Back-calculating the incidence of infection of leprosy in a Bayesian framework.

BACKGROUND: The number of new leprosy cases reported annually is falling worldwide, but remains relatively high in some populations. Because of the long and variable periods between infection, onset of disease, and diagnosis, the recently detected cases are a reflection of infection many years earlier. Estimation of the numbers of sub-clinical and clinical infections would be useful for management of elimination programmes. Back-calculation is a methodology that could provide estimates of prevalence of undiagnosed infections, future diagnoses and the effectiveness of control. METHODS: A basic back-calculation model to investigate the infection dynamics of leprosy has been developed using Markov Chain Monte Carlo in a Bayesian context. The incidence of infection and the detection delay both vary with calendar time. Public data from Thailand are used to demonstrate the results that are obtained as the incidence of diagnosed cases falls. RESULTS: The results show that the underlying burden of infection and short-term future predictions of cases can be estimated with a simple model. The downward trend in new leprosy cases in Thailand is expected to continue. In 2015 the predicted total number of undiagnosed sub-clinical and clinical infections is 1,168 (846-1,546) of which 466 (381-563) are expected to be clinical infections. CONCLUSIONS: Bayesian back-calculation has great potential to provide estimates of numbers of individuals in health/infection states that are as yet unobserved. Predictions of future cases provides a quantitative measure of understanding for programme managers and evaluators. We will continue to develop the approach, and suggest that it might be useful for other NTD in which incidence of diagnosis is not an immediate measure of infection

Cow, farm, and herd management factors in the dry period associated with raised somatic cell counts in early lactation

This study investigated cow characteristics, farm facilities, and herd management strategies during the dry period to examine their joint influence on somatic cell counts (SCC) in early lactation. Data from 52 commercial dairy farms throughout England and Wales were collected over a 2-yr period. For the purpose of analysis, cows were separated into those housed for the dry period (6,419 cow-dry periods) and those at pasture (7,425 cow-dry periods). Bayesian multilevel models were specified with 2 response variables: ln SCC (continuous) and SCC >199,000 cells/mL (binary), both within 30 d of calving. Cow factors associated with an increased SCC after calving were parity, an SCC >199,000 cells/mL in the 60 d before drying off, increasing milk yield 0 to 30 d before drying off, and reduced DIM after calving at the time of SCC estimation. Herd management factors associated with an increased SCC after calving included procedures at drying off, aspects of bedding management, stocking density, and method of pasture grazing. Posterior predictions were used for model assessment, and these indicated that model fit was generally good. The research demonstrated that specific dry-period management strategies have an important influence on SCC in early lactation

Impact of imperfect test sensitivity on determining risk factors : the case of bovine tuberculosis

Background Imperfect diagnostic testing reduces the power to detect significant predictors in classical cross-sectional studies. Assuming that the misclassification in diagnosis is random this can be dealt with by increasing the sample size of a study. However, the effects of imperfect tests in longitudinal data analyses are not as straightforward to anticipate, especially if the outcome of the test influences behaviour. The aim of this paper is to investigate the impact of imperfect test sensitivity on the determination of predictor variables in a longitudinal study. Methodology/Principal Findings To deal with imperfect test sensitivity affecting the response variable, we transformed the observed response variable into a set of possible temporal patterns of true disease status, whose prior probability was a function of the test sensitivity. We fitted a Bayesian discrete time survival model using an MCMC algorithm that treats the true response patterns as unknown parameters in the model. We applied our approach to epidemiological data of bovine tuberculosis outbreaks in England and investigated the effect of reduced test sensitivity in the determination of risk factors for the disease. We found that reduced test sensitivity led to changes to the collection of risk factors associated with the probability of an outbreak that were chosen in the ‘best’ model and to an increase in the uncertainty surrounding the parameter estimates for a model with a fixed set of risk factors that were associated with the response variable. Conclusions/Significance We propose a novel algorithm to fit discrete survival models for longitudinal data where values of the response variable are uncertain. When analysing longitudinal data, uncertainty surrounding the response variable will affect the significance of the predictors and should therefore be accounted for either at the design stage by increasing the sample size or at the post analysis stage by conducting appropriate sensitivity analyses

Quantification and determinants of the amount of respiratory syncytial virus (RSV) shed using real time PCR data from a longitudinal household study.

Background A better understanding of respiratory syncytial virus (RSV) epidemiology requires realistic estimates of RSV shedding patterns, quantities shed, and identification of the related underlying factors. Methods RSV infection data arise from a cohort study of 47 households with 493 occupants, in coastal Kenya, during the 2009/2010 RSV season. Nasopharyngeal swabs were taken every 3 to 4 days and screened for RSV using a real time polymerase chain reaction (PCR) assay. The amount of virus shed was quantified by calculating the 'area under the curve' using the trapezoidal rule applied to rescaled PCR cycle threshold output. Multivariable linear regression was used to identify correlates of amount of virus shed. Results The median quantity of virus shed per infection episode was 29.4 (95% CI: 15.2, 54.2) log10 ribonucleic acid (RNA) copies. Young age (<1 year), presence of upper respiratory symptoms, intra-household acquisition of infection, an individual's first infection episode in the RSV season, and having a co-infection of RSV group A and B were associated with increased amount of virus shed. Conclusions The findings provide insight into which groups of individuals have higher potential for transmission, information which may be useful in designing RSV prevention strategies

The level and duration of RSV-specific maternal IgG in infants in Kilifi Kenya

Background Respiratory syncytial virus (RSV) is the major cause of lower respiratory tract infection in infants. The rate of decay of RSV-specific maternal antibodies (RSV-matAb), the factors affecting cord blood levels, and the relationship between these levels and protection from infection are poorly defined. Methods A birth cohort (n = 635) in rural Kenya, was studied intensively to monitor infections and describe age-related serological characteristics. RSV specific IgG antibody (Ab) in serum was measured by the enzyme linked immunosorbent assay (ELISA) in cord blood, consecutive samples taken 3 monthly, and in paired acute and convalescent samples. A linear regression model was used to calculate the rate of RSV-matAb decline. The effect of risk factors on cord blood titres was investigated. Results The half-life of matAb in the Kenyan cohort was calculated to be 79 days (95% confidence limits (CL): 76–81 days). Ninety seven percent of infants were born with RSV-matAb. Infants who subsequently experienced an infection in early life had significantly lower cord titres of anti-RSV Ab in comparison to infants who did not have any incident infection in the first 6 months (P = 0.011). RSV infections were shown to have no effect on the rate of decay of RSV-matAb. Conclusion Maternal-specific RSV Ab decline rapidly following birth. However, we provide evidence of protection against severe disease by RSV-matAb during the first 6–7 months. This suggests that boosting maternal-specific Ab by RSV vaccination may be a useful strategy to consider

"I always know what's going on." Assessing the Relationship between Perceived and Actual Situation Awareness across Different Scenarios

Effective performance in a situation relies on having a good awareness of that situation or at least, if SA is poor, being aware that this is the case. This study examined the bias (tendency to accept or reject available information) and actual and perceived SA of firefighters across two different situations The data suggested that, although actual SA and bia varied across the situations, perceived SA remained relatively constant. This raises the possibility that individuals may have a ‘resting level’ of perceived SA and that the tasks used in this study were effective in manipulating actual SA while perceived SA remained at the resting level

"I always know what's going on." Assessing the Relationship between Perceived and Actual Situation Awareness across Different Scenarios

Effective performance in a situation relies on having a good awareness of that situation or at least, if SA is poor, being aware that this is the case. This study examined the bias (tendency to accept or reject available information) and actual and perceived SA of firefighters across two different situations The data suggested that, although actual SA and bia varied across the situations, perceived SA remained relatively constant. This raises the possibility that individuals may have a ‘resting level’ of perceived SA and that the tasks used in this study were effective in manipulating actual SA while perceived SA remained at the resting level

Is modelling complexity always needed?:Insights from modelling PrEP introduction in South Africa

BACKGROUND: Mathematical models can be powerful policymaking tools. Simple, static models are user-friendly for policymakers. More complex, dynamic models account for time-dependent changes but are complicated to understand and produce. Under which conditions are static models adequate? We compare static and dynamic model predictions of whether behavioural disinhibition could undermine the impact of HIV pre-exposure prophylaxis (PrEP) provision to female sex workers in South Africa. METHODS: A static model of HIV risk was developed and adapted into a dynamic model. Both models were used to estimate the possible reduction in condom use, following PrEP introduction, without increasing HIV risk. The results were compared over a 20-year time horizon, in two contexts: at epidemic equilibrium and during an increasing epidemic. RESULTS: Over time horizons of up to 5 years, the models are consistent. Over longer timeframes, the static model overstates the tolerated reduction in condom use where initial condom use is reasonably high ($\ge$50%) and/or PrEP effectiveness is low ($\le$45%), especially during an increasing epidemic. CONCLUSIONS: Static models can provide useful deductions to guide policymaking around the introduction of a new HIV intervention over short-medium time horizons of up to 5 years. Over longer timeframes, static models may not sufficiently emphasise situations of programmatic importance, especially where underlying epidemics are still increasing

The human brain in fireground decision-making: trustworthy firefighting equipment?

The research programme to date has involved studies of the response of Fire and Rescue (FRS) crew and commanders in fireground training situations and simulations (tabletop, BA and incident command exercises). The studies have revealed patterns and tendencies with potentially serious consequences for real FRS situations. The main conclusion from these studies is that the fire personnel involved were well-trained professionals with good “Situation Awareness” (SA) or knowledge of the incident under study, but there was also evidence of “bias” in decision-making leading to either a tunneling or broadening of focus that may respectively produce “miss” or “false alarm” errors. This tendency is linked to the limits of the human brain under pressure and could explain tragic errors of decision-making such as may have occurred in real-life fire incidents