Characterizing Influenza surveillance systems performance: application of a Bayesian hierarchical statistical model to Hong Kong surveillance data

Abstract Statistical models are commonly employed in the estimation of influenza-associated excess mortality that, due to various reasons, is often underestimated by laboratory-confirmed influenza deaths reported by healthcare facilities. However, methodology for timely and reliable estimation of that impact remains limited because of the delay in mortality data reporting. We explored real-time estimation of influenza-associated excess mortality by types/subtypes in each year between 2012 and 2018 in Hong Kong using linear regression models fitted to historical mortality and influenza surveillance data. We could predict that during the winter of 2017/2018, there were ~634 (95% confidence interval (CI): (190, 1033)) influenza-associated excess all-cause deaths in Hong Kong in population ⩾18 years, compared to 259 reported laboratory-confirmed deaths. We estimated that influenza was associated with substantial excess deaths in older adults, suggesting the implementation of control measures, such as administration of antivirals and vaccination, in that age group. The approach that we developed appears to provide robust real-time estimates of the impact of influenza circulation and complement surveillance data on laboratory-confirmed deaths. These results improve our understanding of the impact of influenza epidemics and provide a practical approach for a timely estimation of the mortality burden of influenza circulation during an ongoing epidemic.

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A Bayesian Approach to Characterize Hong Kong Influenza Surveillance Systems

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v5i1.4567 ◽

2013 ◽

Vol 5 (1) ◽

Author(s):

Ying Zhang ◽

Ali Arab ◽

Michael A. Stoto

Keyword(s):

Hong Kong ◽

Bayesian Approach ◽

Influenza Surveillance ◽

Surveillance Systems

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Can automated alerts generated from influenza surveillance data reduce institutional outbreaks in Hong Kong

10.5353/th_b3972492 ◽

2006 ◽

Author(s):

Yat-hung Tam

Keyword(s):

Hong Kong ◽

Surveillance Data ◽

Influenza Surveillance

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Comparison of influenza surveillance data from the Republic of Korea, selected northern hemisphere countries and Hong Kong SAR (China) from 2012 to 2017

Western Pacific Surveillance and Response ◽

10.5365/wpsar.2019.10.2.015 ◽

2020 ◽

Vol 11 (3) ◽

pp. 1-9

Author(s):

Bryan Inho Kim ◽

Ok Park ◽

Sangwon Lee

Keyword(s):

Hong Kong ◽

Northern Hemisphere ◽

North China ◽

The United States ◽

Surveillance Data ◽

Republic Of Korea ◽

Temperate Climate ◽

Influenza Surveillance ◽

Hong Kong Sar ◽

The Republic

Influenza surveillance is conducted in many countries; it is one of the most important types of infectious disease surveillance due to the significant impact and burden of the influenza virus. The Republic of Korea has a temperate climate, and influenza activity usually peaks in the winter as in other temperate-climate countries in the northern hemisphere. This descriptive study compared the influenza surveillance data from the Korea Centers for Disease Control and Prevention with that from other countries and areas in the northern hemisphere, namely China, including Hong Kong SAR, Japan and the United States of America, to identify seasonal influenza patterns from 2012 to 2017. Data on influenza-like illnesses (ILIs) and laboratory surveillance were collected from various sources; visual comparisons were conducted on the onset, duration and the peak timing of each influenza season based on subtypes. Correlation coefficients were estimated, and time differences for the beginning of influenza seasons between the Republic of Korea and other countries were measured. ILIs in North China and cases reported from Japan’s sentinel surveillance showed high correlations with the Republic of Korea. The number of confirmed influenza cases in Japan showed a high correlation with the laboratory-confirmed influenza cases in the Republic of Korea. We found that there are similarities in the influenza patterns of the Republic of Korea, Japan and North China. Monitoring these neighbouring countries’ data may be useful for understanding influenza patterns in the Republic of Korea. Continuous monitoring and comparison of influenza surveillance data with neighbouring countries is recommended to enhance preparedness against influenza.

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

10.2196/preprints.25155 ◽

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.

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Malaria elimination in India requires additional surveillance mechanisms

Journal of Public Health ◽

10.1093/pubmed/fdab106 ◽

2021 ◽

Author(s):

Manju Rahi ◽

Payal Das ◽

Amit Sharma

Keyword(s):

Real Time ◽

Malaria Elimination ◽

Surveillance Data ◽

Surveillance Systems ◽

Malaria Surveillance ◽

Aggregated Data ◽

Malaria Burden ◽

The Core ◽

Reporting Systems ◽

Geographic Regions

Abstract Malaria surveillance is weak in high malaria burden countries. Surveillance is considered as one of the core interventions for malaria elimination. Impressive reductions in malaria-associated morbidity and mortality have been achieved across the globe, but sustained efforts need to be bolstered up to achieve malaria elimination in endemic countries like India. Poor surveillance data become a hindrance in assessing the progress achieved towards malaria elimination and in channelizing focused interventions to the hotspots. A major obstacle in strengthening India’s reporting systems is that the surveillance data are captured in a fragmented manner by multiple players, in silos, and is distributed across geographic regions. In addition, the data are not reported in near real-time. Furthermore, multiplicity of malaria data resources limits interoperability between them. Here, we deliberate on the acute need of updating India’s surveillance systems from the use of aggregated data to near real-time case-based surveillance. This will help in identifying the drivers of malaria transmission in any locale and therefore will facilitate formulation of appropriate interventional responses rapidly.

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Effects of School Holidays on Seasonal Influenza in South Korea, 2014–2016

The Journal of Infectious Diseases ◽

10.1093/infdis/jiaa179 ◽

2020 ◽

Vol 222 (5) ◽

pp. 832-835 ◽

Cited By ~ 6

Author(s):

Sukhyun Ryu ◽

Sheikh Taslim Ali ◽

Benjamin J Cowling ◽

Eric H Y Lau

Keyword(s):

South Korea ◽

Seasonal Influenza ◽

School Closure ◽

Surveillance Data ◽

Influenza Surveillance ◽

School Closures ◽

School Aged Children ◽

Severe Influenza ◽

Influenza Transmission

Abstract School closures are considered as a potential nonpharmaceutical intervention to mitigate severe influenza epidemics and pandemics. In this study, we assessed the effects of scheduled school closure on influenza transmission using influenza surveillance data before, during, and after spring breaks in South Korea, 2014–2016. During the spring breaks, influenza transmission was reduced by 27%–39%, while the overall reduction in transmissibility was estimated to be 6%–23%, with greater effects observed among school-aged children.

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Towards an integrated animal health surveillance system in Tanzania: making better use of existing and potential data sources for early warning surveillance

BMC Veterinary Research ◽

10.1186/s12917-021-02789-x ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Janeth George ◽

Barbara Häsler ◽

Erick Komba ◽

Calvin Sindato ◽

Mark Rweyemamu ◽

...

Keyword(s):

Early Warning ◽

Surveillance System ◽

Animal Health ◽

Health Surveillance ◽

Surveillance Data ◽

Data Sources ◽

Surveillance Systems ◽

Cross Sectional Survey ◽

Traceability System ◽

Spatial Coverage

Abstract Background Effective animal health surveillance systems require reliable, high-quality, and timely data for decision making. In Tanzania, the animal health surveillance system has been relying on a few data sources, which suffer from delays in reporting, underreporting, and high cost of data collection and transmission. The integration of data from multiple sources can enhance early detection and response to animal diseases and facilitate the early control of outbreaks. This study aimed to identify and assess existing and potential data sources for the animal health surveillance system in Tanzania and how they can be better used for early warning surveillance. The study used a mixed-method design to identify and assess data sources. Data were collected through document reviews, internet search, cross-sectional survey, key informant interviews, site visits, and non-participant observation. The assessment was done using pre-defined criteria. Results A total of 13 data sources were identified and assessed. Most surveillance data came from livestock farmers, slaughter facilities, and livestock markets; while animal dip sites were the least used sources. Commercial farms and veterinary shops, electronic surveillance tools like AfyaData and Event Mobile Application (EMA-i) and information systems such as the Tanzania National Livestock Identification and Traceability System (TANLITS) and Agricultural Routine Data System (ARDS) show potential to generate relevant data for the national animal health surveillance system. The common variables found across most sources were: the name of the place (12/13), animal type/species (12/13), syndromes (10/13) and number of affected animals (8/13). The majority of the sources had good surveillance data contents and were accessible with medium to maximum spatial coverage. However, there was significant variation in terms of data frequency, accuracy and cost. There were limited integration and coordination of data flow from the identified sources with minimum to non-existing automated data entry and transmission. Conclusion The study demonstrated how the available data sources have great potential for early warning surveillance in Tanzania. Both existing and potential data sources had complementary strengths and weaknesses; a multi-source surveillance system would be best placed to harness these different strengths.

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Dynamic interactions of influenza viruses in Hong Kong during 1998-2018

10.1101/19008987 ◽

2019 ◽

Cited By ~ 1

Author(s):

Wan Yang ◽

Eric H. Y. Lau ◽

Benjamin J. Cowling

Keyword(s):

Hong Kong ◽

Influenza A ◽

Vaccine Development ◽

Immune Escape ◽

Influenza Viruses ◽

Influenza Surveillance ◽

Influenza Epidemic ◽

Inference System ◽

Population Immunity ◽

Cross Immunity

AbstractInfluenza epidemics cause substantial morbidity and mortality every year worldwide. Currently, two influenza A subtypes, A(H1N1) and A(H3N2), and type B viruses co-circulate in humans and infection with one type/subtype could provide cross-protection against the others. However, it remains unclear how such ecologic competition via cross-immunity and antigenic mutations that allow immune escape impact influenza epidemic dynamics at the population level. Here we develop a comprehensive model-inference system and apply it to study the evolutionary and epidemiological dynamics of the three influenza types/subtypes in Hong Kong, a city of global public health significance for influenza epidemic and pandemic control. Utilizing long-term influenza surveillance data since 1998, we are able to estimate the strength of cross-immunity between each virus-pairs, the timing and frequency of punctuated changes in population immunity in response to antigenic mutations in influenza viruses, and key epidemiological parameters over the last 20 years including the 2009 pandemic. We find evidence of cross-immunity in all types/subtypes, with strongest cross-immunity from A(H1N1) against A(H3N2). Our results also suggest that A(H3N2) may undergo antigenic mutations in both summers and winters and thus monitoring the virus in both seasons may be important for vaccine development. Overall, our study reveals intricate epidemiological interactions and underscores the importance of simultaneous monitoring of population immunity, incidence rates, and viral genetic and antigenic changes.

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Accounting for healthcare-seeking behaviours and testing practices in real-time influenza forecasts

10.1101/475756 ◽

2018 ◽

Cited By ~ 1

Author(s):

Robert Moss ◽

Alexander E Zarebski ◽

Sandra J Carlson ◽

James M McCaw

Keyword(s):

Real Time ◽

Clinical Decision Making ◽

Clinical Decision ◽

Surveillance Data ◽

Surveillance Systems ◽

Healthcare Seeking ◽

Public Health Decision ◽

Predictive Skill ◽

Health Decision ◽

Substantial Change

AbstractFor diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., in order to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for RT-PCR influenza tests. In this study we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data.

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