A model based on cellular automata to estimate the social isolation impact on COVID-19 spreading in Brazil

Schimit, Pedro H. T.

doi:10.1016/j.cmpb.2020.105832

Cited by 43 publications

(18 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies show that a high percentage of reduced contacts through social isolation limits the spread of new cases [39] , [47] . The timing of enforcing lockdown restrictions is also crucial, with earlier implementations leading to lesser infections [3] , [5] .…”

Section: Resultsmentioning

confidence: 98%

See 1 more Smart Citation

A model based on cellular automata for investigating the impact of lockdown, migration and vaccination on COVID-19 dynamics

Jithesh

2021

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

B ackground and Objective COVID-19 pandemic continues unabated due to the rapid spread of new mutant strains of the virus. Decentralized cluster containment is an efficient approach to manage the pandemic in the long term, without straining the healthcare system and economy. In this study, the objective is to forecast the peak and duration of COVID-19 spread in a cluster under different conditions, using a probabilistic cellular automata configuration designed to include the observed characteristics of the pandemic with appropriate neighbourhood schemes and transition rules. M ethods The cellular automata, initially configured to have only susceptible and exposed states, enlarges and evolves in discrete time steps to different infection states of the COVID-19 pandemic. The transition rules take into account the probability and proximity of contact between infected hosts and susceptible individuals. A transmittable and transition neighbourhoods are defined to identify the most probable individuals infected from a single host in a time step. R esults The model with novel neighbourhood schemes and transition rules reproduce the macroscopic behaviour of infection and recovery observed in pandemics. The temporal evolution of the pandemic trajectory is sensitive to lattice size, range, latent and recovery periods but has constraints in capturing the changes in the infectious period. A study of lockdown and migration scenarios shows strict social isolation is crucial in controlling the pandemic. The simulations also indicate that earlier vaccination with a higher capacity and rate is essential to mitigate the pandemic. A comparison of simulated and actual data shows a good match. C onclusions The study concludes that social isolation during movement and interaction of people can limit the spread of new infections. Vaccinating a large proportion of the population reduces new cases in subsequent waves of the pandemic. The model and algorithm with real-world data as input can quickly forecast the trajectory of the pandemic, for effective response in cluster containment.

show abstract

Section: Resultsmentioning

confidence: 98%

“…In another important work, Schimit [39] , studied the impact of social isolation on dynamics of COVID-19 using PCA and ordinary differential equations. The model consists of eight disease states and fifteen parameters and uses an extended neighbourhood to establish random contact networks between cells.…”

Section: Introductionmentioning

confidence: 99%

A model based on cellular automata for investigating the impact of lockdown, migration and vaccination on COVID-19 dynamics

Jithesh

2021

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

show abstract

“…Subsequently, Wolfram [39] proposed a simpler version of CA with two states per cell and three-neighbourhood. Research of a large number of authors from diverse disciplines has enriched the field of CA [40][41][42][43][44] and some researchers used CA to understand the dynamics of COVID-19 [45][46][47][48][49]. The book [44] covers a comprehensive survey of CA theory and wide varieties of applications.…”

Section: Cellular Automata (Ca) Preliminariesmentioning

confidence: 99%

Analysis of Coronavirus Envelope Protein with Cellular Automata (CA) Model

Hazari¹,

Chaudhuri²

2022

Preprint

View full text Add to dashboard Cite

The reason of significantly higher transmissibility of SARS Covid (2019 CoV-2) compared to SARS Covid (2003 CoV) and MERS Covid (2012 MERS) can be attributed to mutations reported in structural proteins, and the role played by non-structural proteins (nsps) and accessory proteins (ORFs) for viral replication, assembly, and shedding. Envelope protein E is one of the four structural proteins of minimum length. Recent studies have confirmed critical role played by the envelope protein in the viral life cycle including assembly of virion exported from infected cell for its transmission. However, the determinants of the highly complex viral -host interactions of envelope protein, particularly with host Golgi complex, have not been adequately characterized. CoV-2 and CoV Envelope proteins of length 75 and 76 amino acids differ in four amino acid locations. The additional amino acid Gly (G) at location 70 makes CoV length 76. The amino acid pair EG at location 69-70 of CoV in place of amino acid R in location 69 of CoV-2, has been identified as a major determining factor in the current investigation. This paper concentrates on the design of computational model to compare the structure/function of wild and mutants of CoV-2 with wild and mutants of CoV in the functionally important region of the protein chain pair. We hypothesize that differences of CAML model parameter of CoV-2 and CoV characterize the deviation in structure and function of envelope proteins in respect of interaction of virus with host Golgi complex; and this difference gets reflected in the difference of their transmissibility. The hypothesis has been validated from single point mutational study on-(i) human HBB beta-globin hemoglobin protein associated with sickle cell anemia, (ii) mutants of envelope protein of Covid-2 infected patients reported in recent publications. For each of these case studies, the CAML model parameter differ between wild and specific mutant that represent deviation of the structure-function of the mutant from that of wild leading to a specific disease. From detailed analysis of the characteristics of proteins of three viruses (CoV-2, CoV, MERS

show abstract

“…Another feature that make CA more flexible is the ability to adjust the connectivity strength for cells during simulation [36] , [37] , [38] . This feature is very useful for modeling and analysis of control measures such as blocking the entry of imported cases by mandatory quarantine upon arrival, an entry ban for nonresidents and requiring a negative test result from arrivals, passenger monitoring and other protective protocols applied dynamically during the outbreak of COVID-19 [39] , [40] , [41] , [42] , [43] , [44] , [45] . Figure 4 , presents the transition function of a heterogeneous CA that is capable of dynamic adjusting the weights.…”

Section: Introductionmentioning

confidence: 99%

A symbiosis between cellular automata and dynamic weighted multigraph with application on virus spread modeling

Moghari

Ghorani

2022

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

A model based on cellular automata to estimate the social isolation impact on COVID-19 spreading in Brazil

Cited by 43 publications

References 55 publications

A model based on cellular automata for investigating the impact of lockdown, migration and vaccination on COVID-19 dynamics

A model based on cellular automata for investigating the impact of lockdown, migration and vaccination on COVID-19 dynamics

Analysis of Coronavirus Envelope Protein with Cellular Automata (CA) Model

A symbiosis between cellular automata and dynamic weighted multigraph with application on virus spread modeling

Contact Info

Product

Resources

About