# Modelling death rates due to COVID-19: A Bayesian approach

**Authors:** Cristian Bayes, Victor Sal y Rosas, Luis Valdivieso  
**arXiv:** [2004.02386](https://arxiv.org/abs/2004.02386)  
**Date:** 2020-04-06  
**Summary page:** https://www.summarizepaper.com/fr/arxiv-id/2004.02386v2/

## Abstract

Objective: To estimate the number of deaths in Peru due to COVID-19. Design: With a priori information obtained from the daily number of deaths due to CODIV-19 in China and data from the Peruvian authorities, we constructed a predictive Bayesian non-linear model for the number of deaths in Peru. Exposure: COVID-19. Outcome: Number of deaths. Results: Assuming an intervention level similar to the one implemented in China, the total number of deaths in Peru is expected to be 612 (95%CI: 604.3 - 833.7) persons. Sixty four days after the first reported death, the 99% of expected deaths will be observed. The inflexion point in the number of deaths is estimated to be around day 26 (95%CI: 25.1 - 26.8) after the first reported death. Conclusion: These estimates can help authorities to monitor the epidemic and implement strategies in order to manage the COVID-19 pandemic.