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A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate

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  • Coro, Gianpaolo

Abstract

COVID-19 pandemic is a global threat to human health and economy that requires urgent prevention and monitoring strategies. Several models are under study to control the disease spread and infection rate and to detect possible factors that might favour them, with a focus on understanding the correlation between the disease and specific geophysical parameters. However, the pandemic does not present evident environmental hindrances in the infected countries. Nevertheless, a lower rate of infections has been observed in some countries, which might be related to particular population and climatic conditions.

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  • Coro, Gianpaolo, 2020. "A global-scale ecological niche model to predict SARS-CoV-2 coronavirus infection rate," Ecological Modelling, Elsevier, vol. 431(C).
    • Handle: RePEc:eee:ecomod:v:431:y:2020:i:c:s0304380020302581
    DOI: 10.1016/j.ecolmodel.2020.109187
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    References listed on IDEAS

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    1. George Valiakos & Konstantinos Papaspyropoulos & Alexios Giannakopoulos & Periklis Birtsas & Sotirios Tsiodras & Michael R Hutchings & Vassiliki Spyrou & Danai Pervanidou & Labrini V Athanasiou & Niko, 2014. "Use of Wild Bird Surveillance, Human Case Data and GIS Spatial Analysis for Predicting Spatial Distributions of West Nile Virus in Greece," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-8, May.
    2. Clay, Karen & Lewis, Joshua & Severnini, Edson, 2018. "Pollution, Infectious Disease, and Mortality: Evidence from the 1918 Spanish Influenza Pandemic," The Journal of Economic History, Cambridge University Press, vol. 78(4), pages 1179-1209, December.
    3. Coro, Gianpaolo & Pagano, Pasquale & Ellenbroek, Anton, 2013. "Combining simulated expert knowledge with Neural Networks to produce Ecological Niche Models for Latimeria chalumnae," Ecological Modelling, Elsevier, vol. 268(C), pages 55-63.
    4. Coro, Gianpaolo & Magliozzi, Chiara & Vanden Berghe, Edward & Bailly, Nicolas & Ellenbroek, Anton & Pagano, Pasquale, 2016. "Estimating absence locations of marine species from data of scientific surveys in OBIS," Ecological Modelling, Elsevier, vol. 323(C), pages 61-76.
    5. Coro, Gianpaolo & Vilas, Luis Gonzalez & Magliozzi, Chiara & Ellenbroek, Anton & Scarponi, Paolo & Pagano, Pasquale, 2018. "Forecasting the ongoing invasion of Lagocephalus sceleratus in the Mediterranean Sea," Ecological Modelling, Elsevier, vol. 371(C), pages 37-49.
    6. Coro, Gianpaolo & Magliozzi, Chiara & Ellenbroek, Anton & Pagano, Pasquale, 2015. "Improving data quality to build a robust distribution model for Architeuthis dux," Ecological Modelling, Elsevier, vol. 305(C), pages 29-39.
    7. A. Godzinski & M. Suarez Castillo, 2019. "Short-term health effects of public transport disruptions: air pollution and viral spread channels," Documents de Travail de l'Insee - INSEE Working Papers g2019-03, Institut National de la Statistique et des Etudes Economiques.
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    Cited by:

    1. Agnieszka Sompolska-Rzechuła & Agnieszka Kurdyś-Kujawska, 2021. "Towards Understanding Interactions between Sustainable Development Goals: The Role of Climate-Well-Being Linkages. Experiences of EU Countries," Energies, MDPI, vol. 14(7), pages 1-20, April.
    2. Pitchaimani, M. & Brasanna Devi, M., 2021. "Stochastic probical strategies in a delay virus infection model to combat COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    3. Ping He & Yu Gao & Longfei Guo & Tongtong Huo & Yuxin Li & Xingren Zhang & Yunfeng Li & Cheng Peng & Fanyun Meng, 2021. "Evaluating the Disaster Risk of the COVID-19 Pandemic Using an Ecological Niche Model," Sustainability, MDPI, vol. 13(21), pages 1-23, October.
    4. Ploutarchos Tzampoglou & Dimitrios Loukidis, 2020. "Investigation of the Importance of Climatic Factors in COVID-19 Worldwide Intensity," IJERPH, MDPI, vol. 17(21), pages 1-25, October.

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