Cargando…

Spatial network based model forecasting transmission and control of COVID-19

The SARS-CoV-2 driven infectious novel coronavirus disease (COVID-19) has been declared a pandemic by its brutal impact on the world in terms of loss on human life, health, economy, and other crucial resources. To explore more about its aspects, we adopted the [Formula: see text] (Susceptible–Expose...

Descripción completa

Detalles Bibliográficos
Autores principales: Sharma, Natasha, Verma, Atul Kumar, Gupta, Arvind Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247259/
https://www.ncbi.nlm.nih.gov/pubmed/34230756
http://dx.doi.org/10.1016/j.physa.2021.126223
_version_ 1783716483929473024
author Sharma, Natasha
Verma, Atul Kumar
Gupta, Arvind Kumar
author_facet Sharma, Natasha
Verma, Atul Kumar
Gupta, Arvind Kumar
author_sort Sharma, Natasha
collection PubMed
description The SARS-CoV-2 driven infectious novel coronavirus disease (COVID-19) has been declared a pandemic by its brutal impact on the world in terms of loss on human life, health, economy, and other crucial resources. To explore more about its aspects, we adopted the [Formula: see text] (Susceptible–Exposed–Infected–Recovered–Death) pandemic spread with a time delay on the heterogeneous population and geography in this work. Focusing on the spatial heterogeneity, epidemic spread on the framework of modeling that incorporates population movement within and across the boundaries is studied. The entire population of interest in a region is divided into small distinct geographical sub regions, which interact using migration networks across boundaries. Utilizing the time delay differential equations based model estimations, we analyzed the spread dynamics of disease in India. The numerical outcomes from the model are validated using real time available data for COVID-19 cases. Based on the developed model in the framework of the recent data, we verified total infection cases in India considering the effect of nationwide lockdown at the onset of the pandemic and its unlocking by what seemed to be the end of the first wave. We have forecasted the total number of infection cases in two extreme situations of nationwide no lockdown and strict lockdown scenario. We expect that in future for any change in the key parameters, due to the regional differences, predictions will lie within the bounds of the above mentioned extreme plots. We computed the approximate peak infection in forwarding time and relative timespan when disease outspread halts. The most crucial parameter, the time-dependent generalization of the basic reproduction number, has been estimated. The impact of the social distancing and restricted movement measures that are crucial to contain the pandemic spread has been extensively studied by considering no lockdown scenario. Our model suggests that attaining a reduction in the contact rate between susceptible and infected individuals by practicing strict social distancing is one of the most effective control measures to manage COVID-19 spread in India. The cases can further decrease if social distancing is followed in conjunction with restricted movement.
format Online
Article
Text
id pubmed-8247259
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Elsevier B.V.
record_format MEDLINE/PubMed
spelling pubmed-82472592021-07-02 Spatial network based model forecasting transmission and control of COVID-19 Sharma, Natasha Verma, Atul Kumar Gupta, Arvind Kumar Physica A Article The SARS-CoV-2 driven infectious novel coronavirus disease (COVID-19) has been declared a pandemic by its brutal impact on the world in terms of loss on human life, health, economy, and other crucial resources. To explore more about its aspects, we adopted the [Formula: see text] (Susceptible–Exposed–Infected–Recovered–Death) pandemic spread with a time delay on the heterogeneous population and geography in this work. Focusing on the spatial heterogeneity, epidemic spread on the framework of modeling that incorporates population movement within and across the boundaries is studied. The entire population of interest in a region is divided into small distinct geographical sub regions, which interact using migration networks across boundaries. Utilizing the time delay differential equations based model estimations, we analyzed the spread dynamics of disease in India. The numerical outcomes from the model are validated using real time available data for COVID-19 cases. Based on the developed model in the framework of the recent data, we verified total infection cases in India considering the effect of nationwide lockdown at the onset of the pandemic and its unlocking by what seemed to be the end of the first wave. We have forecasted the total number of infection cases in two extreme situations of nationwide no lockdown and strict lockdown scenario. We expect that in future for any change in the key parameters, due to the regional differences, predictions will lie within the bounds of the above mentioned extreme plots. We computed the approximate peak infection in forwarding time and relative timespan when disease outspread halts. The most crucial parameter, the time-dependent generalization of the basic reproduction number, has been estimated. The impact of the social distancing and restricted movement measures that are crucial to contain the pandemic spread has been extensively studied by considering no lockdown scenario. Our model suggests that attaining a reduction in the contact rate between susceptible and infected individuals by practicing strict social distancing is one of the most effective control measures to manage COVID-19 spread in India. The cases can further decrease if social distancing is followed in conjunction with restricted movement. Elsevier B.V. 2021-11-01 2021-07-01 /pmc/articles/PMC8247259/ /pubmed/34230756 http://dx.doi.org/10.1016/j.physa.2021.126223 Text en © 2021 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Sharma, Natasha
Verma, Atul Kumar
Gupta, Arvind Kumar
Spatial network based model forecasting transmission and control of COVID-19
title Spatial network based model forecasting transmission and control of COVID-19
title_full Spatial network based model forecasting transmission and control of COVID-19
title_fullStr Spatial network based model forecasting transmission and control of COVID-19
title_full_unstemmed Spatial network based model forecasting transmission and control of COVID-19
title_short Spatial network based model forecasting transmission and control of COVID-19
title_sort spatial network based model forecasting transmission and control of covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8247259/
https://www.ncbi.nlm.nih.gov/pubmed/34230756
http://dx.doi.org/10.1016/j.physa.2021.126223
work_keys_str_mv AT sharmanatasha spatialnetworkbasedmodelforecastingtransmissionandcontrolofcovid19
AT vermaatulkumar spatialnetworkbasedmodelforecastingtransmissionandcontrolofcovid19
AT guptaarvindkumar spatialnetworkbasedmodelforecastingtransmissionandcontrolofcovid19