Optimizing use of multistream influenza sentinel surveillance data

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study1

Background

Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance.

Methods

The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models.

Results

Considering the MAPE, RMSE, and R squared values, the ARMA-GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models' predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting.

Conclusions

Our study suggested that the ARMA-GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA-GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.

Impact of non-pharmaceutical interventions during COVID-19 on future influenza trends in Mainland China

BACKGROUND

Influenza is a common illness for its high rates of morbidity and transmission. The implementation of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic to manage its dissemination could affect the transmission of influenza.

METHODS

A retrospective analysis, between 2018 and 2023, was conducted to examine the incidence of influenza virus types A and B among patients in sentinel cities located in North or South China as well as in Wuhan City. For validations, data on the total count of influenza patients from 2018 to 2023 were collected at the Central Hospital of Wuhan, which is not included in the sentinel hospital network. Time series methods were utilized to examine seasonal patterns and to forecast future influenza trends.

RESULTS

Northern and southern cities in China had earlier outbreaks during the NPIs period by about 8 weeks compared to the 2018-2019. The implementation of NPIs significantly reduced the influenza-like illness (ILI) rate and infection durations. Influenza B Victoria and H3N2 were the first circulating strains detected after the relaxation of NPIs, followed by H1N1 across mainland China. The SARIMA model predicted synchronized H1N1 outbreak cycles in North and South China, with H3N2 expected to occur in the summer in southern cities and in the winter in northern cities over the next 3 years. The ILI burden is expected to rise in both North and South China over the next 3 years, with higher ILI% levels in southern cities throughout the year, especially in winter, and in northern cities mainly during winter. In Wuhan City and the Central Hospital of Wuhan, influenza levels are projected to peak in the winter of 2024, with 2 smaller peaks expected during the summer of 2023.

CONCLUSIONS

In this study, we report the impact of NPIs on future influenza trends in mainland China. We recommend that local governments encourage vaccination during the transition period between summer and winter to mitigate economic losses and mortality associated with influenza.

Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study

BACKGROUND

The impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China.

METHODS

We descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011-2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020-2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020-2021.

RESULTS

The weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011-2019, with an obvious winter-spring seasonality in Beijing. During the 2020-2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, "Cancelling public events" and "Restrictions on internal travel" measures played an important role in the reduction of ILI in Beijing, while the "restrictions on international travel" was statistically most associated with ILI reductions in the Hong Kong SAR.

CONCLUSIONS

Our study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future.

Indoor relative humidity shapes influenza seasonality in temperate and subtropical climates in China

OBJECTIVES

The aim of this study was to explore whether indoor or outdoor relative humidity (RH) modulates the influenza epidemic transmission in temperate and subtropical climates.

METHODS

In this study, the daily temperature and RH in 1558 households from March 2017 to January 2019 in five cities across both temperate and subtropical regions in China were collected. City-level outdoor temperature and RH from 2013 to 2019 were collected from the weather stations. We first estimated the effective reproduction number (R_t) of influenza and then used time-series analyses to explore the relationship between indoor/outdoor RH/absolute humidity and the R_t of influenza. Furthermore, we expanded the measured 1-year indoor temperature and the RH data into 5 years and used the same method to examine the relationship between indoor/outdoor RH and the R_t of influenza.

RESULTS

Indoor RH displayed a seasonal pattern, with highs during the summer months and lows during the winter months, whereas outdoor RH fluctuated with no consistent pattern in subtropical regions. The R_t of influenza followed a U-shaped relationship with indoor RH in both temperate and subtropical regions, whereas a U-shaped relationship was not observed between outdoor RH and R_t. In addition, indoor RH may be a better indicator for R_t of influenza than indoor absolute humidity.

CONCLUSION

The findings indicated that indoor RH may be the driver of influenza seasonality in both temperate and subtropical locations in China.

Influenza seasonality and its environmental driving factors in mainland China and Hong Kong

BACKGROUND

Influenza epidemics occur during winter in temperate zones, but have less regular seasonality in the subtropics and tropics. Here we quantified the role of environmental drivers of influenza seasonality in temperate and subtropical China.

METHODS

We used weekly surveillance data on influenza virus activity in mainland China and Hong Kong from 2005 through 2016. We estimated the transmissibility via the instantaneous reproduction number (R_t), a real-time measure of transmissibility, and examined its relationship with different climactic drivers and allowed for the timing of school holidays and the decline in susceptibility in the population as an epidemic progressed. We developed a multivariable regression model for R_t to quantify the contribution of various potential environmental drivers of transmission.

FINDINGS

We found that absolute humidity is a potential driver of influenza seasonality and had a U-shaped association with transmissibility and hence can predict the pattern of influenza virus transmission across different climate zones. Absolute humidity was able to explain up to 15% of the variance in R_t, and was a stronger predictor of R_t across the latitudes. Other climatic drivers including mean daily temperature explained up to 13% of variance in R_t and limited to the locations where the indoor measures of these factors have better indicators of outdoor measures. The non-climatic driver, holiday-related school closures could explain up to 7% of variance in R_t.

INTERPRETATION

A U-shaped association of absolute humidity with influenza transmissibility was able to predict seasonal patterns of influenza virus epidemics in temperate and subtropical locations.

Does Seasonal Influenza Related Hospital Occupancy Surge Impact Hospital Staff Sickness Presenteeism and Productivity Costs?

Background—The workload of public hospital staff is heightened during seasonal influenza surges in hospitals serving densely populated cities. Such work environments may subject staff to increased risk of sickness presenteeism. Presenteeism is detrimental to nurses’ health and may lead to downstream productivity loss, resulting in financial costs for hospital organizations. Aims—This study aims to quantify how seasonal influenza hospital occupancy surge impacts nurses’ sickness presenteeism and related productivity costs in high-intensity inpatient metropolitan hospitals. Methods—Full-time nurses in three Hong Kong acute-care hospitals were surveyed. Generalized estimating equations (GEE) was applied to account for clustering in small number of hospitals. Results—A total of 71.3% of nurses reported two or more presenteeism events last year. A 6.8% increase in hospital inpatient occupancy rate was associated with an increase of 19% (1.19, 95% CI: 1.06–1.34) in nurse presenteeism. Presenteeism productivity loss costs between nurses working healthy (USD1983) and worked sick (USD 2008) were not significantly different, while sick leave costs were highest (USD 2703). Conclusion—Presenteeism prevalence is high amongst acute-care hospital nurses and workload increase during influenza flu surge significantly heightened nurse sickness presenteeism. Annual presenteeism productivity loss costs in this study of USD 24,096 were one of the highest reported worldwide. Productivity loss was also considerably high regardless of nurses’ health states, pointing towards other potential risk factors at play. When scheduling nurses to tackle flu surge, managers may want to consider impaired productivity due to staff presenteeism. Further longitudinal research is essential in identifying management modifiable risk factors that impact nurse presenteeism and impairing downstream productivity loss.

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

Influenza 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.

Using decision fusion methods to improve outbreak detection in disease surveillance

BACKGROUND

When outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where several ODAs analyze the same surveillance signal), the outbreak detection problem should be treated as a decision fusion (DF) problem of multiple sensors.

METHODS

This study evaluated the benefit for a decisions support system of using DF methods (fusing multiple ODA decisions) compared to using a single method of outbreak detection. For each day, we merged the decisions of six ODAs using 5 DF methods (two voting methods, logistic regression, CART and Bayesian network - BN). Classical metrics of accuracy, prediction and timelines were used during the evaluation steps.

RESULTS

In our results, we observed the greatest gain (77%) in positive predictive value compared to the best ODA if we used DF methods with a learning step (BN, logistic regression, and CART).

CONCLUSIONS

To identify disease outbreaks in systems using several ODAs to analyze surveillance data, we recommend using a DF method based on a Bayesian network. This method is at least equivalent to the best of the algorithms considered, regardless of the situation faced by the system. For those less familiar with this kind of technique, we propose that logistic regression be used when a training dataset is available.

A Smart Card-Based Electronic School Absenteeism System for Influenza-Like Illness Surveillance in Hong Kong: Design, Implementation, and Feasibility Assessment

Background

School-aged children have the highest incidence of respiratory virus infections each year, and transmission of respiratory viruses such as influenza virus can be a major concern in school settings. School absenteeism data have been employed as a component of influenza surveillance systems in some locations. Data timeliness and system acceptance remain as key determinants affecting the usefulness of a prospective surveillance system.

Objective

The aim of this study was to assess the feasibility of implementing an electronic school absenteeism surveillance system using smart card–based technology for influenza-like illness (ILI) surveillance among a representative network of local primary and secondary schools in Hong Kong.

Methods

We designed and implemented a surveillance system according to the Protocol for a Standardized information infrastructure for Pandemic and Emerging infectious disease Response (PROSPER). We employed an existing smart card–based education and school administration platform for data capture, customized the user interface, and used additional back end systems built for other downstream surveillance steps. We invited local schools to participate and collected absenteeism data by the implemented system. We compared temporal trend of the absenteeism data with data from existing community sentinel and laboratory surveillance data.

Results

We designed and implemented an ILI surveillance system utilizing smart card–based attendance tracking approach for data capture. We implemented the surveillance system in a total of 107 schools (including 66 primary schools and 41 secondary schools), covering a total of 75,052 children. The system successfully captured information on absences for 2 consecutive academic years (2012-2013 and 2013-2014). The absenteeism data we collected from the system reflected ILI activity in the community, with an upsurge in disease activity detected up to 1 to 2 weeks preceding other existing surveillance systems.

Conclusions

We designed and implemented a novel smart card technology–based school absenteeism surveillance system. Our study demonstrated the feasibility of building a large-scale surveillance system riding on a routinely adopted data collection approach and the use of simple system enhancement to minimize workload implication and enhance system acceptability. Data from this system have potential value in supplementing existing sentinel influenza surveillance for situational awareness of influenza activity in the community.

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data

BACKGROUND

In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland.

RESULTS

In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities.

CONCLUSIONS

Stochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

Forecasting Influenza Epidemics in Hong Kong

Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.

Influenza causes high levels of morbidity, mortality, and economic burden. Accurate forecasts of epidemic timing and magnitude would provide public health sectors valuable advance information in support of the planning and deployment of intervention measures. Such forecast systems have been developed for temperate regions with seasonal winter epidemics (e.g., U.S. cities). In subtropical and tropical regions, however, influenza epidemics can occur throughout the year with varying epidemic intensity; this irregularity makes the generation of accurate forecasts more challenging. For this study we develop alternative forecast systems that are more adept at handling erratic non-seasonal epidemics, using state-of-the-art Bayesian inference methods in conjunction with an epidemiological model. Here we present these forecast systems and apply them to Hong Kong. During 1998–2013, Hong Kong saw 44 influenza epidemics caused by either the A(H1N1), A(H3N2), or B strain, and 19 aggregate epidemics caused by one or more of these influenza strains. The forecast systems are able to forecast both the peak timing and peak magnitude of these epidemics, including the 2009 pandemic. The results suggest that routine forecast of influenza epidemics in other subtropical and tropical regions is possible, as well as forecast of other infectious diseases sharing similar irregular transmission dynamics.

Age-specific epidemic waves of influenza and respiratory syncytial virus in a subtropical city

Both influenza and respiratory syncytial virus (RSV) are active throughout the year in subtropical or tropical regions, but few studies have reported on age-specific seasonal patterns of these viruses. We examined the age-specific epidemic curves of laboratory-confirmed cases of influenza A (subtyped into seasonal A(H1N1), A(H3N2), and pandemic virus A(H1N1)pdm09), influenza B and respiratory syncytial virus (RSV), in subtropical city Hong Kong from 2004 to 2013. We found that different types and subtypes of influenza showed similar two-peak patterns across age groups, with one peak in winter and another in spring/summer. Age differences were found in epidemic onset time and duration, but none could reach statistical significance (p > 0.05). Age synchrony was found in epidemic peak time for both cool and warm seasons. RSV showed less clear seasonal patterns and non-synchronized epidemic curves across age. In conclusion, age synchrony was found in influenza seasonal epidemics and the 2009 pandemic, but not in RSV. None of the age groups consistently appear as the driving force for seasonal epidemics of influenza and RSV in Hong Kong.

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

BACKGROUND

Infectious disease surveillance is a process the product of which reflects both actual disease trends and public awareness of the disease. Decisions made by patients, health care providers, and public health professionals about seeking and providing health care and about reporting cases to health authorities are all influenced by the information environment, which changes constantly. Biases are therefore imbedded in surveillance systems; these biases need to be characterized to provide better situational awareness for decision-making purposes. Our goal is to develop a statistical framework to characterize influenza surveillance systems, particularly their correlation with the information environment.

METHODS

We identified Hong Kong influenza surveillance data systems covering healthcare providers, laboratories, daycare centers and residential care homes for the elderly. A Bayesian hierarchical statistical model was developed to examine the statistical relationships between the influenza surveillance data and the information environment represented by alerts from HealthMap and web queries from Google. Different models were fitted for non-pandemic and pandemic periods and model goodness-of-fit was assessed using common model selection procedures.

RESULTS

Some surveillance systems - especially ad hoc systems developed in response to the pandemic flu outbreak - are more correlated with the information environment than others. General practitioner (percentage of influenza-like-illness related patient visits among all patient visits) and laboratory (percentage of specimen tested positive) seem to proportionally reflect the actual disease trends and are less representative of the information environment. Surveillance systems using influenza-specific code for reporting tend to reflect biases of both healthcare seekers and providers.

CONCLUSIONS

This study shows certain influenza surveillance systems are less correlated with the information environment than others, and therefore, might represent more reliable indicators of disease activity in future outbreaks. Although the patterns identified in this study might change in future outbreaks, the potential susceptibility of surveillance data is likely to persist in the future, and should be considered when interpreting surveillance data.

Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China

Background

Influenza has been associated with heavy burden of mortality and morbidity in subtropical regions. However, timely forecast of influenza epidemic in these regions has been hindered by unclear seasonality of influenza viruses. In this study, we developed a forecasting model by integrating multiple sentinel surveillance data to predict influenza epidemics in a subtropical city Shenzhen, China.

Methods

Dynamic linear models with the predictors of single or multiple surveillance data for influenza-like illness (ILI) were adopted to forecast influenza epidemics from 2006 to 2012 in Shenzhen. Temporal coherence of these surveillance data with laboratory-confirmed influenza cases was evaluated by wavelet analysis and only the coherent data streams were entered into the model. Timeliness, sensitivity and specificity of these models were also evaluated to compare their performance.

Results

Both influenza virology data and ILI consultation rates in Shenzhen demonstrated a significant annual seasonal cycle (p<0.05) during the entire study period, with occasional deviations observed in some data streams. The forecasting models that combined multi-stream ILI surveillance data generally outperformed the models with single-stream ILI data, by providing more timely, sensitive and specific alerts.

Conclusions

Forecasting models that combine multiple sentinel surveillance data can be considered to generate timely alerts for influenza epidemics in subtropical regions like Shenzhen.

A review of the evidence to support influenza vaccine introduction in countries and areas of WHO's Western Pacific Region

Background

Immunization against influenza is considered an essential public health intervention to control both seasonal epidemics and pandemic influenza. According to the World Health Organization (WHO), there are five key policy and three key programmatic issues that decision-makers should consider before introducing a vaccine. These are (a) public health priority, (b) disease burden, (c) efficacy, quality and safety of the vaccine, (d) other inventions, (e) economic and financial issues, (f) vaccine presentation, (g) supply availability and (h) programmatic strength. We analyzed the body of evidence currently available on these eight issues in the WHO Western Pacific Region.

Methodology/Principal Findings

Studies indexed in PubMed and published in English between 1 January 2000 and 31 December 2010 from the 37 countries and areas of the Western Pacific Region were screened for keywords pertaining to the five policy and three programmatic issues. Studies were grouped according to country income level and vaccine target group. There were 133 articles that met the selection criteria, with most (90%) coming from high-income countries. Disease burden (n = 34), vaccine efficacy, quality and safety (n = 27) and public health priority (n = 27) were most frequently addressed by studies conducted in the Region. Many studies assessed influenza vaccine policy and programmatic issues in the general population (42%), in the elderly (24%) and in children (17%). Few studies (2%) addressed the eight issues relating to pregnant women.

Conclusions/Significance

The evidence for vaccine introduction in countries and areas in this Region remains limited, particularly in low- and middle-income countries that do not currently have influenza vaccination programmes. Surveillance activities and specialized studies can be used to assess the eight issues including disease burden among vaccine target groups and the cost-effectiveness of influenza vaccine. Multi-country studies should be considered to maximize resource utilization for cross-cutting issues such as vaccine presentation and other inventions.

Situational awareness of influenza activity based on multiple streams of surveillance data using multivariate dynamic linear model

BACKGROUND

Multiple sources of influenza surveillance data are becoming more available; however integration of these data streams for situational awareness of influenza activity is less explored.

METHODS AND RESULTS

We applied multivariate time-series methods to sentinel outpatient and school absenteeism surveillance data in Hong Kong during 2004-2009. School absenteeism data and outpatient surveillance data experienced interruptions due to school holidays and changes in public health guidelines during the pandemic, including school closures and the establishment of special designated flu clinics, which in turn provided 'drop-in' fever counts surveillance data. A multivariate dynamic linear model was used to monitor influenza activity throughout epidemics based on all available data. The inferred level followed influenza activity closely at different times, while the inferred trend was less competent with low influenza activity. Correlations between inferred level and trend from the multivariate model and reference influenza activity, measured by the product of weekly laboratory influenza detection rates and weekly general practitioner influenza-like illness consultation rates, were calculated and compared with those from univariate models. Over the whole study period, there was a significantly higher correlation (ρ = 0.82, p≤0.02) for the inferred trend based on the multivariate model compared to other univariate models, while the inferred trend from the multivariate model performed as well as the best univariate model in the pre-pandemic and the pandemic period. The inferred trend and level from the multivariate model was able to match, if not outperform, the best univariate model albeit with missing data plus drop-in and drop-out of different surveillance data streams. An overall influenza index combining level and trend was constructed to demonstrate another potential use of the method.

CONCLUSIONS

Our results demonstrate the potential use of multiple streams of influenza surveillance data to promote situational awareness about the level and trend of seasonal and pandemic influenza activity.

A method for selecting and monitoring medication sales for surveillance of gastroenteritis

PURPOSE

Monitoring appropriate categories of medication sales can provide early warning of certain disease outbreaks. This paper presents a methodology for choosing and monitoring medication sales relevant for the surveillance of gastroenteritis and assesses the operational characteristics of the selected medications for early warning.

METHODS

Acute diarrhoea incidences in mainland France were obtained from the Sentinelles network surveillance system for the period 2000-2009. Medication sales grouped by therapeutic classes were obtained on the same period. Hierarchical clustering was used to select therapeutic classes correlating with disease incidence over the period. Alert thresholds were defined for the selected therapeutic classes. Single and multiple voter algorithms were investigated for outbreak detection based on sales crossing the thresholds. Sensitivity and specificity were calculated respective to known outbreaks periods.

RESULTS

Four therapeutic classes were found to cluster with acute diarrhoea incidence. The therapeutic class other antiemetic and antinauseants had the best sensitivity (100%) and timeliness (1.625 weeks before official alerts), for a false alarm rate of 5%. Multiple voter algorithm was the most efficient with the rule: 'Emit an outbreak alert when at least three therapeutic classes are over their threshold' (sensitivity 100%, specificity 95%, timeliness 1.750 weeks before official alerts).

CONCLUSIONS

The presented method allowed selection of relevant therapeutic classes for surveillance of a specific condition. Multiple voter algorithm based on several therapeutic classes performed slightly better than the best therapeutic class alone, while improving robustness against abrupt changes occurring in a single therapeutic class.

Timely detection of localized excess influenza activity in Northern California across patient care, prescription, and laboratory data

Timely detection of clusters of localized influenza activity in excess of background seasonal levels could improve situational awareness for public health officials and health systems. However, no single data type may capture influenza activity with optimal sensitivity, specificity, and timeliness, and it is unknown which data types could be most useful for surveillance. We compared the performance of 10 types of electronic clinical data for timely detection of influenza clusters throughout the 2007/08 influenza season in northern California. Kaiser Permanente Northern California generated zip code-specific daily episode counts for: influenza-like illness (ILI) diagnoses in ambulatory care (AC) and emergency departments (ED), both with and without regard to fever; hospital admissions and discharges for pneumonia and influenza; antiviral drugs dispensed (Rx); influenza laboratory tests ordered (Tests); and tests positive for influenza type A (FluA) and type B (FluB). Four credible events of localized excess illness were identified. Prospective surveillance was mimicked within each data stream using a space-time permutation scan statistic, analyzing only data available as of each day, to evaluate the ability and timeliness to detect the credible events. AC without fever and Tests signaled during all four events and, along with Rx, had the most timely signals. FluA had less timely signals. ED, hospitalizations, and FluB did not signal reliably. When fever was included in the ILI definition, signals were either delayed or missed. Although limited to one health plan, location, and year, these results can inform the choice of data streams for public health surveillance of influenza.

Data-driven approach for creating synthetic electronic medical records

BACKGROUND

New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs) that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed.

METHODS

This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia) and for background records. The method developed has three major steps: 1) synthetic patient identity and basic information generation; 2) identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3) adaptation of these care patterns to the synthetic patient population.

RESULTS

We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified.

CONCLUSIONS

A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders). The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious diseases. The pilot synthetic background records were in the 4-11 year old age group. The adaptations that must be made to the algorithms to produce synthetic background EMRs for other age groups are indicated.

Early detection of influenza outbreaks using the DC Department of Health's syndromic surveillance system

Background

Since 2001, the District of Columbia Department of Health has been using an emergency room syndromic surveillance system to identify possible disease outbreaks. Data are received from a number of local hospital emergency rooms and analyzed daily using a variety of statistical detection algorithms. The aims of this paper are to characterize the performance of these statistical detection algorithms in rigorous yet practical terms in order to identify the optimal parameters for each and to compare the ability of two syndrome definition criteria and data from a children's hospital versus vs. other hospitals to determine the onset of seasonal influenza.

Methods

We first used a fine-tuning approach to improve the sensitivity of each algorithm to detecting simulated outbreaks and to identifying previously known outbreaks. Subsequently, using the fine-tuned algorithms, we examined (i) the ability of unspecified infection and respiratory syndrome categories to detect the start of the flu season and (ii) how well data from Children's National Medical Center (CNMC) did versus all the other hospitals when using unspecified infection, respiratory, and both categories together.

Results

Simulation studies using the data showed that over a range of situations, the multivariate CUSUM algorithm performed more effectively than the other algorithms tested. In addition, the parameters that yielded optimal performance varied for each algorithm, especially with the number of cases in the data stream. In terms of detecting the onset of seasonal influenza, only "unspecified infection," especially the counts from CNMC, clearly delineated influenza outbreaks out of the eight available syndromic classifications. In three of five years, CNMC consistently flags earlier (from 2 days up to 2 weeks earlier) than a multivariate analysis of all other DC hospitals.

Conclusions

When practitioners apply statistical detection algorithms to their own data, fine tuning of parameters is necessary to improve overall sensitivity. With fined tuned algorithms, our results suggest that emergency room based syndromic surveillance focusing on unspecified infection cases in children is an effective way to determine the beginning of the influenza outbreak and could serve as a trigger for more intensive surveillance efforts and initiate infection control measures in the community.

Effects of school closures, 2008 winter influenza season, Hong Kong

In winter 2008, kindergartens and primary schools in Hong Kong were closed for 2 weeks after media coverage indicated that 3 children had died, apparently from influenza. We examined prospective influenza surveillance data before, during, and after the closure. We did not find a substantial effect on community transmission.