A novel methodology for epidemic risk assessment of COVID-19 outbreak

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.

Particulate Matter Short-Term Exposition, Mobility Trips and COVID-19 Diffusion: A Correlation Analyses for the Italian Case Study at Urban Scale

“…These results are more evident, for example, in Milan and Turin with an average seasonal PM 10 (PM 2.5 ) concentrations of about 30 (18) µg/m 3 (the higher of the panel), which had a positive correlation with daily COVID-19 cases ranging between 0.5 and 0.7, according to the Pearson's and Spearman's estimations. This long-term correlation effect, as discussed in the introduction, is consistent with different papers dealing with this topic [23,[29][30][31][32][33].…”

“…Overall, many studies have observed a direct correlation between long-term exposure to outdoor air pollution (e.g., high PM concentrations) with increased risk of respiratory disease [25][26][27][28]. This means that people living in areas with a higher pollution (longterm exposition) have developed a probably chronic inflammatory stimulus which may contribute to more COVID-19 cases and/or deaths [23,[29][30][31][32][33].…”

Early Spread of COVID-19 in the Air-Polluted Regions of Eight Severely Affected Countries

“…The area with the largest number of infections and deaths in Italy is the Po Valley, which is also the foremost place of polluted air in Europe [111]. This result was first hypothesised [112] and later confirmed by another study [76] and a remarkable further one [99] that has controlled for five demographic co-variables. The fact that population density does not play a role in the incidence of COVID-19 in Italy and Iran is a result of our investigation that strongly supports the common hypotheses of these other studies and questions the widespread scepticism maintaining that air pollution usually overlaps with areas of high population density, and that the contribution of each to the virus incidence cannot be discerned (tested in Appendix B, Table A2).…”

Analyzing the Risk to COVID‐19 Infection using Remote Sensing and GIS

“…However, such parameters are still under trials (Franklin et al., 2020). Hence, if the COVID‐19 risk assessment has to be performed, hazard and vulnerability parameters need to be defined (Pluchino et al., 2020). Providing sustainable threat protection from the COVID‐19 requires approaches that are proven in resisting the spread of this pandemic, mainly when it is a fact that it has no drug or vaccine to date (Rothan & Byrareddy, 2020).…”