Hydro-socio-economic implications for water management strategies: the case of Roussillon coastal aquifer

In many Mediterranean coastal areas, agriculture, drinking water supply, tourism and industry strongly depend on the available groundwater resources. As a result of the significant economic development during the last three decades along the coast, abstractions from coastal aquifers have increased tremendously, frequently leading to overexploitation and saltwater intrusion. Geological, hydrogeological and socio-economical studies as a multidisciplinary approach on a coastal Mediterranean aquifer- the Roussillon - have been carried out in order to design effective water management strategies on areas sensitive to seawater intrusion risk. Geology provides geometry and architecture of the different aquifers, hydrogeology assess the seawater intrusion risk while socio-economic study includes consulting the stakeholders with the aim of suggesting water management and policy option acceptable to the majority of population. This paper then highlights the economic interests at stake, diversity of viewpoints expressed by stakeholders and political dimension of the issue, which are likely to be encountered for all similar situations on both sides of the Mediterranean Sea.GESTION DE L'EAU;STRATEGIE;HYDROGEOLOGIE;SOCIOLOGIE;ECONOMIE;MER MEDITERRANEE;ROUSSILLON

A pluridisciplinary methodology for intregrated management of coastal aquifer - Geological, hydrogeological and economic studies of the Roussillon aquifer (Pyrénées-Orientales, France)

In order to study Mediterranean coastal water management, a pluridisciplinarity approach is developed. Reservoir geology and some of its tools, used in oil prospecting, are applied to build a detailed sedimentary model. The analysis of depositional environments and sedimentary process allows the correlation of pre-existing data (outcrop, borehole, and seismic profile) using Genetic stratigraphy (onshore domain) and seismic stratigraphy (offshore domain). The interpretation results in a better knowledge of the sedimentary geometries following correlations between onshore and offshore domains. It is thus possible to differentiate the coastal groundwater aquifers precisely and to establish their relative connections. At the same time, hydrogeological investigations such as hydrochemistry and geophysical prospecting allow us to elaborate the hydrogeological conceptual model of the case studies. Variable-density flow and solute transport simulations constitute the hydrogeological work. Experimental economy constitutes the third part of this integrated methodology. It assesses the effectiveness of institutional arrangements to cope with aquifer overexploitation. Feed-back from these three fields of research will also authenticate our methodology. This approach applied on Roussillon basin (South-west of French Mediterranean coastline) could be exported to many other coastal area

Factors Influencing Performance of Internet-Based Biosurveillance Systems Used in Epidemic Intelligence for Early Detection of Infectious Diseases Outbreaks

Background: Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives. Method and Findings Infectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems’ performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems. 84 events were detected before their publication in the gold-standard. The type of sources utilised by the systems varied significantly (p<0001). I-Se varied significantly from 43% to 71% (p = 0001) whereas other indicators were similar (C-DR: p = 020; C-Se, p = 013). I-Se was significantly associated with individual systems, types of system, languages, regions of occurrence, and types of infectious disease. Conversely, no statistical difference of C-DR was observed after adjustment for other variables. Conclusion: Although differences could result from a biosurveillance system's conceptual design, findings suggest that the combined expertise amongst systems enhances early detection performance for detection of infectious diseases. While all systems showed similar early detection performance, systems including human moderation were found to have a 53% higher I-Se (p = 00001) after adjustment for other variables. Overall, the use of moderation, sources, languages, regions of occurrence, and types of cases were found to influence system performance

Disease surveillance using a hidden Markov model

<p>Abstract</p> <p>Background</p> <p>Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.</p> <p>Methods</p> <p>A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.</p> <p>Results</p> <p>Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms.</p> <p>Conclusion</p> <p>Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.</p

Need and importance of health protection training in Nepal

By investing in health protection, the health of the nation can be safeguarded from future threats of uncontrolled infectious disease epidemics and disasters

Online detection and quantification of epidemics

<p>Abstract</p> <p>Background</p> <p>Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses.</p> <p>Results</p> <p>We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at <url>http://www.u707.jussieu.fr/periodic_regression/</url>. The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea).</p> <p>Conclusion</p> <p>The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners.</p

Probabilistic Daily ILI Syndromic Surveillance with a Spatio-Temporal Bayesian Hierarchical Model

BACKGROUND: For daily syndromic surveillance to be effective, an efficient and sensible algorithm would be expected to detect aberrations in influenza illness, and alert public health workers prior to any impending epidemic. This detection or alert surely contains uncertainty, and thus should be evaluated with a proper probabilistic measure. However, traditional monitoring mechanisms simply provide a binary alert, failing to adequately address this uncertainty. METHODS AND FINDINGS: Based on the Bayesian posterior probability of influenza-like illness (ILI) visits, the intensity of outbreak can be directly assessed. The numbers of daily emergency room ILI visits at five community hospitals in Taipei City during 2006-2007 were collected and fitted with a Bayesian hierarchical model containing meteorological factors such as temperature and vapor pressure, spatial interaction with conditional autoregressive structure, weekend and holiday effects, seasonality factors, and previous ILI visits. The proposed algorithm recommends an alert for action if the posterior probability is larger than 70%. External data from January to February of 2008 were retained for validation. The decision rule detects successfully the peak in the validation period. When comparing the posterior probability evaluation with the modified Cusum method, results show that the proposed method is able to detect the signals 1-2 days prior to the rise of ILI visits. CONCLUSIONS: This Bayesian hierarchical model not only constitutes a dynamic surveillance system but also constructs a stochastic evaluation of the need to call for alert. The monitoring mechanism provides earlier detection as well as a complementary tool for current surveillance programs

A Hidden Markov Model for Analysis of Frontline Veterinary Data for Emerging Zoonotic Disease Surveillance

Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines

Handedness as a neurodevelopmental marker in schizophrenia: Results from the FACE-SZ cohort

Objectives: High rates of non-right-handedness (NRH) including mixed-handedness have been reported in neurodevelopmental disorders. In schizophrenia (SZ), atypical handedness has been inconsistently related to impaired features. We aimed to determine whether SZ subjects with NRH and mixed-handedness had poorer clinical and cognitive outcomes compared to their counterparts. Methods: 667 participants were tested with a battery of neuropsychological tests, and assessed for laterality using the Edinburg Handedness Inventory. Clinical symptomatology was assessed. Learning disorders and obstetrical complications were recorded. Biological parameters were explored. Results: The prevalence of NRH and mixed-handedness was high (respectively, 42.4% and 34.1%). In the multivariable analyses, NRH was associated with cannabis use disorder (p = 0.045). Mixed-handedness was associated with positive symptoms (p = 0.041), current depressive disorder (p = 0.005)), current cannabis use (p = 0.024) and less akathisia (p = 0.019). A history of learning disorder was associated with NRH. No association was found with cognition, trauma history, obstetrical complications, psychotic symptoms, peripheral inflammation. Conclusions: Non-right and mixed-handedness are very high in patients with SZ, possibly reflecting a neurodevelopmental origin. NRH is associated with learning disorders and cannabis use. Mixed-handedness is associated with positive symptoms, current depressive disorder, cannabis use and less akathisia. However, this study did not confirm greater cognitive impairment in these patients. © 2021 Informa UK Limited, trading as Taylor & Francis Group.Sorbonne Universités à Paris pour l'Enseignement et la RechercheFondaMental-Cohorte