Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

syndromic surveillance Related Abstracts

3 Horse Exposition to Coxiella burnetii in France: Antibody Dynamics in Serum, Environmental Risk Assessment and Potential Links with Symptomatology

Authors: Agnès Leblond, Joulié Aurélien, Isabelle Desjardins, Elsa Jourdain, Sophie Pradier, Dufour Philippe, Elodie Rousset

Abstract:

Q fever is a worldwide zoonosis caused by the bacterium Coxiella burnetii. It may infect a broad range of host species, including horses. Although the role of horses in C. burnetii infections remains unknown, their use as sentinel species may be interesting to better assess the human risk exposure. Thus, we aimed to assess the C. burnetii horse exposition in a French endemic area by describing the antibody dynamics detected in serum; investigating the pathogen circulation in the horse environment, and exploring potential links with unexplained syndromes. Blood samples were collected in 2015 and 2016 on 338 and 294 horses, respectively and analyzed by ELISA. Ticks collected on horses were identified, and C. burnetii DNA detection was performed by qPCR targeting the IS1111 gene. Blood sample analyses revealed a significant increase of the seroprevalence in horses between both years, from 11% [7.67; 14.43] to 25% [20.06; 29.94]. On 36 seropositive horses in 2015 and 73 in 2016, 5 and four respectively showed clinical signs compatible with a C. burnetii infection (i.e., chronic fever or respiratory disorders, unfitness and unexplained weight loss). DNA was detected in almost 40% of ticks (n=59/148 in 2015 and n=103/305 in 2016) and exceptionally in dust samples (n=2/46 in 2015 and n=1/14 in 2016) every year. The C. burnetti detection in both the serum and the environment of horses confirm their exposure to the bacterium. Therefore, consideration should be given to target a relevant sentinel species to better assess the Q fever surveillance depending on the epidemiological context.

Keywords: ELISA, qPCR, Q fever, syndromic surveillance

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2 Outputs from the Implementation of 'PHILOS' Programme: Emergency Health Response to Refugee Crisis, Greece, 2017

Authors: K. Mellou, C. Botsi, A. Terzidis, G. Anastopoulos, T. Zakinthinos

Abstract:

‘PHILOS – Emergency health response to refugee crisis’ is a programme of the Greek Ministry of Health, implemented by the Hellenic Center for Disease Control and Prevention (HCDCP). The programme is funded by the Asylum, Migration and Integration Fund (AMIF) of EU’s DG Migration and Home Affairs. With the EU Member States accepting, the last period, accelerating migration flows, Greece inevitably occupies a prominent position in the migratory map due to this geographical location. The main objectives of the programme are a) reinforcement of the capacity of the public health system and enhancement of the epidemiological surveillance in order to cover refugees/migrant population, b) provision of on-site primary health care and psychological support services, and c) strengthening of national health care system task-force. The basic methods for achieving the aforementioned goals are: a) implementation of syndromic surveillance system at camps and enhancement of public health response with the use of mobile medical units (Sub-action A), b) enhancement of health care services inside the camps via increasing human resources and implementing standard operating procedures (Sub-action B), and c) reinforcement of the national health care system (primary healthcare units, hospitals, and emergency care spots) of affected regions with personnel (Sub-action C). As a result, 58 health professionals were recruited under sub-action 2 and 10 mobile unit teams (one or two at each health region) were formed. The main actions taken so far by the mobile units are the evaluation, of syndromic surveillance, of living conditions at camps and medical services. Also, vaccination coverage of children population was assessed, and more than 600 catch-up vaccinations were performed by the end of June 2017. Mobile units supported transportation of refugees/migrants from camps to medical services reducing the load of the National Center for Emergency Care (more than 350 transportations performed). The total number of health professionals (MD, nurses, etc.) placed at camps was 104. Common practices were implemented in the recording and collection of psychological and medical history forms at the camps. Protocols regarding maternity care, gender based violence and handling of violent incidents were produced and distributed at personnel working at camps. Finally, 290 health care professionals were placed at primary healthcare units, public hospitals and the National Center for Emergency Care at affected regions. The program has, also, supported training activities inside the camps and resulted to better coordination of offered services on site.

Keywords: Public Health, Primary Care, Refugees, migrants, syndromic surveillance, national health care system, emergency health response

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1 Syndromic Surveillance Framework Using Tweets Data Analytics

Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

Abstract:

Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Data Mining, Machine Learning, Influenza, syndromic surveillance, Tweets, Latent Dirichlet allocation (LDA)

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