Biodiversity and ecosystem function in soil

1. Soils are one of the last great frontiers for biodiversity research and are home to an extraordinary range of microbial and animal groups. Biological activities in soils drive many of the key ecosystem processes that govern the global system, especially in the cycling of elements such as carbon, nitrogen and phosphorus. 2. We cannot currently make firm statements about the scale of biodiversity in soils, or about the roles played by soil organisms in the transformations of organic materials that underlie those cycles. The recent UK Soil Biodiversity Programme (SBP) has brought a unique concentration of researchers to bear on a single soil in Scotland, and has generated a large amount of data concerning biodiversity, carbon flux and resilience in the soil ecosystem. 3. One of the key discoveries of the SBP was the extreme diversity of small organisms: researchers in the programme identified over 100 species of bacteria, 350 protozoa, 140 nematodes and 24 distinct types of arbuscular mycorrhizal fungi. Statistical analysis of these results suggests a much greater 'hidden diversity'. In contrast, there was no unusual richness in other organisms, such as higher fungi, mites, collembola and annelids. 4. Stable-isotope (C-13) technology was used to measure carbon fluxes and map the path of carbon through the food web. A novel finding was the rapidity with which carbon moves through the soil biota, revealing an extraordinarily dynamic soil ecosystem. 5. The combination of taxonomic diversity and rapid carbon flux makes the soil ecosystem highly resistant to perturbation through either changing soil structure or removing selected groups of organisms

HEPA/Vaccine Plan for Indoor Anthrax Remediation

A mathematical model suggests that a HEPA/vaccine approach is viable for most buildings after a large-scale anthrax attack

Simulated Anthrax Attacks and Syndromic Surveillance

Bioterrorism surveillance systems can be assessed using modeling to simulate real-world attacks

Syndromic Surveillance and Bioterrorism-related Epidemics

To facilitate rapid detection of a future bioterrorist attack, an increasing number of public health departments are investing in new surveillance systems that target the early manifestations of bioterrorism-related disease. Whether this approach is likely to detect an epidemic sooner than reporting by alert clinicians remains unknown. The detection of a bioterrorism-related epidemic will depend on population characteristics, availability and use of health services, the nature of an attack, epidemiologic features of individual diseases, surveillance methods, and the capacity of health departments to respond to alerts. Predicting how these factors will combine in a bioterrorism attack may be impossible. Nevertheless, understanding their likely effect on epidemic detection should help define the usefulness of syndromic surveillance and identify approaches to increasing the likelihood that clinicians recognize and report an epidemic

A simulation study comparing aberration detection algorithms for syndromic surveillance

BACKGROUND: The usefulness of syndromic surveillance for early outbreak detection depends in part on effective statistical aberration detection. However, few published studies have compared different detection algorithms on identical data. In the largest simulation study conducted to date, we compared the performance of six aberration detection algorithms on simulated outbreaks superimposed on authentic syndromic surveillance data. METHODS: We compared three control-chart-based statistics, two exponential weighted moving averages, and a generalized linear model. We simulated 310 unique outbreak signals, and added these to actual daily counts of four syndromes monitored by Public Health – Seattle and King County's syndromic surveillance system. We compared the sensitivity of the six algorithms at detecting these simulated outbreaks at a fixed alert rate of 0.01. RESULTS: Stratified by baseline or by outbreak distribution, duration, or size, the generalized linear model was more sensitive than the other algorithms and detected 54% (95% CI = 52%–56%) of the simulated epidemics when run at an alert rate of 0.01. However, all of the algorithms had poor sensitivity, particularly for outbreaks that did not begin with a surge of cases. CONCLUSION: When tested on county-level data aggregated across age groups, these algorithms often did not perform well in detecting signals other than large, rapid increases in case counts relative to baseline levels

Adherence to Antimicrobial Inhalational Anthrax Prophylaxis among Postal Workers, Washington, D.C., 2001

In October 2001, two envelopes containing Bacillus anthracis spores were processed at the Washington, D.C., Processing and Distribution Center of the U.S. Postal Service; inhalational anthrax developed in four workers at this facility. More than 2,000 workers were advised to complete 60 days of postexposure prophylaxis to prevent inhalational anthrax. Interventions to promote adherence were carried out to support workers, and qualitative information was collected to evaluate our interventions. A quantitative survey was administered to a convenience sample of workers to assess factors influencing adherence. No anthrax infections developed in any workers involved in the interventions or interviews. Of 245 workers, 98 (40%) reported full adherence to prophylaxis, and 45 (18%) had completely discontinued it. Experiencing adverse effects to prophylaxis, anxiety, and being <45 years old were risk factors for discontinuing prophylaxis. Interventions, especially frequent visits by public health staff, proved effective in supporting adherence

Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts

<p>Abstract</p> <p>Background</p> <p>Public health surveillance is the monitoring of data to detect and quantify unusual health events. Monitoring pre-diagnostic data, such as emergency department (ED) patient chief complaints, enables rapid detection of disease outbreaks. There are many sources of variation in such data; statistical methods need to accurately model them as a basis for timely and accurate disease outbreak methods.</p> <p>Methods</p> <p>Our new methods for modeling daily chief complaint counts are based on a seasonal-trend decomposition procedure based on loess (STL) and were developed using data from the 76 EDs of the Indiana surveillance program from 2004 to 2008. Square root counts are decomposed into inter-annual, yearly-seasonal, day-of-the-week, and random-error components. Using this decomposition method, we develop a new synoptic-scale (days to weeks) outbreak detection method and carry out a simulation study to compare detection performance to four well-known methods for nine outbreak scenarios.</p> <p>Result</p> <p>The components of the STL decomposition reveal insights into the variability of the Indiana ED data. Day-of-the-week components tend to peak Sunday or Monday, fall steadily to a minimum Thursday or Friday, and then rise to the peak. Yearly-seasonal components show seasonal influenza, some with bimodal peaks.</p> <p>Some inter-annual components increase slightly due to increasing patient populations. A new outbreak detection method based on the decomposition modeling performs well with 90 days or more of data. Control limits were set empirically so that all methods had a specificity of 97%. STL had the largest sensitivity in all nine outbreak scenarios. The STL method also exhibited a well-behaved false positive rate when run on the data with no outbreaks injected.</p> <p>Conclusion</p> <p>The STL decomposition method for chief complaint counts leads to a rapid and accurate detection method for disease outbreaks, and requires only 90 days of historical data to be put into operation. The visualization tools that accompany the decomposition and outbreak methods provide much insight into patterns in the data, which is useful for surveillance operations.</p

Biosecurity and biodefense

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