2020/05/26 by Marco D'Orazio, Marco D’Orazio, Gabriele Bernardini +1 · 30 citations
Computer Science · Economics, Econometrics and Finance · Engineering · Environmental Science · Mathematics · Medicine · Physics and Astronomy · #2019-20 coronavirus outbreak #Business #COVID-19 Pandemic Impacts #COVID-19 epidemiological studies #COVID-19 impact on air quality #Coronavirus disease 2019 (COVID-19) #Disease #Engineering #Environmental health #Environmental planning #Geography #Human factors and ergonomics #Injury prevention #Medicine #Occupational safety and health #Outbreak #Poison control #Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) #Suicide prevention #Tourism #Transport engineering #Virology #cs.CY #cs.MA #physics.soc-ph
paper · pdf · doi:10.1016/j.ssci.2021.105399
published in Safety Science 142, 105399 (Elsevier BV) · 21 pages; 16 figures; 3 tables; submitted to Safety Science
arxiv created 2020/05/26 · openalex publication_date 2021/07/06 · arxiv updated 2021/07/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Touristic cities will suffer from COVID-19 emergency because of its economic impact on their communities. The first emergency phases involved a wide closure of such areas to support "social distancing" measures (i.e. travels limitation; lockdown of (over)crowd-prone activities). In the second phase, individual's risk-mitigation strategies (facial masks) could be properly linked to "social distancing" to ensure re-opening touristic cities to visitors. Simulation tools could support the effectiveness evaluation of risk-mitigation measures to look for an economic and social optimum for activities restarting. This work modifies an existing Agent-Based Model to estimate the virus spreading in touristic areas, including tourists and residents' behaviours, movement and virus effects on them according to a probabilistic approach. Consolidated proximity-based and exposure-time-based contagion spreading rules are included according to international health organizations and previous calibration through experimental data. Effects of tourists' capacity (as "social distancing"-based measure) and other strategies (i.e. facial mask implementation) are evaluated depending on virus-related conditions (i.e. initial infector percentages). An idealized scenario representing a significant case study has been analysed to demonstrate the tool capabilities and compare the effectiveness of those solutions. Results show that "social distancing" seems to be more effective at the highest infectors' rates, although represents an extreme measure with important economic effects. This measure loses its full effectiveness (on the community) as the infectors' rate decreases and individuals' protection measures become predominant (facial masks). The model could be integrated to consider other recurring issues on tourist-related fruition and schedule of urban spaces and facilities (e.g. cultural/leisure buildings).