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The first is to explain the geo-environmental determinants of the accelerated diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats similar to COVID-19 having an accelerated viral infectivity in society. Using data on sample of N= 55 Italian province capitals, and data of infected individuals at as of April 7th, 2020, results reveal that the accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution of cities measured with days exceeding the limits set for PM10 (particulate matter 10 mu mor less in diameter) or ozone. In particular, hinterland cities with average high number of days exceeding the limits set for PM10 (and also having a lowwind speed) have a very high number of infected people on 7th April 2020 (arithmetic mean is about 2200 infected individuals, with average polluted days greater than 80 days per year), whereas coastal cities also having days exceeding the limits set for PM10 or ozone but with high wind speed have about 944.70 average infected individuals, with about 60 average polluted days per year; moreover, cities having more than 100 days of air pollution (exceeding the limits set for PM10), they have a very high average number of infected people (about 3350 infected individuals, 7th April 2020), whereas cities having less than 100 days of air pollution per year, they have a lower average number of infected people (about 10(14) individuals). The findings here also suggest that tominimize the impact of future epidemics similar to COVID-19, the max number of days per year that Italian provincial capitals or similar industrialized cities can exceed the limits set for PM10 or for ozone, considering theirmeteorological conditions, is about 48 days. Moreover, results here reveal that the explanatory variable of air pollution in cities seems to be a more important predictor in the initial phase of diffusion of viral infectivity (on 17th March 2020, b(1)= 1.27, p > 0.001) than interpersonal contacts (b(2)= 0.31, p < 0.05). In the second phase of maturity of the transmission dynamics of COVID-19, air pollution reduces intensity (on 7th April 2020 with b'(1) = 0.81, p < 0.001) also because of the indirect effect of lockdown, whereas regression coefficient of transmission based on interpersonal contacts has a stable level (b'(2)= 0.31, p < 0.01). This result reveals that accelerated transmission dynamics of COVID-19 is due tomainly to the mechanismof air pollution-to-human transmission (airborne viral infectivity) rather than human-to-human transmission. Overall, then, transmission dynamics of viral infectivity, such as COVID-19, is due to systemic causes: general factors that are the same for all regions (e.g., biological characteristics of virus, incubation period, etc.) and specific factors which are different for each region and/or city (e.g., complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity) and health level of individuals (habits, immune system, age, sex, etc.). Lessons learned for COVID-19 in the case study here suggest that a proactive strategy to cope with future epidemics is also to apply especially an environmental and sustainable policy based on reduction of levels of air pollution mainly in hinterland and polluting cities- (having low wind speed, high percentage of moisture and number of fog days)-that seem to have an environment that foster a fast transmission dynamics of viral infectivity in society. Hence, in the presence of polluting industrialization in regions that can trigger the mechanism of air pollutionto-human transmission dynamics of viral infectivity, this study must conclude that a comprehensive strategy to prevent future epidemics similar to COVID-19 has to be also designed in environmental and socioeconomic terms, that is also based on sustainability science and environmental science, and not only in terms of biology, medicine, healthcare and health sector. (C) 2020 Elsevier B.V. All rights reserved. https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/ http://schema.org/encodingFormat application/ld+json https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/ http://schema.org/hasPart https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/resources/fb6f51e8-125e-4761-9068-4c81a55c8b44 https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/ http://schema.org/identifier https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487 https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/ http://schema.org/license https://choosealicense.com/no-permission/ https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/ http://schema.org/name Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/ http://w3id.org/ro-id/rohub/model#creation_mode MANUAL https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/ro#ResearchObject https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/roevo#LiveRO https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Dataset https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://w3id.org/ro/earth-science#BibliographyResearchObject https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/ro/terms/earth-science#BibliographyResearchObject https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/ https://www.w3.org/ns/iana/link-relations/relation#cite-as Foglini, Federica. "Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID." ROHub. Dec 10 ,2021. https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487. https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/resources/fb6f51e8-125e-4761-9068-4c81a55c8b44 http://schema.org/author mailto:service-account-generation-service https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/resources/fb6f51e8-125e-4761-9068-4c81a55c8b44 http://schema.org/contentSize 184 https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/resources/fb6f51e8-125e-4761-9068-4c81a55c8b44 http://schema.org/contentUrl https://api.rohub.org/api/resources/fb6f51e8-125e-4761-9068-4c81a55c8b44/download/ https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/resources/fb6f51e8-125e-4761-9068-4c81a55c8b44 http://schema.org/creator mailto:service-account-generation-service https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/resources/fb6f51e8-125e-4761-9068-4c81a55c8b44 http://schema.org/dateCreated 2021-12-10 09:59:04.387384+00:00 https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/resources/fb6f51e8-125e-4761-9068-4c81a55c8b44 http://schema.org/dateModified 2021-12-10 09:59:04.388609+00:00 https://w3id.org/ro-id/814757a0-5d2f-4573-90c5-4b6676714487/resources/fb6f51e8-125e-4761-9068-4c81a55c8b44 http://schema.org/description This study has two goals. The first is to explain the geo-environmental determinants of the accelerated diffusion of COVID-19 that is generating a high level of deaths. The second is to suggest a strategy to cope with future epidemic threats similar to COVID-19 having an accelerated viral infectivity in society. Using data on sample of N= 55 Italian province capitals, and data of infected individuals at as of April 7th, 2020, results reveal that the accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution of cities measured with days exceeding the limits set for PM10 (particulate matter 10 mu mor less in diameter) or ozone. In particular, hinterland cities with average high number of days exceeding the limits set for PM10 (and also having a lowwind speed) have a very high number of infected people on 7th April 2020 (arithmetic mean is about 2200 infected individuals, with average polluted days greater than 80 days per year), whereas coastal cities also having days exceeding the limits set for PM10 or ozone but with high wind speed have about 944.70 average infected individuals, with about 60 average polluted days per year; moreover, cities having more than 100 days of air pollution (exceeding the limits set for PM10), they have a very high average number of infected people (about 3350 infected individuals, 7th April 2020), whereas cities having less than 100 days of air pollution per year, they have a lower average number of infected people (about 10(14) individuals). The findings here also suggest that tominimize the impact of future epidemics similar to COVID-19, the max number of days per year that Italian provincial capitals or similar industrialized cities can exceed the limits set for PM10 or for ozone, considering theirmeteorological conditions, is about 48 days. Moreover, results here reveal that the explanatory variable of air pollution in cities seems to be a more important predictor in the initial phase of diffusion of viral infectivity (on 17th March 2020, b(1)= 1.27, p > 0.001) than interpersonal contacts (b(2)= 0.31, p < 0.05). In the second phase of maturity of the transmission dynamics of COVID-19, air pollution reduces intensity (on 7th April 2020 with b'(1) = 0.81, p < 0.001) also because of the indirect effect of lockdown, whereas regression coefficient of transmission based on interpersonal contacts has a stable level (b'(2)= 0.31, p < 0.01). This result reveals that accelerated transmission dynamics of COVID-19 is due tomainly to the mechanismof air pollution-to-human transmission (airborne viral infectivity) rather than human-to-human transmission. Overall, then, transmission dynamics of viral infectivity, such as COVID-19, is due to systemic causes: general factors that are the same for all regions (e.g., biological characteristics of virus, incubation period, etc.) and specific factors which are different for each region and/or city (e.g., complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity) and health level of individuals (habits, immune system, age, sex, etc.). Lessons learned for COVID-19 in the case study here suggest that a proactive strategy to cope with future epidemics is also to apply especially an environmental and sustainable policy based on reduction of levels of air pollution mainly in hinterland and polluting cities- (having low wind speed, high percentage of moisture and number of fog days)-that seem to have an environment that foster a fast transmission dynamics of viral infectivity in society. Hence, in the presence of polluting industrialization in regions that can trigger the mechanism of air pollutionto-human transmission dynamics of viral infectivity, this study must conclude that a comprehensive strategy to prevent future epidemics similar to COVID-19 has to be also designed in environmental and socioeconomic terms, that is also based on sustainability science and environmental science, and not only in terms of biology, medicine, healthcare and health sector. (C) 2020 Elsevier B.V. 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