Sequential detection of influenza epidemics by the Kolmogorov-Smirnov test

Upregulation of RasGRF1 ameliorates spatial cognitive dysfunction in mice after chronic cerebral hypoperfusion

Chronic cerebral hypoperfusion (CCH)-mediated cognitive impairment is a serious problem worldwide. However, given its complexity, the underlying mechanisms by which CCH induces cognitive dysfunction remain unclear, resulting in a lack of effective treatments. In this study, we aimed to determine whether changes in the expression of RasGRF1, an important protein associated with cognition and synaptic plasticity, underlie the associated impairments in cognition after CCH. We found that RasGRF1 levels markedly decreased following CCH. Through prediction and validation studies, we observed that miRNA-323-3p was upregulated after CCH and could bind to the 3'-untranslated region of Rasgrf1 mRNA and regulate its expression in vitro. Moreover, the inhibition of miRNA-323-3p upregulated Rasgrf1 expression in the hippocampus after CCH, which was reversed by Rasgrf1 siRNA. This suggests that miRNA-323-3p is an important regulator of Rasgrf1. The Morris water maze and Y maze tests showed that miRNA-323-3p inhibition and Rasgrf1 upregulation improved spatial learning and memory, and electrophysiological measurements revealed deficits in long-term potentiation after CCH that were reversed by Rasgrf1 upregulation. Dendritic spine density and mature mushroom spine density were also improved after miRNA-323-3p inhibition and Rasgrf1 upregulation. Furthermore, Rasgrf1 upregulation by miRNA-323-3p inhibition improved dendritic spine density and mature mushroom spine density and ameliorated the deterioration of synapses and postsynaptic density. Overall, RasGRF1 regulation attenuated cognitive impairment, helped maintain structural and functional synaptic plasticity, and prevented synapse deterioration after CCH. These results suggest that Rasgrf1 downregulation by miRNA-323-3p plays an important role in cognitive impairment after CCH. Thus, RasGRF1 and miRNA-323-3p may represent potential therapeutic targets for cognitive impairment after CCH.

Early Detection of the Start of the Influenza Epidemic Using Surveillance Systems in Catalonia (PREVIGrip Study)

Sentinel physician networks are the method of influenza surveillance recommended by the World Health Organization. Weekly clinical diagnoses based on clinical history are a surveillance method that provides more immediate information. The objective of this study is to evaluate which influenza surveillance system is capable of the earliest detection of the start of the annual influenza epidemic. We carried out an ecological time-series study based on influenza data from the population of Catalonia from the 2010-2011 to the 2018-2019 seasons. Rates of clinical diagnoses and of confirmed cases in Catalonia were used to study the changes in trends in the different surveillance systems, the differences in area and time lag between the curves of the different surveillance systems using Joinpoint regression, Simpson's 1/3 method and cross-correlation, respectively. In general, changes in the trend of the curves were detected before the beginning of the epidemic in most seasons, using the rates for the complete seasons and the pre-epidemic rates. No time lag was observed between clinical diagnoses and the total confirmed cases. Therefore, clinical diagnoses in Primary Care could be a useful tool for early detection of the start of influenza epidemics in Catalonia.

Concordance between the Clinical Diagnosis of Influenza in Primary Care and Epidemiological Surveillance Systems (PREVIGrip Study)

Introduction: Health authorities use different systems of influenza surveillance. Sentinel networks, which are recommended by the World Health Organization, provide information on weekly influenza incidence in a monitored population, based on laboratory-confirmed cases. In Catalonia there is a public website, DiagnostiCat, that publishes the number of weekly clinical diagnoses at the end of each week of disease registration, while the sentinel network publishes its reports later. The objective of this study was to determine whether there is concordance between the number of cases of clinical diagnoses and the number of confirmed cases of influenza, in order to evaluate the predictive potential of a clinical diagnosis-based system. Methods: Population-based ecological time series study in Catalonia. The period runs from the 2010–2011 to the 2018–2019 season. The concordance between the clinical diagnostic cases and the confirmed cases was evaluated. The degree of agreement and the concordance were analysed using Bland–Altman graphs and intraclass correlation coefficients. Results: There was greater concordance between the clinical diagnoses and the sum of the cases confirmed outside and within the sentinel network than between the diagnoses and the confirmed sentinel cases. The degree of agreement was higher when influenza rates were low. Conclusions: There is concordance between the clinical diagnosis and the confirmed cases of influenza. Registered clinical diagnostic cases could provide a good alternative to traditional surveillance, based on case confirmation. Cases of clinical diagnosis of influenza may have the potential to predict the onset of annual influenza epidemics.

Risk Diagrams Based on Primary Care Electronic Medical Records and Linked Real-Time PCR Data to Monitor Local COVID-19 Outbreaks During the Summer 2020: A Prospective Study Including 7,671,862 People in Catalonia

Monitoring transmission is a prerequisite for containing COVID-19. We report on effective potential growth (EPG) as a novel measure for the early identification of local outbreaks based on primary care electronic medical records (EMR) and PCR-confirmed cases. Secondly, we studied whether increasing EPG precedes local hospital and intensive care (ICU) admissions and mortality. Population-based cohort including all Catalan citizens' PCR tests, hospitalization, intensive care (ICU) and mortality between 1/07/2020 and 13/09/2020; linked EMR covering 88.6% of the Catalan population was obtained. Nursing home residents were excluded. COVID-19 counts were ascertained based on EMR and PCRs separately. Weekly empirical propagation (ρ₇) and 14-day cumulative incidence (A₁₄) and 95% confidence intervals were estimated at care management area (CMA) level, and combined as EPG = ρ₇ × A₁₄. Overall, 7,607,201 and 6,798,994 people in 43 CMAs were included for PCR and EMR measures, respectively. A14, ρ₇, and EPG increased in numerous CMAs during summer 2020. EMR identified 2.70-fold more cases than PCRs, with similar trends, a median (interquartile range) 2 (1) days earlier, and better precision. Upticks in EPG preceded increases in local hospital admissions, ICU occupancy, and mortality. Increasing EPG identified localized outbreaks in Catalonia, and preceded local hospital and ICU admissions and subsequent mortality. EMRs provided similar estimates to PCR, but some days earlier and with better precision. EPG is a useful tool for the monitoring of community transmission and for the early identification of COVID-19 local outbreaks.

Divergences on expected pneumonia cases during the COVID-19 epidemic in Catalonia: a time-series analysis of primary care electronic health records covering about 6 million people

BACKGROUND

Pneumonia is one of the complications of COVID-19. Primary care electronic health records (EHR) have shown the utility as a surveillance system. We therefore analyse the trends of pneumonia during two waves of COVID-19 pandemic in order to use it as a clinical surveillance system and an early indicator of severity.

METHODS

Time series analysis of pneumonia cases, from January 2014 to December 2020. We collected pneumonia diagnoses from primary care EHR, a software system covering > 6 million people in Catalonia (Spain). We compared the trend of pneumonia in the season 2019-2020 with that in the previous years. We estimated the expected pneumonia cases with data from 2014 to 2018 using a time series regression adjusted by seasonality and influenza epidemics.

RESULTS

Between 4 March and 5 May 2020, 11,704 excess pneumonia cases (95% CI: 9909 to 13,498) were identified. Previously, we identified an excess from January to March 2020 in the population older than 15 years of 20%. We observed another excess pneumonia period from 22 october to 15 november of 1377 excess cases (95% CI: 665 to 2089). In contrast, we observed two great periods with reductions of pneumonia cases in children, accounting for 131 days and 3534 less pneumonia cases (95% CI, 1005 to 6064) from March to July; and 54 days and 1960 less pneumonia cases (95% CI 917 to 3002) from October to December.

CONCLUSIONS

Diagnoses of pneumonia from the EHR could be used as an early and low cost surveillance system to monitor the spread of COVID-19.

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

OBJECTIVES

The moving epidemic method (MEM) has been well used for assessing seasonal influenza epidemics in temperate regions. This study used the MEM to establish epidemic threshold for influenza in Guangdong, a subtropical province in China.

METHODS

Influenza virology surveillance data from 2011/2012 to 2017/2018 seasons in Guangdong were used with the MEM to calculate the epidemic thresholds and timeously detect the 2018/2019 influenza season epidemic. The weekly positive proportion of influenza A(H1N1)pdm09, A(H3N2), B/Victoria-lineage and B/Yamagata-lineage were separately adapted to calculate the subtype-specific epidemic thresholds. The performance of MEM was evaluated using a cross-validation procedure.

RESULTS

For the 2018/2019 influenza season, the epidemic threshold of a weekly positive proportion was 15.08%. Epidemic detection for the 2018/2019 season was 1 week in advance. Influenza A(H1N1)pdm09, B/Yamagata-lineage and B/Victoria-lineage prevailed during winter and spring and their epidemic thresholds were 5.12%, 4.53% and 4.38%, respectively. Influenza A(H3N2) was active in the summer, with an epidemic threshold of 11.99%.

CONCLUSIONS

Using influenza virology surveillance data stratified by types of influenza virus, the MEM was effectively used in Guangdong, China. This study provided a practical way for subtropical regions to establish local influenza epidemic thresholds.

Transmissibility of influenza during the 21st-century epidemics, Spain, influenza seasons 2001/02 to 2017/18

BackgroundUnderstanding influenza seasonality is necessary for determining policies for influenza control.AimWe characterised transmissibility during seasonal influenza epidemics, including one influenza pandemic, in Spain during the 21th century by using the moving epidemic method (MEM) to calculate intensity levels and estimate differences across seasons and age groups.MethodsWe applied the MEM to Spanish Influenza Sentinel Surveillance System data from influenza seasons 2001/02 to 2017/18. A modified version of Goldstein's proxy was used as an epidemiological-virological parameter. We calculated the average starting week and peak, the length of the epidemic period and the length from the starting week to the peak of the epidemic, by age group and according to seasonal virus circulation.ResultsIndividuals under 15 years of age presented higher transmissibility, especially in the 2009 influenza A(H1N1) pandemic. Seasons with dominance/co-dominance of influenza A(H3N2) virus presented high intensities in older adults. The 2004/05 influenza season showed the highest influenza-intensity level for all age groups. In 12 seasons, the epidemic started between week 50 and week 3. Epidemics started earlier in individuals under 15 years of age (-1.8 weeks; 95% confidence interval (CI):-2.8 to -0.7) than in those over 64 years when influenza B virus circulated as dominant/co-dominant. The average time from start to peak was 4.3 weeks (95% CI: 3.6-5.0) and the average epidemic length was 8.7 weeks (95% CI: 7.9-9.6).ConclusionsThese findings provide evidence for intensity differences across seasons and age groups, and can be used guide public health actions to diminish influenza-related morbidity and mortality.

Excess cases of influenza and the coronavirus epidemic in Catalonia: a time-series analysis of primary-care electronic medical records covering over 6 million people

OBJECTIVES

There is uncertainty about when the first cases of COVID-19 appeared in Spain. We aimed to determine whether influenza diagnoses masked early COVID-19 cases and estimate numbers of undetected COVID-19 cases.

DESIGN

Time-series study of influenza and COVID-19 cases, 2010-2020.

SETTING

Primary care, Catalonia, Spain.

PARTICIPANTS

People registered in primary-care practices, covering >6 million people and >85% of the population.

MAIN OUTCOME MEASURES

Weekly new cases of influenza and COVID-19 clinically diagnosed in primary care.

ANALYSES

Daily counts of both cases were computed using the total cases recorded over the previous 7 days to avoid weekly effects. Epidemic curves were characterised for the 2010-2011 to 2019-2020 influenza seasons. Influenza seasons with a similar epidemic curve and peak case number as the 2019-2020 season were used to model expected case numbers with Auto Regressive Integrated Moving Average models, overall and stratified by age. Daily excess influenza cases were defined as the number of observed minus expected cases.

RESULTS

Four influenza season curves (2011-2012, 2012-2013, 2013-2014 and 2016-2017) were used to estimate the number of expected cases of influenza in 2019-2020. Between 4 February 2020 and 20 March 2020, 8017 (95% CI: 1841 to 14 718) excess influenza cases were identified. This excess was highest in the 15-64 age group.

CONCLUSIONS

COVID-19 cases may have been present in the Catalan population when the first imported case was reported on 25 February 2020. COVID-19 carriers may have been misclassified as influenza diagnoses in primary care, boosting community transmission before public health measures were taken. The use of clinical codes could misrepresent the true occurrence of the disease. Serological or PCR testing should be used to confirm these findings. In future, this surveillance of excess influenza could help detect new outbreaks of COVID-19 or other influenza-like pathogens, to initiate early public health responses.

Influenza surveillance: determining the epidemic threshold for influenza by using the Moving Epidemic Method (MEM), Montenegro, 2010/11 to 2017/18 influenza seasons

Background: In 2009, an improved influenza surveillance system was implemented and weekly reporting to the World Health Organization on influenza-like illness (ILI) began. The goals of the surveillance system are to monitor and analyse the intensity of influenza activity, to provide timely information about circulating strains and to help in establishing preventive and control measures. In addition, the system is useful for comparative analysis of influenza data from Montenegro with other countries.

Aim: We aimed to evaluate the performance and usefulness of the Moving Epidemic Method (MEM), for use in the influenza surveillance system in Montenegro.

Methods: Historical ILI data from 2010/11 to 2017/18 influenza seasons were modelled with MEM. Epidemic threshold for Montenegro 2017/18 season was calculated using incidence rates from 2010/11–2016/17 influenza seasons.

Results: Pre-epidemic ILI threshold per 100,000 population was 19.23, while the post-epidemic threshold was 17.55. Using MEM, we identified an epidemic of 10 weeks’ duration. The sensitivity of the MEM epidemic threshold in Montenegro was 89% and the warning signal specificity was 99%.

Conclusions: Our study marks the first attempt to determine the pre/post-epidemic threshold values for the epidemic period in Montenegro. The findings will allow a more detailed examination of the influenza-related epidemiological situation, timely detection of epidemic and contribute to the development of more efficient measures for disease prevention and control aimed at reducing the influenza-associated morbidity and mortality.

A systematic review of aberration detection algorithms used in public health surveillance

The algorithms used for detecting anomalies have evolved substantially over the last decade to take advantage of advances in informatics and to accommodate changes in surveillance data. We identified 145 studies since 2007 that evaluated statistical methods used to detect aberrations in public health surveillance data. For each study, we classified the analytic methods and reviewed the evaluation metrics. We also summarized the practical usage of the detection algorithms in public health surveillance systems worldwide. Traditional methods (e.g., control charts, linear regressions) were the focus of most evaluation studies and continue to be used commonly in practice. There was, however, an increase in the number of studies using forecasting methods and studies applying machine learning methods, hidden Markov models, and Bayesian framework to multivariate datasets. Evaluation studies demonstrated improved accuracy with more sophisticated methods, but these methods do not appear to be used widely in public health practice.

Performances of statistical methods for the detection of seasonal influenza epidemics using a consensus-based gold standard

Influenza epidemics are monitored using influenza-like illness (ILI) data reported by health-care professionals. Timely detection of the onset of epidemics is often performed by applying a statistical method on weekly ILI incidence estimates with a large range of methods used worldwide. However, performance evaluation and comparison of these algorithms is hindered by: (1) the absence of a gold standard regarding influenza epidemic periods and (2) the absence of consensual evaluation criteria. As of now, performance evaluations metrics are based only on sensitivity, specificity and timeliness of detection, since definitions are not clear for time-repeated measurements such as weekly epidemic detection. We aimed to evaluate several epidemic detection methods by comparing their alerts to a gold standard determined by international expert consensus. We introduced new performance metrics that meet important objective of influenza surveillance in temperate countries: to detect accurately the start of the single epidemic period each year. Evaluations are presented using ILI incidence in France between 1995 and 2011. We found that the two performance metrics defined allowed discrimination between epidemic detection methods. In the context of performance detection evaluation, other metrics used commonly than the standard could better achieve the needs of real-time influenza surveillance.

Influenza detection and prediction algorithms: comparative accuracy trial in Östergötland county, Sweden, 2008–2012

Methods for the detection of influenza epidemics and prediction of their progress have seldom been comparatively evaluated using prospective designs. This study aimed to perform a prospective comparative trial of algorithms for the detection and prediction of increased local influenza activity. Data on clinical influenza diagnoses recorded by physicians and syndromic data from a telenursing service were used. Five detection and three prediction algorithms previously evaluated in public health settings were calibrated and then evaluated over 3 years. When applied on diagnostic data, only detection using the Serfling regression method and prediction using the non-adaptive log-linear regression method showed acceptable performances during winter influenza seasons. For the syndromic data, none of the detection algorithms displayed a satisfactory performance, while non-adaptive log-linear regression was the best performing prediction method. We conclude that evidence was found for that available algorithms for influenza detection and prediction display satisfactory performance when applied on local diagnostic data during winter influenza seasons. When applied on local syndromic data, the evaluated algorithms did not display consistent performance. Further evaluations and research on combination of methods of these types in public health information infrastructures for ‘nowcasting’ (integrated detection and prediction) of influenza activity are warranted.

Early and Real-Time Detection of Seasonal Influenza Onset

Every year, influenza epidemics affect millions of people and place a strong burden on health care services. A timely knowledge of the onset of the epidemic could allow these services to prepare for the peak. We present a method that can reliably identify and signal the influenza outbreak. By combining official Influenza-Like Illness (ILI) incidence rates, searches for ILI-related terms on Google, and an on-call triage phone service, Saúde 24, we were able to identify the beginning of the flu season in 8 European countries, anticipating current official alerts by several weeks. This work shows that it is possible to detect and consistently anticipate the onset of the flu season, in real-time, regardless of the amplitude of the epidemic, with obvious advantages for health care authorities. We also show that the method is not limited to one country, specific region or language, and that it provides a simple and reliable signal that can be used in early detection of other seasonal diseases.

Influenza, generally referred to as the flu, is a common infectious disease that affects millions of people. Every year, we expect this seasonal disease to occur during the Winter, but exactly when it will start and how severe it will be is not known. This places a strong burden on health services, as often the spread can be felt as very fast and emergency rooms become flooded with patients. With this work, we propose a new method that identifies the beginning of the yearly flu season. This is done by using several different data sources, including searches for flu-related symptoms on Google and phone call logs to a specialized medical phone service. These data sources, together with our method, can provide a daily or weekly report, making it much faster than current methods, which require lab testing or centralized medical reports. Our method was applied to different European countries and can anticipate current official alerts by several weeks.

Pilot study to harmonize the reported influenza intensity levels within the Spanish Influenza Sentinel Surveillance System (SISSS) using the Moving Epidemic Method (MEM)

The intensity of annual Spanish influenza activity is currently estimated from historical data of the Spanish Influenza Sentinel Surveillance System (SISSS) using qualitative indicators from the European Influenza Surveillance Network. However, these indicators are subjective, based on qualitative comparison with historical data of influenza-like illness rates. This pilot study assesses the implementation of Moving Epidemic Method (MEM) intensity levels during the 2014-2015 influenza season within the 17 sentinel networks covered by SISSS, comparing them to historically reported indicators. Intensity levels reported and those obtained with MEM at the epidemic peak of the influenza wave, and at national and regional levels did not show statistical difference (P = 0·74, Wilcoxon signed-rank test), suggesting that the implementation of MEM would have limited disrupting effects on the dynamic of notification within the surveillance system. MEM allows objective influenza surveillance monitoring and standardization of criteria for comparing the intensity of influenza epidemics in regions in Spain. Following this pilot study, MEM has been adopted to harmonize the reporting of intensity levels of influenza activity in Spain, starting in the 2015-2016 season.

Algorithms for detecting and predicting influenza outbreaks: metanarrative review of prospective evaluations

OBJECTIVES

Reliable monitoring of influenza seasons and pandemic outbreaks is essential for response planning, but compilations of reports on detection and prediction algorithm performance in influenza control practice are largely missing. The aim of this study is to perform a metanarrative review of prospective evaluations of influenza outbreak detection and prediction algorithms restricted settings where authentic surveillance data have been used.

DESIGN

The study was performed as a metanarrative review. An electronic literature search was performed, papers selected and qualitative and semiquantitative content analyses were conducted. For data extraction and interpretations, researcher triangulation was used for quality assurance.

RESULTS

Eight prospective evaluations were found that used authentic surveillance data: three studies evaluating detection and five studies evaluating prediction. The methodological perspectives and experiences from the evaluations were found to have been reported in narrative formats representing biodefence informatics and health policy research, respectively. The biodefence informatics narrative having an emphasis on verification of technically and mathematically sound algorithms constituted a large part of the reporting. Four evaluations were reported as health policy research narratives, thus formulated in a manner that allows the results to qualify as policy evidence.

CONCLUSIONS

Awareness of the narrative format in which results are reported is essential when interpreting algorithm evaluations from an infectious disease control practice perspective.

Harmonizing influenza primary-care surveillance in the United Kingdom: piloting two methods to assess the timing and intensity of the seasonal epidemic across several general practice-based surveillance schemes

General Practitioner consultation rates for influenza-like illness (ILI) are monitored through several geographically distinct schemes in the UK, providing early warning to government and health services of community circulation and intensity of activity each winter. Following on from the 2009 pandemic, there has been a harmonization initiative to allow comparison across the distinct existing surveillance schemes each season. The moving epidemic method (MEM), proposed by the European Centre for Disease Prevention and Control for standardizing reporting of ILI rates, was piloted in 2011/12 and 2012/13 along with the previously proposed UK method of empirical percentiles. The MEM resulted in thresholds that were lower than traditional thresholds but more appropriate as indicators of the start of influenza virus circulation. The intensity of the influenza season assessed with the MEM was similar to that reported through the percentile approach. The MEM pre-epidemic threshold has now been adopted for reporting by each country of the UK. Further work will continue to assess intensity of activity and apply standardized methods to other influenza-related data sources.

Do intensive care data on respiratory infections reflect influenza epidemics?

OBJECTIVES

Severe influenza can lead to Intensive Care Unit (ICU) admission. We explored whether ICU data reflect influenza like illness (ILI) activity in the general population, and whether ICU respiratory infections can predict influenza epidemics.

METHODS

We calculated the time lag and correlation between ILI incidence (from ILI sentinel surveillance, based on general practitioners (GP) consultations) and percentages of ICU admissions with a respiratory infection (from the Dutch National Intensive Care Registry) over the years 2003-2011. In addition, ICU data of the first three years was used to build three regression models to predict the start and end of influenza epidemics in the years thereafter, one to three weeks ahead. The predicted start and end of influenza epidemics were compared with observed start and end of such epidemics according to the incidence of ILI.

RESULTS

Peaks in respiratory ICU admissions lasted longer than peaks in ILI incidence rates. Increases in ICU admissions occurred on average two days earlier compared to ILI. Predicting influenza epidemics one, two, or three weeks ahead yielded positive predictive values ranging from 0.52 to 0.78, and sensitivities from 0.34 to 0.51.

CONCLUSIONS

ICU data was associated with ILI activity, with increases in ICU data often occurring earlier and for a longer time period. However, in the Netherlands, predicting influenza epidemics in the general population using ICU data was imprecise, with low positive predictive values and sensitivities.