A Bayesian Approach to Characterize Hong Kong Influenza Surveillance Systems

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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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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International Real Estate Review

International Real Estate Review ◽

10.53383/100117 ◽

2010 ◽

Vol 13 (1) ◽

pp. 1-29

Author(s):

Sam K. Hui ◽

◽

Alvin Cheung ◽

Jimmy Pang ◽

◽

...

Keyword(s):

Hong Kong ◽

Real Estate ◽

Statistical Method ◽

Bayesian Approach ◽

Investment Decisions ◽

Average Price ◽

Residential Real Estate ◽

Property Market ◽

Residential Properties ◽

Expert Assessments

We have developed a statistical method for the valuation of residential properties using a hierarchical Bayesian approach, which takes into consideration the unique structure of the Hong Kong property market. Our model is calibrated on a dataset that covers all residential real estate transactions in ten major Hong Kong residential complexes from February 2008 to February 2009. Although parsimonious, our model outperforms other valuation methods that are based on average price-per-square- feet or expert assessments. By providing our model-based valuations online without charge, we hope to improve transparency in the Hong Kong housing market, thus enabling consumers to make better investment decisions.

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A comparison of coronavirus disease 2019 and seasonal influenza surveillance in five European countries: France, Germany, Italy, Spain and the United Kingdom

10.22541/au.163629278.88925629/v1 ◽

2021 ◽

Author(s):

Hélène Bricout ◽

Rigoine de Fougerolles Thierry ◽

Joan Puig-Barbera ◽

Georges Kassianos ◽

Philippe Vanhems ◽

...

Keyword(s):

United Kingdom ◽

Medical Information ◽

Public Awareness ◽

Data Communication ◽

European Countries ◽

World Health ◽

Influenza Surveillance ◽

Molecular Testing ◽

Surveillance Systems ◽

The United Kingdom

Background: In response to the coronavirus disease (COVID-19) outbreak that unfolded across Europe in 2020, the World Health Organisation called for repurposing existing influenza surveillance systems to monitor COVID-19. This analysis aimed to compare descriptively the extent to which influenza surveillance systems were adapted and enhanced, and how COVID-19 surveillance could ultimately benefit or disrupt routine influenza surveillance. Methods: We used a previously developed framework in France, Germany, Italy, Spain and the United Kingdom to describe COVID-19 surveillance and its impact on influenza surveillance. The framework divides surveillance systems into 7 sub-systems and 20 comparable outcomes of interest, and uses 5 evaluation criteria based on WHO guidance. Information on influenza and COVID-19 surveillance systems were collected from publicly available resources shared by European and national public health agencies. Results: Overall, non-medically attended, virological, primary care and mortality surveillance were adapted in most countries to monitor COVID-19, whilst community, outbreak, and hospital surveillance were reinforced in all countries. Data granularity improved, with more detailed demographic and medical information recorded. A shift to systematic notification for cases and deaths enhanced both geographic and population representativeness whilst the sampling strategy benefited from the roll out of widespread molecular testing. Data communication was greatly enhanced, contributing to improved public awareness. Conclusions: Well-established influenza surveillance systems are a key component of pandemic preparedness and their upgrade allowed European countries to respond to the COVID-19 pandemic. However, uncertainties remain on how both influenza and COVID-19 surveillance can be jointly and durably implemented.

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Quantifying differences in the epidemic curves from three influenza surveillance systems: a nonlinear regression analysis

Epidemiology and Infection ◽

10.1017/s0950268814000764 ◽

2014 ◽

Vol 143 (2) ◽

pp. 427-439 ◽

Cited By ~ 4

Author(s):

E. G. THOMAS ◽

J. M. McCAW ◽

H. A. KELLY ◽

K. A. GRANT ◽

J. McVERNON

Keyword(s):

Nonlinear Regression ◽

Influenza Surveillance ◽

Future Research ◽

Surveillance Systems ◽

Epidemic Curve ◽

Incidence Data ◽

Disease Patterns ◽

Department Of Health ◽

Baseline Activity ◽

Victorian Department

SUMMARYInfluenza surveillance enables systematic collection of data on spatially and demographically heterogeneous epidemics. Different data collection mechanisms record different aspects of the underlying epidemic with varying bias and noise. We aimed to characterize key differences in weekly incidence data from three influenza surveillance systems in Melbourne, Australia, from 2009 to 2012: laboratory-confirmed influenza notified to the Victorian Department of Health, influenza-like illness (ILI) reported through the Victorian General Practice Sentinel Surveillance scheme, and ILI cases presenting to the Melbourne Medical Deputising Service. Using nonlinear regression, we found that after adjusting for the effects of geographical region and age group, characteristics of the epidemic curve (including season length, timing of peak incidence and constant baseline activity) varied across the systems. We conclude that unmeasured factors endogenous to each surveillance system cause differences in the disease patterns recorded. Future research, particularly data synthesis studies, could benefit from accounting for these differences.

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Real-time estimation of the influenza-associated excess mortality in Hong Kong

Epidemiology and Infection ◽

10.1017/s0950268819001067 ◽

2019 ◽

Vol 147 ◽

Cited By ~ 1

Author(s):

Jessica Y. Wong ◽

Edward Goldstein ◽

Vicky J. Fang ◽

Benjamin J. Cowling ◽

Peng Wu

Keyword(s):

Hong Kong ◽

Real Time ◽

Excess Mortality ◽

Time Estimation ◽

Surveillance Data ◽

Control Measures ◽

Influenza Surveillance ◽

Mortality Data ◽

Real Time Estimation ◽

The Impact

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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Characteristics of hospitalizations with influenza diagnosis, France, 2012-2017

European Journal of Public Health ◽

10.1093/eurpub/ckz187.074 ◽

2019 ◽

Vol 29 (Supplement_4) ◽

Author(s):

M Pivette ◽

V de Lauzun ◽

N Nicolay ◽

A Scanff ◽

B Hubert

Keyword(s):

Length Of Stay ◽

Diagnosis Related Groups ◽

Laboratory Data ◽

Incidence Rates ◽

H3n2 Virus ◽

Influenza Surveillance ◽

Surveillance Systems ◽

Age Group ◽

Hospital System ◽

The Impact

Abstract Background Seasonal influenza surveillance in France is based on several data sources (ambulatory data, emergency department and intensive care unit (ICU) admissions, laboratory data, mortality). However, the data do not provide a complete measure of the impact of the epidemics on the hospital system. The objective of the study was to describe the characteristics of influenza hospitalizations from the French national hospital discharge database (PMSI) between 2012 and 2017 and to precise the burden of influenza by age group and by season. Methods All hospitalizations in metropolitan France with at least one ICD-10 code related to influenza (J09, J10, J11) as a principal, related or associated diagnosis between 1 July 2012 to 30 June 2017 were extracted from the PMSI. For each season, the total number of hospitalizations, admissions to ICU, incidence and lethality rates, lengths of stay and classification in diagnosis-related groups were described by age group. Results During the 5 seasons, 91 255 hospitalizations with an influenza-diagnosis were identified. The incidence varied significantly between seasons, from 12.7/100 000 in 2013-2014 to 45.9/100 000 in 2016-2017. A high number of cases was observed in elderlies in 2014-2015 and 2016-2017, marked by the circulation of A (H3N2) virus. The proportion of hospitalizations with an admission in ICU was 10%, and was higher in the 40-79 age group (19%). Lethality increased steadily with age, from 0.5% under 20 years to 10% in 80 years and older. Length of stay also increased with age. Significant regional disparities were observed, with higher incidence rates in South-Eastern France each season. Conclusions The analysis of influenza hospitalizations from the PMSI provides important elements on influenza burden, not available in the current surveillance systems. An annual analysis, stratified by age group, would provide an indicator of the impact of the epidemics on hospital system at the end of each influenza season. Key messages Important influenza incidence variations were observed between seasons by age groups. Severity and impact of influenza (mortality, ICU, length of stay) varied significantly by age group.

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