Mathematical Population Dynamics and Epidemiology in Temporal and Spatio-Temporal Domains

Singh, Harkaran; Dhar, Joydip

doi:10.1201/9781351251709

Cited by 12 publications

(12 citation statements)

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“…The logistic growth model is a regression model that is widely used in epidemiology mathematical models to estimate the growth and decline rate of pathogens [14]. The model assumes an exponential growth at the beginning of the epidemic, followed by steady increase and finally ending with a declining growth rate.…”

Section: Logistics Growth Modelmentioning

confidence: 99%

Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models

et al. 2020

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The state of Kuwait is facing a substantial challenge in responding to the spread of the novel coronavirus 2019 (COVID-19). The government’s decision to repatriate stranded citizens back to Kuwait from various COVID-19 epicenters has generated a great concern. It has heightened the need for prediction models to estimate the epidemic size. Mathematical modeling plays a pivotal role in predicting the spread of infectious diseases to enable policymakers to implement various health and safety measures to contain the spread. This research presents a forecast of the COVID-19 epidemic size in Kuwait based on the confirmed data. Deterministic and stochastic modeling approaches were used to estimate the size of COVID-19 spread in Kuwait and determine its ending phase. In addition, various simulation scenarios were conducted to demonstrate the effectiveness of nonpharmaceutical intervention measures, particularly with time-varying infection rates and individual contact numbers. Results indicate that, with data until 19 April 2020 and before the repatriation plan, the estimated reproduction number in Kuwait is 2.2. It also confirms the efficiency of the containment measures of the state of Kuwait to control the spread even after the repatriation plan. The results show that a high contact rate among the population implies that the epidemic peak value is yet to be reached and that more strict intervention measures must be incorporated

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Section: Logistics Growth Modelmentioning

confidence: 99%

Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models

et al. 2020

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“…The logistic growth model is a regression model that has been widely applied in epidemiological mathematical models to estimate the growth rate and the reduction in the cumulative infected cases [30]. This model assumes that the exponential growth of the cumulative infected cases at the start of the epidemic is followed by a steadily increasing growth, and is ended by a decreasing growth rate.…”

Section: The Logistic Growth Regressionmentioning

confidence: 99%

Forecasting the Long-Term Trends of Coronavirus Disease 2019 (COVID-19) Epidemic Using the Susceptible-Infectious-Recovered (SIR) Model

Kartono

Karimah

Wahyudi

et al. 2021

Infectious Disease Reports

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A simple model for predicting Coronavirus Disease 2019 (COVID-19) epidemic is presented in this study. The prediction model is presented based on the classic Susceptible-Infectious-Recovered (SIR) model, which has been widely used to describe the epidemic time evolution of infectious diseases. The original version of the Kermack and McKendrick model is used in this study. This included the daily rates of infection spread by infected individuals when these individuals interact with a susceptible population, which is denoted by the parameter β, while the recovery rates to determine the number of recovered individuals is expressed by the parameter γ. The parameters estimation of the three-compartment SIR model is determined through using a mathematical sequential reduction process from the logistic growth model equation. As the parameters are the basic characteristics of epidemic time evolution, the model is always tested and applied to the latest actual data of confirmed COVID-19 cases. It seems that this simple model is still reliable enough to describe the dynamics of the COVID-19 epidemic, not only qualitatively but also quantitatively with a high degree of correlation between actual data and prediction results. Therefore, it is possible to apply this model to predict cases of COVID-19 in several countries. In addition, the parameter characteristics of the classic SIR model can provide information on how these parameters reflect the efforts by each country to prevent the spread of the COVID-19 outbreak. This is clearly seen from the changes of the parameters shown by the classic SIR model.

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“…Drop/delete the date column from the time series data. The sequence of observations was as follows: [10,7,15,20,24,36,40,31,47,53,45]. The sequence of observations must be transformed into multiple samples from which the LSTM can learn.…”

Section: Data Preparationmentioning

confidence: 99%

“…Table 1 Inputs/output patterns for time-series data example Inputs Output [10,7,15,20,24,36,40] 31 [7,15,20,24,36,40,31] 47 [15,20,24,36,40,31,47] 53 [20,24,36,40,31,47,53] The output layer had a dense layer with 1 unit for predicting the output. The learning rate is set to 0.001, and it decays every 5 epochs.…”

Section: The Proposed Lstm Modelmentioning

confidence: 99%

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Gulf Area COVID-19 Cases Prediction Using Deep Learning

Ghany

Zawbaa

Sabri

2021

Digital Transformation Technology

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is the most catastrophic news of 2020. The initial onset of the virus is an unexpected extreme outlier event. Still, the human response to restrain the virus is also creating massive ripples through systems that run the world, such as health, economy, education, and tourism. This paper focuses on research and applying artificial intelligence (AI) algorithms for predicting COVID-19 propagation using the available time-series data and studies the effect of the lifestyle and the awareness of citizens on the virus in the gulf area, especially in the Kingdom of Saudi Arabia (KSA), United Arab of Emirates (UAE) and Qatar. For this aim, we access time-series real-datasets collected from Johns Hopkins University Center for Systems Science and Engineering [1]. We have implemented the proposed model based on long short-term memory (LSTM) with 100 hidden units (neurons). We confirmed that the KSA and Qatar would take the most extended period to recover from COVID-19 virus from the experimental results. The situation will be controllable in March 2021 in UAE.

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Mathematical Population Dynamics and Epidemiology in Temporal and Spatio-Temporal Domains

Cited by 12 publications

References 0 publications

Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models

Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models

Forecasting the Long-Term Trends of Coronavirus Disease 2019 (COVID-19) Epidemic Using the Susceptible-Infectious-Recovered (SIR) Model

Gulf Area COVID-19 Cases Prediction Using Deep Learning

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