On the Temporal Analysis of COVID-19 Pandemic and Prediction of R0

Autor: Piyali Ganguli, Kshitij Patil, Sutanu Nandi, Ram Rup Sarkar, Anirudh Murali
Rok vydání: 2020
Předmět:
DOI: 10.21203/rs.3.rs-40499/v1
Popis: The COVID-19 pandemic has affected millions of people and claimed numerous lives already. As of now, there are no available treatments and it has become both imperative and challenging to forecast the COVID-19 cases, which will help to design effective clinical management and policy to fight the pandemic. With the objective to forecast the COVID-19 cases and Basic Reproductive Number (R0) country-wise, for more than a month ahead, we have adopted a data driven approach that employs Multiple Aggregation Prediction Algorithm (MAPA) for temporal predictions. Our strategy applies MAPA in two ways. The first is the direct application on the number of cases and second is by calculation of R0, followed by MAPA. This novel workflow generates a Principal and an Exponential Prediction that provides a range of values within which the total number of cases is expected to lie. The strategy and workflow have been validated for long term predictions with 51 countries in different growth phases. Thereafter, we have made predictions of the possible number of COVID-19 cases for the next 45 days of these 51 countries, the world as a whole and the other 160 countries combined, that are affected by the pandemic.
Databáze: OpenAIRE