scholarly journals Prediction of the Peak, Effect of Intervention and Total Infected by the Coronavirus Disease in India

2020 ◽  
Author(s):  
Parth Vipul Shah
Keyword(s):  
Infection Rate ◽  
Compartmental Model ◽  
Total Population ◽  
Reproduction Number ◽  
Least Square Method ◽  
Peak Effect ◽  
Poisson Noise ◽  
Least Square ◽  
Intervention Period ◽  
Social Distancing

AbstractWe study the effect of the coronavirus disease 2019 (COVID-19) in India using the SEIR compartmental model. After it’s outbreak in Wuhan, China, it has been imported to India which is a densely populated country. India is fighting against this disease by ensuring nationwide social distancing. We estimate the infection rate to be 0.258 using a least square method with Poisson noise and estimate the reproduction number to be 2.58. We approximate the peak of the epidemic to be August 11, 2020. We estimate that a 25% drop in infection rate will delay the peak by 38 days for a 1 month intervention period. We estimate that the total individuals infected in India will be approximately 9% of the total population.

scholarly journals Prediction of the Peak, Effect of Intervention, and Total Infected by COVID-19 in India

2020 ◽  
pp. 1-11
Author(s):  
Parth Vipul Shah
Keyword(s):  
Health Care ◽  
Infection Rate ◽  
Compartmental Model ◽  
Total Population ◽  
Reproduction Number ◽  
Least Square Method ◽  
Peak Effect ◽  
Least Square ◽  
Intervention Period ◽  
Care Capacity

ABSTRACT Objectives: We study the effect of the coronavirus disease 2019 (COVID-19) in India and model the epidemic to guide those involved in formulating policy and building health-care capacity. Methods: This effect is studied using the Susceptible-Exposed-Infected-Recovered (SEIR) compartmental model. We estimate the infection rate using a least square method with Poisson noise and calculate the reproduction number. Results: The infection rate is estimated to be 0.270 and the reproduction number to be 2.70. The approximate peak of the epidemic will be August 9, 2020. A 25% drop in infection rate will delay the peak by 11 d for a 1-mo intervention period. The total infected individuals in India will be 9% of the total population. Conclusions: The predictions are sensitive to changes in the behavior of people and their practice of social distancing.

scholarly journals Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020

2020 ◽  
Vol 9 (3) ◽  
pp. 789 ◽  
Author(s):  
Toshikazu Kuniya
Keyword(s):  
Compartmental Model ◽  
Reproduction Number ◽  
Least Square ◽  
Time Data ◽  
Long Period ◽  
Epidemic Size ◽  
First Case ◽  
Final Epidemic Size ◽  
Positive Effect ◽  
Day By Day

The first case of coronavirus disease 2019 (COVID-19) in Japan was reported on 15 January 2020 and the number of reported cases has increased day by day. The purpose of this study is to give a prediction of the epidemic peak for COVID-19 in Japan by using the real-time data from 15 January to 29 February 2020. Taking into account the uncertainty due to the incomplete identification of infective population, we apply the well-known SEIR compartmental model for the prediction. By using a least-square-based method with Poisson noise, we estimate that the basic reproduction number for the epidemic in Japan is R 0 = 2.6 ( 95 % CI, 2.4 – 2.8 ) and the epidemic peak could possibly reach the early-middle summer. In addition, we obtain the following epidemiological insights: (1) the essential epidemic size is less likely to be affected by the rate of identification of the actual infective population; (2) the intervention has a positive effect on the delay of the epidemic peak; (3) intervention over a relatively long period is needed to effectively reduce the final epidemic size.

scholarly journals Estimation of COVID-19 outbreak size in Harbin, China

2020 ◽  
Author(s):  
Haitao Song ◽  
Zhongwei Jia ◽  
Zhen Jin
Keyword(s):  
Reproduction Number ◽  
Least Square Method ◽  
Threshold Value ◽  
Least Square ◽  
Transmission Risk ◽  
Heilongjiang Province ◽  
Infected People ◽  
Imported Case ◽  
Effective Interventions ◽  
Outbreak Size

Abstract Background: Since the first level response to public health emergencies (FLRPHE) was launched on January 25, 2020 in Heilongjiang province, China, the outbreak of COVID-19 seems to be under control. However, an outbreak of COVID-19 caused by imported case developed in Harbin during April 2020. Here, we provide an estimate of the COVID-19 outbreak size in Harbin in April based on the number of found infected cases, assess the proportion of found and unfound infected cases in infected cases and evaluate the effective reproduction number which shows the transmission risk of COVID-19. Methods: We used data from April 9 to April 30, 2020, on the number of found infected cases from the outbreak in Harbin to infer the number of infections in Harbin in the outbreak of COVID-19 and give the proportion of found and unfound infected cases in infected cases. Data on found infected cases were obtained from the the reports of Health Commission of Heilongjiang Province. A Susceptible-Unfound infected-Found infected-Removed model was used to fit the data on found infected cases of COVID-19 in Harbin using the least square method and simulate the transmission of COVID-19. The effective reproduction number was estimated. Results: The COVID-19 outbreak size estimated in Harbin in April reaches 174, where 54% of infected cases were found and 46% of infected cases were not found out. Our findings suggest that the effective reproduction number decreased drastically in contrast with the value of 3.6 on April 9 after that the effective interventions were implemented by Heilongjiang province government. Finally, the effective reproduction number arrived the value of 0.04 which is immensely below the threshold value 1, which means that Heilongjiang province government got the outbreak of COVID-19 in Harbin under control. Conclusions: The COVID-19 outbreak size in Harbin based on the assumptions that infected people with COVID-19 in the incubation period have the same infectivity with infectious people with COVID-19 could have been overestimated. As an increasing number of imported infected cases got into China and a growing number of asymptomatic infected people were found, our study provides evidence that unfound infected cases would increase the risk of local outbreak of COVID-19 in China.

scholarly journals A model for COVID-19 with isolation, quarantine and testing as control measures

2020 ◽  
Author(s):  
M.S. Aronna ◽  
R. Guglielmi ◽  
L.M. Moschen
Keyword(s):  
Compartmental Model ◽  
Reproduction Number ◽  
Risk Groups ◽  
Control Measures ◽  
Contact Rate ◽  
Control Parameters ◽  
Social Distancing ◽  
Proposed Model ◽  
Asymptomatic Infections ◽  
The Basic Reproduction Number

AbstractIn this article we propose a compartmental model for the dynamics of Coronavirus Disease 2019 (COVID-19). We take into account the presence of asymptomatic infections and the main policies that have been adopted so far to contain the epidemic: isolation (or social distancing) of a portion of the population, quarantine for confirmed cases and testing. We model isolation by separating the population in two groups: one composed by key-workers that keep working during the pandemic and have a usual contact rate, and a second group consisting of people that are enforced/recommended to stay at home. We refer to quarantine as strict isolation, and it is applied to confirmed infected cases.In the proposed model, the proportion of people in isolation, the level of contact reduction and the testing rate are control parameters that can vary in time, representing policies that evolve in different stages. We obtain an explicit expression for the basic reproduction number in terms of the parameters of the disease and of the control policies. In this way we can quantify the effect that isolation and testing have in the evolution of the epidemic. We present a series of simulations to illustrate different realistic scenarios. From the expression of and the simulations we conclude that isolation (social distancing) and testing among asymptomatic cases are fundamental actions to control the epidemic, and the stricter these measures are and the sooner they are implemented, the more lives can be saved. Additionally, we show that people that remain in isolation significantly reduce their probability of contagion, so risk groups should be recommended to maintain a low contact rate during the course of the epidemic.

Approximating the asymptomatic infectious cases of the COVID-19 disease in Algeria and India using a mathematical model

2021 ◽  
Author(s):  
Salih Djilali ◽  
Soufiane Bentout ◽  
Sunil Kumar ◽  
Tarik Mohammed Touaoula
Keyword(s):  
Mathematical Model ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Real Data ◽  
Least Square Method ◽  
Least Square ◽  
Regular Treatment ◽  
Asymptomatic Individuals ◽  
The Basic Reproduction Number ◽  
Artificial Solution

In this research, we are interested in discussing the evolution of the COVID-19 infection cases and predicting the spread of COVID-19 disease in Algeria and India. To this aim, we will approximate the transmission rate in terms of the measures taken by the governments. The least square method is used with an accuracy of 95% for fitting the artificial solution with the real data declared by WHO for the purpose of approximating the density of asymptomatic individuals for COVID-19 disease. As a result, we obtained the different values of the basic reproduction number (BRN) corresponding to each measure taken by the governments. Moreover, we estimate the number of asymptomatic infected persons at the epidemic peak for each country. Further, we will determine the needed ICU beds (intense medical carte beds) and regular treatment beds. Also, we provide the outcome of governmental strategies in reducing the spread of disease. Combining all these components, we offer some suggestions about the necessity of using the recently discovered vaccines as Pfizer/Bioentec and Moderna for limiting the spread of the COVID-19 disease in the studied countries.

scholarly journals Parameterization and Forecasting of Childhood Pneumonia Model Using Least Square Approximation, Lagrange Polynomial and Monte Carlo Simulation

2020 ◽  
pp. 102-114
Author(s):  
Cyrus Gitonga Ngari ◽  
Dominic Makaa Kitavi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Basic Reproduction Number ◽  
Total Population ◽  
Reproduction Number ◽  
Research Result ◽  
Least Square ◽  
Under Five

Despite a study by [1] proposing a simple model of under five years pneumonia, doubt lingers regarding its reliability, sufficiency and validity. The research question is whether the model is valid for use or not?  The objectives of this study were to: incorporate exit rate from under five-year age bracket in the model, use Kenya data to parameterize the model, taking into account the uncertainties and finally to predict the dynamics of pneumonia. The model was rescaled through nondimensionalization. Data was fitted using theory of general solutions of nonlinear Ordinary differential equations, numerical differentiation using Lagrange polynomials and least square approximation method. Uncertainties due to disparities and round off errors were simulated using Monte Carlo simulation. Predictions of dynamics of pneumonia were carried out using MATLAB inbuilt ode solvers. Excel software was used to predict dynamics of discrete ordinary differential equations and to fit data. The basic reproduction number (  and effective reproduction number ( ) were obtained as  Iteration of uncertainties on R was carried out 1000 times by Monte Carlo simulation. The maximum and minimum R were obtained as 90 and 55, respectively. Using MATLAB software and effective reproduction number, the ratio of infective class to the total population and the ratio of class under treatment to the total population will remain constant at 0.095 and 0.2297 respectively for the years 2021, 2022 and 2023. Research result indicted that it is more effective and efficient to use effective reproduction number ( ) than basic reproduction number (  in mathematical modelling of Infectious diseases whenever study focuses on proportion of population. On basis of large absolute errors in fitting data to model, findings cast doubt on model formulation and/or observed data.

scholarly journals Dynamical Analysis of an SEIT Epidemic Model with Application to Ebola Virus Transmission in Guinea

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Zhiming Li ◽  
Zhidong Teng ◽  
Xiaomei Feng ◽  
Yingke Li ◽  
Huiguo Zhang
Keyword(s):  
Basic Reproduction Number ◽  
Epidemic Model ◽  
Ebola Virus ◽  
Reproduction Number ◽  
Rank Correlation ◽  
Least Square Method ◽  
Least Square ◽  
Dynamical Analysis ◽  
Infectious Individual ◽  
The Basic Reproduction Number

In order to investigate the transmission mechanism of the infectious individual with Ebola virus, we establish an SEIT (susceptible, exposed in the latent period, infectious, and treated/recovery) epidemic model. The basic reproduction number is defined. The mathematical analysis on the existence and stability of the disease-free equilibrium and endemic equilibrium is given. As the applications of the model, we use the recognized infectious and death cases in Guinea to estimate parameters of the model by the least square method. With suitable parameter values, we obtain the estimated value of the basic reproduction number and analyze the sensitivity and uncertainty property by partial rank correlation coefficients.

Direct Assessment of Effective Renal Plasma Flow from Renoscintigraphy with a Gamma Camera and an On-Line Computer

1981 ◽  
Vol 20 (06) ◽  
pp. 274-278
Author(s):  
J. Liniecki ◽  
J. Bialobrzeski ◽  
Ewa Mlodkowska ◽  
M. J. Surma
Keyword(s):  
Count Rate ◽  
Renal Plasma Flow ◽  
Early Period ◽  
Least Square Method ◽  
Least Square ◽  
Uptake Coefficient ◽  
Positive Linear Correlation ◽  
On Line ◽  
Best Fit ◽  
Plasma Activity

A concept of a kidney uptake coefficient (UC) of 131I-o-hippurate was developed by analogy from the corresponding kidney clearance of blood plasma in the early period after injection of the hippurate. The UC for each kidney was defined as the count-rate over its ROI at a time shorter than the peak in the renoscintigraphic curve divided by the integral of the count-rate curve over the "blood"-ROI. A procedure for normalization of both curves against each other was also developed. The total kidney clearance of the hippurate was determined from the function of plasma activity concentration vs. time after a single injection; the determinations were made at 5, 10, 15, 20, 30, 45, 60, 75 and 90 min after intravenous administration of 131I-o-hippurate and the best-fit curve was obtained by means of the least-square method. When the UC was related to the absolute value of the clearance a positive linear correlation was found (r = 0.922, ρ > 0.99). Using this regression equation the clearance could be estimated in reverse from the uptake coefficient calculated solely on the basis of the renoscintigraphic curves without blood sampling. The errors of the estimate are compatible with the requirement of a fast appraisal of renal function for purposes of clinical diagknosis.

PENGARUH KNOWLEDGE MANAGEMENT, KOMPETENSI, MOTIVASI TERHADAP KINERJA KARYAWAN

2015 ◽  
Vol 5 (2) ◽  
pp. 1
Author(s):  
Miftahol Arifin
Keyword(s):  
Knowledge Management ◽  
Linear Regression ◽  
Significant Influence ◽  
Employee Performance ◽  
Least Square Method ◽  
Least Square ◽  
Analysis Tool ◽  
Ordinary Least Square ◽  
Competence Motivation ◽  
Performance Analyze

The purpose of this research is to analyze the influence of knowledge management on employee performance, analyze the effect of competence on employee performance, analyze the influence of motivation on employee performance). In this study, samples taken are structural employees PT.centris Kingdom Taxi Yogyakarta. The analysis tool in this study using multiple linear regression with Ordinary Least Square method (OLS). The conclusion of this study showed that the variables of knowledge management has a significant influence on employee performance, competence variables have an influence on employee performance, motivation variables have an influence on employee performance, The analysis showed that the variables of knowledge management, competence, motivation on employee performance.Keywords: knowledge management, competence, motivation, employee performance.

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