Videos-Webinar. SEIMR/R-S/OPT Epidemic Management Optimization Model. Multi: Infected States, Socio-Demographics Segments, Region Mobility & Vaccines

Next Webinars: 10/03/2021 (Spanish) - 24/03/2021 (English)

VIDEOS/WEBINARs: in Spanish & in English (17-24/02/2001)

Organizers:

• Instituto Tecnologico de Ciudad Madero
• DecisionWare

Video Webinar in English (24/02/2021)

Video Webinar in Spanish (17/02/2021)

PDF Presentation: https://www.doanalytics.net/Documents/Webinar-SEIMR-R-S-OPT-ML-February-2021.pdf 

Previous Webinar/Video in Spanish (23/12/2020)

General Epidemic Management Optimization Models

i)            SEIMR/R-S General Epidemic Simulation (a new approach to simulate epidemic process)

ii)           SEIMR/R-S/OPT General Epidemic Management Optimization Model

?            Epidemic, Control Policies & Vital Health Resources & Vaccination

?            Modeling Epidemic Regional & Socio-Demographic Parameters

?            Epidemic & Socio-Economic

iii)          SEIMR/R-S/ML Dynamic Machine Learning of Epidemic Parameters

(Epidemic State and Parameter Estimation Using Dynamic Machine Learning Based on a Dual Multi-State Kalman Filter, DMS-KF).

Some questions a high-complexity mathematical model can answer (topics of the webinar):

1.           There are different between a simple simulation model and a complex optimization model. What kind of model best protects humanity ?

 2.           Is it possible to integrate large simulation models of differential equations with highly non-linear optimization models ?

 3.           In an unknown process such as COVID-19, it will be advisable to estimate epidemic model parameters, biological and socio-economic, based on state estimation models, which integrate differential equations, control actions and uncertainty measurement systems, to improve knowledge and save more lives ?

 4.           What's better: a two-phase early vaccine? or a late single-phase vaccine ?

 5.           Which if the impact of the lack of knowledge in the vaccination process ?

 6.           In a pandemic whose vaccination period will be the order of one year, which is better: vaccinate the most vulnerable first ?, or vaccinate first those who for their work are more exposed to spread the infection ?

 7.          What do we need to be prepared for the next pandemic?

 These impacts can be measured in terms of avoided death, without involving the economic impact of vaccination policy.

The models SEIMR/R-S (simulation, JAVA) and SEIMR/R-S/OPT (optimization, GAMS, AMPL, PYTHON ) are available for free use in the cloud by organizations linked to COVID-19 pandemic control. Interested please contact [email protected]

Video Presentation Simulation Model (JAVA)

SEIMR/R-S/OPT - General Epidemic Optimization Model

Multi-Infected States - Multi Socio-Demographics Segments - Multi-Region Mobility - Multi Vaccines

• Management Epidemics: Optimization versus Simulation Models
• Optimal Control Policies & Health Resource Capacity Expansion
• Study Cases :

1. México: Tampico, C. Madero & Altamira
2. Colombia: Bogotá

PDF Document:

DW invite you to participate in the LinkedIn Group: Mathematical Modeling for Coronavirus Pandemic:

https://www.dhirubhai.net/groups/12383318/

The mathematical models developed may be used in the following environments:

• Government: Institutions of different countries that must decide regarding the management of the epidemic.
• Information Technology Companies: Private organizations that find highly complex mathematical tools useful to complement the services they offer to governments and/or other companies.
• Research Centers: individual researchers or groups of researchers, universities, research centers, who consider that the mathematical models incorporated in OPCHAIN-HEALTH : i) may be useful to complement their research or ii) who wish to perform joint optimization with DecisionWare (DW).
• Industrial Enterprises: private companies, or private public associations, that require support to provide their services during the time of the pandemic.

The presentation of OPCHAIN HEALTH is realized in the series of papers Management Epidemics using High Complexity Mathematical Modeling, that is composed for the following papers:

• PART I: SEIMR/R-S Public Health and High-Precision Decision-Making contains a general presentation of OPCHAIN HEALTH and its information system.

https://www.doanalytics.net/Documents/DW-OPCHAIN-Health-General-Framework.pdf 

• PART II: SEIMR/R-S General Epidemic Model. Theory, Validation and Applications contains the conceptualization of SEIMR/R-S that correspond to a generalized mathematical model of pandemics that enhances traditional, aggregated simulation models when considering inter-regional impacts in a macro region (conurbed); SEIMR/R-S also considers the impact of modeling the population divided into socio-demographic segments based on age and economic stratum.

https://www.doanalytics.net/Documents/DW-ITM-SEIMR-R-S-Epidemic-Model.pdf

• PART III: SEIMR/R-S Optimization Epidemic of Control Policies & Vital Health Resources Model, this document, it contains the mathematical conceptualization and algebraic formulation of the models: EPI&CON and EPI&HCM

https://www.doanalytics.net/Documents/DW-OPCHAIN-Health-Optimization-Epidemic-Model.pdf 

• PART IV: SEIMR/R-S Epidemic Optimization Model Applied to Bogotá City

https://www.doanalytics.net/Documents/DW-OPCHAIN-Health-Epidemic-Model-Case-Bogota.pdf 

• PART V: SEIMR/R-S Epidemic & Socio-Economic Model, it contains the mathematical conceptualization and algebraic formulation of the model

https://www.doanalytics.net/Documents/DW-OPCHAIN-Health-Epidemic-Economics-Model.pdf 

• DW invites to participate in the LinkedIn Group: Mathematical Modeling for Coronavirus Pandemic

https://www.dhirubhai.net/groups/12383318/

SEIMR/R-S/OPT Optimization Epidemic Management Model. Theory & Applications

Abstract.

OPCHAIN-HEALTH is a Decision Support System (DSS) composed of a suite of mathematical programming models of prescriptive analytics; to address epidemics, and more specifically the current COVID 19 pandemic. The aim is to support governments and private companies in decision-making in high complex environments.

The concept of the system is based on the goal of the planning/management process should be:

i)     Short term: minimize the number of deaths during the epidemic; and

ii)    Long term: maximize a quality of life index of the society.

OPCHAIN-HEALTH's fundamental justification is to use high complexity mathematical technologies as the basis for decision-making, and within those methodologies use mathematical optimization as the basic support for developing models of high complexity, since in the scientific environment it is well known that the optimization methodologies is the one that produces the most added value to the societies that use it; in the specific case of epidemic management, high-complexity mathematical methodologies will prevent the most deaths.

These models integrate what is called the hypothalamus of the decision-making system which consists of:

1.    Relational information system, based on a data-model that is common for all mathematical models, and

2.    High complex mathematical models which can be used in two ways:

i)     Coordinated, independent models in which the results of one model are converted into the input

ii)    data of another model, and/or

iii)   Integrated, when the models are integrated into a single mathematical model, with holistic vision, integrated by means of equations and coordination variables that endogenize the exchange of data between models.

The optimal approach is to be able to handle a single integrated model, which is not always possible.

The optimization model can be visualized as the coordinated integration of multiple modeling layers: i) epidemic model, represents the dynamic process of the evolution of epidemic, ii) control policies, allows the user to coordinate the policies of spread control of the epidemic with the simulation model, and iii) vital resource capacity model, allows to integrate an epidemic management model in order to determine the optimal management policies of according to the criteria of the decision makers. In addition, it is possible to include a fourth layer oriented to evaluate the "detail" economic impact of the pandemic. This document presents the modelling of the first three layers, leaving for a supplementary document the integrated epidemic-economy modeling.

The integrated model consists of two main models: i) the optimization of epidemic management, and ii) calculation of the parameters required by the optimization model. The calculation of the model parameters corresponds to an information pre-processing model that is made prior to the optimization model, its complexity can be very large to the extent that the decision-maker want to represent reality in the greatest degree of detail. In this case the preprocessing model consists of two sub-models: i) biological parameters and ii) socio-demographic parameters.

The construction of the data follows a "bottom-up" methodology, that is, the most detailed data is located and added, the calculations must be performed at the lowest level, so as not to lose the detail. This approach makes the difference with aggregated parameters (trending to average values) that are disaggregated with logical rules and that lose the detail of what happens at the level of each atom (in this case each socio-demographic segment in a region).

SEIMR/R-S/OPT epidemic optimization model was carried out in i) JAVA program and a GAMS and/or AMPL algebraic languages. It was developed using the cognitive robot OPTEX Optimization Expert System.

These programs may be used by any organization that considers the SEIMR/R-S/OPT will be useful for management the COVID-19 pandemic. 

DW invite to know about OPCHAIN-HEALTH a Decision Support System that integrates a set of Mathematical Models for Control Policies & Resources & Patients Allocation During Coronavirus Pandemic.

https://www.dhirubhai.net/pulse/opchain-health-dss-integrates-mathematical-models-during-velasquez/