We have developed a new general approach for handling misclassification in discrete covariates or responses in regression models. The simulation and extrapolation (SIMEX) method, which was originally designed for handling additive covariate measurement error, is applied to the case of misclassification. The statistical model for characterizing misclassification is given by the transition matrix Pi from the true to the observed variable. We exploit the relationship between the size of misclassification and bias in estimating the parameters of interest. Assuming that Pi is known or can be estimated from validation data, we simulate data with higher misclassification and extrapolate back to the case of no misclassification. We show that our method is quite general and applicable to models with misclassified response and/or misclassified discrete regressors. In the case of a binary response with misclassification, we compare our method to the approach of Neuhaus, and to the matrix method of Morrissey and Spiegelman in the case of a misclassified binary regressor. We apply our method to a study on caries with a misclassified longitudinal response.

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Zero-inflated models for count data are becoming quite popular nowadays and are found in many application areas, such as medicine, economics, biology, sociology and so on. However, in practice these counts are often prone to measurement error which in this case boils down to misclassification. Methods to deal with misclassification of counts have been suggested recently, but only for the binomial model and the Poisson model. Here we look at a more complex model, that is, the zero-inflated negative binomial, and illustrate how correction for misclassification can be achieved. Our approach is illustrated on the dmft-index which is a popular measure for caries experience in caries research. An extra problem was the fact that several dental examiners were involved in scoring caries experience. Using our example, we illustrate how a non-differential misclassification process for each examiner can lead to differential misclassification overall.

Objective Coronavirus disease 2019 (COVID-19) is a pandemic respiratory illness spreading from person-to-person caused by a novel coronavirus and poses a serious public health risk. The goal of this study was to apply a modified susceptible-exposed-infectious-recovered (SEIR) compartmental mathematical model for prediction of COVID-19 epidemic dynamics incorporating pathogen in the environment and interventions. The next generation matrix approach was used to determine the basic reproduction number . The model equations are solved numerically using fourth and fifth order Runge–Kutta methods. Results We found an of 2.03, implying that the pandemic will persist in the human population in the absence of strong control measures. Results after simulating various scenarios indicate that disregarding social distancing and hygiene measures can have devastating effects on the human population. The model shows that quarantine of contacts and isolation of cases can help halt the spread on novel coronavirus.

Objective: Coronavirus disease 2019 (COVID-19) is a pandemic of respiratory disease spreading from person-to-person caused by a novel coronavirus and poses a serious public health risk. The goal of this study is to apply SEIR compartmental mathematical model for prediction of COVID-19 epidemic dynamics incorporating pathogen in the environment and interventions. The next generation matrix approach was used to determine the basic reproduction number R0. The model equations are solved numerically using fourth and fifth order Runge–Kutta methods. Results: The value of basic reproduction number R0 was determined as 2.03, implying that the pandemic will persist in the human population. Results after simulating various scenarios indicate that disregarding social distancing, wearing of masks and frequent washing of hands can have devastating effects on the human population. The model shows that quarantine and isolation are key winners to this pandemic.

In conclusion, the implemented minimal school-based oral health education programme did not result in a significant reduction of the caries prevalence measured. The programme has been effective in improving reported dietary habits and the proper use of topical fluorides and resulted in a higher care index.

VL results from V-DBS, M-DBS, and D-DBS were comparable with those from plasma for determining VF using the Abbott platform but not with CAP/CTM. A 1000-copies/mL threshold was optimal and should be considered for VF determination using DBS in adults and children.

BackgroundKenya is home to several high-performing internationally-accredited research laboratories, whilst most public sector laboratories have historically lacked functioning quality management systems. In 2010, Kenya enrolled an initial eight regional and four national laboratories into the Strengthening Laboratory Management Toward Accreditation (SLMTA) programme. To address the challenge of a lack of mentors for the regional laboratories, three were paired, or ‘twinned’, with nearby accredited research laboratories to provide institutional mentorship, whilst the other five received standard mentorship.ObjectivesThis study examines results from the eight regional laboratories in the initial SLMTA group, with a focus on mentorship models.MethodsThree SLMTA workshops were interspersed with three-month periods of improvement project implementation and mentorship. Progress was evaluated at baseline, mid-term, and exit using the Stepwise Laboratory Quality Improvement Process Towards Accreditation (SLIPTA) audit checklist and scores were converted into a zero- to five-star scale.ResultsAt baseline, the mean score for the eight laboratories was 32%; all laboratories were below the one-star level. At mid-term, all laboratories had measured improvements. However, the three twinned laboratories had increased an average of 32 percentage points and reached one to three stars; whilst the five non-twinned laboratories increased an average of 10 percentage points and remained at zero stars. At exit, twinned laboratories had increased an average 12 additional percentage points (44 total), reaching two to four stars; non-twinned laboratories increased an average of 28 additional percentage points (38 total), reaching one to three stars.ConclusionThe partnership used by the twinning model holds promise for future collaborations between ministries of health and state-of-the-art research laboratories in their regions for laboratory quality improvement. Where they exist, such laboratories may be valuable resources to be used judiciously so as to accelerate sustainable quality improvement initiated through SLMTA.