ArticleScientific reports2021
A novel methodology for epidemic risk assessment of COVID-19 outbreak.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A rapid review of best practices in the development of risk registers for public health emergency management.Frontiers in public health · 2023Pooled it
- Networked SIRS model with Kalman filter state estimation for epidemic monitoring in Europe.Communications medicine · 2026Article
- A coupled model for public health risk: hazard and urban vulnerability in 18 cities in Sichuan, China.Frontiers in public health · 2025Article
- Research gaps and priorities for quantitative microbial risk assessment (QMRA).Risk analysis : an official publication of the Society for Risk Analysis · 2024Article
- Spatial spread of COVID-19 during the early pandemic phase in Italy.BMC infectious diseases · 2024Article
- COVID-19 Pandemic Risk Assessment: Systematic Review.Risk management and healthcare policy · 2024Review
- Data sources on COVID-19 infection and vaccination in pregnancy on the island of Ireland: strengths, weaknesses, and recommendations for future pandemic preparedness.HRB open research · 2024Article
- Acute stress symptoms in general population during the first wave of COVID lockdown in Italy: Results from the COMET trial.Brain and behavior · 2023Article
- A new method for accurate calculation of case fatality rates during a pandemic: Mathematical deduction based on population-level big data.Infectious medicine · 2023Article
- Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil.Cities (London, England) · 2023Article
- Nexus between drivers of COVID-19 and country risks.Socio-economic planning sciences · 2023Article
- Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns.Frontiers in public health · 2023Article
- Hip osteoarthritis: A novel network analysis of subchondral trabecular bone structures.PNAS nexus · 2022Article
- Assessment of community vulnerability during the COVID-19 pandemic: Hong Kong as a case study.International journal of applied earth observation and geoinformation : ITC journal · 2022Article
- Connected in health: Place-to-place commuting networks and COVID-19 spillovers.Health & place · 2022Article
- Population genetic models for the spatial spread of adaptive variants: A review in light of SARS-CoV-2 evolution.PLoS genetics · 2022Review
- Pandemic policy assessment by artificial intelligence.Scientific reports · 2022Article
- Were anxiety, depression and psychological distress associated with local mortality rates during COVID-19 outbreak in Italy? Findings from the COMET study.Journal of psychiatric research · 2022Article
- The effects of local homogeneity assumptions in metapopulation models of infectious disease.Royal Society open science · 2022Article
- An analysis of the dynamic spatial spread of COVID-19 across South Korea.Scientific reports · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
We propose a novel data-driven framework for assessing the a-priori epidemic risk of a geographical area and for identifying high-risk areas within a country. Our risk index is evaluated as a function of three different components: the hazard of the disease, the exposure of the area and the vulnerability of its inhabitants. As an application, we discuss the case of COVID-19 outbreak in Italy. We characterize each of the twenty Italian regions by using available historical data on air pollution, human mobility, winter temperature, housing concentration, health care density, population size and age. We find that the epidemic risk is higher in some of the Northern regions with respect to Central and Southern Italy. The corresponding risk index shows correlations with the available official data on the number of infected individuals, patients in intensive care and deceased patients, and can help explaining why regions such as Lombardia, Emilia-Romagna, Piemonte and Veneto have suffered much more than the rest of the country. Although the COVID-19 outbreak started in both North (Lombardia) and Central Italy (Lazio) almost at the same time, when the first cases were officially certified at the beginning of 2020, the disease has spread faster and with heavier consequences in regions with higher epidemic risk. Our framework can be extended and tested on other epidemic data, such as those on seasonal flu, and applied to other countries. We also present a policy model connected with our methodology, which might help policy-makers to take informed decisions.
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