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RESEARCH PRODUCT

COVID-19 Infection Process in Italy and Spain: Are the Data Talking?

Ana MontoroLaura GabrieliPaloma MonllorPaloma Taltavull De La PazZhenyu Su

subject

2019-20 coronavirus outbreakARIMA methodGeographyContagion patternCoronavirus disease 2019 (COVID-19)ItalySpainSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)COVID-19Economía AplicadaChinaSocioeconomicsForecasting

description

Background: COVID-19 has spread successfully worldwide in a matter of weeks. After the example of China, all the affected countries are taking hard-confinement measures to control the infection and to gain some time to diminish the big amount of cases that arrive to hospital. Although the measures in China reduced the percentages of new cases, this is not seen in other countries that have taken similar measures, such as Italy and Spain. Now we are in the middle of a battle trying to prevent the healthcare system from collapsing while it effectively responds to the needs of patients who are infected and require hospitalization. Methods: Using China as a mirror of what could happen in our countries and with the data available, we calculated a model that forecasts when are the peak of the curve going to happen. We aim to review the patterns of spread of the virus in the three countries and their regions, looking for similarities that reflect the existence of a common pattern in this expansive virulence and the effects of the intervention of the authorities with drastic isolation measures, to contain the outbreak. Findings: A model based on ARMA methodology and including Chinese disease pattern as a proxy predicts a reduction of the speed of contagious during the last week of March in Italy and smooth reduction until the peak in the second week of April. In the case of Spain, the infections still will follow growing fast until the end of March. Interpretation: We present the following predictions to inform political leaders since they have the responsibility to maintain the national health systems away from collapsing. We are confident this data could help them into decision taking and place the capitals (from hospital beds to human resources) into the right place.

10.2139/ssrn.3566150https://hdl.handle.net/10045/105567