Preprint Technical Note Version 1 Preserved in Portico This version is not peer-reviewed

Cancer-COVID-19 Mortality Prediction: An Algorithm by Bayesian Autoregressive Model

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. Preprints 2020, 2020040427 (doi: 10.20944/preprints202004.0427.v1). Bhattacharjee, A. Cancer-COVID-19 Mortality Prediction: An Algorithm by Bayesian Autoregressive Model. Preprints 2020, 2020040427 (doi: 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.

Subject Areas

COVID-19; cancer; decision; Bayesian; autoregressive model

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.