Moving epidemic method (MEM) applied to virology data as a novel real time tool to predict peak in seasonal influenza healthcare utilisation. The Scottish experience of the 2017/18 season to date

Murray, Josephine L K; Marques, Diogo F P; Cameron, Ross; Potts, Alison; Bishop, Jennifer; Wissmann, Beatrix von; William, N; Reynolds, Arlene; Robertson, Chris; McMenamin, Jim

doi:10.2807/1560-7917.es.2018.23.11.18-00079

Cited by 20 publications

(25 citation statements)

References 5 publications

(5 reference statements)

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“…Our study depicts a possible development for infectious disease surveillance systems. While our study utilizes a database unique for Finland, similar medical databases can be used to assess data in European countries and internationally . The combination of Internet‐based query data and other surveillance data could enhance current surveillance systems.…”

Section: Discussionmentioning

confidence: 99%

“…While our study utilizes a database unique for Finland, similar medical databases can be used to assess data in European countries and internationally. 9,19 The combination of Internet-based query data and other surveillance data could enhance current surveillance systems. In the future, it may be possible to create algorithms that analyze HCPs' queries in real time in order to help the detection of the beginning epidemic.…”

Section: Discussionmentioning

confidence: 99%

“…The Moving Epidemic Method (MEM) has been developed to assess the timing of influenza epidemics and to estimate their baseline and threshold . It is implemented by WHO and the European Centres for Disease Prevention and Control (ECDC) to monitor influenza circulation in European countries . Historical data on influenza weekly rates are analyzed with MEM, and the method includes three stages .…”

Section: Introductionmentioning

confidence: 99%

“…17 It is implemented by WHO and the European Centres for Disease Prevention and Control (ECDC) to monitor influenza circulation in European countries. [17][18][19] Historical data on influenza weekly rates are analyzed with MEM, and the method includes three stages. [17][18][19] In the first stage, the length of each influenza season with start and end points is determined forming a pre-epidemic, an epidemic, and a post-epidemic period.…”

Section: Introductionmentioning

confidence: 99%

“…[17][18][19] Historical data on influenza weekly rates are analyzed with MEM, and the method includes three stages. [17][18][19] In the first stage, the length of each influenza season with start and end points is determined forming a pre-epidemic, an epidemic, and a post-epidemic period. In the second stage, the epidemic baseline and thresholds are calculated using pre-epidemic and post-epidemic values from historical seasons.…”

Section: Introductionmentioning

confidence: 99%

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Healthcare professionals’ queries on oseltamivir and influenza in Finland 2011‐2016—Can we detect influenza epidemics with specific online searches?

Pesälä

Virtanen

Mukka

et al. 2019

Influenza Resp Viruses

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Background Healthcare professionals (HCPs) search medical information during their clinical work using Internet sources. In Finland, Physician's Databases (PD) serve as an Internet medical portal aimed at HCPs. Influenza epidemics appear seasonal outbreaks causing public health concern. Oseltamivir can be used to treat influenza. Little is known about HCPs’ queries on oseltamivir and influenza from dedicated online medical portals and whether queries could be used as an additional source of information for disease surveillance when detecting influenza epidemics. Methods We compared HCPs’ queries on oseltamivir and influenza from PD to influenza diagnoses from the primary healthcare register in Finland 2011‐2016. The Moving Epidemic Method (MEM) calculated the starts of influenza epidemics. Laboratory reports of influenza A and influenza B were assessed. Paired differences compared queries, diagnoses, and laboratory reports by using starting weeks. Kendall's correlation test assessed the season‐to‐season similarity. Results We found that PD and the primary healthcare register showed visually similar patterns annually. Paired differences in the mean showed that influenza epidemics based on queries on oseltamivir started earlier than epidemics based on diagnoses by −0.80 weeks (95% CI: −1.0, 0.0) with high correlation ( τ = 0.943). Queries on influenza preceded queries on oseltamivir by −0.80 weeks (95% CI: −1.2, 0.0) and diagnoses by −1.60 weeks (95% CI: −1.8, −1.0). Conclusions HCPs’ queries on oseltamivir and influenza from Internet medical databases correlated with register diagnoses of influenza. Therefore, they should be considered as a supplementary source of information for disease surveillance when detecting influenza epidemics.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Healthcare professionals’ queries on oseltamivir and influenza in Finland 2011‐2016—Can we detect influenza epidemics with specific online searches?

Pesälä

Virtanen

Mukka

et al. 2019

Influenza Resp Viruses

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The moving epidemic method applied to influenza surveillance in Guangdong, China

Kang

Tan

et al. 2021

International Journal of Infectious Diseases

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Developing influenza and respiratory syncytial virus activity thresholds for syndromic surveillance in England

et al. 2019

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Influenza and respiratory syncytial virus (RSV) are common causes of respiratory tract infections and place a burden on health services each winter. Systems to describe the timing and intensity of such activity will improve the public health response and deployment of interventions to these pressures. Here we develop early warning and activity intensity thresholds for monitoring influenza and RSV using two novel data sources: general practitioner out-of-hours consultations (GP OOH) and telehealth calls (NHS 111). Moving Epidemic Method (MEM) thresholds were developed for winter 2017–2018. The NHS 111 cold/flu threshold was breached several weeks in advance of other systems. The NHS 111 RSV epidemic threshold was breached in week 41, in advance of RSV laboratory reporting. Combining the use of MEM thresholds with daily monitoring of NHS 111 and GP OOH syndromic surveillance systems provides the potential to alert to threshold breaches in real-time. An advantage of using thresholds across different health systems is the ability to capture a range of healthcare-seeking behaviour, which may reflect differences in disease severity. This study also provides a quantifiable measure of seasonal RSV activity, which contributes to our understanding of RSV activity in advance of the potential introduction of new RSV vaccines.

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Moving epidemic method (MEM) applied to virology data as a novel real time tool to predict peak in seasonal influenza healthcare utilisation. The Scottish experience of the 2017/18 season to date

Cited by 20 publications

References 5 publications

Healthcare professionals’ queries on oseltamivir and influenza in Finland 2011‐2016—Can we detect influenza epidemics with specific online searches?

Healthcare professionals’ queries on oseltamivir and influenza in Finland 2011‐2016—Can we detect influenza epidemics with specific online searches?

The moving epidemic method applied to influenza surveillance in Guangdong, China

Developing influenza and respiratory syncytial virus activity thresholds for syndromic surveillance in England

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