scholarly journals A Pivotal Restructuring of Modeling the Control of COVID-19 During and After Massive Vaccinations for the Next Few Years

2021 ◽  
Author(s):  
Jose B Cruz ◽  
Tirso A Ronquillo ◽  
Ralph G B Sangalang ◽  
Albertson D Amante ◽  
Divina G D Ronquillo ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Closed Loop ◽  
The Other ◽  
Disease Spread ◽  
Contact Tracing ◽  
Loop Model ◽  
Input Output ◽  
Time Rate ◽  
Direct Observations ◽  
The Social

Abstract This paper presents a pivotal restructuring of modeling the control of COVID-19 even when massive vaccination is in progress. A new closed loop mathematical model to demonstrate how direct observations of the epidemiological compartments of population could be mapped to inputs, such that the social spread of the disease is asymptotically subdued. Mathematical details of the stabilization and robustness are included. A new engineered closed loop model is designed to control the spread of COVID-19 or its variants—that is, one input directly increases the time-rate of the compartment of population free of virus, and the other input directly changes the time-rate of the susceptible compartment of population. Both inputs have collateral opposite influences on the time-rate of the infected compartment of population. The loop is closed around the new input-output model and designed so that the outputs reach the desired asymptotes. New surges of disease spread are not possible in appropriately designed stable closed loop models. However, extensive testing, contact tracing, and medical treatment of those found infected, must be maintained.

scholarly journals A pivotal restructuring of modelling the control of COVID-19 during and after massive vaccination for the next few years

2021 ◽  
Author(s):  
Jose B Cruz ◽  
Tirso A Ronquillo ◽  
Ralph G B Sangalang ◽  
Albertson D Amante ◽  
Divina G D Ronquillo ◽  
...  
Keyword(s):  
Medical Treatment ◽  
World Wide ◽  
Herd Immunity ◽  
Contact Tracing ◽  
Direct Observations ◽  
Feedback Model ◽  
Corona Virus ◽  
The Social ◽  
One Year

Abstract This paper presents a new mathematical feedback model to demonstrate how direct observations of the epidemiological compartments of population could be mapped to inputs, such that the social spread of the disease is asymptotically subdued. Details of the stabilization and robustness are included. This is a pivotal restructuring of modelling the control of corona virus from the current models in use world-wide which do not utilize feedback of functions of epidemiological compartments of population to construct the inputs. Although several vaccines have received Emergency Use Authorization (EUA) massive vaccination would take several years to reach herd immunity in most countries. Furthermore, the period of efficacy of the vaccination may be approximately one year only resulting in an unending vaccination. Even during the vaccination, there would be an urgent need to control the spread of the virus. When herd immunity is reached and vaccination is discontinued, there would be new surges of the disease. These surges of disease are not possible in appropriately designed stable feedback models. However, extensive testing, contact tracing, and medical treatment of those found infected, must be maintained.

scholarly journals A pivotal restructuring of modelling the control of COVID-19 during and after massive vaccination for the next few years

2021 ◽  
Author(s):  
Jose B Cruz ◽  
Tirso A Ronquillo ◽  
Ralph G B Sangalang ◽  
Albertson D Amante ◽  
Divina G D Ronquillo ◽  
...  
Keyword(s):  
Medical Treatment ◽  
World Wide ◽  
Herd Immunity ◽  
Contact Tracing ◽  
Direct Observations ◽  
Feedback Model ◽  
Corona Virus ◽  
The Social ◽  
One Year

Abstract This paper presents a new mathematical feedback model to demonstrate how direct observations of the epidemiological compartments of population could be mapped to inputs, such that the social spread of the disease is asymptotically subdued. Details of the stabilization and robustness are included. This is a pivotal restructuring of modelling the control of corona virus from the current models in use world-wide which do not utilize feedback of functions of epidemiological compartments of population to construct the inputs. Although several vaccines have received Emergency Use Authorization (EUA) massive vaccination would take several years to reach herd immunity in most countries. Furthermore, the period of efficacy of the vaccination may be approximately one year only resulting in an unending vaccination. Even during the vaccination, there would be an urgent need to control the spread of the virus. When herd immunity is reached and vaccination is discontinued, there would be new surges of the disease. These surges of disease are not possible in appropriately designed stable feedback models. However, extensive testing, contact tracing, and medical treatment of those found infected, must be maintained.

scholarly journals A pivotal restructuring of modelling the control of COVID-19 during and after massive vaccination for the next few years

2021 ◽  
Author(s):  
Jose B Cruz ◽  
Tirso A Ronquillo ◽  
Ralph G B Sangalang ◽  
Albertson D Amante ◽  
Divina G D Ronquillo ◽  
...  
Keyword(s):  
Medical Treatment ◽  
World Wide ◽  
Herd Immunity ◽  
Contact Tracing ◽  
Direct Observations ◽  
Feedback Model ◽  
Corona Virus ◽  
The Social ◽  
One Year

Abstract This paper presents a new mathematical feedback model to demonstrate how direct observations of the epidemiological compartments of population could be mapped to inputs, such that the social spread of the disease is asymptotically subdued. Details of the stabilization and robustness are included. This is a pivotal restructuring of modelling the control of corona virus from the current models in use world-wide which do not utilize feedback of functions of epidemiological compartments of population to construct the inputs. Although several vaccines have received Emergency Use Authorization (EUA) massive vaccination would take several years to reach herd immunity in most countries. Furthermore, the period of efficacy of the vaccination may be approximately one year only resulting in an unending vaccination. Even during the vaccination, there would be an urgent need to control the spread of the virus. When herd immunity is reached and vaccination is discontinued, there would be new surges of the disease. These surges of disease are not possible in appropriately designed stable feedback models. However, extensive testing, contact tracing, and medical treatment of those found infected, must be maintained.

scholarly journals A pivotal restructuring of modelling the control of COVID-19 during and after massive vaccination for the next few years

2021 ◽  
Author(s):  
Jose B Cruz ◽  
Tirso A Ronquillo ◽  
Ralph G B Sangalang ◽  
Albertson D Amante ◽  
Divina G D Ronquillo ◽  
...  
Keyword(s):  
Medical Treatment ◽  
World Wide ◽  
Herd Immunity ◽  
Contact Tracing ◽  
Direct Observations ◽  
Feedback Model ◽  
Corona Virus ◽  
The Social ◽  
One Year

Abstract This paper presents a new mathematical feedback model to demonstrate how direct observations of the epidemiological compartments of population could be mapped to inputs, such that the social spread of the disease is asymptotically subdued. Details of the stabilization and robustness are included. This is a pivotal restructuring of modelling the control of corona virus from the current models in use world-wide which do not utilize feedback of functions of epidemiological compartments of population to construct the inputs. Although several vaccines have received Emergency Use Authorization (EUA) massive vaccination would take several years to reach herd immunity in most countries. Furthermore, the period of efficacy of the vaccination may be approximately one year only resulting in an unending vaccination. Even during the vaccination, there would be an urgent need to control the spread of the virus. When herd immunity is reached and vaccination is discontinued, there would be new surges of the disease. These surges of disease are not possible in appropriately designed stable feedback models. However, extensive testing, contact tracing, and medical treatment of those found infected, must be maintained.

scholarly journals Mathematical Modeling to Perform an Analysis of Social Isolation Periods in the Dissemination of COVID-19

2021 ◽  
Vol 22 (4) ◽  
pp. 595-608
Author(s):  
A. Molter ◽  
R. S. Quadros ◽  
M. Rafikov ◽  
D. Buske ◽  
G. A. Gonçalves
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Model ◽  
Mathematical Models ◽  
Social Isolation ◽  
Computer Simulations ◽  
The Other ◽  
Stable Level ◽  
The Social ◽  
The World ◽  
Second Peak

The outbreak of COVID-19 has made scientists from all over the world do not measureefforts to understand the dynamics of the disease caused by this coronavirus. Several mathematical models have been proposed to describe the dynamics and make predictions. This work proposes a mathematical model that includes social isolation of susceptible individuals as a strategy of suppression and mitigation of the disease. The Susceptible-Infectious-Isolated-Recovered-Dead (SIQRD) model is proposed to analyze three important issues about the dynamics of the disease taking into account social isolation: when the isolation should begin? How long to keep the isolation? How to get out of this isolation? To get answers, computer simulations are provided and their results discussed. The results obtained show that beginning social isolation on the 10th or 15th days, after confirmation of the 50th case, and with 70% of the population in isolation, seems to be promising, since the infected curve does not grow much until it enters the isolation and remains at a stable level during the isolation. On the other hand an abrupt release of the social isolation will imply a second peak of infected individuals above the first one, which is not desired. Therefore, the release from social isolation should be gradual.

scholarly journals Analysis and Forecast of novel Coronavirus (Covid-19) Cases in India: A Simplified Mathematical Model based on Growth and Recovery

2020 ◽  
Author(s):  
R R Rajalaxmi ◽  
Chockalingam Aravind Vaithilingam ◽  
Gayathri Sivasubramanian ◽  
Lalitha R
Keyword(s):  
Mathematical Model ◽  
Reference Data ◽  
Epidemiological Data ◽  
The Other ◽  
Disease Spread ◽  
Epidemic Spreading ◽  
Effective Measure ◽  
Model Predictions ◽  
Novel Coronavirus ◽  
Simplified Mathematical Model

This work analyses the different phases of Covid-19 outbreak in India and performs progress of the disease spread. The data is collected from John Hopkins epidemiological data providing the latest data from January 31,2020 to May 31, 2020. A simple mathematical model is developed to gather a quantitative picture of the epidemic spreading with limited reference data. Further, we performed an analysis and forecast of the disease spread in different phases of lock down in the country. The profound model predictions considering the overall data exhibit that the numbers to reach peak between 28 August 2020 to 6 Sep 2020. As the pandemic still increases the number of infected cases, different quarantine levels would serve as an effective measure in containing the spread much earlier than the other similar cases.

scholarly journals Analysis and Forecast of novel Coronavirus (Covid-19) Cases in India: A Simplified Mathematical Model based on Growth and Recovery

2020 ◽  
Author(s):  
R R Rajalaxmi ◽  
Chockalingam Aravind Vaithilingam ◽  
Gayathri Sivasubramanian ◽  
Lalitha R
Keyword(s):  
Mathematical Model ◽  
Reference Data ◽  
Epidemiological Data ◽  
The Other ◽  
Disease Spread ◽  
Epidemic Spreading ◽  
Effective Measure ◽  
Model Predictions ◽  
Novel Coronavirus ◽  
Simplified Mathematical Model

This work analyses the different phases of Covid-19 outbreak in India and performs progress of the disease spread. The data is collected from John Hopkins epidemiological data providing the latest data from January 31,2020 to May 31, 2020. A simple mathematical model is developed to gather a quantitative picture of the epidemic spreading with limited reference data. Further, we performed an analysis and forecast of the disease spread in different phases of lock down in the country. The profound model predictions considering the overall data exhibit that the numbers to reach peak between 28 August 2020 to 6 Sep 2020. As the pandemic still increases the number of infected cases, different quarantine levels would serve as an effective measure in containing the spread much earlier than the other similar cases.

scholarly journals A pivotal restructuring of modelling the control of COVID-19 during and after massive vaccination for the next few years

2021 ◽  
Author(s):  
Jose B Cruz ◽  
Tirso A Ronquillo ◽  
Ralph G B Sangalang ◽  
Albertson D Amante ◽  
Divina G D Ronquillo ◽  
...  
Keyword(s):  
Medical Treatment ◽  
World Wide ◽  
Herd Immunity ◽  
Contact Tracing ◽  
Direct Observations ◽  
Feedback Model ◽  
Corona Virus ◽  
The Social ◽  
One Year

Abstract This paper presents a new mathematical feedback model to demonstrate how direct observations of the epidemiological compartments of population could be mapped to inputs, such that the social spread of the disease is asymptotically subdued. Details of the stabilization and robustness are included. This is a pivotal restructuring of modelling the control of corona virus from the current models in use world-wide which do not utilize feedback of functions of epidemiological compartments of population to construct the inputs. Although several vaccines have received Emergency Use Authorization (EUA) massive vaccination would take several years to reach herd immunity in most countries. Furthermore, the period of efficacy of the vaccination may be approximately one year only resulting in an unending vaccination. Even during the vaccination, there would be an urgent need to control the spread of the virus. When herd immunity is reached and vaccination is discontinued, there would be new surges of the disease. These surges of disease are not possible in appropriately designed stable feedback models. However, extensive testing, contact tracing, and medical treatment of those found infected, must be maintained.

scholarly journals Investigation of the Efficiency of Mask Wearing, Contact Tracing, and Case Isolation during the COVID-19 Outbreak

2021 ◽  
Vol 10 (13) ◽  
pp. 2761
Author(s):  
Tatiana Filonets ◽  
Maxim Solovchuk ◽  
Wayne Gao ◽  
Tony Wen-Hann Sheu
Keyword(s):  
Mathematical Model ◽  
Reproduction Number ◽  
Control Measures ◽  
Disease Spread ◽  
Contact Tracing ◽  
Imported Cases ◽  
Quantitative Results ◽  
High Level ◽  
Pharmaceutical Interventions ◽  
The Basic Reproduction Number

Case isolation and contact tracing are two essential parts of control measures to prevent the spread of COVID-19, however, additional interventions, such as mask wearing, are required. Taiwan successfully contained local COVID-19 transmission after the initial imported cases in the country in early 2020 after applying the above-mentioned interventions. In order to explain the containment of the disease spread in Taiwan and understand the efficiency of different non-pharmaceutical interventions, a mathematical model has been developed. A stochastic model was implemented in order to estimate the effectiveness of mask wearing together with case isolation and contact tracing. We investigated different approaches towards mask usage, estimated the effect of the interventions on the basic reproduction number (R0), and simulated the possibility of controlling the outbreak. With the assumption that non-medical and medical masks have 20% and 50% efficiency, respectively, case isolation works on 100%, 70% of all people wear medical masks, and R0 = 2.5, there is almost 80% probability of outbreak control with 60% contact tracing, whereas for non-medical masks the highest probability is only about 20%. With a large proportion of infectiousness before the onset of symptoms (40%) and the presence of asymptomatic cases, the investigated interventions (isolation of cases, contact tracing, and mask wearing by all people), implemented on a high level, can help to control the disease spread. Superspreading events have also been included in our model in order to estimate their impact on the outbreak and to understand how restrictions on gathering and social distancing can help to control the outbreak. The obtained quantitative results are in agreement with the empirical COVID-19 data in Taiwan.

scholarly journals Observations and conversations: how communities learn about infection risk can impact the success of non-pharmaceutical interventions against epidemics

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew J. Silk ◽  
Simon Carrignon ◽  
R. Alexander Bentley ◽  
Nina H. Fefferman
Keyword(s):  
Social Norms ◽  
Social Communication ◽  
Physical Contact ◽  
Disease Spread ◽  
Contact Tracing ◽  
Accurate Information ◽  
The Social ◽  
Communication Layer ◽  
Perceived Social Norms ◽  
Pharmaceutical Interventions

Abstract Background Individual behavioural decisions are responses to a person’s perceived social norms that could be shaped by both their physical and social environment. In the context of the COVID-19 pandemic, these environments correspond to epidemiological risk from contacts and the social construction of risk by communication within networks of friends. Understanding the circumstances under which the influence of these different social networks can promote the acceptance of non-pharmaceutical interventions and consequently the adoption of protective behaviours is critical for guiding useful, practical public health messaging. Methods We explore how information from both physical contact and social communication layers of a multiplex network can contribute to flattening the epidemic curve in a community. Connections in the physical contact layer represent opportunities for transmission, while connections in the communication layer represent social interactions through which individuals may gain information, e.g. messaging friends. Results We show that maintaining focus on awareness of risk among each individual’s physical contacts promotes the greatest reduction in disease spread, but only when an individual is aware of the symptoms of a non-trivial proportion of their physical contacts (~ ≥ 20%). Information from the social communication layer without was less useful when these connections matched less well with physical contacts and contributed little in combination with accurate information from physical contacts. Conclusions We conclude that maintaining social focus on local outbreak status will allow individuals to structure their perceived social norms appropriately and respond more rapidly when risk increases. Finding ways to relay accurate local information from trusted community leaders could improve mitigation even where more intrusive/costly strategies, such as contact-tracing, are not possible.

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