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

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

scholarly journals Semi-Markov Modelling of HIV/AIDS Disease Progression

2021 ◽  
Author(s):  
Tilahun Asena ◽  
Ayele Goshu ◽  
Mebratu Senbeta ◽  
Derbachew Teni
Keyword(s):  
Body Mass Index ◽  
Disease Progression ◽  
Body Mass ◽  
Waiting Time ◽  
Time Distribution ◽  
The Body ◽  
Waiting Time Distribution ◽  
Body Mass Index Increase ◽  
Hiv Aids

Abstract Background: HIV/AIDS epidemic continues to be the main challenge in the world. According to United Nations Program on HIV/AIDS (UNAIDS) and the World Health Organization (WHO) reports of 2013, 35 million people were living with HIV worldwide, with 2.1 million new infections and with 1.5 million deaths occurred each year. Among these, 24.7 million lived in sub-Saharan Africa with 1.5 million new infections and 1.1 million AIDS deaths.Method: The main objective of this study is finding factors affecting HIV/AIDS disease progression. This study was conducted to investigate the effect of factors on HIV/AIDS disease progression. Patient follow-up data is obtained at Yirgalim General Hospital. A sample of 370 Patient data from a follow-up cohort is obtained at Yirgalim General Hospital. Multivariate generalized hazard regression model was employed to investigate the disease progression using both time independent and time dependent covariates. Result: The study revealed that the risk of transition differs by patient's body mass index. Increase in the body mass index reduces the risk of transiting into the next worst states. The effects of sex, weight, age and body mass index of patients are significantly associated with AIDS disease progression. The risk of transition differs by patient's body mass index. Increase in the body mass index reduces the risk of transiting into the next worst states. The effect of sex, weight, age and body mass index of patients are significantly associated with AIDS disease progression. The results further revealed that the semi-Markov model with Weibull waiting time distribution has smaller log likelihood and AIC values compared to a semi-Markov model with exponential waiting time distribution.Conclusion: Transition probabilities are highly dependent on the choice of waiting times. We recommend that while choosing waiting time distributions for semi-Marko models one should consider appropriate distributions as waiting time distribution effect have a significant change on the estimated model parameters. In addition, this study recommends that concerned bodies should look at deferent contributing factors of AIDS diseases progression in addition to the ART services administered for slowing the current level of high diseased population in the country.

scholarly journals Estimation and prediction of the HIV–AIDS-epidemic under conditions of HAART using mixtures of incubation time distributions

2008 ◽  
Vol 27 (6) ◽  
pp. 781-794 ◽  
Author(s):  
S. H. Heisterkamp ◽  
R. de Vries ◽  
H. G. Sprenger ◽  
G. A. A. Hubben ◽  
M. J. Postma
Keyword(s):  
Incubation Time ◽  
Estimation And Prediction ◽  
Aids Epidemic ◽  
Hiv Aids
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