ArticleBMC infectious diseases2025
Human mobility and sewage data correlate with COVID-19 epidemic evolution in a 3-year surveillance of the metropolitan area of Bologna.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundThe COVID-19 pandemic has significantly impacted human society at many levels, from public health to economics and transports, highlighting the need of approaches integrating all available information to better understand and model similar phenomena, also in order to develop early detection and responses.
methodsIn this paper we present an analysis of COVID-19 pandemic in the metropolitan area of Bologna, Italy, integrating an epidemiological mathematical model, SARS-CoV-2 virus quantification in wastewater, clinical hospitalization, vaccination campaign, virus genotypization and human mobility data in the period 2020-2022.
resultsWe were able to follow the evolution of epidemic, observing the effect of vaccination and other factors that produced significant changes in hospitalizations. Moreover, by considering a mathematical model of COVID-19 epidemics spread, with parameters selected partly from literature and partly adapted to the local situation on a weekly basis, we identified a strict relation between human mobility at mesoscopic level and a sociability rate (related to model reproduction number).
conclusionOur results demonstrate the value of a interdisciplinary approach in monitoring and modeling epidemic trends. The observed relationships between mobility and sociability reveals the mutual impact of health issues on human activity and vice versa, providing insights for the implementation of effective response strategies in future pandemics.
Indexed as
Identifiers
What Socratic 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 Socratic graph.