scholarly journals Prediction of daily and cumulative cases for COVID-19 infection based on reproductive number (R0) in Karnataka: a data-driven analytics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kuralayanapalya Puttahonnappa Suresh ◽  
Sharanagouda S. Patil ◽  
Bharath Prasad Cholanayakanahalli Thyagaraju ◽  
Srikantha Gowda Ramkrishnappa ◽  
Divakar Hemadri ◽  
...  
Keyword(s):  
Data Driven ◽  
Reproductive Number ◽  
Bootstrap Resampling ◽  
Medical Facility ◽  
Expected Number ◽  
R Program ◽  
Serial Interval ◽  
Karnataka State ◽  
Resampling Method ◽  
Rapid Pace

AbstractTo estimate the reproductive number (R0) of the coronavirus in the present scenario and to predict the incidence of daily and probable cumulative cases, by 20 August, 2020 for Karnataka state in India. The model used serial interval with a gamma distribution and applied ‘early R’ to estimate the R0 and ‘projections’ package in R program. This was performed to mimic the probable cumulative epidemic trajectories and predict future daily incidence by fitting the data to existing daily incidence and the estimated R0 by a model based on the assumption that daily incidence follows Poisson distribution. The maximum-likelihood (ML) value of R0 was 2.242 for COVID-19 outbreak, as on June 2020. The median with 95% CI of R0 values was 2.242 (1.50–3.00) estimated by bootstrap resampling method. The expected number of new cases for the next 60 days would progressively increase, and the estimated cumulative cases would reach 27,238 (26,008–28,467) at the end of 60th day in the future. But, if R0 value was doubled the estimated total number of cumulative cases would increase up to 432,411 (400,929–463,893) and if, R0 increase by 50%, the cases would increase up to 86,386 (80,910–91,861). The probable outbreak size and future daily cumulative incidence are largely dependent on the change in R0 values. Hence, it is vital to expedite the hospital provisions, medical facility enhancement work, and number of random tests for COVID-19 at a very rapid pace to prepare the state for exponential growth in next 2 months.

scholarly journals Quantifying TB transmission: a systematic review of reproduction number and serial interval estimates for tuberculosis

2018 ◽  
Vol 146 (12) ◽  
pp. 1478-1494 ◽  
Author(s):  
Y. Ma ◽  
C. R. Horsburgh ◽  
L. F. White ◽  
H. E. Jenkins
Keyword(s):  
Systematic Review ◽  
Parameter Estimation ◽  
The Netherlands ◽  
Cause Of Death ◽  
Reproduction Number ◽  
Reproductive Number ◽  
Interval Estimates ◽  
Transmission Parameters ◽  
Serial Interval ◽  
Number Of Publications

AbstractTuberculosis (TB) is the leading global infectious cause of death. Understanding TB transmission is critical to creating policies and monitoring the disease with the end goal of TB elimination. To our knowledge, there has been no systematic review of key transmission parameters for TB. We carried out a systematic review of the published literature to identify studies estimating either of the two key TB transmission parameters: the serial interval (SI) and the reproductive number. We identified five publications that estimated the SI and 56 publications that estimated the reproductive number. The SI estimates from four studies were: 0.57, 1.42, 1.44 and 1.65 years; the fifth paper presented age-specific estimates ranging from 20 to 30 years (for infants <1 year old) to <5 years (for adults). The reproductive number estimates ranged from 0.24 in the Netherlands (during 1933–2007) to 4.3 in China in 2012. We found a limited number of publications and many high TB burden settings were not represented. Certain features of TB dynamics, such as slow transmission, complicated parameter estimation, require novel methods. Additional efforts to estimate these parameters for TB are needed so that we can monitor and evaluate interventions designed to achieve TB elimination.

Analysis of stress intensity factor for fatigue crack using bootstrap S-version finite element model

2020 ◽  
Vol 11 (4) ◽  
pp. 579-589
Author(s):  
Muhamad Husnain Mohd Noh ◽  
Mohd Akramin Mohd Romlay ◽  
Chuan Zun Liang ◽  
Mohd Shamil Shaari ◽  
Akiyuki Takahashi
Keyword(s):  
Stress Intensity Factor ◽  
Finite Element ◽  
Finite Element Model ◽  
Simulation Analysis ◽  
Element Model ◽  
Bootstrap Resampling ◽  
Confidence Bounds ◽  
Content Type ◽  
Resampling Method ◽  
The Mean

PurposeFailure of the materials occurs once the stress intensity factor (SIF) overtakes the material fracture toughness. At this level, the crack will grow rapidly resulting in unstable crack growth until a complete fracture happens. The SIF calculation of the materials can be conducted by experimental, theoretical and numerical techniques. Prediction of SIF is crucial to ensure safety life from the material failure. The aim of the simulation study is to evaluate the accuracy of SIF prediction using finite element analysis.Design/methodology/approachThe bootstrap resampling method is employed in S-version finite element model (S-FEM) to generate the random variables in this simulation analysis. The SIF analysis studies are promoted by bootstrap S-version Finite Element Model (BootstrapS-FEM). Virtual crack closure-integral method (VCCM) is an important concept to compute the energy release rate and SIF. The semielliptical crack shape is applied with different crack shape aspect ratio in this simulation analysis. The BootstrapS-FEM produces the prediction of SIFs for tension model.FindingsThe mean of BootstrapS-FEM is calculated from 100 samples by the resampling method. The bounds are computed based on the lower and upper bounds of the hundred samples of BootstrapS-FEM. The prediction of SIFs is validated with Newman–Raju solution and deterministic S-FEM within 95 percent confidence bounds. All possible values of SIF estimation by BootstrapS-FEM are plotted in a graph. The mean of the BootstrapS-FEM is referred to as point estimation. The Newman–Raju solution and deterministic S-FEM values are within the 95 percent confidence bounds. Thus, the BootstrapS-FEM is considered valid for the prediction with less than 6 percent of percentage error.Originality/valueThe bootstrap resampling method is employed in S-FEM to generate the random variables in this simulation analysis.

scholarly journals Estimation of the serial interval and basic reproduction number of COVID‐19 in Qom, Iran, and three other countries: A data‐driven analysis in the early phase of the outbreak

2020 ◽  
Vol 67 (6) ◽  
pp. 2860-2868 ◽  
Author(s):  
Mohammad Aghaali ◽  
Goodarz Kolifarhood ◽  
Roya Nikbakht ◽  
Hossein Mozafar Saadati ◽  
Seyed Saeed Hashemi Nazari
Keyword(s):  
Basic Reproduction Number ◽  
Early Phase ◽  
Reproduction Number ◽  
Data Driven ◽  
Serial Interval

scholarly journals Serial Interval and Reproductive Number of COVID-19 Among 116 Infector-infectee Pairs — Jingzhou City, Hubei Province, China, 2020

2020 ◽  
Vol 2 (27) ◽  
pp. 491-495 ◽  
Author(s):  
Tian Liu ◽  
◽  
Li Qi ◽  
Menglei Yao ◽  
Keqing Tian ◽  
...  
Keyword(s):  
Hubei Province ◽  
Reproductive Number ◽  
Serial Interval

scholarly journals Non-Parametric Statistics on Extreme Rainfall

1994 ◽  
Vol 25 (4) ◽  
pp. 267-278 ◽  
Author(s):  
K. Arnbjerg-Nielsen ◽  
P. Harremoës ◽  
H. Spliid
Keyword(s):  
Monitoring Program ◽  
Extreme Rainfall ◽  
Bootstrap Resampling ◽  
Return Periods ◽  
Peak Intensity ◽  
Resampling Method ◽  
Storm Drainage ◽  
Design Values ◽  
Parametric Statistics ◽  
Rain Data

This article focuses on rain as input data to problems related to urban storm drainage. The rain data originate from a monitoring program consisting of 56 gauges in Denmark. The gauges have observation periods ranging from 2 to 14 years. The gauges sample rain in a quantity of 0.2 mm with a resolution in time of 1 minute. Two variables have been investigated: peak intensity and depth. Design values for return periods in the range from 0.1 to 2 years have been estimated for each gauge separately by means of the bootstrap resampling method. The estimation includes expected value, standard deviation and confidence intervals of the design value. For large return periods the uncertainty of the estimates prevents a distinction by gauge between different statistical populations. However, for small return periods a test shows significant variation between gauges, i.e. the uncertainty of the estimates may not be assumed to be due to sampling variability only.

scholarly journals COVID-19 pandemic scenario in India compared to China and rest of the world: a data driven and model analysis

2020 ◽  
Author(s):  
Manisha Mandal ◽  
Shyamapada Mandal
Keyword(s):  
Reproduction Number ◽  
Case Fatality ◽  
Respiratory Illness ◽  
Data Driven ◽  
Daily Growth Rate ◽  
Daily Growth ◽  
Epidemic Size ◽  
Serial Interval ◽  
The World ◽  
Before And After

AbstractThe COVID-19 is a rapidly spreading respiratory illness caused with the infection of SARS-CoV-2. The COVID-19 data from India was compared with China and rest of the world. The average values of daily growth rate (DGR), case recovery rate (CRR), case fatality rate (CFR), serial interval (SI) of COVID-19 in India was 17%, 8.25%, and 1.87%, and 5.76 days respectively, as of April 9, 2020. The data driven estimates of basic reproduction number (R0), average reproduction number (R) and effective reproduction number (Re) were 1.03, 1.73, and 1.35, respectively. The results of exponential and SIR model showed higher estimates of R0, R and Re. The data driven as well as estimated COVID-19 cases reflect the growing nature of the epidemic in India and world excluding China, whereas the same in China reveal the involved population became infected with the disease and moved into the recovered stage. The epidemic size of India was estimated to be ∼30,284 (as of April 15, 2020 with 12,370 infectious cases) with an estimated end of the epidemic on June 9, 2020. The Re values in India before and after lockdown were 1.62 and 1.37 respectively, with SI 5.52 days and 5.98 days, respectively, as of April 17, 2020, reflecting the effectiveness of lockdown strategies. Beyond April 17, 2020, our estimate of 24,431 COVID-19 infected cases with lockdown is 78% lower compared to the 112,042 case estimates in absence of lockdown, on April 27, 2020. To early end of the COVID-19 epidemic, strong social distancing is important.

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