cases have been reported, with > 755 786 deaths worldwide. This day’s novel coronavirus-2019 is the most infectious virus with high infectivity and low mortality rate where a high mortality rate was observed among people above the age of sixteen (60) years and with the pre-existing health condition. To date, there is no clinically approved antiviral drug or vaccine available to be used against COVID-19. However, Preventive measures such as masks, hand hygiene practices, avoidance of public contact, case detection, contact tracing, and quarantines have been discussed as ways to reduce transmission. Therefore, the purpose of this review is to summarize the basic biological properties of novel coronavirus 2019. Int. J. Appl. Sci. Biotechnol. Vol 9(1): 16-22

scholarly journals Forecasting Analysis of Covid-19 Cases with Wavelet Neural Network and Time Series Approach

2021 ◽  
Vol 8 ◽  
pp. 6-11
Author(s):  
Asli Kaya ◽  
Fatih Cemrek ◽  
Ozer Ozdemir
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Wavelet Neural Networks ◽  
Government Officials ◽  
Response Planning ◽  
Time Series Approach ◽  
Novel Coronavirus ◽  
Different Response

COVID-19 is a respiratory disease caused by a novel coronavirus first detected in December 2019. As the number of new cases increases rapidly, pandemic fatigue and public disinterest in different response strategies are creating new challenges for government officials in tackling the pandemic. Therefore, government officials need to fully understand the future dynamics of COVID-19 to develop strategic preparedness and flexible response planning. In the light of the above-mentioned conditions, in this study, autoregressive integrated moving average (ARIMA) time series model and Wavelet Neural Networks (WNN) methods are used to predict the number of new cases and new deaths to draw possible future epidemic scenarios. These two methods were applied to publicly available data of the COVID-19 pandemic for Turkey, Italy, and the United Kingdom. In our analysis, excluding Turkey data, the WNN algorithm outperformed the ARIMA model in terms of forecasting consistency. Our work highlighted the promising validation of using wavelet neural networks when making predictions with very few features and a smaller amount of historical data.

Exact Forecasting for COVID-19 Data: Case Study for Turkey

2021 ◽  
pp. 2150006
Author(s):  
Çiğdem Dinçkal
Keyword(s):  
Mortality Rate ◽  
Recovery Rate ◽  
Moving Average ◽  
Reproductive Number ◽  
Percentage Error ◽  
The Novel ◽  
A New Technique ◽  
Novel Coronavirus ◽  
Short Time

The novel coronavirus COVID-19 (SARS-CoV-2) with the first clinical case emerged in the city of Wuhan in China in December 2019. Then it has spread to the entire world in very short time and turned into a global problem, namely, it has rapidly become a pandemic. Within this context, many studies have attempted to predict the consequences of the pandemic in certain countries. Nevertheless, these studies have focused on some parameters such as reproductive number, recovery rate and mortality rate when performing forecasting. This study aims to forecast COVID-19 data in Turkey with use of a new technique which is a combination of classical exponential smoothing and moving average. There is no need for reproductive number, recovery rate and mortality rate computation in this proposed technique. Simulations are carried out for the number of daily cases, active cases (those are cases with no symptoms), daily tests, recovering patients, patients in the intensive care unit, daily intubated patients, and deaths forecasting and results are tested on Mean Absolute Percentage Error (MAPE) criterion. It is shown that this technique captured the system dynamic behavior in Turkey and made exact predictions with the use of real time dataset.

Characteristics and Outcomes of Critically Ill Patients with COVID-19 Disease in Jordan

2020 ◽  
pp. 1-3
Author(s):  
Hasan Ibrahim Al-Balas ◽  
Keyword(s):  
Health Care ◽  
Mortality Rate ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Tertiary Hospital ◽  
Published Data ◽  
First Case ◽  
Global Health Care ◽  
Novel Coronavirus ◽  
Overall Mortality

Introduction: Coronavirus disease 2019 (COVID-19) is an emerging global health care threat that is caused by a novel coronavirus named 2019-nCoV (SARS-CoV-2). The first case of diagnosed COVID-19 patient was declared in Jordan in early March 2020. As of June 8, Jordan had confirmed 831 cases, with 9 deaths, with an overall mortality rate of 1.08%. As there is no published data about critically ill patients in Jordan, we aimed to describe the characteristics and outcomes of critically ill COVID-19 patients in a tertiary hospital in Jordan.

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 Taranto’s long shadow? Cancer mortality shows alarming peaks for specific types in the most polluted city of Italy but also in surrounding towns

2020 ◽  
Author(s):  
R. Roberto Cazzolla Gatti ◽  
Alena Velichevskaya
Keyword(s):  
Mortality Rate ◽  
Industrial Area ◽  
National Scale ◽  
Policy Makers ◽  
Specific Cancer ◽  
Cancer Types ◽  
Heavy Pollution ◽  
High Level ◽  
Epidemiological Effects ◽  
The City

AbstractA national-scale study in Italy showed an incidence of cancer higher in the territories indicated as highly polluted compared to the regional average. One of them, the city of Taranto in Apulia (Italy), which is considered one of the most polluted cities in Europe, has numerous industrial activities that impact population health. We studied the epidemiological effects of a high level of pollution produced by the industrial area of Taranto in increasing the mortality rate for some specific cancer types in the city and towns of the two provinces located downwind. We analysed 10-year mortality rates for 14 major types of tumours reported among the residents of Taranto, of 6 surrounding towns, randomly placed within an imaginary cone in the main wind direction from the vertex of the industrial zone of Taranto. Our results confirm our hypothesis that the mortality rate for some specific types of cancer (namely, Hodgkin and non-Hodgkin lymphomas, leukaemia, liver and bladder tumours) are higher than the norm in the municipality of Taranto and we have evidence that other local causes may be implicated in the excess of mortality besides the potential dispersal of pollutants from the industrial area of Taranto. The proximity to the industrial area of Taranto cannot, therefore, explain alone the anomalies detected in some populations. It is likely that other site-specific sources of heavy pollution are playing a role in worsening the death toll of these towns and this must be taken into serious consideration by environmental policy-makers and local authorities.

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.

scholarly journals SARS-CoV-2 Isolation and Propagation from Turkish COVID-19 patients

2020 ◽  
Author(s):  
Cihan Tastan ◽  
Bulut Yurtsever ◽  
Gozde Sir ◽  
Derya Dilek Kancagi ◽  
Sevda Demir ◽  
...  
Keyword(s):  
Rna Virus ◽  
The Novel ◽  
Isolation And Characterization ◽  
Characterization Studies ◽  
Positive Strand Rna Virus ◽  
The Republic ◽  
Novel Coronavirus ◽  
Positive Strand Rna ◽  
Vaccine Testing ◽  
Republic Of Turkey

AbstractThe novel coronavirus pneumonia, which was named later as Coronavirus Disease 2019 (COVID-19), is caused by the Severe Acute Respiratory Syndrome Coronavirus 2, namely SARS-CoV-2. It is a positive-strand RNA virus that is the seventh coronavirus known to infect humans. The COVID-19 outbreak presents enormous challenges for global health behind the pandemic outbreak. The first diagnosed patient in Turkey has been reported by the Republic of Turkey Ministry of Health on March 11, 2020. Today, over ninety thousand cases in Turkey, and two million cases around the world have been declared. Due to the urgent need for vaccine and anti-viral drug, isolation of the virus is crucial. Here, we report one of the first isolation and characterization studies of SARS-CoV-2 from nasopharyngeal and oropharyngeal specimens of diagnosed patients in Turkey. This study provides an isolation and replication methodology, and cell culture tropism of the virus that will be available to the research communities.Article SummaryScientists have isolated virus from Turkish COVID-19 patients. The isolation, propagation, and plaque and immune response assays of the virus described here will serve in following drug discovery and vaccine testing.

Modern Retailers in Transition Economies

2011 ◽  
Vol 32 (1) ◽  
pp. 31-51 ◽  
Author(s):  
Masayoshi Maruyama ◽  
Le Viet Trung
Keyword(s):  
Chief Executive Officers ◽  
Chief Executive ◽  
Government Officials ◽  
Policy Makers ◽  
Comprehensive Picture ◽  
Executive Officers ◽  
Almost All ◽  
Retail Format ◽  
Depth Interviews ◽  
Modern Retailers

This article reports the findings of a study on modern domestic retailers in Vietnam. The authors based this study on (1) a survey of fifty-six firms that control almost all the modern retail format stores in Vietnam, (2) in-depth interviews with chief executive officers (CEOs) and government officials, and (3) store visits and observations that were carried out by the authors. The authors discuss the operation and retail renovations of local modern retailers, the structure and the background of competitors, the problems retailers face, and their prospects for future development. These findings provide a comprehensive picture of modern retailers in Vietnam and have significant implications for policy makers as well as for local and foreign retailers.

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