Mine water outbreak and stability risks : examples and challenges from England and Wales

Abstract Although their frequency of occurrence is rare, the sudden outbreak of mine water from abandoned mines, or collapse of waste rock stores can be environmentally significant and represent significant postclosure legacies. This paper reports on a national survey of abandoned non-coal mine sites where concerns over mine water outbreak or stability are apparent across England and Wales. A range of respondents across environmental regulators and local authorities were consulted to populate a geodatabase. Outbreak risk was highlighted as a documented or suspected concern at 19 mine sites. Typical issues were related to adit blockages and associated perched mine water alongside issues of sudden ingress of surface waters into mines under high flow conditions. The majority of the responses concerning stability issues (72 sites in total) were related to fluvial erosion of riparian waste rock heaps. While successful management of such issues is highlighted in some cases, these are generally isolated examples. In both cases, the fact that stability or outbreak issues are often caused or exacerbated by extreme rainfall events highlights a potential future management issue with the predicted effects of climate change in north west Europe

What's unusual in online disease outbreak news?

Background: Accurate and timely detection of public health events of international concern is necessary to help support risk assessment and response and save lives. Novel event-based methods that use the World Wide Web as a signal source offer potential to extend health surveillance into areas where traditional indicator networks are lacking. In this paper we address the issue of systematically evaluating online health news to support automatic alerting using daily disease-country counts text mined from real world data using BioCaster. For 18 data sets produced by BioCaster, we compare 5 aberration detection algorithms (EARS C2, C3, W2, F-statistic and EWMA) for performance against expert moderated ProMED-mail postings. Results: We report sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), mean alerts/100 days and F1, at 95% confidence interval (CI) for 287 ProMED-mail postings on 18 outbreaks across 14 countries over a 366 day period. Results indicate that W2 had the best F1 with a slight benefit for day of week effect over C2. In drill down analysis we indicate issues arising from the granular choice of country-level modeling, sudden drops in reporting due to day of week effects and reporting bias. Automatic alerting has been implemented in BioCaster available from http://born.nii.ac.jp. Conclusions: Online health news alerts have the potential to enhance manual analytical methods by increasing throughput, timeliness and detection rates. Systematic evaluation of health news aberrations is necessary to push forward our understanding of the complex relationship between news report volumes and case numbers and to select the best performing features and algorithms

Predicting the extinction of Ebola spreading in Liberia due to mitigation strategies

The Ebola virus is spreading throughout West Africa and is causing thousands of deaths. In order to quantify the effectiveness of different strategies for controlling the spread, we develop a mathematical model in which the propagation of the Ebola virus through Liberia is caused by travel between counties. For the initial months in which the Ebola virus spreads, we find that the arrival times of the disease into the counties predicted by our model are compatible with World Health Organization data, but we also find that reducing mobility is insufficient to contain the epidemic because it delays the arrival of Ebola virus in each county by only a few weeks. We study the effect of a strategy in which safe burials are increased and effective hospitalisation instituted under two scenarios: (i) one implemented in mid-July 2014 and (ii) one in mid-August—which was the actual time that strong interventions began in Liberia. We find that if scenario (i) had been pursued the lifetime of the epidemic would have been three months shorter and the total number of infected individuals 80% less than in scenario (ii). Our projection under scenario (ii) is that the spreading will stop by mid-spring 2015.H.E.S. thanks the NSF (grants CMMI 1125290 and CHE-1213217) and the Keck Foundation for financial support. L.D.V. and L.A.B. wish to thank to UNMdP and FONCyT (Pict 0429/2013) for financial support. (CMMI 1125290 - NSF; CHE-1213217 - NSF; Keck Foundation; UNMdP; Pict 0429/2013 - FONCyT)Published versio

Towards cross-lingual alerting for bursty epidemic events

Background: Online news reports are increasingly becoming a source for event based early warning systems that detect natural disasters. Harnessing the massive volume of information available from multilingual newswire presents as many challenges as opportunities due to the patterns of reporting complex spatiotemporal events. Results: In this article we study the problem of utilising correlated event reports across languages. We track the evolution of 16 disease outbreaks using 5 temporal aberration detection algorithms on text-mined events classified according to disease and outbreak country. Using ProMED reports as a silver standard, comparative analysis of news data for 13 languages over a 129 day trial period showed improved sensitivity, F1 and timeliness across most models using cross-lingual events. We report a detailed case study analysis for Cholera in Angola 2010 which highlights the challenges faced in correlating news events with the silver standard. Conclusions: The results show that automated health surveillance using multilingual text mining has the potential to turn low value news into high value alerts if informed choices are used to govern the selection of models and data sources. An implementation of the C2 alerting algorithm using multilingual news is available at the BioCaster portal http://born.nii.ac.jp/?page=globalroundup

Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019.

BackgroundAs of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak.MethodsFor short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott's rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts.ResultsDuring validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872-1054) and 955 cases by March 4 (95% prediction interval: 874-1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876-933) and 898 (95% prediction interval: 877-983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013-2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone.ConclusionsOur projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges

The Global Ability to Respond: Applying SARS Knowledge to H1N1 and Beyond

Influenza outbreaks may be alarming, but they are nothing new in the 21st century. At this point, the various strains of influenza have broken into cities and homes, acted as silent killers by causing fear, death and destruction, and spreading uncontrollably. This repetitive cycle arouses the question of when people will learn how to take care of these epidemics. Well, according to Flahault and Zylberman, knowledge may not be the only factor necessary to stop influenza from disrupting lives. The authors reveal that “Influenza epidemics occur regularly and prediction of their conversion to pandemics and their impact is difficult” meaning there is no tangible definition of each strain or explanation of what it will do (319). Despite this reminder of the lack of control humans have at the viral level, there are aspects of hope that are visible from one outbreak to the next. Specifically, response to the H1N1 epidemic of 2009 was much calmer than the typical reaction to epidemics in the past. This reveals that people were able to learn from past outbreaks, such as SARS in 2003. The effectiveness of response increased in a mere six years which offers a great deal of hope for the way future outbreaks will be handled

Integral chain management of wildlife diseases

The chytrid fungus Batrachochytrium dendrobatidis has caused the most prominent loss of vertebrate diversity ever recorded, which peaked in the 1980s. Recent incursion by its sister species B. salamandrivorans in Europe raised the alarm for a new wave of declines and extinctions in western Palearctic urodeles. The European Commission has responded by restricting amphibian trade. However, private amphibian collections, the main end consumers, were exempted from the European legislation. Here, we report how invasion by a released, exotic newt coincided with B. salamandrivorans invasion at over 1000 km from the nearest natural outbreak site, causing mass mortality in indigenous marbled newts (Triturus marmoratus), and posing an acute threat to the survival of nearby populations of the most critically endangered European newt species (Montseny brook newt, Calotriton arnoldi). Disease management was initiated shortly after detection in a close collaboration between policy and science and included drastic on site measures and intensive disease surveillance. Despite these efforts, the disease is considered temporarily contained but not eradicated and continued efforts will be necessary to minimize the probability of further pathogen dispersal. This precedent demonstrates the importance of tackling wildlife diseases at an early stage using an integrated approach, involving all stakeholders and closing loopholes in existing regulations

Spatial and Temporal Pattern of Rift Valley Fever Outbreaks in Tanzania; 1930 to 2007

Rift Valley fever (RVF)-like disease was first reported in Tanzania more than eight decades ago and the last large outbreak of the disease occurred in 2006–07. This study investigates the spatial and temporal pattern of RVF outbreaks in Tanzania over the past 80 years in order to guide prevention and control strategies. A retrospective study was carried out based on disease reporting data from Tanzania at district or village level. The data were sourced from the Ministries responsible for livestock and human health, Tanzania Meteorological Agency and research institutions involved in RVF surveillance and diagnosis. The spatial distribution of outbreaks was mapped using ArcGIS 10. The space-time permutation model was applied to identify clusters of cases, and a multivariable logistic regression model was used to identify risk factors associated with the occurrence of outbreaks in the district. RVF outbreaks were reported between December and June in 1930, 1947, 1957, 1960, 1963, 1968, 1977– 79, 1989, 1997–98 and 2006–07 in 39.2% of the districts in Tanzania. There was statistically significant spatio-temporal clustering of outbreaks. RVF occurrence was associated with the eastern Rift Valley ecosystem (OR = 6.14, CI: 1.96, 19.28), total amount of rainfall of .405.4 mm (OR = 12.36, CI: 3.06, 49.88), soil texture (clay [OR = 8.76, CI: 2.52, 30.50], and loam [OR = 8.79, CI: 2.04, 37.82]). RVF outbreaks were found to be distributed heterogeneously and transmission dynamics appeared to vary between areas. The sequence of outbreak waves, continuously cover more parts of the country. Whenever infection has been introduced into an area, it is likely to be involved in future outbreaks. The cases were more likely to be reported from the eastern Rift Valley than from the western Rift Valley ecosystem and from areas with clay and loam rather than sandy soil texture