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Showing 4 results for Prediction
Statistical map analysis of the mean and the gini coefficient of healthcare expenses in Iran
Yadollah Waghei, Samaneh Nictinat, Gholam Reza Mohtashami Barzadaran,
Volume 5, Issue 1 (3-2015)
Abstract
In many societies fairness and equality are the most significant concepts among the governments and peoples. In the health systems, the fast increase in expenses takes expert’s attention to measure and control the inequality. The aim of this paper is to investigate the inequality of household’s health expense’s in the Iran and making its statistical map. The health expenses data for doing this research have been gotten from the statistical center of Iran (SCI). The process of preparing data and estimating the statistical indexes have been done using the S-PLUS software. Spatial analysis and predictions have been done by geoR. For making statistical maps we have used Arcgis9.2 software. We conclude that the mean of costs and its gini coefficient increases, from east to the west of Iran. The Gini coefficient of household’s health expenses differs from 0.64 to 0.84, which show the existence of high inequality in this type of expenses. For eastern and north-western areas the least Gini coefficient had been predicted. In Sistan and Baluchestan and Hormozgan provinces the most inequality are predicted. Governments should try by financial aids decrease the inequality and receive fairness in health systems.
A model to predict the sequential behavior of healthy blood donors using data mining
Maryam Ashoori, Somaye Alizade, Hoda Sadat Hosseiny Eivary, Saber Rastad, Somaye Sadat Hossieny Eivary,
Volume 5, Issue 2 (6-2015)
Abstract

Estimating Algorithms for Prediction and Spread of a Factor as a Pandemic: A Case Study of Global COVID-19 Prevalence
Hamideh Rezaei Nezhad, Farshid Keynia, Amir Sabagh Mola Hoseini,
Volume 14, Issue 1 (1-2024)
Abstract
Background: This paper aims to present open-source computer simulation programs developed to simulate, track, and estimate the COVID-19 outbreak.
Methods: The programs included two separate parts, one set of programs built in Simulink with a block diagram display, and another one coded as a script in MATLAB R2020b. The mathematical model used in this package was the suspectable-infected-removed (SIR), suspectable-exposed-infected-removed (SEIR), and susceptible-exposed-infected-recovered-deceased (SEIRD) models represented by a set of differential-algebraic equations. It can be easily modified to develop new models for the problem. A generalized method was adopted to simulate worldwide outbreaks in an efficient, fast, and simple way. 
Results: To get a good tracking of the virus spread, a sum of sigmoid functions was proposed to capture any dynamic changes in the data. The parameters used for the input (infection and recovery rate functions) were computed using the parameter estimation tool in MATLAB. Several statistical methods were applied for the rate function, including linear, Mean±SD and root mean square (RMS). In addition, an adaptive neuro-fuzzy inference system (ANFIS) was employed and proposed to train the model and predict its output.
Conclusion: This procedure is presented in such a way that it can be generalized and applied in other parts and applications of estimating the scenarios of an event, including the potential of several models, including suspectable-infected-removed (SIR), which is sensitive to pollution, etc. This program can be used as an educational tool or for research studies and this article promises some lasting contributions in the field of COVID-19.

Risk Score Model for Predicting COVID-19 Progression in Iranian Patients: Development and Validation Study
Fatemeh Mohammadzadeh, Ali Delshad Noughabi, Sina Sabeti Bilondi, Mitra Tavakolizadeh, Jafar Hajavi, Hosein Aalami, Mohsen Sahebanmaleki,
Volume 14, Issue 3 (5-2024)
Abstract
Background: The recent novel coronavirus disease 2019 (COVID-19) pandemic has underlined the importance of risk score models in public health emergencies. This study aimed to develop a risk prediction score to identify high-risk hospitalized patients for disease progression on admission.
Methods: This prospective cohort study included 171 COVID-19 patients, identified through the reverse transcription polymerase chain reaction test, admitted to Bohlool Hospital in Gonabad City, Iran, between April 4 and June 5, 2021. The patients’ demographic, clinical, and laboratory data were collected upon admission, and clinical outcomes were monitored until the end of the study. The discovery dataset (80% of the data) was used to develop the risk score model based on clinical and laboratory features and patient characteristics to predict COVID-19 progression. An additive risk score model was developed based on the regression coefficients of the significant variables in a multiple logistic regression model. The performance of the risk score model was evaluated on the validation dataset (20% of the data) using the receiver operating characteristic (ROC) curve. Statistical analyses were performed with SPSS software, version 21.
Results: The Mean±SD for age of participants was 59.54±20.52 years, and 48.6% were male. Most patients (82.5%) fully recovered or showed improvement, while 5.2% experienced disease progression and 12.3% died. Three variables, interleukin-6, neutrophil-to-lymphocyte ratio, and lung involvement, were found to be significant in predicting risk, with a good discriminatory ability, having an area under the ROC curve of 0.970 (95% CI, 0.935%, 1.00%) in the discovery set and 0.973 (95% CI, 0.923%, 1.00%) in the validation set.
Conclusion: The developed risk score model in this study can be used as a clinical diagnostic tool to identify COVID-19 patients at higher risk of disease progression and aid in informed decision-making and resource utilization in similar situations, such as respiratory disease outbreaks in the post-corona era. 

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