Laboratory

Population Dynamics and Sustainable Well-Being

At a Glance Projects Publications Team

Project

Analyzing and Monitoring the Early Spread of COVID-19: A Demographic Perspective

Ugofilippo Basellini, Diego Alburez-Gutierrez, Emilio Zagheni; in Collaboration with Marco Bonetti (Bocconi University, Milan, Italy), Carlo Giovanni Camarda (French National Institute for Demographic Studies, Paris, France)

Detailed Description

The global impact of the novel coronavirus disease (COVID-19) has been severe. We provide some demographic insights into the first phases of the COVID-19 pandemic.

This project has three main goals. The first goal is to estimate and visualize the spread of COVID-19 in Italian regions and provinces. To this end, we introduced a new software tool (Bonetti and Basellini 2021). The software is based on open-access coronavirus data provided by the Department of Civil Protection. The data are modeled using standard Poisson regression, with parametric and nonparametric functional forms for the hazard function. The results are readily visualized for each province or region on a map for the entire country. This allows for the dynamic monitoring of the spread of the disease, which makes it possible to quickly identify areas at risk of being affected by the pandemic and to reallocate health care resources across the country.

The second goal is to understand the socioeconomic factors that have contributed to the diverse regional experiences of COVID-19 mortality in a range of contexts. In one study, we analyzed the relationship between the number of COVID-19-related deaths and a set of explanatory variables at the regional level in Italy. We found that COVID-19 mortality was negatively associated with the degree of intergenerational co-residence, the number of intensive care unit beds per capita, and the extent of the delay in the outbreak of the epidemic. Conversely, we did not find strong associations for several variables highlighted in recent research literature (e.g., the population density and the share of the older population) (Basellini and Camarda 2022). In another context, we analyzed the relationship between excess mortality and human mobility in England and Wales, having leveraged novel sources of digital trace data. We found a robust association between mobility reductions and lower excess mortality, and we predicted that the number of excess deaths could have more than doubled in the absence of the observed mobility reductions (Basellini et al. 2021). In a third study, we analyzed the relationship between digital mis- and disinformation and population health outcomes across 31 countries. Our results showed that greater public engagement with unreliable online content predicted significant future increases in new infection rates (Wang et al. 2026).

Our third goal is to measure excess mortality using established demographic forecasting models that allow us to consider correlations among different demographic and geographic groups, or autocorrelations over time. So far, we have proposed and employed the Lee-Carter method to compute excess mortality estimates. We illustrated this approach by using weekly age- and sex-specific mortality data for 19 countries, and we documented the heterogeneity of excess mortality across countries, sex, and age groups in Europe during 2020 (Vanella et al. 2021). In another study, we documented the levels of excess mortality in Guatemala (Martinez-Folgar et al. 2021).

Covid-19 in Italy

Maps of Italy showing, for each region, the magnitude of newly reported SARS-CoV-2 infections (left) and their rates of change (right) on October 27, 2020. © Bonetti and Basellini (2021)

Covid-19 mortality rates in Italian regions by epidemic onset

Start date of the epidemic by region (left-hand panel) and smoothed Covid-19 log mortality rates over time (right-hand panels): Italy, late February to mid-July 2020. Regions with earlier epidemic outbreaks show on average higher levels of Covid-19 mortality than regions with later outbreaks. © Basellini and Camarda (2022)

Publications

Wang, Y.; Basellini, U.; Camarda, C. G.:

The infodemic–pandemic nexus: cross-national analysis of online misinformation’s impact on population health

Social Science and Medicine 390:118881, 1–11. (2026)    

Basellini, U.; Camarda, C. G.:

Explaining regional differences in mortality during the first wave of COVID-19 in Italy

Population Studies 76:1, 99–118. (2022)    

Basellini, U.; Alburez-Gutierrez, D.; Del Fava, E.; Perrotta, D.; Bonetti, M.; Camarda, C. G.; Zagheni, E.:

Linking excess mortality to mobility data during the first wave of COVID-19 in England and Wales

SSM-Population Health 14:100799, 1–18. (2021)    

Bonetti, M.; Basellini, U.:

Epilocal: a real-time tool for local epidemic monitoring

Demographic Research 44:12, 307–332. (2021)    

Martinez-Folgar, K.; Alburez-Gutierrez, D.; Paniagua-Avila, A.; Ramirez-Zea, M.; Bilal, U.:

Excess mortality during the COVID-19 pandemic in Guatemala

American Journal of Public Health 111:10, 1839–1846. (2021)    

Vanella, P.; Basellini, U.; Lange, B.:

Assessing excess mortality in times of pandemics based on principal component analysis of weekly mortality data: the case of COVID-19

Genus 77:16, 1–36. (2021)