COVID-19 Impact of NPIs

Overview

The COVID-19 pandemics was a singular event where scientific activity proved to be instrumental in fighting against the disease and better decision making. Scientists worked round the clock from their homes during lockdown to establish networks, gather and process data, elaborate models and draft reports to help decision makers.

In this context, the main mathematical spanish society CEMAT (Comité Español de Matemáticas) established a Committee of experts called “Acción Matemática contra el Coronavirus” from the 4 main societies (SEMA, RSME, SCM and SEIO) whose role was to elaborate a mathematical response to the challenges posed by the pandemics.

The Committee elaborated a meta-prediction model where many modeling groups participated to predict short term prevalence and spread of the disease.

The Committee started working with technical experts from the Ministry of Health and the Ministry of Economy, and it was tasked with modeling the effect of Non-Pharmaceutical Interventions (NPIs) both from a public health and an economic point of view. This was quite important since decision makers were often faced with the choice of adopting more restrictive measures with a considerable economic impact, whose effectiveness had to be predicted.

Our study analyzed the effectiveness of non-pharmaceutical interventions (NPIs) implemented in Spain during the second wave of COVID-19 (September 2020 to May 2021). Researchers compiled detailed provincial and municipal data on restrictions across nine areas of activity and constructed a daily “restriction intensity index” ranging from 0 to 1 to quantify the strength of measures over time. Using statistical modeling under the framework of the Spanish Committee of Mathematics’ “Mathematical Action against Coronavirus” initiative, the team evaluated how changes in restriction intensity affected virus transmission.

The results showed that increasing the overall intensity of measures by 34% was associated with a 22% reduction in transmission within one week. Interventions related to social distancing and indoor hospitality were found to be particularly effective, while measures affecting leisure, cultural activities, places of worship, religious celebrations, and indoor sports showed less clear effects—though these differences should be interpreted cautiously, as many measures were implemented simultaneously. The project also made all collected data publicly available to support transparency and future research, highlighting the critical role of mathematical modeling and data analysis in managing public health crises.

My role in this project was mainly involved in coding and processing the NPIs into useful variables for the statistical model that matched the NPI intensity time series to incidence metrics. On a separate project, we made a predictive tool to assist hospitals in planning for extra beds in ICUs, leveraging what was known on disease dynamics and observed infected individuals.

Media Coverage

• Interview in eldiario.es “To fight the pandemic, we need transparency and access to good data.” (17/04/20) link
• Interview for Real Sociedad Matemática Española (09/04/21) link
• Las matemáticas frente a la Covid-19, Fundación Ramón Areces en colaboración con Real Sociedad Matemática Española video
• M. Salomone “Spanish mathematicians look for a model to predict how the pandemic will evolve”, Fundación BBVA (07/04/20) link
• “Big Data contra el coronavirus y ¿nuestra privacidad?” , Fallo de Sistema, Radio 3 (19/04/20) link
• “Desarrollan un modelo predictivo de ocupación de camas en las UCI de los hospitales andaluces” Fundación Descubre, Junta de Andalucía link
• Acción Matemática contra la COVID confirma que el incremento de las restricciones redujo la transmisión del virus en un 22% a la semana. CITIC-UDC (17/04/23) link