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

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

BMC Public Health ◽

10.1186/1471-2458-14-850 ◽

2014 ◽

Vol 14 (1) ◽

Cited By ~ 6

Author(s):

Ying Zhang ◽

Ali Arab ◽

Benjamin J Cowling ◽

Michael A Stoto

Keyword(s):

Hong Kong ◽

Statistical Model ◽

Surveillance Data ◽

Influenza Surveillance ◽

Surveillance Systems ◽

Bayesian Hierarchical ◽

Hierarchical Statistical Model

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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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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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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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FluHMM: A simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection

Statistical Methods in Medical Research ◽

10.1177/0962280218776685 ◽

2018 ◽

Vol 28 (6) ◽

pp. 1826-1840 ◽

Cited By ~ 1

Author(s):

Theodore Lytras ◽

Kassiani Gkolfinopoulou ◽

Stefanos Bonovas ◽

Baltazar Nunes

Keyword(s):

Public Health ◽

Public Health Practice ◽

R Package ◽

Sentinel Surveillance ◽

Surveillance Data ◽

Outbreak Detection ◽

Influenza Surveillance ◽

Bayesian Algorithm ◽

Public Health Action ◽

Health Action

Timely detection of the seasonal influenza epidemic is important for public health action. We introduce FluHMM, a simple but flexible Bayesian algorithm to detect and monitor the seasonal epidemic on sentinel surveillance data. No comparable historical data are required for its use. FluHMM segments a typical influenza surveillance season into five distinct phases with clear interpretation (pre-epidemic, epidemic growth, epidemic plateau, epidemic decline and post-epidemic) and provides the posterior probability of being at each phase for every week in the period under surveillance, given the available data. An alert can be raised when the probability that the epidemic has started exceeds a given threshold. An accompanying R package facilitates the application of this method in public health practice. We apply FluHMM on 12 seasons of sentinel surveillance data from Greece, and show that it achieves very good sensitivity, timeliness and perfect specificity, thereby demonstrating its usefulness. We further discuss advantages and limitations of the method, providing suggestions on how to apply it and highlighting potential future extensions such as with integrating multiple surveillance data streams.

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