Spatial behavioural responses to the spread of an infectious disease can suppress Turing and Turing–Hopf patterning of the disease

d’Onofrio, Alberto; Banerjee, Malay; Manfredi, Piero

doi:10.1016/j.physa.2019.123773

Cited by 17 publications

(11 citation statements)

References 62 publications

(134 reference statements)

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“…A list of hospitals in New York state with Pulmonology and Lung surgery departments was obtained from the New [6] 2016 HIV genotype ----Sung [17] 2016 EMS data -----Gryseels [7] 2017 GenBank ----Wesolowski [8] 2017 GISDiva --Guzzetta [9] 2018 Dengue Transmission ---Ray [13] 2018 Influenza ----Li [10] 2019 A(H7N9) ---Nandana [14] 2019 Delhi-Dengue ---Grabowski [11] 2020 RCCS -----Nelli [12] 2020 Malaria ---Kang [15] 2020 Covid-19 China ---d'Onofrio [16] 2020 ----Guevara 2020 Covid-19 NY, USA -- York Department of Health (https://profiles.health.ny.gov).…”

Section: B Hospital Datasetmentioning

confidence: 99%

“…The third constraint (15) represents the integrality constraints of zero-one variables x ij (x ij ≥ 0). The last constraint (16) ensures that each city (node) in the final route will be visited one time and that no sub-routes will be formed.…”

Section: ) Application Of the Tsp Algorithmmentioning

confidence: 99%

“…The article published by d’Onofrio et al [16] also focused on the spread of an endemic infectious disease. Models were generated that represent changes in people’s social and mobility behavior, such as avoiding visiting areas with a high prevalence of infection.…”

Section: Related Workmentioning

confidence: 99%

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Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm

Guevara

Peñas

2020

IEEE Access

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In this study, the Intelligent Infectious Diseases Algorithm (IIDA) has been developed to locate the sources of infection and survival rate of coronavirus disease 2019 (COVID-19), in order to propose health care routes for population affected by COVID-19. The main goal of this computational algorithm is to reduce the spread of the virus and decrease the number of infected people. To do so, health care routes are generated according to the priority of certain population groups. The algorithm was applied to New York state data. Based on infection rates and reported deaths, hot spots were determined by applying the kernel density estimation (KDE) to the groups that have been previously obtained using a clustering algorithm together with the elbow method. For each cluster, the survival rate-the key information to prioritize medical care-was determined using the proportional hazards model. Finally, ant colony optimization (ACO) and the traveling salesman problem (TSP) optimization algorithms were applied to identify the optimal route to the closest hospital. The results obtained efficiently covered the points with the highest concentration of COVID-19 cases. In this way, its spread can be prevented and health resources optimized. INDEX TERMS Clustering, computational intelligence, coronavirus disease 2019 (COVID-19), kernel density estimation (KDE), medical care routing, optimization.

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Section: B Hospital Datasetmentioning

confidence: 99%

Section: ) Application Of the Tsp Algorithmmentioning

confidence: 99%

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Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm

Guevara

Peñas

2020

IEEE Access

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“… 2020 ), models with time varying or nonlinear disease transmission rate (d’Onofrio et al. 2020 ; Sun et al. 2008 ).…”

Section: Introductionmentioning

confidence: 99%

An Epidemic Model with Time-Distributed Recovery and Death Rates

2022

Self Cite

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A compartmental epidemiological model with distributed recovery and death rates is proposed. In some particular cases, the model can be reduced to the conventional SIR model. However, in general, the dynamics of epidemic progression in this model is different. Distributed recovery and death rates are evaluated from COVID-19 data. The model is validated by the epidemiological data for different countries, and it shows better agreement with the data than the SIR model. The time-dependent disease transmission rate is estimated.

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“…Mathematical model structures and their analytical techniques have capitulated research interests of numerous biological scientists as they bestow them liberty to experiment without any direct species interaction yet furnishing exquisite results in compliance with experimental outcomes. Researchers rely on model methodologies as they evolved as indispensable tools to augment perspicacity into dynamics of crises and serve as vital exemplary artificial platform to analyze the dynamics in longer run at both ecological [5] , [6] , [7] and epidemic frontiers [8] , [9] , [10] , [11] .…”

Section: Introductionmentioning

confidence: 99%

Stochastic probical strategies in a delay virus infection model to combat COVID-19

Pitchaimani

Devi

2021

Chaos, Solitons & Fractals

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In disease model systems, random noises and time delay factors play key role in interpreting disease dynamics to comprehend deeper insights into the course of dynamics. An endeavor to forecast intercellular behavioral dynamics of SARS-CoV-2 virus via Infection model with responsive host immune mechanisms forms the crux of this research study. Incorporation of time delay factor into infection transmission rates in noisy system epitomizes spectacular view on internal viral dynamics and stability properties are rigorously analyzed around equilibrium steady states to probe feasible strategies in mitigating rapid spread. Efforts to perceive inocular view on infection dynamics are not limited to theoretical frontiers but are substantiated with empirically simulated outcomes and visualized as graphical upshots. Discussions on numerical investigations emphasized shorter incubation periods and vaccination at pertinent time intervals to restrain massive spread and exhibit total immunity against SARS-CoV-2 infections.

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Spatial behavioural responses to the spread of an infectious disease can suppress Turing and Turing–Hopf patterning of the disease

Cited by 17 publications

References 62 publications

Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm

Surveillance Routing of COVID-19 Infection Spread Using an Intelligent Infectious Diseases Algorithm

An Epidemic Model with Time-Distributed Recovery and Death Rates

Stochastic probical strategies in a delay virus infection model to combat COVID-19

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