Simulating the spread of COVID-19 with cellular automata: A new approach Article Swipe
Sourav Chowdhury
,
Suparna Roychowdhury
,
Indranath Chaudhuri
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.14576
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.14576
Between the years 2020 to 2022, the world was hit by the pandemic of COVID-19 giving rise to an extremely grave situation. The global economy was badly hurt due to the consequences of various intervention strategies (like social distancing, lockdown) which were applied by different countries to control this pandemic. There are multiple speculations that humanity will again face such pandemics in the future. Thus it is very important to learn and gain knowledge about the spread of such infectious diseases and the various factors which are responsible for it. In this study, we have extended our previous work (Chowdhury et.al., 2022) on the probabilistic cellular automata (CA) model to reproduce the spread of COVID-19 in several countries by modifying its earlier used neighbourhood criteria. This modification gives us the liberty to adopt the effect of different restrictions like lockdown and social distancing in our model. We have done some theoretical analysis for initial infection and simulations to gain insights into our model. We have also studied the data from eight countries for COVID-19 in a window of 876 days and compared it with our model. We have developed a proper framework to fit our model on the data for confirmed cases of COVID-19 and have also re-checked the goodness of the fit with the data of the deceased cases for this pandemic. This model fits well with different peaks of COVID-19 data for all the eight countries and can be possibly generalized for a global prediction.
Related Topics
Concepts
Social distance
Pandemic
Coronavirus disease 2019 (COVID-19)
Cellular automaton
Econometrics
Computer science
Development economics
Geography
Economics
Artificial intelligence
Medicine
Pathology
Disease
Infectious disease (medical specialty)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.14576
- https://arxiv.org/pdf/2307.14576
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385373864
All OpenAlex metadata
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https://openalex.org/W4385373864Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2307.14576Digital Object Identifier
- Title
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Simulating the spread of COVID-19 with cellular automata: A new approachWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-27Full publication date if available
- Authors
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Sourav Chowdhury, Suparna Roychowdhury, Indranath ChaudhuriList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.14576Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2307.14576Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2307.14576Direct OA link when available
- Concepts
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Social distance, Pandemic, Coronavirus disease 2019 (COVID-19), Cellular automaton, Econometrics, Computer science, Development economics, Geography, Economics, Artificial intelligence, Medicine, Pathology, Disease, Infectious disease (medical specialty)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.infectious | 79 |
| abstract_inverted_index.re-checked | 207 |
| abstract_inverted_index.situation. | 21 |
| abstract_inverted_index.strategies | 35 |
| abstract_inverted_index.distancing, | 38 |
| abstract_inverted_index.generalized | 242 |
| abstract_inverted_index.prediction. | 246 |
| abstract_inverted_index.responsible | 87 |
| abstract_inverted_index.simulations | 156 |
| abstract_inverted_index.theoretical | 150 |
| abstract_inverted_index.consequences | 31 |
| abstract_inverted_index.intervention | 34 |
| abstract_inverted_index.modification | 126 |
| abstract_inverted_index.restrictions | 137 |
| abstract_inverted_index.speculations | 53 |
| abstract_inverted_index.neighbourhood | 123 |
| abstract_inverted_index.probabilistic | 104 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile |