An early warning indicator for monitoring infectious animal diseases and its application in the case of a sheep pox epidemic

We describe an early warning indicator for monitoring infectious diseases. The approach is illustrated with data from a major sheep pox epidemic, which occurred in the Evros prefecture of Greece, from August 2013 until the end of 2014. We present the epidemic data and identify factors affecting this major epidemic using a suitable methodological framework for the sequential analysis of such outbreaks. Environmental conditions and seasonality affect the spread of the epidemic: autumn seems to enhance disease occurrence and higher average temperatures facilitate disease spread. The on-line prediction of the disease potential is investigated through an early warning system that is based on the probability of the disease going extinct. The temporal stability of this indicator is extensively explored.     

A web-based system for public–private sector collaborative ecosystem management

 To preserve biodiversity over centuries, ecosystem management will need to be accepted and practiced by individuals from a broad spectrum of society's strata. Also, management decisions will need to be based on reliable judgments of the cause and effect relationships that govern an ecosystem's dynamics. This article describes an extant, web-based ecosystem management system (EMS) that allows (a) wide participation in ecosystem assessment and policy impact predictions, (b) convenient construction of probabilistic models of ecosystem processes through an influence diagram, and (c) automatic creation of ecosystem assessment reports. For illustration, the system is used to first model the cheetah population in Kenya, and then to assess the impact on this population of different management options. The influence diagram used herein extends standard influence diagram theory to allow representation of variables governed by stochastic differential equations, birth–death processes, and other nongaussian, continuous probability distributions. For many ecosystems, data sets on ecosystem health indicators can be incomplete, small, and contain unknown measurement errors. Some amount of knowledge of an ecosystem's dynamics however, may exist in the form of expert opinion derived from ecological theory. The proposed EMS uses a nonbayesian parameter estimation method, called consistency analysis that finds parameter estimates such that the fitted ecosystem model is as faithful as possible to both the available data and the collected body of expert opinion. For illustration, consistency analysis is used to estimate the cheetah viability influence diagram using all known cheetah surveys in the country of Kenya plus current understanding of factors such as habitat and prey availability that affect cheetah population dynamics.     

A big-data spatial,temporal and network analysis of bovine tuberculosis between wildlife (badgers) and cattle

Bovine tuberculosis (TB) poses a serious threat for agricultural industry in several countries, it involves potential interactions between wildlife and cattle and creates societal problems in terms of human-wildlife conflict. This study addresses connectedness network analysis, the spatial, and temporal dynamics of TB between cattle in farms and the European badger (Meles meles) using a large dataset generated by a calibrated agent based model. Results showed that infected network connectedness was lower in badgers than in cattle. The contribution of an infected individual to the mean distance of disease spread over time was considerably lower for badger than cattle; badgers mainly spread the disease locally while cattle infected both locally and across longer distances. The majority of badger-induced infections occurred when individual badgers leave their home sett, and this was positively correlated with badger population growth rates. Point pattern analysis indicated aggregation in the spatial pattern of TB prevalence in badger setts across all scales. The spatial distribution of farms that were not TB free was aggregated at different scales than the spatial distribution of infected badgers and became random at larger scales. The spatial cross correlation between infected badger setts and infected farms revealed that generally infected setts and farms do not coexist except at few scales. Temporal autocorrelation detected a two year infection cycle for badgers, while there was both within the year and longer cycles for infected cattle. Temporal cross correlation indicated that infection cycles in badgers and cattle are negatively correlated. The implications of these results for understanding the dynamics of the disease are discussed.     

Assessing risk perception and behavioral responses to influenza epidemics: linking information theory to probabilistic risk modeling

Risk perception plays a crucial role in shaping health-related behaviors in a variety of infectious disease control settings. The purpose of this study was to assess risk perception and behavioral changes in response to influenza epidemics. We present a risk perception assessment model that uses information theory linking with a probabilistic risk model to investigate the interplay between risk perception spread and health behavioral changes for an influenza outbreak. Building on human influenza data, we predicted risk perception spread as the amount of risk information. A negative feedback-based information model was used to explore whether health behavioral changes can increase the control effectiveness. Finally, a probabilistic risk assessment framework was used to predict influenza infection risk based on maximal information-derived risk perception. We found that (i) an individual who perceived more accurate knowledge of influenza can substantially increase the amount of mutual risk perception information, (ii) an intervening network over which individuals communicate overlap can be more effective in risk perception transfer, (iii) collective individual responses can increase risk perception information transferred, but may be limited by contact numbers of infectious individuals, and (iv) higher mutual risk perception information gains lower infection risk probability. We also revealed that when people increased information about the benefits of vaccination and antiviral drug used, future infections could significantly be prevented. We suggest that increasing mutual risk perception information through a negative feedback mechanism plays an important role in adaptation and mitigation behavior and policy support.     

Runoff sensitivity to climate change for hydro-climatically different catchments in China

Communities are facing severe water stress due to the rapid development of agriculture and industry, climate change, as well as population growth. Climate variability has a big impact on runoff variation and it is important to understand these hydrological responses. Using a water balance model, monthly discharges of 21 climatically different catchments in China were simulated. Sensitivities of runoff to climate change were investigated by adopting hypothetical climate scenarios. Results indicate that the water balance model performs well for monthly discharge simulations of climatically different catchments with Nash–Sutcliffe coefficients >65 % and relative errors falling in the range of ±5 %. In general, runoff in arid north China are more sensitive to climate change than those in humid south China. A 1 °C rise in temperature would probably lead to 1.2–4.4 % decreases in runoff. A decrease in precipitation of 10 % would result in 9.4–17.4 % of decreases in runoff. It is essential to consider the implications of climate change in future water resources management.     

Assessment of the Accuracy of SRTM C- and X-Band High Mountain Elevation Data: a Case Study of the Polish Tatra Mountains

Global digital elevation models (DEMs) are an invaluable source of information in large area studies. Of particular interest are shuttle radar topography mission (SRTM) data that are freely available for the scientific community worldwide. Prior to any application, global datasets should be evaluated using reference data of higher accuracy. Therefore, the main objective of this study was to assess the accuracy of the SRTM C-band (version 4) DEM and SRTM X-band DEM of mountainous areas located in Poland and to examine the quality of data in relation to topographic parameters, radar beam geometry, initial voids in data and the presence of forest cover. A DEM from the Central National Geodetic and Cartographic Inventory, Poland, served as a reference. The study consisted of three steps: (i) the computation of vertical errors of the SRTM C- and X-band DEMs, (ii) the examination of any systematic bias in the data, and (iii) the analysis of the relationships between the elevation errors and terrain slope, aspect, local incidence angle, occurrence of voids and land cover. We found that the SRTM C- and X-band DEMs have mean errors equal to 4.31 ± 14.09 and 9.03 ± 37.40 m and root mean square errors equal to 14.74 and 38.47 m, respectively. Only 82 % of the C-band DEM and 74 % of the X-band DEM vertical errors had absolute values below 16 m. We found that the most important factors determining the occurrence of high errors were the distribution of initial voids and high slope angles for the C-band DEM, and local incidence angle, slope, aspect and radar beam geometry for the X-band DEM. In both cases, the presence of forest cover increased the mean error by approximately 10 m.     

Probabilistic assessment of aggregate risk for bisphenol A by integrating the currently available environmental data

Bisphenol A (BPA) is an endocrine disruptor widely used in the production of polycarbonate plastics and epoxy resins. Exposures to BPA have been associated with reproductive, developmental, and cardiovascular effects. In this study, the CalTOX model was used to assess the aggregate health risks on BPA by integrating the currently available BPA data in various environmental media in Taiwan. Local parameters such as chemical properties, local landscape data, and exposure factors were used as model inputs under the continuous source mode. A reference dose (RfD) of 50 μg/kg-day was adopted in this assessment. Monte Carlo simulation was used to simulate great variability of the environmental data. Our results show that an upper limit of 95 % confidence interval of aggregate exposures for the adults (19–64 years old) was 1.05 μg/kg-day, corresponding to a hazard index (HI) of 0.021. The chemical properties (BPA half-life in surface water), intake rates (fruit, vegetable, and fluid intake), and landscape data (average depth of surface waters and leaf wet density) are critical parameters. Finally, HI value would approach to 1 as BPA concentrations in ambient air, surface water, and sediment was greater than 20 ng/m³, 100 μg/L, and 3.3 mg/kg. The quality of the risk assessment on BPA can be further improved by reduction of uncertainty of the abovementioned critical parameters as well as considering additional BPA exposures from canned and packaged goods.     

The Twentieth-Century Sea Level Budget: Recent Progress and Challenges

For coastal areas, given the large and growing concentration of population and economic activity, as well as the importance of coastal ecosystems, sea level rise is one of the most damaging aspects of the warming climate. Huge progress in quantifying the cause of sea level rise and closure of sea level budget for the period since the 1990s has been made mainly due to the development of the global observing system for sea level components and total sea levels. We suggest that a large spread (1.2 ± 0.2–1.9 ± 0.3 mm year^?1) in estimates of sea level rise during the twentieth century from several reconstructions demonstrates the need for and importance of the rescue of historical observations from tide gauges, with a focus on the beginning of the twentieth century. Understanding the physical mechanisms contributing to sea level rise and controlling the variability of sea level over the past few 100 years are a challenging task. In this study, we provide an overview of the progress in understanding the cause of sea level rise during the twentieth century and highlight the main challenges facing the interdisciplinary sea level community in understanding the complex nature of sea level changes.     

Metal accumulation within salt marsh environments: A review

A comprehensive assessment of the chemical and physical factors affecting metal accumulation and cycling within salt marshes is presented. The effects that changes in physico-chemical properties (redox potential, salinity, pH, etc.) have upon metal mobility, speciation and consequent biological availability are described together with the implications for salt marsh habitat loss. Salt marshes act as very efficient sinks for metal contaminants although metal concentrations in halophytes do not generally reflect environmental contamination levels. Marine angiosperms, particularly Zostera marina, do however, reflect external metal concentrations and can therefore be used as biomonitors. Evidence suggests that the concentration of heavy metals in the sediments of most estuaries is not sufficiently high to cause ill effects to salt marsh plants although further investigations are necessary to assess potential threats of pollutants upon the health of these intertidal ecosystems.     

Decisions under uncertainty: a computational framework for quantification of policies addressing infectious disease epidemics

Emerging infectious diseases continue to place a strain on the welfare of the population by decreasing the population’s general health and increasing the burden on public health infrastructure. This paper addresses these issues through the development of a computational framework for modeling and simulating infectious disease outbreaks in a specific geographic region facilitating the quantification of public health policy decisions. Effectively modeling and simulating past epidemics to project current or future disease outbreaks will lead to improved control and intervention policies and disaster preparedness. In this paper, we introduce a computational framework that brings together spatio–temporal geography and population demographics with specific disease pathology in a novel simulation paradigm termed, global stochastic field simulation (GSFS). The primary aim of this simulation paradigm is to facilitate intelligent what-if-analysis in the event of health crisis, such as an influenza pandemic. The dynamics of any epidemic are intrinsically related to a region’s spatio–temporal characteristics and demographic composition and as such, must be considered when developing infectious disease control and intervention strategies. Similarly, comparison of past and current epidemics must include demographic changes into any effective public health policy for control and intervention strategies. GSFS is a hybrid approach to modeling, implicitly combining agent-based modeling with the cellular automata paradigm. Specifically, GSFS is a computational framework that will facilitate the effective identification of risk groups in the population and determine adequate points of control, leading to more effective surveillance and control of infectious diseases epidemics. The analysis of past disease outbreaks in a given population and the projection of current or future epidemics constitutes a significant challenge to Public Health. The corresponding design of computational models and the simulation that facilitates epidemiologists’ understanding of the manifestation of diseases represents a challenge to computer and mathematical sciences.     

A macroseismic intensity prediction equation for intermediate depth earthquakes in the Vrancea region,Romania

The use of shake maps in terms of macroseismic intensity in earthquake early warning systems as well as intensity based seismic hazard assessments provides a valuable supplement to typical studies based on recorded ground motion parameters. A requirement for such applications is ground motion prediction equations (GMPE) in terms of macroseismic intensity, which have the advantages of good data availability and the direct relation of intensity to earthquake damage. In the current study, we derive intensity prediction equations for the Vrancea region in Romania, which is characterized by the frequent occurrence of large intermediate depth earthquakes giving rise to a peculiar anisotropic ground shaking distribution. The GMPE have a physical basis and take the anisotropic intensity distribution into account through an empirical regional correction function. Furthermore, the relations are easy to implement for the user. Relations are derived in terms of epicentral, rupture and Joyner–Boore distance and the obtained relations all provide a new intensity estimate with an uncertainty of ca. 0.6 intensity units.     

Hydrogeochemical investigation of irrigation water in the vicinity of metallic ore deposits in Kiraz-İzmir,Turkey: Understanding the crucial nexus between “geology and food safety”

The increasing drought due to climate change poses a threat to issues such as safe and accessible drinking water, food safety, and protection from diseases. The provision of water supply is vital for agricultural and livestock activities, which are commonly practiced around natural ore deposits. Examining traditional “irrigation water quality” methods alone is insufficient; investigating potentially toxic metal content in the region's waters is vital, especially around metallic ore deposits. This study focused on the Kiraz district in Turkey, known for its agricultural activities, to assess the impact of geogenic water pollution on irrigation water quality and its implications for food safety and human health. Geology determines nutrient availability, water resources, and land suitability for agriculture. Conventional irrigation water quality parameters indicate groundwater suitability for irrigation in the study area, considering Na%, sodium adsorption ratio, residual sodium carbonate, permeability index, Kelly ratio, magnesium hazard, and potential salinity. However, when examining the potential toxic metal content in the region, it was determined that the values of Al ranged from 96 to 8676 ppb, Ni values ranged from 27 to 360 ppb, and Sb concentrations varied between 9 and 53 432 ppb. Utilizing geogenically contaminated water for irrigation and its indiscriminate use in livestock, dairy, and food industries can lead to foodborne illnesses (cancer, endocrine disruptors, tuberculosis, antimony spots, thyroid tumors, goiter, neurologic and cardiovascular diseases) that endanger human health. The use of low-quality water throughout the agricultural sector and food production chain increases food safety risks.     

Elucidating transmission parameters of African swine fever through wild boar carcasses by combining spatio-temporal notification data and agent-based modelling

Mechanistic epidemiological modelling has a role in predicting the spatial and temporal spread of emerging disease outbreaks and purposeful application of control treatment in animal populations. Especially in the case of infectious diseases newly emerging in an ecological habitat, lack of knowledge may hamper direct parameterisation of model algorithms. Along with experimental studies observational data is usually based on case notifications. These data are widely acknowledged as having “biological precision” due to e.g. convenient sampling procedures, host or human activity patterns or diagnostic limitations under field conditions. Nevertheless, the data comprises the complex spatio-temporal distribution patterns of the infection. In the literature, this data value is non-systematically used to inform model development although the need for and value of the data is well recognised. Here we address the newly emerging epidemic of African swine fever spreading in Eurasian wild boar using an existing spatio-temporally explicit individual-based model of wild boar. The disease etiology required the implementation of a sub-model regarding transmission by carcasses left after infected individuals have died. However, the experimental evidence about the mechanism involved in carcass-mediated spread of the infection still has to be established. We propose a mechanistic quantitative procedure to optimise calibration of several uncertain parameters based on the spatio-temporal model output from the simulation environment and the spatio-temporal case data of infectious disease notifications. The best agreement with the spatio-temporal spreading pattern was achieved by parameterisation that suggests ubiquitous accessibility to carcasses but with marginal chance of being contacted by conspecifics e.g., avoidance behaviour. The parameter estimation procedure is fully general and applicable to problems where spatio-temporal explicit data recording and spatial-explicit dynamic modelling was performed.     

Water safety: management,perception and health implications in Santa agricultural area,North-West Region,Cameroon

ABSTRACT

Santa agricultural area is a key production site for crops in Cameroon. This study aimed to look at the risk factors, knowledge and health implications of water pollution across 10 villages, in the area: 140 water points were visited and questionnaires randomly administered to water users, while health data were collected from the two local hospitals. Water sources are tap, stream, rain, well and spring and the water is used for agriculture, domestic activities, hygiene and sanitation. Pesticide pollution was illustrated by activities such as spraying, mixing and management of waste containers and purification methods are chlorination, boiling, sedimentation, filtration and refrigeration. Waterborne diseases are cholera, typhoid (the most prevalent), diarrhea, dysentery and skin diseases. Many water sources are close to toilets, farms and dumping sites. We found that 75% of respondents were not satisfied with the quality of water. Our results will be interest for water management, and to educate users on the risks linked to current practices.     

Multivariate Prediction of Total Water Storage Changes Over West Africa from Multi-Satellite Data

West African countries have been exposed to changes in rainfall patterns over the last decades, including a significant negative trend. This causes adverse effects on water resources of the region, for instance, reduced freshwater availability. Assessing and predicting large-scale total water storage (TWS) variations are necessary for West Africa, due to its environmental, social, and economical impacts. Hydrological models, however, may perform poorly over West Africa due to data scarcity. This study describes a new statistical, data-driven approach for predicting West African TWS changes from (past) gravity data obtained from the gravity recovery and climate experiment (GRACE), and (concurrent) rainfall data from the tropical rainfall measuring mission (TRMM) and sea surface temperature (SST) data over the Atlantic, Pacific, and Indian Oceans. The proposed method, therefore, capitalizes on the availability of remotely sensed observations for predicting monthly TWS, a quantity which is hard to observe in the field but important for measuring regional energy balance, as well as for agricultural, and water resource management. Major teleconnections within these data sets were identified using independent component analysis and linked via low-degree autoregressive models to build a predictive framework. After a learning phase of 72 months, our approach predicted TWS from rainfall and SST data alone that fitted to the observed GRACE-TWS better than that from a global hydrological model. Our results indicated a fit of 79 % and 67 % for the first-year prediction of the two dominant annual and inter-annual modes of TWS variations. This fit reduces to 62 % and 57 % for the second year of projection. The proposed approach, therefore, represents strong potential to predict the TWS over West Africa up to 2 years. It also has the potential to bridge the present GRACE data gaps of 1 month about each 162 days as well as a—hopefully—limited gap between GRACE and the GRACE follow-on mission over West Africa. The method presented could also be used to generate a near-real-time GRACE forecast over the regions that exhibit strong teleconnections.     

三层BP神经网络地震灾害人员伤亡预测模型

选择地震发生时刻、震级、震中烈度、建筑物倒塌和严重破坏率、抗震设防水准、人口密度、地震预报等7个评价指标,以20次严重地震灾害为示例（其中,17个作训练样本,3个作验证样本）,建立了三层BP神经网络地震灾害人员伤亡预测模型。基于MATLAB6,5BP神经网络训练,得出的预测结果与各个示例的实际数值比较吻合。验证样本的训练结果表明,该模型适用于地震灾害人员伤亡评估。通过对评价指标的权重计算,确认人口密度、建筑物倒塌与严重破坏率、震中烈度是影响地震灾害人员伤亡的主要因素,地震预报、抗震设防水准、地震发生时刻和震级次之。作为人为可控预测指标,减少人口密度特别是城市人口密度,提高建（构）筑物抗震能力及预测预报水平,对于减少地震灾害人员伤亡起更重要的作用。     

Advances in the use of observed spatial patterns of catchment hydrological response

Over the past two decades there have been repeated calls for the collection of new data for use in developing hydrological science. The last few years have begun to bear fruit from the seeds sown by these calls, through increases in the availability and utility of remote sensing data, as well as the execution of campaigns in research catchments aimed at providing new data for advancing hydrological understanding and predictive capability. In this paper we discuss some philosophical considerations related to model complexity, data availability and predictive performance, highlighting the potential of observed patterns in moving the science and practice of catchment hydrology forward. We then review advances that have arisen from recent work on spatial patterns, including in the characterisation of spatial structure and heterogeneity, and the use of patterns for developing, calibrating and testing distributed hydrological models. We illustrate progress via examples using observed patterns of snow cover, runoff occurrence and soil moisture. Methods for the comparison of patterns are presented, illustrating how they can be used to assess hydrologically important characteristics of model performance. These methods include point-to-point comparisons, spatial relationships between errors and landscape parameters, transects, and optimal local alignment. It is argued that the progress made to date augers well for future developments, but there is scope for improvements in several areas. These include better quantitative methods for pattern comparisons, better use of pattern information in data assimilation and modelling, and a call for improved archiving of data from field studies to assist in comparative studies for generalising results and developing fundamental understanding.     

Using geographically weighted regression to explore the spatially heterogeneous spread of bovine tuberculosis in England and Wales

An understanding of the factors that affect the spread of endemic bovine tuberculosis (bTB) is critical for the development of measures to stop and reverse this spread. Analyses of spatial data need to account for the inherent spatial heterogeneity within the data, or else spatial autocorrelation can lead to an overestimate of the significance of variables. This study used three methods of analysis—least-squares linear regression with a spatial autocorrelation term, geographically weighted regression (GWR) and boosted regression tree (BRT) analysis—to identify the factors that influence the spread of endemic bTB at a local level in England and Wales. The linear regression and GWR methods demonstrated the importance of accounting for spatial differences in risk factors for bTB, and showed some consistency in the identification of certain factors related to flooding, disease history and the presence of multiple genotypes of bTB. This is the first attempt to explore the factors associated with the spread of endemic bTB in England and Wales using GWR. This technique improves on least-squares linear regression approaches by identifying regional differences in the factors associated with bTB spread. However, interpretation of these complex regional differences is difficult and the approach does not lend itself to predictive models which are likely to be of more value to policy makers. Methods such as BRT may be more suited to such a task. Here we have demonstrated that GWR and BRT can produce comparable outputs.     

Thermodynamical Structure of Atmosphere during Pre-monsoon Thunderstorm Season over Kharagpur as Revealed by STORM Data

Variation of atmospheric thermodynamical structure parameters between days of thunderstorm occurrence and non-occurrence is presented based on data sets obtained during Severe Thunderstorm-Observations and Regional Modeling (STORM) experiments conducted over Kharagpur (22.3°N, 87.2°E) in pre-monsoon season of 2009 and 2010. Potential instability (stable to neutral) is noticed in the lower layers and enhanced (suppressed) convection in the middle troposphere during thunderstorm (non-thunderstorm) days. Low-level jets are observed during all days of the experimental period but with higher intensity on thunderstorm days. Convective available potential energy (CAPE) builds up until thunderstorm occurrence and becomes dissipated soon after, whereas convective inhibition (CIN) is greatly decreased prior to the event on thunderstorm days. In contrast, higher CAPE and CIN are noticed on non-thunderstorm days. Analysis of thermodynamic indices showed that indices including moisture [humidity index (HI) and dew point temperature at 850 hPa (DPT850)] are useful in differentiating thunderstorm from non-thunderstorm days. The present study reveals that significant moisture availability in the lower troposphere in the presence of convective instability conditions results in thunderstorm occurrence at Kharagpur.     

Bivalve population health: Multistress to identify hot spots

This study investigated some stress (metals, parasites) and response (immunity, metallothionein) factors in two cockle and two Manila clam populations. Data from eight seasons were averaged to obtain global baseline values. Stress/response characteristics of each population were compared to population health status that was determined through population dynamics parameters. Four different scenarios were discussed: (1) a lightly stressed cockle population with correct population health but with a risk of deterioration (hot spot); (2) a lightly stressed introduced cockle population threatened of extinction. In this case ecological factors were suspected; (3) a moderately stressed clam population with moderate adaptative response. The population was sustainable but the level of stress should not increase (hotspot); and (4) a stressed clam population and unfavourable ecological conditions preventing clam settlement. This monitoring highlighted that the discrepancy between population health and stress levels could be due to insufficient response by bivalves and/or by unfavourable ecological factors.