Article Sidebar
Published:
Apr 16, 2021DOI:
10.4314/ahs.v21i1.26Keywords:
Article Details
While African Health Sciences has been freely accessible online there have been questions on whether it is Open Access or not. We wish to clearly state that indeed African Health Sciences is Open Access. There are key issues regarding Open Access needing clarification for avoidance of doubt:
- 1. Henceforth, papers in African Health Sciences will be published under the CC BY (Creative Commons Attribution License) 4.0 International. See details on https://creativecomons.org/)
- 2. The copyright owners or the authors grant the 3rd party (perpetually and in advance) the right to disseminate, reproduce, or use the research papers in part or in full, format/medium as long as:
- No substantive errors are introduced in the process
- Attribution of authorship and correct citation details are given
- The referencing details are not changed.
Should the papers be reproduced in part, this must be clearly stated.
- 3. The papers will be freely and universally accessible online in an easily readable format such as XML in at least one widely recognized open access repository such as PUBMED CENTRAL.
B. ABRIDGED LICENCE AGREEMENT BETWEEN AUTHORS AND African Health Sciences
I submitted my manuscript to African Health Sciences and would like to affirm that:
1.0 I am authorized by my co-authors to enter into these arrangements.
2.0 I guarantee, on behalf of self and co-authors:
- That the paper is original, and has not been published in any other peer-reviewed journal; nor is it under consideration by other journal (s). It does not infringe existing copyright or any other person’s rights
- That we are/I am the sole author(s) of the paper and with authority to enter into this agreement. My granting rights to African Health Sciences is not in breach of any other obligation
- That the paper contains nothing unlawful, or libelous. Nor anything that would constitute a breach of contract, confidence or commitment given to secrecy, if published
- That I/we have taken care to ensure the integrity of the article.
3.0 I and all co-authors, agree that the paper, if accepted for publication, shall be licensed under the Creative Commons Attribution License 4.0. (see https://creativecommons.org/)
Main Article Content
COVID-19 mortality rate prediction for India using statistical neural networks and gaussian process regression model
Abstract
The primary purpose of this research is to identify the best COVID-19 mortality model for India using regression models and is to estimate the future COVID-19 mortality rate for India. Specifically, Statistical Neural Networks ( Radial Basis Function Neural Network (RBFNN), Generalized Regression Neural Network (GRNN)), and Gaussian Process Regression (GPR) are applied to develop the COVID-19 Mortality Rate Prediction (MRP) model for India. For that purpose, there are two types of dataset used in this study: One is COVID-19 Death cases, a Time Series Data and the other is COVID-19 Confirmed Case and Death Cases where Death case is dependent variable and the Confirmed case is an independent varia- ble. Hyperparameter optimization or tuning is used in these regression models, which is the process of identifying a set of optimal hyperparameters for any learning process with minimal error. Here, sigma (σ) is a hyperparameter whose value is used to constrain the learning process of the above models with minimum Root Mean Squared Error (RMSE). The perfor- mance of the models is evaluated using the RMSE and 'R2 values, which shows that the GRP model performs better than the GRNN and RBFNN.
Keywords: Covid-19; India; mortality rate; mortality prediction; regression model; hyperparameter tuning; GPR; GRNN; RBFNN.
