Comparison of smoothing techniques for CD4 data in a Markov model with states defined by CD4: an example on the estimation of the HIV incubation time distribution

2001 ◽  
Vol 20 (24) ◽  
pp. 3667-3676 ◽  
Author(s):  
V. Sypsa ◽  
G. Touloumi ◽  
M. Kenward ◽  
A. Karafoulidou ◽  
A. Hatzakis
Keyword(s):  
Markov Model ◽  
Incubation Time ◽  
Time Distribution ◽  
Smoothing Techniques

Estimating time to onset of swine influenza symptoms after initial novel A(H1N1v) viral infection

2010 ◽  
Vol 139 (9) ◽  
pp. 1418-1424 ◽  
Author(s):  
B. D. M. TOM ◽  
A. J. VAN HOEK ◽  
R. PEBODY ◽  
J. McMENAMIN ◽  
C. ROBERTSON ◽  
...  
Keyword(s):  
Incubation Time ◽  
Time Distribution ◽  
Age Groups ◽  
Swine Influenza ◽  
Interval Censoring ◽  
Limited Data ◽  
History Of ◽  
Influenza Case ◽  
Time To Onset

SUMMARYCharacterization of the incubation time from infection to onset is important for understanding the natural history of infectious diseases. Attempts to estimate the incubation time distribution for novel A(H1N1v) have been, up to now, based on limited data or peculiar samples. We characterized this distribution for a generic group of symptomatic cases using laboratory-confirmed swine influenza case-information. Estimates of the incubation distribution for the pandemic influenza were derived through parametric time-to-event analyses of data on onset of symptoms and exposure dates, accounting for interval censoring. We estimated a mean of about 1·6–1·7 days with a standard deviation of 2 days for the incubation time distribution in those who became symptomatic after infection with the A(H1N1v) virus strain. Separate analyses for the <15 years and ⩾15 years age groups showed a significant (P<0·02) difference with a longer mean incubation time in the older age group.

On the Incubation Time Distribution and the Danish AIDS Data

1988 ◽  
Vol 151 (1) ◽  
pp. 42 ◽  
Author(s):  
Jesper Lier Boldsen ◽  
Jens Ledet Jensen ◽  
Jes Sogaard ◽  
Michael Sorensen
Keyword(s):  
Incubation Time ◽  
Time Distribution

Resting Eggs in Pontella meadi (Copepoda: Calanoida)

1977 ◽  
Vol 34 (3) ◽  
pp. 410-412 ◽  
Author(s):  
George D. Grice ◽  
Victoria R. Gibson
Keyword(s):  
Key Words ◽  
Incubation Time ◽  
Time Distribution ◽  
Resting Eggs

Pontella meadi Wheeler produces resting eggs in fall which hatch the following summer. Experiments show that these eggs require 4–8 wk of incubation at 2–3 or 5–6 °C for substantial hatching to occur. Eggs occur in sediment in winter. Resting eggs serve to repopulate temperate inshore areas with this species after its winter disappearance from the plankton. Key words: Copepoda, Calanoida, Pontella, resting eggs, incubation time, distribution

RELIABILITY ANALYSIS OF PHASED MISSIONS

1995 ◽  
Vol 02 (04) ◽  
pp. 405-417
Author(s):  
WINFRID G. SCHNEEWEISS
Keyword(s):  
Markov Model ◽  
Reliability Analysis ◽  
Time Distribution ◽  
Life Time ◽  
Reliability Measures ◽  
Model Case ◽  
Component Failure ◽  
Triple Modular Redundancy ◽  
Mission Success ◽  
Modular Redundancy

It is shown that in a number of cases mission success probabilities and related reliability measures can be determined, once appropriate mission phases are defined. The results are general, i.e., not limited to the Markov model case, but intentionally kept “simple” by considering only few components and few different phases. The problems specifically addressed (and solved) are: (1) Non-ideal switches for activating spares, (2) and (3) degraded operation, and change from TMR (Triple Modular Redundancy) to simplex structure on the first component failure, respectively, (4) preventive renewals after alternating periods, and (5) load-dependent two-phased life time distribution.

scholarly journals A multistate first-order Markov model for modeling time distribution of extreme rainfall events

2020 ◽  
Author(s):  
A. N. Rohith ◽  
Margaret W. Gitau ◽  
I. Chaubey ◽  
K. P. Sudheer
Keyword(s):  
Markov Model ◽  
Stormwater Management ◽  
Time Distribution ◽  
Rainfall Intensity ◽  
Extreme Rainfall ◽  
Peak Discharge ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Urban Stormwater Management ◽  
First Order

AbstractThe time distribution of extreme rainfall events is a significant property that governs the design of urban stormwater management structures. Accuracy in characterizing this behavior can significantly influence the design of hydraulic structures. Current methods used for this purpose either tend to be generic and hence sacrifice on accuracy or need a lot of model parameters and input data. In this study, a computationally efficient multistate first-order Markov model is proposed for use in characterizing the inherently stochastic nature of the dimensionless time distribution of extreme rainfall. The model was applied to bivariate extremes at 10 stations in India and 205 stations in the United States (US). A comprehensive performance evaluation was carried out with one-hundred stochastically generated extremes for each historically observed extreme rainfall event. The comparisons included: 1-h (15-min); 2-h (30-min); and, 3-h (45-min) peak rainfall intensities for India and (US) stations, respectively; number of first, second, third, and fourth-quartile storms; the dependence of peak rainfall intensity on total depth and duration; and, return levels and return periods of peak discharge when these extremes were applied on a hypothetical urban catchment. Results show that the model efficiently characterizes the time distribution of extremes with: Nash–Sutcliffe-Efficiency > 0.85 for peak rainfall intensity and peak discharge; < 20% error in reproducing different quartile storms; and, < 0.15 error in correlation analysis at all study locations. Hence the model can be used to effectively reproduce the time distribution of extreme rainfall events, thus increasing the confidence of design of urban stormwater management structures.

scholarly journals The Milwaukee Cryptosporidium outbreak: assessment of incubation time and daily attack rate

2004 ◽  
Vol 2 (2) ◽  
pp. 59-69 ◽  
Author(s):  
Mukul Gupta ◽  
Charles N. Haas
Keyword(s):  
Incubation Time ◽  
Time Distribution ◽  
Time Course ◽  
Equation System ◽  
Differential Equation System ◽  
Epidemic Curve ◽  
Force Of Infection ◽  
Disease Occurrence ◽  
Functional Forms ◽  
Precise Assessment

The time course of reported illnesses (epidemic curve) in the 1993 Milwaukee outbreak of cryptosporidiosis was analysed using a dynamic model considering time variant force of infection and incubation time distributions. Different functional forms for the force of infection and incubation time distribution were tested. The resulting model is a coupled integro-differential equation system. These models gave a good fit to the data, although depending upon the functional forms of the underlying distributions, different incubation time and force of infection curves were obtained. However there was reasonable agreement with respect to a baseline illness rate that existed. This demonstrates that useful information may be obtained in this manner, although it should be supplemented with other data (e.g. serology) for a precise assessment of dynamics of disease occurrence during waterborne epidemic conditions.

scholarly journals Incubation-time distribution in back-calculation applied to HIV/AIDS data in India

2005 ◽  
Vol 2 (2) ◽  
pp. 263-277 ◽  
Author(s):  
Arni S.R. Srinivasa Rao ◽  
◽  
Masayuki Kakehashi ◽  
Keyword(s):  
Incubation Time ◽  
Time Distribution ◽  
Back Calculation ◽  
Hiv Aids
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