The scale of the underlying dataset is what sets this analysis apart. The investigators assembled a cohort of every individual registered with the Lombardy Regional Health Service as of January 2020, aggregating monthly data by sex, age, country of origin, municipality or ZIP code area, and pre-existing comorbidities. The region’s eight local public health authorities, which administer surveillance and prevention across distinct catchment areas, contributed records that allowed the researchers to track three separate outcomes: confirmed COVID-19 incidence, hospital admissions, and mortality. Crucially, the team also had access to SARS-CoV-2 lineage data from the regional genomic repository, which enabled them to divide the study period into four sub-periods defined by the predominant viral variants circulating at the time. This variant-based segmentation meant the researchers could ask not only how the epidemic changed over time, but how the relationship between risk factors and outcomes itself evolved as the virus mutated.

The demographic profile of the population under study helps explain why Lombardy was hit so hard. Of the more than ten million residents included in the cohort, 45 percent were aged 50 or older, and 22 percent carried a history of cardiac disease. These figures describe a region with a substantial reservoir of older adults and cardiovascular vulnerability, precisely the characteristics epidemiologists associate with severe COVID-19 outcomes. The study’s multivariable regression models, run separately for each of the four variant-defined sub-periods, confirmed that advancing age remained the dominant risk factor for both infection and death throughout the entire pandemic. Neurologic and renal comorbidities also emerged as consistent predictors of mortality, alongside cardiac conditions, underscoring that the burden of severe disease fell disproportionately on people with chronic organ dysfunction.

One of the most striking findings concerns the geography of the early epidemic. Contrary to the intuitive expectation that a respiratory virus would ignite first in the largest urban centers, the outbreak in Lombardy was initially concentrated in a handful of Local Health Districts, none of which contained large cities. This observation aligns with the now well-understood story of the virus’s silent spread through smaller provincial towns in the Po Valley before the first cases were even recognized. The geographic heterogeneity persisted throughout the study period: incidence, hospitalization rates, and mortality varied substantially across districts, and the researchers found that geographical location itself remained an independent predictor of outcomes even after accounting for age, sex, comorbidities, and other individual-level characteristics.

That persistent geographic signal carries an important interpretive message. Because the models adjusted for measurable individual risk factors, the residual effect of location hints at the influence of local epidemic dynamics, such as the timing and intensity of transmission waves in each community, and of health system factors, including differences in testing capacity, care pathways, and hospital load between districts. The authors suggest that areas impacted early in the pandemic experienced distinct trajectories compared with those hit later, and that these local dynamics shaped outcomes in ways that individual-level data alone cannot capture. In practical terms, the same person’s risk of severe outcomes depended not only on their age and health status but also on where and when they lived through the pandemic, a finding with clear implications for how regional health systems should allocate resources during future epidemic emergencies.

The temporal trends documented in the study reveal a pattern that public health officials observed qualitatively but had rarely been able to quantify so systematically. As overall SARS-CoV-2 transmission increased in the population, driven by successive waves of more transmissible variants and eventually by widespread immunity from infection and vaccination, hospital admissions and mortality declined over time. The decline was especially pronounced in areas that had been impacted early, such as the districts at the epicenter of the first wave, where the combination of prior infection, vaccination coverage, and adapted clinical management appears to have progressively decoupled case numbers from severe outcomes. The researchers also tracked test positivity rates and vaccination coverage across the local public health authorities and districts, providing a parallel account of how diagnostic intensity and immunization campaigns varied geographically and interacted with the epidemic’s course.

Methodologically, the study exemplifies the power of routinely collected administrative health data when linked at population scale. Rather than relying on sampled surveys or voluntary reporting, the team exploited the complete registry of Regional Health Service beneficiaries, pseudonymized according to the ISO 25237:2017 standard by each health protection agency before analysis. Only fully anonymized, aggregated data were made available to the study group, a design that satisfied Italian data protection regulations and the EU General Data Protection Regulation while preserving the statistical power of a true total-population cohort. The multivariable regression framework, applied within each variant-defined sub-period, allowed the investigators to estimate how the hazard of infection and death associated with each risk factor changed as the virus and the population’s immunity evolved, a level of temporal resolution that few previous studies have achieved.

The findings also carry lessons that extend well beyond Lombardy. The confirmation that neurologic and renal comorbidities predict mortality, in addition to the more widely recognized cardiac and pulmonary conditions, refines the clinical risk stratification that guided vaccination prioritization and protective measures during the pandemic. The demonstration that geographic location independently shapes outcomes even in a region with a single, relatively uniform health system suggests that epidemic management cannot rely on one-size-fits-all policies; sub-regional surveillance and locally tailored interventions matter. And the observation that severity declined as transmission rose, reflecting the transition from a naive population facing a lethal novel virus to an immunized population facing endemic circulation, provides a quantitative template for anticipating how future respiratory pathogens might behave as they make the same transition.

Lombardy’s experience was in many respects the harshest test any European health system faced in 2020, when hospitals in Bergamo, Brescia, and Cremona became global symbols of the pandemic’s toll. This new analysis converts that traumatic experience into structured evidence, mapping exactly where and when the virus struck, who was most vulnerable, and how the determinants of severe disease shifted across the ancestral, Alpha, Delta, and Omicron eras. The work was funded by Fondazione Cariplo as part of a broader initiative on post-COVID syndrome research, and it forms part of the PASCNET network’s effort to build lasting analytical capacity from the pandemic’s data legacy. As the authors conclude, the findings contribute essential insights into the dynamics of COVID-19 in one of Europe’s earliest and most severely affected regions, evidence that will inform pandemic preparedness, health system planning, and epidemiological modeling for years to come. For a region that served as the pandemic’s European ground zero, the three-year map now drawn represents both a record of what was endured and a guide for what may come.

Subject of Research: Population-based epidemiological mapping of COVID-19 incidence, hospitalization, and mortality in Lombardy, Italy, from 2020 to 2022

Article Title: Mapping the COVID-19 epidemic in Lombardy, Italy, years 2020–2022: a population-based study of temporal trends, geographic distribution, and risk factors for incidence, hospitalization, and mortality

Article References: Villa, S., Magnoni, P., Mazzali, C., Zucchi, A., Maifredi, G., Cavalieri d’Oro, L., Gambino, M. L., Fanetti, A. C., Perotti, P. G., Villa, M., Valsecchi, M. G., Vigani, D., Lucifora, C., Pregliasco, F. E., Cereda, D., Leoni, O., Russo, A. G., the PASCNET study group, Franzoni, F., … Zuccaro, V. (2026). Mapping the COVID-19 epidemic in Lombardy, Italy, years 2020–2022: a population-based study of temporal trends, geographic distribution, and risk factors for incidence, hospitalization, and mortality. BMC Public Health. https://doi.org/10.1186/s12889-026-29682-2

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29682-2

Keywords: COVID-19, Lombardy, SARS-CoV-2, epidemiology, spatial analysis, risk factors, hospitalization, mortality, variants of concern, vaccination coverage, public health, population-based study