Predictive model for COVID 19 curve - An evolutionary approach

Genetic Algorithm based Predictive model for COVID 19 – Theoretical Model based on an evolutionary approach

10.21203/rs.3.rs-107714/v2 ◽

2021 ◽

Author(s):

Srikanth Rangarajan ◽

Srikanth Poranki ◽

Bahgat Sammakia

Keyword(s):

Genetic Algorithm ◽

Real Time ◽

State Of The Art ◽

Contact Tracing ◽

Reproductive Number ◽

Time Data ◽

Proposed Model ◽

Fitness Value ◽

Real Time Data ◽

Novel Method

Abstract In this manuscript we propose a novel method that models the evolution, spread and transmission of COVID 19 pandemic. The proposed model is inspired partly from the evolutionary based state of the art genetic algorithm. The rate of virus evolution, spread and transmission of the COVID 19 and its associated recovery and death rate are modeled using the principle inspired from evolutionary algorithm. Furthermore, the interaction within a community and interaction outside the community is modeled. Using this model, the maximum healthcare threshold is fixed as a constraint. Our evolutionary based model distinguishes between individuals in the population depending on the severity of their symptoms/infection based on the fitness value of the individuals. There is a need to differentiate between virus infected diagnosed (Self isolated) and virus infected non-diagnosed (Highly interacting) sub populations/group. In this study the model results does not compare the number outcomes with any actual real time data based curves. However, the results from the model demonstrates that a strict lockdown, social-distancing measures in conjunction with more number of testing and contact tracing is required to flatten the ongoing COVID-19 pandemic curve. A reproductive number of 2.4 during the initial spread of virus is predicted from the model for the randomly considered population. The proposed model has the potential to be further fine-tuned and matched accurately against real time data.

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Genetic Algorithm based Predictive model for COVID 19 – Theoretical Model based on an evolutionary approach

10.21203/rs.3.rs-107714/v3 ◽

2021 ◽

Author(s):

Srikanth Rangarajan ◽

Srikanth Poranki ◽

Bahgat Sammakia

Keyword(s):

Genetic Algorithm ◽

Real Time ◽

Theoretical Method ◽

Contact Tracing ◽

Reproductive Number ◽

Time Data ◽

Proposed Model ◽

Fitness Value ◽

Real Time Data ◽

Unique Part

Abstract In this manuscript we propose a novel theoretical method that models the evolution, spread and transmission of COVID 19 pandemic. The proposed model is inspired partly from the evolutionary based state of the art genetic algorithm. The rate of virus evolution, spread and transmission of the COVID 19 and its associated recovery and death rate are modeled using the principle inspired from evolutionary algorithm. Furthermore, the interaction within a community and interaction outside the community is modeled. The constraint with respect to interaction has been implemented by a machine learning type algorithm and becomes the unique part of our study . Using this model, the maximum healthcare threshold is fixed as a constraint. Our evolutionary based model distinguishes between individuals in the population depending on the severity of their symptoms/infection based on the fitness value of the individuals. There is a need to differentiate between virus infected diagnosed (Self isolated) and virus infected non-diagnosed (Highly interacting) sub populations/group. In this study the model results does not compare the number outcomes with any actual real time data based curves. However, the results from the model demonstrates that a strict lockdown, social-distancing measures in conjunction with more number of testing and contact tracing is required to flatten the ongoing COVID-19 pandemic curve. A reproductive number of 2.4 during the initial spread of virus is predicted from the model for the randomly considered population. The proposed model has the potential to be further fine-tuned and matched accurately against real time data.

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Multivariate Data Quality Enhancement by Ranked Imputation

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c9027.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 2387-2391

Keyword(s):

State Of The Art ◽

Multivariate Data ◽

Quality Enhancement ◽

Data Imputation ◽

Time Data ◽

Proposed Model ◽

Real Time Data ◽

Attribute Order ◽

Negative Impacts

Organizational decisions are based on data-based-analysis and predictions. Effective decisions require accurate predictions, which in-turn depend on the quality of the data. Real time data is prone to inconsistencies, which exhibit negative impacts on the quality of the predictions. This mandates the need for data imputation techniques. This work presents a prediction-based data imputation technique, Rank Based Multivariate Imputation (RBMI) that operates on multivariate data. The proposed model is composed of the ranking phase and the imputation phase. Ranking dictates, the attribute order in which imputation is to be performed. The proposed model utilizes tree-based approach for the actual imputation process. Experiments were performed on Pima, a diabetes dataset. The data was amputed in range between 5% - 30%. The obtained results were compared with existing state-of-the-art models in terms of MAE and MSE levels. The proposed RBMI model exhibits a reduction of 0.03 in MAE levels and 0.001 in MSE levels.

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Reliability Assessment of State-of-the-Art Real-Time Data Reception and Analysis System for the Indian Seas

Marine Technology Society Journal ◽

10.4031/mtsj.49.3.6 ◽

2015 ◽

Vol 49 (3) ◽

pp. 127-134 ◽

Cited By ~ 5

Author(s):

Ramasamy Venkatesan ◽

Subramaniam Ramasundaram ◽

Ranganathan Sundar ◽

Narayanaswamy Vedachalam ◽

Rajagopalan Lavanya ◽

...

Keyword(s):

Real Time ◽

State Of The Art ◽

Time Data ◽

Control Center ◽

System Integrity ◽

Societal Needs ◽

Real Time Data ◽

Safety And Reliability ◽

Analysis System ◽

Early Restoration

Abstract This paper describes the Advanced Data Reception and Analysis System (ADDRESS) used for monitoring Indian Ocean moored buoys and the reliability analysis done on the data reception and dissemination performances. The system needs to be highly reliable, as the critical real-time data received from the tsunami and meteorological buoys are used for societal protection decision support systems during tsunami and cyclonic events. Driven by demanding societal needs, the developed ADDRESS with a 24/7 manned and automated Mission Control Center and the implemented system integrity proof test interval is found to conform with the IEC 61508 SIL 4 safety and reliability levels and to have an availability of 99.9938%. The graphical display capabilities developed are used for buoy deployment support, early restoration of failed moorings, and for up-keep of system components operating in remote harsh offshore environments by monitoring their performance over time.

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A Novel Method to Compress Real-Time Process Data

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.430-432.1298 ◽

2012 ◽

Vol 430-432 ◽

pp. 1298-1301

Author(s):

Xiao Jian Zheng

Keyword(s):

Real Time ◽

Sampling Interval ◽

Compression Algorithm ◽

Time Process ◽

Time Data ◽

Process Data ◽

Real Time Data ◽

Novel Method

Most existing real-time data compressing algorithms are focused on dynamic and inconstancy of the process data, but a basic observation is ignored with some unexpectedness: on condition that sampling interval is not large, difference between amplitudes of real-time process data from two neighboring samples is relatively small, and most of data amplitudes are in the same range. In this paper we propose a compression algorithm based on the observation and experimentally evaluate the proposed approach and demonstrate that our algorithm is promising and efficient.

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Hybrid online model based multi seasonal decompose for short-term electricity load forecasting using ARIMA and online RNN

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189884 ◽

2021 ◽

pp. 1-14

Author(s):

Nguyen Quang Dat ◽

Nguyen Thi Ngoc Anh ◽

Nguyen Nhat Anh ◽

Vijender Kumar Solanki

Keyword(s):

Time Series ◽

Real Time ◽

Load Forecasting ◽

Seasonal Patterns ◽

Time Data ◽

Short Term ◽

Proposed Model ◽

Real Time Data ◽

Electricity Load ◽

Electricity Load Forecasting

Short-term electricity load forecasting (STLF) plays a key role in operating the power system of a nation. A challenging problem in STLF is to deal with real-time data. This paper aims to address the problem using a hybrid online model. Online learning methods are becoming essential in STLF because load data often show complex seasonality (daily, weekly, annual) and changing patterns. Online models such as Online AutoRegressive Integrated Moving Average (Online ARIMA) and Online Recurrent neural network (Online RNN) can modify their parameters on the fly to adapt to the changes of real-time data. However, Online RNN alone cannot handle seasonality directly and ARIMA can only handle a single seasonal pattern (Seasonal ARIMA). In this study, we propose a hybrid online model that combines Online ARIMA, Online RNN, and Multi-seasonal decomposition to forecast real-time time series with multiple seasonal patterns. First, we decompose the original time series into three components: trend, seasonality, and residual. The seasonal patterns are modeled using Fourier series. This approach is flexible, allowing us to incorporate multiple periods. For trend and residual components, we employ Online ARIMA and Online RNN respectively to obtain the predictions. We use hourly load data of Vietnam and daily load data of Australia as case studies to verify our proposed model. The experimental results show that our model has better performance than single online models. The proposed model is robust and can be applied in many other fields with real-time time series.

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Delayed dynamics of SIR model for COVID-19 spread forecasting

10.21203/rs.3.rs-101284/v1 ◽

2020 ◽

Author(s):

Hameed K. Ebraheem ◽

Nizar Alkhateeb ◽

Hussein Badran ◽

Ali Hajjiah ◽

Ebraheem Sultan

Keyword(s):

Real Time ◽

Geographical Area ◽

Sir Model ◽

Accurate Prediction ◽

World Health ◽

Reproductive Number ◽

Time Data ◽

New Model ◽

The World ◽

Real Time Data

Abstract BackgroundThe global spread of the COVID-19 pandemic has been one of the most challenging tasks the world has faced since the last pandemic outbreak of 1918. Early on countries felt the strength and persistence of the virus infections spreading with no means of estimating the dispersion rates. Officials in infected countries followed several guidelines set by the World Health Organization (WHO) to try and flatten the infection curve and maintain a low number of infectives. Nonetheless, the virus kept on spreading with impunity and all predictions of how containments or peak detections have been a fail so far. Therefore, a need for a more accurate model to predict the peaking of infections and help guide officials on what best to enact as a measure of public health safety from a multitude of choices outlined by the WHO. Earlier studies of compartmental model of Susceptible-Infected-Recovered (SIR) did not predict the peaking of a hot spots flairs of viral infections and a new model needed to provide a more realistic results to serve public officials battling the pandemic worldwideMethodsA new modified SIR model which incorporates appropriate delay parameters leading to a more precise prediction of COVID-19 real time data. The predictions of the new model are compared to real data obtained from four countries, namely Germany, Italy, Kuwait, and Oman. Two included delay periods for incubation and recovery within the SIR model produces a sensible and more accurate representation of the real time data. The reproductive number 𝑅0 is defined for the model for values of recovery time delay 𝜏2 of the infective case.ResultsIncorporating two delay periods that corresponds to the duration of the incubational and recovery periods measured for COVID-19 gives a more accurate prediction of the peak pandemic infections per geographical area. The parameter variations in the model 𝛽,𝛾,𝛼,𝜏1,𝑎𝑛𝑑 𝜏2 makeup different cases corresponding to different situations. The variations are estimated a priori based on what is being observed and collected data of an infected region to give officials better guidelines on what health policies should be enacted in the future.2 of 15ConclusionsThe empirical data provided by WHO show that the proposed new SIR model gives a better more accurate prediction of COVID-19 pandemic spreading curve. The model is shown to closely fit real time data for four countries. Simulation results are consistent with data and generated curves are well constrained. The parameters can be varied and adjusted for producing and/or reproduction of numbers within the range of each country

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Relevance- and Frequency-Enabled Trip Planning Model Based on Socio-economic Status

Journal of Intelligent Systems ◽

10.1515/jisys-2016-0012 ◽

2017 ◽

Vol 26 (3) ◽

pp. 545-559 ◽

Cited By ~ 1

Author(s):

Anand Sesham ◽

P. Padmanabham ◽

A. Govardhan ◽

Rajesh Kulkarni

Keyword(s):

Real Time ◽

Pattern Mining ◽

Economic Status ◽

Planning Model ◽

Time Data ◽

Trip Planning ◽

Socio Economic Status ◽

Proposed Model ◽

Real Time Data ◽

Economic Constraints

AbstractPlanning a trip not only depends on the traveling cost, time, and path, but also on the socio-economic status of the traveler. This paper attempts to introduce a new trip planning model that is able to work on real-time data with multiple socio-economic constraints. The proposed trip planning model processes real-time data to extract the relevant socio-economic attributes; later, it mines the most frequent as well as the feasible attributes to plan the trip. The relevance of the socio-economic constraints is defined using correlations, whereas the frequent as well as the feasible attributes are mined through the sequential pattern mining approach. Real-time travel information of about 38,303 trips was acquired from the Indian city of Hyderabad, and the proposed model was subjected to experimentation. The proposed model maintained a substantial trade-off between multiple performance metrics, though the trip mean model performed statistically.

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