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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/82| Modeling risk of infectious diseases: a case of Coronavirus outbreak in four countries | |
| Md. Jamal Hossain Faroque Ahmed Md. Monirul Islam Md. Mazharul Islam | |
| Novel Coronavirus | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 10.1101/2020.04.01.20049973 | |
| Background The novel coronavirus (2019-nCOV) outbreak has been a serious concern around the globe. Since people are in tremor due to the massive spread of Coronavirus in the major parts of the world, it requires to predict the risk of this infectious disease. In this situation, we develop a model to measure the risk of infectious disease and predict the risk of 2019-nCOV transmission by using data of four countries - United States, Australia, Canada and China. Methods The model underlies that higher the population density, higher the risk of transmission of infectious disease from human to human. Besides, population size, case identification rate and travel of infected passengers in different regions are also incorporated in this model. Results According to the calculated risk index, our study identifies New York State in United States (US) to be the most vulnerable area affected by the novel Coronavirus. Besides, other areas (province/state/territory) such as Hubei (China, 2nd), Massachusetts (US, 3rd), District of Columbia (US, 4th), New Jersey (US, 5th), Quebec (Canada, 20th), Australian Capital Territory (Australia, 29th) are also found as the most risky areas in US, China, Australia and Canada. Conclusion The study suggests avoiding any kind of mass gathering, maintaining recommended physical distances and restricting inbound and outbound flights of highly risk prone areas for tackling 2019-nCOV transmission. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The authors received no funding for this work. ### Author Declarations All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript. Yes All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data are publicly available. 1. The total number of confirmed COVID-19 cases (up to 26 March 2020) and the dates of first identification in geographic areas (territory/province/state) are extracted from Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) (https://systems.jhu.edu/research/public-health/ncov/) Current Github repository - https://github.com/CSSEGISandData/COVID-19 2. Data on population size and geometric area are collected from the website https://www.citypopulation.de 3. Air travel data are obtained from FLIRT (https://flirt.eha.io), a flight network analysis tool developed by EcoHealth Alliance. | |
| Cold Spring Harbor Laboratory Press | |
| 2020 | |
| Preimpreso | |
| https://www.medrxiv.org/content/10.1101/2020.04.01.20049973v1 | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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| Modeling risk.pdf | 1.04 MB | Adobe PDF | Visualizar/Abrir |