Estimating Household and Community Transmission of Ocular Chlamydia trachomatis

Trachoma is a major cause of blindness worldwide and results from ocular infection with the bacterium Chlamydia trachomatis. Mass distribution of antibiotics in communities is part of the strategy to eliminate blindness due to trachoma. Targeted treatment of infected households could be more efficient, but the success of such a strategy will depend on the extent of transmission of infection between members of the same household and between members of the community. In this work, we estimated the magnitude of household and community transmission in four populations, two from The Gambia and two from Tanzania. We found that, in general, transmission of the bacteria within households is very efficient. In three of the four populations, persistent infection within households was predicted by the high level of household transmission (a phenomenon observed in longitudinal studies of trachoma). In all of the studied populations, individuals who live in households with more individuals contribute more to the number of new infections in the community than those who live with fewer individuals. Further studies are required to identify and examine household-targeted approaches to treatment

A dual-chamber method for quantifying the effects of atmospheric perturbations on secondary organic aerosol formation from biomass burning emissions

Biomass burning (BB) is a major source of atmospheric pollutants. Field and laboratory studies indicate that secondary organic aerosol (SOA) formation from BB emissions is highly variable. We investigated sources of this variability using a novel dual-smog-chamber method that directly compares the SOA formation from the same BB emissions under two different atmospheric conditions. During each experiment, we filled two identical Teflon smog chambers simultaneously with BB emissions from the same fire. We then perturbed the smoke with UV lights, UV lights plus nitrous acid (HONO), or dark ozone in one or both chambers. These perturbations caused SOA formation in nearly every experiment with an average organic aerosol (OA) mass enhancement ratio of 1.78 ± 0.91 (mean ± 1σ). However, the effects of the perturbations were highly variable ranging with OA mass enhancement ratios ranging from 0.7 (30% loss of OA mass) to 4.4 across the set of perturbation experiments. There was no apparent relationship between OA enhancement and perturbation type, fuel type, and modified combustion efficiency. To better isolate the effects of different perturbations, we report dual-chamber enhancement (DUCE), which is the quantity of the effects of a perturbation relative to a reference condition. DUCE values were also highly variable, even for the same perturbation and fuel type. Gas measurements indicate substantial burn-to-burn variability in the magnitude and composition of SOA precursor emissions, even in repeated burns of the same fuel under nominally identical conditions. Therefore, the effects of different atmospheric perturbations on SOA formation from BB emissions appear to be less important than burn-to-burn variability

The role of rapid diagnostics in managing Ebola epidemics

Ebola emerged in West Africa around December 2013 and swept through Guinea, Sierra Leone and Liberia, giving rise to 27,748 confirmed, probable and suspected cases reported by 29 July 2015. Case diagnoses during the epidemic have relied on polymerase chain reaction-based tests. Owing to limited laboratory capacity and local transport infrastructure, the delays from sample collection to test results being available have often been 2 days or more. Point-of-care rapid diagnostic tests offer the potential to substantially reduce these delays. We review Ebola rapid diagnostic tests approved by the World Health Organization and those currently in development. Such rapid diagnostic tests could allow early triaging of patients, thereby reducing the potential for nosocomial transmission. In addition, despite the lower test accuracy, rapid diagnostic test-based diagnosis may be beneficial in some contexts because of the reduced time spent by uninfected individuals in health-care settings where they may be at increased risk of infection; this also frees up hospital beds. We use mathematical modelling to explore the potential benefits of diagnostic testing strategies involving rapid diagnostic tests alone and in combination with polymerase chain reaction testing. Our analysis indicates that the use of rapid diagnostic tests with sensitivity and specificity comparable with those currently under development always enhances control, whether evaluated at a health-care-unit or population level. If such tests had been available throughout the recent epidemic, we estimate, for Sierra Leone, that their use in combination with confirmatory polymerase chain-reaction testing might have reduced the scale of the epidemic by over a third

Potential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks

Estimating the case-fatality risk (CFR)—the probability that a person dies from an infection given that they are a case—is a high priority in epidemiologic investigation of newly emerging infectious diseases and sometimes in new outbreaks of known infectious diseases. The data available to estimate the overall CFR are often gathered for other purposes (e.g., surveillance) in challenging circumstances. We describe two forms of bias that may affect the estimation of the overall CFR—preferential ascertainment of severe cases and bias from reporting delays—and review solutions that have been proposed and implemented in past epidemics. Also of interest is the estimation of the causal impact of specific interventions (e.g., hospitalization, or hospitalization at a particular hospital) on survival, which can be estimated as a relative CFR for two or more groups. When observational data are used for this purpose, three more sources of bias may arise: confounding, survivorship bias, and selection due to preferential inclusion in surveillance datasets of those who are hospitalized and/or die. We illustrate these biases and caution against causal interpretation of differential CFR among those receiving different interventions in observational datasets. Again, we discuss ways to reduce these biases, particularly by estimating outcomes in smaller but more systematically defined cohorts ascertained before the onset of symptoms, such as those identified by forward contact tracing. Finally, we discuss the circumstances in which these biases may affect non-causal interpretation of risk factors for death among cases

A simple approach to measure transmissibility and forecast incidence

Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola epidemic in West Africa, a forecasting exercise was convened by a network of infectious disease modellers. The challenge was to forecast unseen “future” simulated data for four different scenarios at five different time points. In a similar method to that used during the recent Ebola epidemic, we estimated current levels of transmissibility, over variable time-windows chosen in an ad hoc way. Current estimated transmissibility was then used to forecast near-future incidence. We performed well within the challenge and often produced accurate forecasts. A retrospective analysis showed that our subjective method for deciding on the window of time with which to estimate transmissibility often resulted in the optimal choice. However, when near-future trends deviated substantially from exponential patterns, the accuracy of our forecasts was reduced. This exercise highlights the urgent need for infectious disease modellers to develop more robust descriptions of processes – other than the widespread depletion of susceptible individuals – that produce non-exponential patterns of incidence

Targeting Antibiotics to Households for Trachoma Control

Repeated ocular infection with the bacterium Chlamydia trachomatis leads to the development of trachoma, a major cause of infectious blindness worldwide. Mass distribution of antibiotics, a component of the current trachoma control strategy, has had success in reducing infection in some areas, but results in a large number of uninfected people receiving antibiotics. We have previously shown that transmission of the bacteria between people in the same household is very efficient. Here, we investigated the effectiveness and cost-effectiveness of targeting antibiotics to households with active trachoma (inflammatory disease) compared to mass distribution, using data from four trachoma-endemic populations and a mathematical model of transmission. We found a high correspondence between households with active trachoma and infected households. In all populations the household targeted approach was predicted to be as effective as mass distribution, but it reduced the number of uninfected individuals receiving antibiotics, making the targeted strategy more cost-effective when antibiotics are not donated. Assuming antibiotics are donated, we predicted the targeted strategy to be more cost effective if it increases the proportion of infected individuals receiving treatment. Further work to address the feasibility and the cost variability in implementing the targeted approach in different settings is now required

The Development of an Age-Structured Model for Trachoma Transmission Dynamics, Pathogenesis and Control

Trachoma is the worldwide leading infectious cause of blindness and is due to repeated conjunctival infection with Chlamydia trachomatis bacteria. The effects of control interventions on population levels of infection and active disease can be promptly measured, but the effects on severe ocular disease outcomes require long-term monitoring. We present a mathematical model of trachoma transmission and disease to predict the impact of interventions on blinding trachoma. The model is based on the concept of multiple re-infections leading to progressive scarring of the eye and the potentially blinding disease sequelae. It includes aspects of trachoma natural history such as an increasing rate of recovery from infection, and a decreasing chlamydial load with subsequent infections. The model reproduces key features of trachoma epidemiology such as the age-profile of infection prevalence; a shift in the prevalence peak toward younger ages in higher-transmission environments; and a rising profile of the prevalence of the severe sequelae (scarring, trichiasis), as well as estimates of the number of infections experienced before these sequelae appear. The model can be used to examine the outcomes of various control strategies on infection and disease and can help to plan treatment interventions for different endemic settings

Exposure Patterns Driving Ebola Transmission in West Africa:A Retrospective Observational Study

BackgroundThe ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved.Methods and findingsOver 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola ("cases") were asked if they had exposure to other potential Ebola cases ("potential source contacts") in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO's response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p ConclusionsAchieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population