Version 1
: Received: 23 April 2020 / Approved: 24 April 2020 / Online: 24 April 2020 (06:28:41 CEST)
How to cite:
Bhattacharjee, A. Cancer-COVID-19 Mortality Prediction: An Algorithm by Bayesian Autoregressive Model. Preprints2020, 2020040427. https://doi.org/10.20944/preprints202004.0427.v1
Bhattacharjee, A. Cancer-COVID-19 Mortality Prediction: An Algorithm by Bayesian Autoregressive Model. Preprints 2020, 2020040427. https://doi.org/10.20944/preprints202004.0427.v1
Bhattacharjee, A. Cancer-COVID-19 Mortality Prediction: An Algorithm by Bayesian Autoregressive Model. Preprints2020, 2020040427. https://doi.org/10.20944/preprints202004.0427.v1
APA Style
Bhattacharjee, A. (2020). Cancer-COVID-19 Mortality Prediction: An Algorithm by Bayesian Autoregressive Model. Preprints. https://doi.org/10.20944/preprints202004.0427.v1
Chicago/Turabian Style
Bhattacharjee, A. 2020 "Cancer-COVID-19 Mortality Prediction: An Algorithm by Bayesian Autoregressive Model" Preprints. https://doi.org/10.20944/preprints202004.0427.v1
Abstract
This pandemic of COVID-19 is tedious to control. The only lockdown is the way to stop the spread of this infection. Conventional health care is facing a real challenge to operate. Primarily the challenge is to provide health care support for COVID-19 patients with limited resources and continue the health care services like earlier.Perhaps, this challenge is the same but magnitude is different from different geographical locations around the globe. In this article, we presented a Bayesian algorithm with the Code to predict cancer death due to COVID-19. This code is possible to run at different time points and different geographical locations around the world. This code will help us to get the best strategy and shift the treatment option for cancer treatment. The model would provide physicians with an objective tool for counseling and decision making at different hotspots and small areas to implement.
Keywords
COVID-19; cancer; decision; Bayesian; autoregressive model
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
