scholarly journals Evaluation of Ethiopian Influenza Sentinel Surveillance System

2021 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Desalegn Takele ◽  
Shikur Mohamed
Keyword(s):  
Surveillance System ◽  
Sentinel Surveillance ◽  
Sentinel Surveillance System

scholarly journals Is the HIV sentinel surveillance system adequate in China? Findings from an evaluation of the national HIV sentinel surveillance system

2012 ◽  
Vol 3 (4) ◽  
pp. 61-68 ◽  
Author(s):  
Wen Lin ◽  
Sanny Chen ◽  
Nicole Seguy ◽  
Zhongdan Chen ◽  
Keith Sabin ◽  
...  
Keyword(s):  
Surveillance System ◽  
Sentinel Surveillance ◽  
Sentinel Surveillance System

scholarly journals An evaluation of the Zambia influenza sentinel surveillance system, 2011–2017

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Paul Simusika ◽  
Stefano Tempia ◽  
Edward Chentulo ◽  
Lauren Polansky ◽  
Mazyanga Lucy Mazaba ◽  
...  
Keyword(s):  
Surveillance System ◽  
Sentinel Surveillance ◽  
Sentinel Surveillance System

scholarly journals Evaluation of National Influenza Sentinel Surveillance System in Nigeria, Jan-Dec 2014

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Amaka P. Onyiah ◽  
Muhammad S. Balogun ◽  
Adebayo A. Adedeji ◽  
Patrick M. Nguku
Keyword(s):  
Surveillance System ◽  
Sentinel Surveillance ◽  
Factors Affecting ◽  
Sentinel Surveillance System

National Influenza Sentinel Surveillance (NISS) was established in Nigeria in 2006 to monitor influenza occurrence in humans in Nigeria and provide a foundation for detecting outbreaks of novel strains of influenza. The evaluation was conducted to assess the performance of the surveillance system from January to December 2014 and identify factors affecting the performance. The system was determined to be useful, flexible, acceptable, and simple. However, timeliness and stability need to be strengthened.

scholarly journals Nosocomial infections and community clusters of pertussis in France, 2000-2005

2007 ◽  
Vol 12 (11) ◽  
pp. 11-12 ◽  
Author(s):  
I Bonmarin ◽  
I Poujol ◽  
D Lévy-Bruhl
Keyword(s):  
Health Authority ◽  
Surveillance System ◽  
Sentinel Surveillance ◽  
Control Measures ◽  
Health Staff ◽  
Local Health Authority ◽  
Local Health ◽  
Healthcare Facilities ◽  
Paediatric Hospital ◽  
Sentinel Surveillance System

Pertussis is not a notifiable disease in France. In addition to a paediatric hospital sentinel surveillance system, pertussis epidemiological data have, since 1996, been gathered through the voluntary notification of community clusters by general practitioners, and since 2001 by the statutory notification of nosocomial infection to the relevant local health authority. The local health authority forwards the information to the French National Institute for Surveillance (InVS). The objective of this study was to analyse pertussis data outside the routine paediatric hospital sentinel surveillance system. We gathered all the information concerning healthcare-associated infections and community clusters of pertussis (specific forms, investigation reports, emails etc.) reported to the InVS between 2000 and 2005. The InVS received and analysed 67 reports with a total of 595 cases. Almost half of the reports (n=31) came from hospitals, and healthcare workers were usually first affected. Control measures were put in place in 22 healthcare facilities and the average duration of an outbreak episode was 48 days. Outside healthcare facilities, clusters were reported also from 17 daycare facilities or schools and five workplaces. Among the 595 cases, six deaths occurred in children under seven months of age. Pertussis is still occurring in France and affects those who are not or who are no longer protected by the vaccine. Infection of infants within the household could be prevented if their parents and siblings were immunised. The number and size of pertussis clusters in hospitals could be reduced through immunisation of health staff, and timely and adequate outbreak management.

scholarly journals Evaluation of the Influenza Sentinel Surveillance System in the Democratic Republic of Congo, 2012-2015

2019 ◽  
Author(s):  
Pélagie Diambalula Babakazo ◽  
Joelle Kabamba-Tshilobo ◽  
Emile Okitolonda Wemakoy ◽  
Léopold Lubula ◽  
Léonie Kitoko Manya ◽  
...  
Keyword(s):  
Data Quality ◽  
Surveillance System ◽  
Democratic Republic ◽  
Democratic Republic Of Congo ◽  
Sentinel Surveillance ◽  
World Health ◽  
Influenza Surveillance ◽  
Surveillance Systems ◽  
Republic Of Congo ◽  
Sentinel Surveillance System

Abstract Background The World Health Organization recommends periodic evaluations of influenza surveillance systems to identify areas for improvement and provide evidence of data reliability for policymaking. However, data about the performance of established influenza surveillance systems are limited in Africa, including in the Democratic Republic of Congo (DRC). Methods We used the Centers for Disease Control and Prevention guidelines to evaluate the performance of the influenza sentinel surveillance system (ISSS) in DRC during 2012-2015. The performance of the system was evaluated using eight surveillance attributes: (i) data quality and completeness for key variables, (ii) timeliness, (iii) representativeness, (iv) flexibility, (v) simplicity, (vi) acceptability, (vii) stability and (viii) utility. For each attribute, specific indicators were developed and described using quantitative and qualitative methods. Scores for each indicator were as follows: <60% weak performance; 60-79% moderate performance; ≥80% good performance. Results During 2012-2015, we enrolled and tested 4,339 patients with influenza-like illness (ILI) and 2,869 patients with severe acute respiratory illness (SARI) from 11 sentinel sites situated in 5 of 11 provinces. Influenza viruses were detected in 446 (10.3%) samples from patients with ILI and in 151 (5.5%) samples from patients with SARI with higher detection during December-May. Data quality and completeness was >90% for all evaluated indicators. Other strengths of the system were timeliness, representativeness, simplicity, stability and utility that scored >70% each. Flexibility and acceptability had moderate to week performance. It was reported that the ISSS contributed to: (i) a better understanding of the epidemiology, circulating patterns and proportional contribution of influenza virus among patients with ILI or SARI; (ii) acquisition of new key competences related to influenza surveillance and diagnosis; and (iii) continuous education of surveillance staff and clinicians at sentinel sites about influenza. However, due to limited resources no actions were undertaken to mitigate the impact of seasonal influenza epidemics. Conclusions The system performed overall satisfactorily and provided reliable and timely data about influenza circulation in DRC. The simplicity of the system contributed to its stability. A better use of the available data could be made to inform and promote prevention interventions especially among the most vulnerable groups.

scholarly journals Enhanced Sentinel Surveillance System for COVID-19 Outbreak Prediction in a Large European Dialysis Clinics Network

2021 ◽  
Vol 18 (18) ◽  
pp. 9739
Author(s):  
Francesco Bellocchio ◽  
Paola Carioni ◽  
Caterina Lonati ◽  
Mario Garbelli ◽  
Francisco Martínez-Martínez ◽  
...  
Keyword(s):  
Surveillance System ◽  
Sentinel Surveillance ◽  
Risk Scores ◽  
Epidemic Dynamics ◽  
Organizational Interventions ◽  
Risk Estimates ◽  
Timely Response ◽  
Distance Weighted ◽  
Input Variables ◽  
Sentinel Surveillance System

Accurate predictions of COVID-19 epidemic dynamics may enable timely organizational interventions in high-risk regions. We exploited the interconnection of the Fresenius Medical Care (FMC) European dialysis clinic network to develop a sentinel surveillance system for outbreak prediction. We developed an artificial intelligence-based model considering the information related to all clinics belonging to the European Nephrocare Network. The prediction tool provides risk scores of the occurrence of a COVID-19 outbreak in each dialysis center within a 2-week forecasting horizon. The model input variables include information related to the epidemic status and trends in clinical practice patterns of the target clinic, regional epidemic metrics, and the distance-weighted risk estimates of adjacent dialysis units. On the validation dates, there were 30 (5.09%), 39 (6.52%), and 218 (36.03%) clinics with two or more patients with COVID-19 infection during the 2-week prediction window. The performance of the model was suitable in all testing windows: AUC = 0.77, 0.80, and 0.81, respectively. The occurrence of new cases in a clinic propagates distance-weighted risk estimates to proximal dialysis units. Our machine learning sentinel surveillance system may allow for a prompt risk assessment and timely response to COVID-19 surges throughout networked European clinics.

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