scholarly journals The Exponentially Increasing Rate of Patients Infected with COVID-19 in Iran

2020 ◽  
Vol 23 (4) ◽  
pp. 235-238 ◽  
Author(s):  
Leila Moftakhar ◽  
Mozhgan Seif
Keyword(s):  
Health Care ◽  
Moving Average ◽  
Health Care Systems ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Exponential Increase ◽  
Johns Hopkins University ◽  
Number Of Patients ◽  
The World ◽  
Care Systems

Background: Coronavirus, the cause of severe acute respiratory syndrome (COVID-19), is rapidly spreading around the world. Since the number of corona positive patients is increasing sharply in Iran, this study aimed to forecast the number of newly infected patients in the coming days in Iran. Methods: The data used in this study were obtained from daily reports of the Iranian Ministry of Health and the datasets provided by the Johns Hopkins University including the number of new infected cases from February 19, 2020 to March 21, 2020. The autoregressive integrated moving average (ARIMA) model was applied to predict the number of patients during the next thirty days. Results: The ARIMA model forecasted an exponential increase in the number of newly detected patients. The result of this study also show that if the spreading pattern continues the same as before, the number of daily new cases would be 3574 by April 20. Conclusion: Since this disease is highly contagious, health politicians need to make decisions to prevent its spread; otherwise, even the most advanced and capable health care systems would face problems for treating all infected patients and a substantial number of deaths will become inevitable.

scholarly journals Community system of social support

2017 ◽  
Vol 127 (1) ◽  
pp. 37-40
Author(s):  
Krzysztof Włoch ◽  
Janusz Jaroszyński ◽  
Ewa Warchoł-Sławińska ◽  
Anna Jurek ◽  
Bartłomiej Drop ◽  
...  
Keyword(s):  
Social Support ◽  
Health Care ◽  
Health Care Systems ◽  
Family Doctors ◽  
Community System ◽  
Modern Health Care ◽  
Number Of Patients ◽  
The World ◽  
Care Systems ◽  
Is Evaluation

Abstract Community System of Social Support is the basis of modern health care systems not only in Poland but all over the world in the prophylaxis-treatment-rehabilitation procedure. In Poland, family doctors, commune councils and territorial self-governments are involved in it. Family doctors perform a substantial function, whereas commune councils and territorial self-governments an administrative one. The principle of the system work is evaluation of its benefits. Its aim is the reduction of the number of patients who abuse alcohol and those who abuse alcohol despite treatment.

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 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 An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy

2020 ◽  
Author(s):  
Gaetano Perone
Keyword(s):  
Inflection Point ◽  
Social Impact ◽  
Moving Average ◽  
Arima Model ◽  
Epidemiological Data ◽  
List Type ◽  
Arima Models ◽  
Final Size ◽  
Autoregressive Integrated Moving Average ◽  
The World

AbstractCoronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly across the world. Italy, that is widely considered one of the main epicenters of the pandemic, has registered the highest COVID-2019 death rates and death toll in the world, to the present day. In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4, 2020, by using the Italian epidemiological data at national and regional level. The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to April 4, 2020. The main advantage of this model is that it is easy to manage and fit. Moreover, it may give a first understanding of the basic trends, by suggesting the hypothetic epidemic’s inflection point and final size.Highlights❖ARIMA models allow in an easy way to investigate COVID-2019 trends, which are nowadays of huge economic and social impact.❖These data may be used by the health authority to continuously monitor the epidemic and to better allocate the available resources.❖The results suggest that the epidemic spread inflection point, in term of cumulative cases, will be reached at the end of May.❖Further useful and more precise forecasting may be provided by updating these data or applying the model to other regions and countries.

scholarly journals Real-Time Forecasting of COVID-19 prevalence in India using ARIMA Model

2020 ◽  
Vol 4 (10) ◽  
pp. 78-83
Keyword(s):  
Time Series ◽  
Virus Disease ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
The World ◽  
Increasing Trend ◽  
Short Term Prediction

Corona virus disease (COVID -19) has changed the world completely due to unavailability of its exact treatment. It has affected 215 countries in the world in which India is no exception where COVID patients are increasing exponentially since 15th of Feb. The objective of paper is to develop a model which can predict daily new cases in India. The autoregressive integrated moving average (ARIMA) models have been used for time series prediction. The daily data of new COVID-19 cases act as an exogenous variable in this framework. The daily data cover the sample period of 15th February, 2020 to 24th May, 2020. The time variable under study is a non-stationary series as 𝒚𝒕 is regressed with 𝒚𝒕−𝟏 and the coefficient is 1. The time series have clearly increasing trend. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. In PACF graph. Lag 1 and Lag 13 is significant. Regressed values implies Lag 1 and Lag 13 is significant in predicting the current values. The model predicted maximum COVID-19 cases in India at around 8000 during 5thJune to 20th June period. As per the model, the number of new cases shall start decreasing after 20th June in India only. The results will help governments to make necessary arrangements as per the estimated cases. The limitation of this model is that it is unable to predict jerks on either lower or upper side of daily new cases. So, in case of jerks re-estimation will be required.

Oral Health Care Systems: An International Collaborative Study Coordinated by the World Health Organization

1987 ◽  
Vol 8 (1) ◽  
pp. 125
Author(s):  
Max H. Schoen ◽  
Harald A. Arnljot ◽  
David E. Barmes ◽  
Lois K. Cohen ◽  
Peter B. V. Hunter ◽  
...  
Keyword(s):  
Health Care ◽  
Oral Health ◽  
World Health Organization ◽  
Health Care Systems ◽  
Collaborative Study ◽  
World Health ◽  
The World ◽  
Care Systems ◽  
Health Organization ◽  
International Collaborative Study

scholarly journals Big data modeling to predict platelet usage and minimize wastage in a tertiary care system

2017 ◽  
Vol 114 (43) ◽  
pp. 11368-11373 ◽  
Author(s):  
Leying Guan ◽  
Xiaoying Tian ◽  
Saurabh Gombar ◽  
Allison J. Zemek ◽  
Gomathi Krishnan ◽  
...  
Keyword(s):  
Health Care ◽  
Census Data ◽  
Blood Product ◽  
Complete Blood Count ◽  
Health Care Systems ◽  
Tertiary Care ◽  
The United States ◽  
Number Of Patients ◽  
Usage Patterns ◽  
Care Systems

Maintaining a robust blood product supply is an essential requirement to guarantee optimal patient care in modern health care systems. However, daily blood product use is difficult to anticipate. Platelet products are the most variable in daily usage, have short shelf lives, and are also the most expensive to produce, test, and store. Due to the combination of absolute need, uncertain daily demand, and short shelf life, platelet products are frequently wasted due to expiration. Our aim is to build and validate a statistical model to forecast future platelet demand and thereby reduce wastage. We have investigated platelet usage patterns at our institution, and specifically interrogated the relationship between platelet usage and aggregated hospital-wide patient data over a recent consecutive 29-mo period. Using a convex statistical formulation, we have found that platelet usage is highly dependent on weekday/weekend pattern, number of patients with various abnormal complete blood count measurements, and location-specific hospital census data. We incorporated these relationships in a mathematical model to guide collection and ordering strategy. This model minimizes waste due to expiration while avoiding shortages; the number of remaining platelet units at the end of any day stays above 10 in our model during the same period. Compared with historical expiration rates during the same period, our model reduces the expiration rate from 10.5 to 3.2%. Extrapolating our results to the ∼2 million units of platelets transfused annually within the United States, if implemented successfully, our model can potentially save ∼80 million dollars in health care costs.

Rethinking the Place of the Psychein Health: Toward the Integration of Health Care Systems

2005 ◽  
Vol 39 (9) ◽  
pp. 816-825 ◽  
Author(s):  
Roger G. Kathol ◽  
David Clarke
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Medical Care ◽  
Psychiatric Care ◽  
Health Care Systems ◽  
Medical Setting ◽  
General Medical Care ◽  
Behavioural Health ◽  
The World ◽  
Care Systems

Objective: To review the value provided when health care systems independently manage medical and psychiatric care. Method: The authors draw on data from the world literature, their own experiences and reflections (one author as an international consultant in the coordination of physical and behavioural health care), and input from colleagues throughout the world who face similar challenges to improve outcomes for complex, high cost patients in their own health care systems. Results: Most health care systems in the world approach the administration and delivery of mental health care separately from that of general medical care. This practice is no longer supported as effective, efficient or inexpensive. Rather accumulating data indicates that concurrent and coordinated medical and psychiatric care, which can only be accomplished by integrating physical and behavioural health through infrastructure change, should replace the present system of independently provided sequential care; that is, one which provides first medical and then psychiatric treatment, or vice versa, with little communication between clinicians in the two sectors. Conclusions: By making mental health treatment an integral part of general medical care through reorganization of the funding system, a higher percentage of those now untreated for their psychiatric disorders, both within and outside of the medical setting, can have their mental health needs addressed in coordination with their physical disorders. At the same time, the number of patients that can be treated within the same budget will be expanded.

Sign in / Sign up

Export Citation Format

Share Document