scholarly journals Deterministic and stochastic models of infection spread and testing in an isolated contingent

2021 ◽  
pp. 57-67
Author(s):  
Anatoliy V. Chigarev ◽  
Michael A. Zhuravkov ◽  
Vitaliy A. Chigarev
Keyword(s):  
Space Dimension ◽  
Weather Forecast ◽  
Sir Model ◽  
Kolmogorov Equations ◽  
Process Dynamics ◽  
Fuzzy Methods ◽  
Distributed Parameters ◽  
Systems Models ◽  
And Control ◽  
Modelling Methods

The mathematical SIR model generalisation for description of the infectious process dynamics development by adding a testing model is considered. The proposed procedure requires the expansion of states’ space dimension due to variables that cannot be measured directly, but allow you to more adequately describe the processes that occur in real situations. Further generalisation of the SIR model is considered by taking into account randomness in state estimates, forecasting, which is achieved by applying the stochastic differential equations methods associated with the application of the Fokker – Planck – Kolmogorov equations for posterior probabilities. As COVID-19 practice has shown, the widespread use of modern means of identification, diagnosis and monitoring does not guarantee the receipt of adequate information about the individual’s condition in the population. When modelling real epidemic processes in the initial stages, it is advisable to use heuristic modelling methods, and then refine the model using mathematical modelling methods using stochastic, uncertain-fuzzy methods that allow you to take into account the fact that flow, decision-making and control occurs in systems with incomplete information. To develop more realistic models, spatial kinetics must be taken into account, which, in turn, requires the use of systems models with distributed parameters (for example, models of continua mechanics). Obviously, realistic models of epidemics and their control should include models of economic, sociodynamics. The problems of forecasting epidemics and their development will be no less difficult than the problems of climate change forecasting, weather forecast and earthquake prediction.

scholarly journals SIR Model for Dengue Disease with Effect of Dengue Vaccination

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Pratchaya Chanprasopchai ◽  
I. Ming Tang ◽  
Puntani Pongsumpun
Keyword(s):  
Mathematical Model ◽  
Mortality Rate ◽  
Dengue Virus ◽  
Numerical Solutions ◽  
Specific Treatment ◽  
Sir Model ◽  
Dengue Vaccine ◽  
Dengue Disease ◽  
Modelling Methods

The dengue disease is caused by dengue virus, and there is no specific treatment. The medical care by experienced physicians and nurses will save life and will lower the mortality rate. A dengue vaccine to control the disease is available in Thailand since late 2016. A mathematical model would be an important way to analyze the effects of the vaccination on the transmission of the disease. We have formulated an SIR (susceptible-infected-recovered) model of the transmission of the disease which includes the effect of vaccination and used standard dynamical modelling methods to analyze the effects. The equilibrium states and their stabilities are investigated. The trajectories of the numerical solutions plotted into the 2D planes and 3D spaces are presented. The main contribution is determining the role of dengue vaccination in the model. From the analysis, we find that there is a significant reduction in the total hospitalization time needed to treat the illness.

scholarly journals Spatial Analysis and Geographic Factors Associated with Cutaneous Leishmaniasis in Southern Iran

2020 ◽  
Author(s):  
Mehdi Sharafi ◽  
Zahra Poormotaseri ◽  
Jalal Karimi ◽  
Shahab Rezaeian ◽  
Seyedeh Leila Dehghani ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Cutaneous Leishmaniasis ◽  
Weather Forecast ◽  
Weather Conditions ◽  
Least Square ◽  
Environmental Data ◽  
Maximum Temperature ◽  
Fars Province ◽  
Disease Pattern ◽  
And Control

Introduction: This study aimed to determine the hotspot areas for Cutaneous Leishmaniasis (CL) in Fasa city and assess the relations between the geographical factors with CL incidence using spatial analysis. Materials and Methods: This ecological study was conducted in Fasa city, data of the CL disease such as the total number of CL cases and the population at risk from 2009 to 2014. Weather conditions' data including the means of temperature, humidity, rainfall, sunny days, rainy days, and evaporation were collected from the weather forecast centers in Fars province. The disease cases' information such as the number of disease cases was collected from all healthcare centers located in Fasa City. Ordinary Least Square (OLS) and Global Moran’s Index (GMI) were used to assess the associations of the various environmental variables with CL incidence and to map clustering of CL cases across the region. Results: The cumulative incidence of CL was 16 per 10,000 populations during a six-year period. The results showed the southern area of Fasa as a hotspot area which is considered as hyperendemic foci for CL. OLS revealed a high incidence of CL in areas with maximum temperature, mean of temperature, mean of evaporation, sunny days and wind velocity. Conclusion: A spatial disease pattern was found in the present study. Hence, substantial consideration to environmental data leads to not only suitable protection against CL but also designing a suitable measure for the prevention and control of the disease.

scholarly journals The attainment of herd immunity with a reducing risk of contact infection in modelling 

2021 ◽  
Author(s):  
Kian Boon Law ◽  
Kalaiarasu M Peariasamy ◽  
Hishamshah Ibrahim ◽  
Noor Hisham Abdullah
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Herd Immunity ◽  
Sir Model ◽  
Mixed Population ◽  
Transmission Model ◽  
Force Of Infection ◽  
Initial Stage ◽  
And Control ◽  
Model Features

Abstract The risk of contact infection among susceptible individuals in a randomly mixed population can be reduced by the presence of immune individuals and this principle forms the fundamental of herd immunity. The conventional susceptible-infectious-recovered (SIR) model features an infection-induced herd immunity model, but does not include the reducing risk of contact infection among susceptible individuals in the transmission model, therefore tends to overestimate the transmission dynamics of infectious diseases. Here we show that the reducing risk of contact infection among susceptible individuals can be achieved by incorporating the proportion of susceptible individuals (model A) or the inverse of proportion of recovered individuals (model B) in the force of infection of the SIR model. We numerically simulated the conventional SIR model and both new SIR models A and B under the exact condition with a basic reproduction number of 3·0. Prior to the numerical simulation, the threshold for the eradication of infectious disease through herd immunity was expected to be 0·667 (66·7%) for all three models. All three models performed likewise at the initial stage of disease transmission. In the conventional SIR model, the infectious disease subsided when 94·0 % of the population had been infected and recovered, way above the expected threshold for eradication and control of the infectious disease. Both models A and B simulated the infectious disease to diminish when 66·7% and 75·6% of the population had been infected, showing herd immunity might protect more susceptible individuals from the infectious disease as compared to the projection generated by the conventional SIR. Our study shows that model A provides a better framework for modelling herd immunity through vaccination, while model B provides a better framework for modelling herd immunity through infection. Both models overcome the insufficiency of the conventional SIR model in attaining the effect of herd immunity in modelling outputs, which is important and relevant for modelling infectious disease, such as the COVID-19 in a randomly mixed population.

scholarly journals Predicting the early depleting transmission dynamics of COVID-19: A time-varying SIR model

2020 ◽  
Author(s):  
Kian Boon Law ◽  
Kalaiarasu M Peariasamy ◽  
Balvinder Singh Gill ◽  
Sarbhan Singh Lakha Singh ◽  
Bala Murali Sundram ◽  
...  
Keyword(s):  
Temporal Trends ◽  
Model Fitting ◽  
Sir Model ◽  
Transmission Dynamics ◽  
Time Varying ◽  
Force Of Infection ◽  
Planning And Control ◽  
The Mean ◽  
And Control ◽  
Absolute Percent Error

Abstract The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I), and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, βt, and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily, and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4·7% each day with a decreased capacity of 40%. For 7–day and 14–day projections, the modified SIR model accurately predicted I total, I, and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19.

Evolutionary Control of Helicopter Hovering Based on Genetic Programming

2013 ◽  
pp. 1-19
Author(s):  
Dimitris C. Dracopoulos ◽  
Dimitrios Effraimidis
Keyword(s):  
Reinforcement Learning ◽  
Genetic Programming ◽  
Control Problems ◽  
Complex Control ◽  
Fuzzy Methods ◽  
Neuro Fuzzy ◽  
Complex Control System ◽  
And Control ◽  
Intelligent Controllers ◽  
Hovering Control

Computational intelligence techniques such as neural networks, fuzzy logic, and hybrid neuroevolutionary and neuro-fuzzy methods have been successfully applied to complex control problems in the last two decades. Genetic programming, a field under the umbrella of evolutionary computation, has not been applied to a sufficiently large number of challenging and difficult control problems, in order to check its viability as a general methodology to such problems. Helicopter hovering control is considered a challenging control problem in the literature and has been included in the set of benchmarks of recent reinforcement learning competitions for deriving new intelligent controllers. This chapter shows how genetic programming can be applied for the derivation of controllers in this nonlinear, high dimensional, complex control system. The evolved controllers are compared with a neuroevolutionary approach that won the first position in the 2008 helicopter hovering reinforcement learning competition. The two approaches perform similarly (and in some cases GP performs better than the winner of the competition), even in the case where unknown wind is added to the dynamic system and control is based on structures evolved previously, that is, the evolved controllers have good generalization capability.

Smart Temperature Sensors

2019 ◽  
pp. 223-250
Author(s):  
Ashraf A. Zaher
Keyword(s):  
Weather Forecast ◽  
Temperature Sensors ◽  
Consumer Electronics ◽  
Data Logging ◽  
Industrial Processing ◽  
Wide Range ◽  
Scada Systems ◽  
New Generation ◽  
And Control ◽  
Measurement And Control

Many real-world applications depend on temperature sensing and/or control. This includes a wide range of industrial processes, chemical reactors, and SCADA systems, in addition to other physical, mechanical, and biological systems. With the advancement of technology, it became possible to produce a new generation of smart and compact temperature sensors, which are capable of providing digital outputs that are more accurate, robust, and easily interfaced and integrated into measurement and control systems. This chapter first surveys traditional analog temperature sensors, such as RTDs and thermocouples, to provide a strong motivation for the need to adopt better and smarter techniques that mainly rely on digital technology (e.g., CMOS designs). Different interfacing techniques that do not need ADCs are introduced, including the programmable Arduino microcontrollers. Different applications will be explored that include automotive accessories, weather forecast, healthcare, industrial processing, firefighting, and consumer electronics. Both wired and wireless technologies, including the IoT, will be investigated as means for transmitting the sensed data for further processing and data logging. A special case study to provide information redundancy in industrial SCADA systems will be analyzed to illustrate the advantages and limitations of smart temperature sensors. The chapter concludes with a summary of the design effort, accuracy, performance, and cost effectiveness of smart temperature sensors while highlighting future trends in this field for different applications.

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