Establishing a standard method for analysing case detection delay in leprosy using a Bayesian modelling approach

Background Leprosy is an infectious disease caused by Mycobacterium leprae and remains a source of preventable disability if left undetected.Case detection delay is an important epidemiological indicator for progress in interrupting transmission and preventing disability in a community.However,no standard method exists to effectively analyse and interpret this type of data.In this study,we aim to evaluate the characteristics of leprosy case detection delay data and select an appropriate model for the variability of detection delays based on the best fitting distribution type.Methods Two sets of leprosy case detection delay data were evaluated:a cohort of 181 patients from the post exposure prophylaxis for leprosy(PEP4LEP)study in high endemic districts of Ethiopia,Mozambique,and Tanzania;and self-reported delays from 87 individuals in 8 low endemic countries collected as part of a systematic literature review.Bayesian models were fit to each dataset to assess which probability distribution(log-normal,gamma or Weibull)best describes variation in observed case detection delays using leave-one-out cross-validation,and to estimate the effects of individual factors.Results For both datasets,detection delays were best described with a log-normal distribution combined with covariates age,sex and leprosy subtype[expected log predictive density(ELPD)for the joint model:-1123.9].Patients with multibacillary(MB)leprosy experienced longer delays compared to paucibacillary(PB)leprosy,with a relative difference of 1.57[95%Bayesian credible interval(BCI):1.14-2.15].Those in the PEP4LEP cohort had 1.51(95%BCI:1.08-2.13)times longer case detection delay compared to the self-reported patient delays in the systematic review.Conclusions The log-normal model presented here could be used to compare leprosy case detection delay datasets,including PEP4LEP where the primary outcome measure is reduction in case detection delay.We recommend the application of this modelling approach to test different probability distributions and covariate effects in studies with similar outcomes in the field of leprosy and other skin-NTDs.

H_-/H_∞ fault detection filter design for interval time-varying delays switched systems

The problem of the robust fault detection filter design for time-varying delays switched systems is considered in the framework of mixed H-/H∞. Firstly, the weighted H∞ performance index is utilized as the robustness performance, and the H- index is used as the sensitivity performance for obtaining the robust fault detection filter. Then a novel multiple Lyapunov-Krasovskii function is proposed for deriving sufficient existence conditions of the robust fault detection filter based on the average dwell time technique. By introducing slack matrix variable, the coupling between the Lyapunov matrix and system matrix is removed, and the conservatism of results is reduced. Based on the robust fault detection filter, residual is generated and evaluated for detecting faults. In addition, the results of this paper are dependent on time delays,and represented in the form of linear matrix inequalities. Finally,the simulation example verifies the effectiveness of the proposed method.

Detection of schedule delay risk of empirical construction projects

In Ethiopian construction projects, schedule delay risk is a predominant issue because it is not properly addressed. Although several studies have been focused on the various effects of risk in construction projects, limited efforts have been made to investigate the typical and the overall schedule delay risk. In this study, our aim is to detect the typical and overall schedule delay risk throughout the construction project lifecycle, which consists of the pre-construction, construction, and post-construction stages, and compare the stages with each other. Common criteria, sub-criteria, and attributes were developed for all alternatives for the purpose of making a risk decision. The methodology that was followed integrated the multiplecriteria decision-making(MCDM) model of fuzzy analytic hierarchy process comprehensive evaluation(FAHPCE)and the relative important index(RII). Data were collected from 77 participants, who were selected through purposive sampling from different contracting organizations in Ethiopian construction projects by means of questionnaires that were distributed to experienced experts. The findings showed that there is a typical delay risk either in the type or in the level of the different construction activities.Consequently, the most influenced alternative is the construction stage because of the high-risk responsibility,resource, and contract condition related criteria. The postconstruction stage was the second most influenced stage because of the high-risk responsibility-related criteria. The pre-constructed stage was the least influenced stage that consist high-risk criteria of responsibility, resource, and contract condition related. These differences provided noteworthy information about risk mitigation in construction projects by identifying the exact risk level on specific activity to make appropriate decision.