scholarly journals Modeling Early Phases of COVID-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion

2021 ◽  
Vol 9 ◽  
Author(s):  
Daniela Gandolfi ◽  
Giuseppe Pagnoni ◽  
Tommaso Filippini ◽  
Alessia Goffi ◽  
Marco Vinceti ◽  
...  
Keyword(s):  
Predictive Validity ◽  
Computational Models ◽  
Control Strategies ◽  
Causal Modeling ◽  
Analytic Framework ◽  
Social Responses ◽  
Italian Regions ◽  
And Control ◽  
Early Phases ◽  
Intense Debate

The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Among these tools, the analytic framework known as “dynamic causal modeling” (DCM) has been applied to the COVID-19 pandemic, shedding new light on the factors underlying the dynamics of the outbreak. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak, and analyzed the predictive validity of the model and also its suitability in highlighting the hidden factors governing the pandemic diffusion. By taking into account data from the beginning of the pandemic, the model could faithfully predict the dynamics of outbreak diffusion varying from region to region. The DCM appears to be a reliable tool to investigate the mechanisms governing the spread of the SARS-CoV-2 to identify the containment and control strategies that could efficiently be used to counteract further waves of infection.

scholarly journals Dynamic causal modeling of the COVID-19 pandemic in northern Italy predicts possible scenarios for the second wave

2020 ◽  
Author(s):  
Daniela Gandolfi ◽  
Giuseppe Pagnoni ◽  
Tommaso Filippini ◽  
Alessia Goffi ◽  
Marco Vinceti ◽  
...  
Keyword(s):  
Control Strategies ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Model Inversion ◽  
Italian Regions ◽  
Potential Factors ◽  
Using Data ◽  
The One ◽  
And Control ◽  
Second Wave

The COVID-19 pandemic has sparked an intense debate about the factors underlying the dynamics of the outbreak. Mitigating virus spread could benefit from reliable predictive models that inform effective social and healthcare strategies. Crucially, the predictive validity of these models depends upon incorporating behavioral and social responses to infection that underwrite ongoing social and healthcare strategies. Formally, the problem at hand is not unlike the one faced in neuroscience when modelling brain dynamics in terms of the activity of a neural network: the recent COVID19 pandemic develops in epicenters (e.g. cities or regions) and diffuses through transmission channels (e.g., population fluxes). Indeed, the analytic framework known as "Dynamic Causal Modeling" (DCM) has recently been applied to the COVID-19 pandemic, shedding new light on the mechanisms and latent factors driving its evolution. The DCM approach rests on a time-series generative model that provides - through Bayesian model inversion and inference - estimates of the factors underlying the progression of the pandemic. We have applied DCM to data from northern Italian regions, which were the first areas in Europe to contend with the COVID-19 outbreak. We used official data on the number of daily confirmed cases, recovered cases, deaths and performed tests. The model - parameterized using data from the first months of the pandemic phase - was able to accurately predict its subsequent evolution (including social mobility, as assessed through GPS monitoring, and seroprevalence, as assessed through serologic testing) and revealed the potential factors underlying regional heterogeneity. Importantly, the model predicts that a second wave could arise due to a loss of effective immunity after about 7 months. This second wave was predicted to be substantially worse if outbreaks are not promptly isolated and contained. In short, dynamic causal modelling appears to be a reliable tool to shape and predict the spread of the COVID-19, and to identify the containment and control strategies that could efficiently counteract its second wave, until effective vaccines become available.

Understanding and predicting the global spread of emergent infectious diseases

2014 ◽  
Vol 22 (3) ◽  
Author(s):  
Dirk Brockmann
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Network Theory ◽  
Computational Models ◽  
Control Strategies ◽  
Global Scale ◽  
Disease Spread ◽  
Complex Network Theory ◽  
Global Spread ◽  
And Control

AbstractThe emergence and global spread of human infectious diseases has become one of the most serious public health threats of the 21st century. Sophisticated computer simulations have become a key tool for understanding and predicting disease spread on a global scale. Combining theoretical insights from nonlinear dynamics, stochastic processes and complex network theory these computational models are becoming increasingly important in the design of efficient mitigation and control strategies and for public health in general.

scholarly journals Impacts of Reopening Strategies for COVID-19 Epidemic: A Modeling Study in Piedmont Region

2020 ◽  
Author(s):  
Simone Pernice ◽  
Paolo Castagno ◽  
Linda Marcotulli ◽  
Milena Maria Maule ◽  
Lorenzo Richiardi ◽  
...  
Keyword(s):  
Computational Models ◽  
Control Strategies ◽  
Control Measures ◽  
New Wave ◽  
Policy Makers ◽  
Individual Level ◽  
Infection Control Measures ◽  
Proposed Model ◽  
Italian Regions ◽  
The Government

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), the causative agent of the coronavirus disease 19 (COVID-19), is a highly transmittable virus. Since the first person-to-person transmission of SARS-CoV-2 was reported in Italy on February 21 st , 2020, the number of people infected with SARS-COV-2 increased rapidly, mainly in northern Italian regions, including Piedmont. A strict lockdown was imposed on March 21 st until May 4 th when a gradual relaxation of the restrictions started. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to understand the spread of the diseases and to evaluate social measures to counteract, mitigate or delay the spread of the epidemic. Methods This study presents an extended version of the Susceptible-Exposed-Infected-Removed-Susceptible (SEIRS) model accounting for population age structure. The infectious population is divided into three sub-groups: (i) undetected infected individuals, (ii) quarantined infected individuals and (iii) hospitalized infected individuals. Moreover, the strength of the government restriction measures and the related population response to these are explicitly represented in the model. Results The proposed model allows us to investigate different scenarios of the COVID-19 spread in Piedmont and the implementation of different infection-control measures and testing approaches. The results show that the implemented control measures have proven effective in containing the epidemic, mitigating the potential dangerous impact of a large proportion of undetected cases. We also forecast the optimal combination of individual-level measures and community surveillance to contain the new wave of COVID-19 spread after the re-opening work and social activities. Conclusions Our model is an effective tool useful to investigate different scenarios and to inform policy makers about the potential impact of different control strategies. This will be crucial in the upcoming months, when very critical decisions about easing control measures will need to be taken.

scholarly journals Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Simone Pernice ◽  
Paolo Castagno ◽  
Linda Marcotulli ◽  
Milena Maria Maule ◽  
Lorenzo Richiardi ◽  
...  
Keyword(s):  
Computational Models ◽  
Control Strategies ◽  
Control Measures ◽  
New Wave ◽  
Policy Makers ◽  
Individual Level ◽  
Infection Control Measures ◽  
Proposed Model ◽  
Italian Regions ◽  
The Government

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), the causative agent of the coronavirus disease 19 (COVID-19), is a highly transmittable virus. Since the first person-to-person transmission of SARS-CoV-2 was reported in Italy on February 21st, 2020, the number of people infected with SARS-COV-2 increased rapidly, mainly in northern Italian regions, including Piedmont. A strict lockdown was imposed on March 21st until May 4th when a gradual relaxation of the restrictions started. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to understand the spread of the diseases and to evaluate social measures to counteract, mitigate or delay the spread of the epidemic. Methods This study presents an extended version of the Susceptible-Exposed-Infected-Removed-Susceptible (SEIRS) model accounting for population age structure. The infectious population is divided into three sub-groups: (i) undetected infected individuals, (ii) quarantined infected individuals and (iii) hospitalized infected individuals. Moreover, the strength of the government restriction measures and the related population response to these are explicitly represented in the model. Results The proposed model allows us to investigate different scenarios of the COVID-19 spread in Piedmont and the implementation of different infection-control measures and testing approaches. The results show that the implemented control measures have proven effective in containing the epidemic, mitigating the potential dangerous impact of a large proportion of undetected cases. We also forecast the optimal combination of individual-level measures and community surveillance to contain the new wave of COVID-19 spread after the re-opening work and social activities. Conclusions Our model is an effective tool useful to investigate different scenarios and to inform policy makers about the potential impact of different control strategies. This will be crucial in the upcoming months, when very critical decisions about easing control measures will need to be taken.

scholarly journals Impacts of Reopening Strategies for COVID-19 Epidemic: A Modeling Study in Piedmont Region

2020 ◽  
Author(s):  
Simone Pernice ◽  
Paolo Castagno ◽  
Linda Marcotulli ◽  
Milena Maria Maule ◽  
Lorenzo Richiardi ◽  
...  
Keyword(s):  
Computational Models ◽  
Control Strategies ◽  
Control Measures ◽  
New Wave ◽  
Policy Makers ◽  
Individual Level ◽  
Infection Control Measures ◽  
Proposed Model ◽  
Italian Regions ◽  
The Government

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), the causative agent of the coronavirus disease 19 (COVID-19), is a highly transmittable virus. Since the first person-to-person transmission of SARS-CoV-2 was reported in Italy on February 21 st , 2020, the number of people infected with SARS-COV-2 increased rapidly, mainly in northern Italian regions, including Piedmont. A strict lockdown was imposed on March 21 st until May 4 th when a gradual relaxation of the restrictions started. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to understand the spread of the diseases and to evaluate social measures to counteract, mitigate or delay the spread of the epidemic. Methods This study presents an extended version of the Susceptible-Exposed-Infected-Removed-Susceptible (SEIRS) model accounting for population age structure. The infectious population is divided into three sub-groups: (i) undetected infected individuals, (ii) quarantined infected individuals and (iii) hospitalized infected individuals. Moreover, the strength of the government restriction measures and the related population response to these are explicitly represented in the model. Results The proposed model allows us to investigate different scenarios of the COVID-19 spread in Piedmont and the implementation of different infection-control measures and testing approaches. The results show that the implemented control measures have proven effective in containing the epidemic, mitigating the potential dangerous impact of a large proportion of undetected cases. We also forecast the optimal combination of individual-level measures and community surveillance to contain the new wave of COVID-19 spread after the re-opening work and social activities. Conclusions Our model is an effective tool useful to investigate different scenarios and to inform policy makers about the potential impact of different control strategies. This will be crucial in the upcoming months, when very critical decisions about easing control measures will need to be taken.

Effective infection prevention and control strategies in a large accredited psychiatric facility in Singapore

2020 ◽  
Author(s):  
Daniel Poremski ◽  
Sandra Henrietta Subner ◽  
Grace Lam Fong Kin ◽  
Raveen Dev Ram Dev ◽  
Mok Yee Ming ◽  
...  
Keyword(s):  
Mental Health ◽  
Prevention And Control ◽  
Infection Prevention ◽  
Control Strategies ◽  
Infection Prevention And Control ◽  
Psychiatric Facility ◽  
Essential Services ◽  
Community Transmission ◽  
And Control

The Institute of Mental Health in Singapore continues to attempt to prevent the introduction of COVID-19, despite community transmission. Essential services are maintained and quarantine measures are currently unnecessary. To help similar organizations, strategies are listed along three themes: sustaining essential services, preventing infection, and managing human and consumable resources.

Optimization and Control Petroleum Refinery Wastewater Treatment Systems — Status, Trends and Needs

1989 ◽  
Vol 24 (3) ◽  
pp. 463-477
Author(s):  
Stephen G. Nutt
Keyword(s):  
Wastewater Treatment ◽  
Process Control ◽  
Petroleum Refinery ◽  
Control Strategies ◽  
Refinery Wastewater ◽  
Statistical Process ◽  
Petroleum Refinery Wastewater ◽  
Optimization And Control ◽  
And Control ◽  
Wastewater Treatment Systems

Abstract Based on discussions in workshop sessions, several recurring themes became evident with respect to the optimization and control of petroleum refinery wastewater treatment systems to achieve effective removal of toxic contaminants. It was apparent that statistical process control (SPC) techniques are finding more widespread use and have been found to be effective. However, the implementation of real-time process control strategies in petroleum refinery wastewater treatment systems is in its infancy. Considerable effort will need to be expended to demonstrate the practicality of on-line sensors, and the utility of automated process control in petroleum refinery wastewater treatment systems. This paper provides a summary of the discussions held at the workshop.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

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