ArticleRegional science policy & practice2021
Unraveling spatial patterns of COVID-19 in Italy: Global forces and local economic drivers.
Article in Regional science policy & practice, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Mapping the COVID-19 pandemic in Burkina Faso: spatial patterns, socioeconomic factors, and public health implications.International journal of health geographics · 2026Article
- Which factors influence mobility change during COVID-19 in Germany? Evidence from German county data.Regional science policy & practice · 2022Article
- Unraveling spatial patterns of COVID-19 in Italy: Global forces and local economic drivers.Regional science policy & practice · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This article investigates the spatial patterns of coronavirus disease 2019 (COVID-19) infection in Italy and its determinants from March 9 to June 15, 2020, a time interval covering the so-called first wave of COVID pandemics in Europe. The results, based on negative binomial regressions and linear spatial models, confirm the importance of multiple factors that positively correlate with the number of recorded cases. Economic forces, including urban agglomeration, industrial districts, concentration of large companies (both before and after the beginning of the 'lockdown') and a north-south gradient, are the most significant predictors of the strength of COVID-19 infection. These effects are statistically more robust in the spatial models than in the aspatial ones. We interpretate our results in the light of pitfalls related to data reliability, and we discuss policy implications and possible avenues for future research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.