Flutracking Australia Influenza-like Illness Surveillance System: Evolution of methods from 2006-2016 (Preprint)

2018 ◽  
Author(s):  
Sandra Jane Carlson ◽  
Craig Brian Dalton
Keyword(s):  
Surveillance System ◽  
Influenza Pandemic ◽  
Assessment System ◽  
Influenza Surveillance ◽  
Local Health ◽  
Surveillance Systems ◽  
Australian Government ◽  
Health Seeking ◽  
Influenza Like Illness ◽  
Government Department

UNSTRUCTURED This paper documents the evolution of Flutracking from a pilot online influenza-like illness (ILI) survey of 394 participants in a local health region to a national surveillance system with over 30,000 participants in 2016. In 2018, there were over 40,000 survey responses per week in most weeks. In particular, this paper will describe how the methods have developed to meet the 1) changing aims of the system; 2) developing capabilities of the system; and 3) participant growth. The aim of this paper is to provide insights to other groups initiating participatory public health surveillance systems and to assist users of our data and reports to better understand Flutracking methods. Some of the key changes to Flutracking from 2006 to 2016 include: allowing participants to respond on behalf of other household members from 2008; adding health seeking behaviour questions in 2011; and offering an express survey and follow-up of unknown test results from 2016 onwards. The Flutracking system has demonstrated its ability to adapt to changes with minimal disruption to participants, and maintain consistency in data collection and reporting. As an example of success, Flutracking has been integrated in the Australian Government Department of Health’s regular influenza reports, and is now contributing weekly data to the transmissibility and impact measures for the Australian Government Department of Health’s application of the Pandemic Influenza Severity Assessment system. Flutracking data have consistently aligned with the timing of the peak level of influenza activity from other Australian influenza surveillance systems. Flutracking provided a unique insight into 2009 H1N1 influenza pandemic in 2009 demonstrating that the community level ILI attack rates were only slightly higher than 2008, and lower than 2007 in the community. Flutracking has demonstrated to be significantly less biased by treatment seeking behaviour and laboratory testing protocols than other surveillance systems during the 2009 influenza pandemic. In 2018, Flutracking expanded to New Zealand, with an average of over 2,800 surveys per week so far. The evolution of Flutracking’s methods has been pivotal to the success of this surveillance system.

Derivation of New Surveillance System for Influenza-like Illness Using Claims Data 2014-2018 in Korea (Preprint)

2020 ◽  
Author(s):  
HeeKyung Choi ◽  
Won Suk Choi ◽  
Euna Han
Keyword(s):  
Surveillance System ◽  
Claims Data ◽  
Health Concern ◽  
Influenza Surveillance ◽  
Utilization Review ◽  
Surveillance Systems ◽  
Influenza Like Illness ◽  
System P ◽  
National Patient ◽  
Antitussive Agents

BACKGROUND Influenza is an important public health concern. A national surveillance system that easily and rapidly detects influenza epidemics is lacking. OBJECTIVE We assumed that the rate of influenza-like illness (ILI) related-claims is similar to the current ILI surveillance system. METHODS We used the Health Insurance Review and Assessment Service-National Patient Samples (HIRA-NPS), 2014-2018. We defined ILI-related claims as outpatient claims that contain both antipyretic and antitussive agents and calculated the weekly rate of ILI-related claims. We compared ILI-related claims and weekly ILI rates from clinical sentinel surveillance data. RESULTS We observed a strong correlation between the two surveillance systems each season. The absolute thresholds for the four-years were 84.64 and 86.19 cases claims per 1,000 claims for claims data and 12.27 and 16.82 per 1,000 patients for sentinel data (Figure 5). Both the claims and sentinel data surpassed the epidemic thresholds each season. The peak epidemic in the claims data was reached one to two weeks later than in the sentinel data. The epidemic patterns were more similar in the 2016-2017 and 2017-2018 seasons than the 2014-2015 and 2015-2016 seasons. CONCLUSIONS Based on hospital reports, ILI-related claims rates were similar to the ILI surveillance system. ILI claims data can be loaded to a drug utilization review system in Korea to make an influenza surveillance system.

scholarly journals Suggestion of a simpler and faster influenza-like illness surveillance system using 2014–2018 claims data in Korea

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
HeeKyoung Choi ◽  
Won Suk Choi ◽  
Euna Han
Keyword(s):  
Drug Utilization ◽  
Surveillance System ◽  
Claims Data ◽  
Absolute Threshold ◽  
Health Concern ◽  
Influenza Surveillance ◽  
Utilization Review ◽  
Surveillance Systems ◽  
Influenza Like Illness ◽  
Cross Correlation Analysis

AbstractInfluenza is an important public health concern. We propose a new real-time influenza-like illness (ILI) surveillance system that utilizes a nationwide prospective drug utilization monitoring in Korea. We defined ILI-related claims as outpatient claims that contain both antipyretic and antitussive agents and calculated the weekly rate of ILI-related claims, which was compared to weekly ILI rates from clinical sentinel surveillance data during 2014–2018. We performed a cross-correlation analysis using Pearson’s correlation, time-series analysis to explore actual correlations after removing any dubious correlations due to underlying non-stationarity in both data sets. We used the moving epidemic method (MEM) to estimate an absolute threshold to designate potential influenza epidemics for the weeks with incidence rates above the threshold. We observed a strong correlation between the two surveillance systems each season. The absolute thresholds for the 4-years were 84.64 and 86.19 cases per 1000claims for claims data and 12.27 and 16.82 per 1000 patients for sentinel data. The epidemic patterns were more similar in the 2016–2017 and 2017–2018 seasons than the 2014–2015 and 2015–2016 seasons. ILI claims data can be loaded to a drug utilization review system in Korea to make an influenza surveillance system.

scholarly journals Evaluation of Point of Need Diagnostic Tests for Use in California Influenza Outbreaks

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Ashlynn Daughton ◽  
Alina Deshpande
Keyword(s):  
Data Streams ◽  
Surveillance System ◽  
Laboratory Data ◽  
Developed Countries ◽  
The United States ◽  
Diagnostic Tools ◽  
Influenza Surveillance ◽  
Mortality Data ◽  
Surveillance Systems ◽  
Influenza Outbreaks

Because of the potential threats flu viruses pose, the United States, like many developed countries, has a very well established flu surveillance system consisting of 10 components collecting laboratory data, mortality data, hospitalization data and sentinel outpatient care data. Currently, this surveillance system is estimated to lag behind the actual seasonal outbreak by one to two weeks. As new data streams come online, it is important to understand what added benefit they bring to the flu surveillance system complex. For data streams to be effective, they should provide data in a more timely fashion or provide additional data that current surveillance systems cannot provide. Two multiplexed diagnostic tools designed to test syndromically relevant pathogens and wirelessly upload data for rapid integration and interpretation were evaluated to see how they fit into the influenza surveillance scheme in California.

scholarly journals FluWatchers: Evaluation of a crowdsourced influenza-like illness surveillance application for Canadian influenza seasons 2015–2016 to 2018–2019

2021 ◽  
Vol 47 (09) ◽  
pp. 357-363
Author(s):  
Liza Lee ◽  
Mireille Desroches ◽  
Shamir Mukhi ◽  
Christina Bancej
Keyword(s):  
Respiratory Virus ◽  
Evaluation Criteria ◽  
Influenza Surveillance ◽  
Surveillance Program ◽  
Surveillance Systems ◽  
Virus Infections ◽  
Detection Rates ◽  
Influenza Like Illness ◽  
Syncytial Virus ◽  
Participatory Surveillance

Background: Sentinel influenza-like illness (ILI) surveillance is an essential component of a comprehensive influenza surveillance program. Community-based ILI surveillance systems that rely solely on sentinel healthcare practices omit important segments of the population, including those who do not seek medical care. Participatory surveillance, which relies on community participation in surveillance, may address some limitations of traditional ILI systems. Objective: We aimed to evaluate FluWatchers, a crowdsourced ILI application developed to complement and complete ILI surveillance in Canada. Methods: Using established frameworks for surveillance evaluations, we assessed the acceptability, reliability, accuracy and usefulness of the FluWatchers system 2015–2016, through 2018–2019. Evaluation indicators were compared against national surveillance indicators of ILI and of laboratory confirmed respiratory virus infections. Results: The acceptability of FluWatchers was demonstrated by growth of 50%–100% in season-over-season participation, and a consistent season-over-season retention of 80%. Reliability was greater for FluWatchers than for our traditional ILI system, although both systems had week-over-week fluctuations in the number of participants responding. FluWatchers’ ILI rates had moderate correlation with weekly influenza laboratory detection rates and other winter seasonal respiratory virus detections including respiratory syncytial virus and seasonal coronaviruses. Finally, FluWatchers has demonstrated its usefulness as a source of core FluWatch surveillance information and has the potential to fill data gaps in current programs for influenza surveillance and control. Conclusion: FluWatchers is an example of an innovative digital participatory surveillance program that was created to address limitations of traditional ILI surveillance in Canada. It fulfills the surveillance system evaluation criteria of acceptability, reliability, accuracy and usefulness.

scholarly journals Performance of Avian Influenza Surveillance System, Ogun State Nigeria, 2015-2019

2020 ◽  
pp. 25-35
Author(s):  
Folajimi. O. Shorunke ◽  
Aisha Usman ◽  
Tade Adeniyi Olanrewaju ◽  
Ndadilnasiya Endie Waziri ◽  
S. N. Grace
Keyword(s):  
Avian Influenza ◽  
Surveillance System ◽  
Highly Pathogenic Avian Influenza ◽  
Health Workers ◽  
Action Plan ◽  
Influenza Surveillance ◽  
Surveillance Systems ◽  
Highly Pathogenic ◽  
Pathogenic Avian Influenza ◽  
Ogun State

Background: In 2019, two Highly pathogenic avian influenza (HPAI) A(H5N8) outbreaks in poultry establishments in Bulgaria, two of wild birds in Denmark and one low pathogenic avian influenza (LPAI) A(H5N3) in captive birds in the Netherlands were reported. Nigeria recorded the first outbreak of Highly Pathogenic Avian Influenza (HPAI) in February 2006 in a commercial poultry farm. Nigerian Pandemic Preparedness and Action Plan for Avian Influenza were then used to respond. Although influenza sentinel surveillance has been established in several African countries including Nigeria, data about the performance of established surveillance systems are limited on the continent. We described the avian influenza (AI) surveillance system in Ogun State, accessed veterinary health workers and farmers knowledge, evaluated all its attributes and made recommendations to improve the AI surveillance system. Methods: We adopted 2001 CDC Updated Guidelines for Evaluating Public Health Surveillance Systems. We reviewed and analyzed passive surveillance data from Ogun State Ministry of Agric, key informant interviews were conducted for relevant stakeholders at the state level and Local Government divisional veterinary clinics and farms to obtain additional information on the operations of the system.

scholarly journals Community transmission of influenza A (H1N1)v virus at a rock festival in Belgium, 2-5 July 2009

2009 ◽  
Vol 14 (31) ◽  
Author(s):  
I Gutiérrez ◽  
A Litzroth ◽  
S Hammadi ◽  
H Van Oyen ◽  
C Gérard ◽  
...  
Keyword(s):  
Surveillance System ◽  
Influenza A ◽  
Influenza Pandemic ◽  
Control Measures ◽  
Influenza Like Illness ◽  
Health Authorities ◽  
Influenza A H1n1 ◽  
Community Transmission ◽  
And Control

On 6 July 2009 the Belgian enhanced surveillance system for influenza-like illness among travellers returning from influenza A(H1N1)v affected areas detected a case linked to a rock festival which took place on 2-5 July. The health authorities implemented communication and control measures leading to the detection of aditional cases. This paper describes the outbreak and its impact on the management of the influenza pandemic in Belgium.

scholarly journals Implementation of Real-Time Laboratory-Based Influenza Surveillance System, Thailand

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Phunlerd Piyaraj ◽  
Nira Pet-hoi ◽  
Chaiyos Kunanusont ◽  
Supanee Sangiamsak ◽  
Somsak Wankijcharoen ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Influenza Virus ◽  
Real Time ◽  
Surveillance System ◽  
Influenza A ◽  
Medical Services ◽  
Influenza Surveillance ◽  
Surveillance Systems ◽  
Private Hospitals ◽  
Influenza A H1n1

Objective: We describe the Bangkok Dusit Medical Services Surveillance System (BDMS-SS) and use of surveillance efforts for influenza as an example of surveillance capability in near real-time among a network of 20 hospitals in the Bangkok Dusit Medical Services group (BDMS).Introduction: Influenza is one of the significant causes of morbidity and mortality globally. Previous studies have demonstrated the benefit of laboratory surveillance and its capability to accurately detect influenza outbreaks earlier than syndromic surveillance.1-3 Current laboratory surveillance has an approximately 4-week lag due to laboratory test turn-around time, data collection and data analysis. As part of strengthening influenza virus surveillance in response to the 2009 influenza A (H1N1) pandemic, the real-time laboratory-based influenza surveillance system, the Bangkok Dusit Medical Services Surveillance System (BDMS-SS), was developed in 2010 by the Bangkok Health Research Center (BHRC). The primary objective of the BDMS-SS is to alert relevant stakeholders on the incidence trends of the influenza virus. Type-specific results along with patient demographic and geographic information were available to physicians and uploaded for public health awareness within 24 hours after patient nasopharyngeal swab was collected. This system advances early warning and supports better decision making during infectious disease events.2 The BDMS-SS operates all year round collecting results of all routinely tested respiratory clinical samples from participating hospitals from the largest group of private hospitals in Thailand.Methods: The BDMS has a comprehensive network of laboratory, epidemiologic, and early warning surveillance systems which represents the largest body of information from private hospitals across Thailand. Hospitals and clinical laboratories have deployed automatic reporting mechanisms since 2010 and have effectively improved timeliness of laboratory data reporting. In April 2017, the capacity of near real-time influenza surveillance in BDMS was found to have a demonstrated and sustainable capability.Results: From October 2010 to April 2017, a total of 482,789 subjects were tested and 86,110 (17.8%) cases of influenza were identified. Of those who tested positive for influenza they were aged <2 years old (4.6%), 2-4 year old (10.9%), 5-14 years old (29.8%), 15-49 years old (41.9%), 50-64 years old (8.3%) and >65 years old (3.7%). Approximately 50% of subjects were male and female. Of these, 40,552 (47.0%) were influenza type B, 31,412 (36.4%) were influenza A unspecified subtype, 6,181 (7.2%) were influenza A H1N1, 4,001 (4.6%) were influenza A H3N2, 3,835 (4.4%) were influenza A seasonal and 196 (0.4%) were respiratory syncytial virus (RSV).The number of influenza-positive specimens reported by the real-time influenza surveillance system were from week 40, 2015 to week 39, 2016. A total of 117,867 subjects were tested and 17,572 (14.91%) cases tested positive for the influenza virus (Figure 1). Based on the long-term monitoring of collected information, this system can delineate the epidemiologic pattern of circulating viruses in near real-time manner, which clearly shows annual peaks in winter dominated by influenza subtype B in 2015-1016 season. This surveillance system helps to provide near real-time reporting, enabling rapid implementation of control measures for influenza outbreaks.Conclusions: This surveillance system was the first real-time, daily reporting surveillance system to report on the largest data base of private hospitals in Thailand and provides timely reports and feedback to all stakeholders. It provides an important supplement to the routine influenza surveillance system in Thailand. This illustrates a high level of awareness and willingness among the BDMS hospital network to report emerging infectious diseases, and highlights the robust and sensitive nature of BDMS’s surveillance system. This system demonstrates the flexibility of the surveillance systems in BDMS to evaluate to emerging infectious disease and major communicable diseases. Through participation in the Thailand influenza surveillance network, BDMS can more actively collaborate with national counterparts and use its expertise to strengthen global and regional surveillance capacity in Southeast Asia, in order to secure advances for a world safe and secure from infectious disease. Furthermore, this system can be quickly adapted and used to monitor future influenzas pandemics and other major outbreaks of respiratory infectious disease, including novel pathogens.

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 Estimating the Early Outbreak Cumulative Incidence of COVID-19 in the United States: Three Complementary Approaches

2020 ◽  
Author(s):  
Fred S. Lu ◽  
Andre T. Nguyen ◽  
Nicholas B. Link ◽  
Marc Lipsitch ◽  
Mauricio Santillana
Keyword(s):  
United States ◽  
Cumulative Incidence ◽  
State Level ◽  
The United States ◽  
Positive Test ◽  
Influenza Surveillance ◽  
Accurate Estimation ◽  
Surveillance Systems ◽  
Influenza Like Illness ◽  
The Us

AbstractEffectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the weekly incidence of COVID-19. Unfortunately, a lack of systematic testing across the United States (US) due to equipment shortages and varying testing strategies has hindered the usefulness of the reported positive COVID-19 case counts. We introduce three complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 during the early outbreak in each state in the US as well as in New York City, using a combination of excess influenza-like illness reports, COVID-19 test statistics, and COVID-19 mortality reports. Instead of relying on an estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our three approaches, there is a consistent conclusion that estimated state-level COVID-19 symptomatic case counts from March 1 to April 4, 2020 varied from 5 to 50 times greater than the official positive test counts. Nationally, our estimates of COVID-19 symptomatic cases in the US as of April 4 have a likely range of 2.2 to 5.1 million cases, with possibly as high as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 12.2 million, which compares with 1.5 million positive test counts. Our approaches demonstrate the value of leveraging existing influenza-like-illness surveillance systems during the flu season for measuring the burden of new diseases that share symptoms with influenza-like-illnesses. Our methods may prove useful in assessing the burden of COVID-19 during upcoming flu seasons in the US and other countries with comparable influenza surveillance systems.

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