Transmission dynamics and control of trachoma

Trachoma continues to be the leading cause of infectious blindness. Mass administration of antibiotics is part of the current control effort, an approach which is costly when drugs are not donated. Trachoma has been shown to cluster by household and therefore this unit could be a means to target treatment. This thesis shows that active inflammatory disease is a more sensitive marker of infection within an individual’s household than just in that individual. The potential impact of a more efficient, targeted treatment of households with at least one member with active disease depends on the relative contributions of community and household transmission of infection. A mathematical model of the household transmission of ocular Chlamydia trachomatis was fitted to detailed demographic and prevalence data from four endemic populations, two in The Gambia and two in Tanzania. Maximum likelihood estimates of the household and community transmission coefficients were obtained. The estimated household transmission coefficient exceeded both the community transmission coefficient and the rate of clearance of infection by individuals in three of the study populations, indicating persistent transmission of infection within households. Allowing children and adults to have a different duration of infection improved the fit of the model to the data in three populations. For a given level of treatment coverage, targeting antibiotics to households with active disease was predicted to have similar post‐treatment dynamics to those observed after mass treatment but to be much more drug sparing. Using available cost data this approach was shown to be more cost effective when antibiotics are not donated. If targeting increases treatment coverage of diseased households, it was found to be more effective and more cost‐effective than mass treatment even if antibiotics are donated. Further work is now required to explore the feasibility of incorporating household targeted treatment into trachoma control programmes

Population Immunity against Serotype-2 Poliomyelitis Leading up to the Global Withdrawal of the Oral Poliovirus Vaccine: Spatio-temporal Modelling of Surveillance Data.

BACKGROUND: Global withdrawal of serotype-2 oral poliovirus vaccine (OPV2) took place in April 2016. This marked a milestone in global polio eradication and was a public health intervention of unprecedented scale, affecting 155 countries. Achieving high levels of serotype-2 population immunity before OPV2 withdrawal was critical to avoid subsequent outbreaks of serotype-2 vaccine-derived polioviruses (VDPV2s). METHODS AND FINDINGS: In August 2015, we estimated vaccine-induced population immunity against serotype-2 poliomyelitis for 1 January 2004-30 June 2015 and produced forecasts for April 2016 by district in Nigeria and Pakistan. Population immunity was estimated from the vaccination histories of children 70% population immunity among children <36 mo old. Districts with lower immunity were clustered in northeastern Nigeria and northwestern Pakistan. The accuracy of immunity estimates was limited by the small numbers of non-polio AFP cases in some districts, which was reflected by large uncertainty intervals. Forecasted improvements in immunity for April 2016 were robust to the uncertainty in estimates of baseline immunity (January-June 2015), vaccine coverage, and vaccine efficacy. CONCLUSIONS: Immunity against serotype-2 poliomyelitis was forecasted to improve in April 2016 compared to the first half of 2015 in Nigeria and Pakistan. These analyses informed the endorsement of OPV2 withdrawal in April 2016 by the WHO Strategic Advisory Group of Experts on Immunization

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

Boreal Forest Fire Emissions in Fresh Canadian Smoke Plumes: C-1-C-10 Volatile Organic Compounds (Vocs), Co2, Co, No2, No, Hcn and Ch3Cn

Boreal regions comprise about 17% of the global land area, and they both affect and are influenced by climate change. To better understand boreal forest fire emissions and plume evolution, 947 whole air samples were collected aboard the NASA DC-8 research aircraft in summer 2008 as part of the ARCTAS-B field mission, and analyzed for 79 non-methane volatile organic compounds (NMVOCs) using gas chromatography. Together with simultaneous measurements of CO2, CO, CH4, CH2O, NO2, NO, HCN and CH3CN, these measurements represent the most comprehensive assessment of trace gas emissions from boreal forest fires to date. Based on 105 air samples collected in fresh Canadian smoke plumes, 57 of the 80 measured NMVOCs (including CH2O) were emitted from the fires, including 45 species that were quantified from boreal forest fires for the first time. After CO2, CO and CH4, the largest emission factors (EFs) for individual species were formaldehyde (2.1 +/- 0.2 g kg(-1)), followed by methanol, NO2, HCN, ethene, alpha-pinene, beta-pinene, ethane, benzene, propene, acetone and CH3CN. Globally, we estimate that boreal forest fires release 2.4 +/- 0.6 TgC yr(-1) in the form of NMVOCs, with approximately 41% of the carbon released as C-1-C-2 NMVOCs and 21% as pinenes. These are the first reported field measurements of monoterpene emissions from boreal forest fires, and we speculate that the pinenes, which are relatively heavy molecules, were detected in the fire plumes as the result of distillation of stored terpenes as the vegetation is heated. Their inclusion in smoke chemistry models is expected to improve model predictions of secondary organic aerosol (SOA) formation. The fire-averaged EF of dichloromethane or CH2Cl2, (6.9 +/- 8.6) x 10(-4) g kg(-1), was not significantly different from zero and supports recent findings that its global biomass burning source appears to have been overestimated. Similarly, we found no evidence for emissions of chloroform (CHCl3) or methyl chloroform (CH3CCl3) from boreal forest fires. The speciated hydrocarbon measurements presented here show the importance of carbon released by short-chain NMVOCs, the strong contribution of pinene emissions from boreal forest fires, and the wide range of compound classes in the most abundantly emitted NMVOCs, all of which can be used to improve biomass burning inventories in local/global models and reduce uncertainties in model estimates of trace gas emissions and their impact on the atmosphere

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

Studying interactions among anthropogenic stressors in freshwater ecosystems: A systematic review of 2396 multiple‐stressor experiments

Understanding the interactions among anthropogenic stressors is critical for effective conservation and management of ecosystems. Freshwater scientists have invested considerable resources in conducting factorial experiments to disentangle stressor interactions by testing their individual and combined effects. However, the diversity of stressors and systems studied has hindered previous syntheses of this body of research. To overcome this challenge, we used a novel machine learning framework to identify relevant studies from over 235,000 publications. Our synthesis resulted in a new dataset of 2396 multiple‐stressor experiments in freshwater systems. By summarizing the methods used in these studies, quantifying trends in the popularity of the investigated stressors, and performing co‐occurrence analysis, we produce the most comprehensive overview of this diverse field of research to date. We provide both a taxonomy grouping the 909 investigated stressors into 31 classes and an open‐source and interactive version of the dataset (https://jamesaorr.shinyapps.io/freshwater‐multiple‐stressors/). Inspired by our results, we provide a framework to help clarify whether statistical interactions detected by factorial experiments align with stressor interactions of interest, and we outline general guidelines for the design of multiple‐stressor experiments relevant to any system. We conclude by highlighting the research directions required to better understand freshwater ecosystems facing multiple stressors