Effectiveness of MF59? Adjuvanted Influenza A(H1N1)pdm09 Vaccine in Risk Groups in the Netherlands

Background

The aim of the present study was to estimate the effectiveness of the MF59™-adjuvanted influenza A(H1N1)pdm09 vaccine against medically attended influenza-like illness and RT-PCR confirmed influenza in the at-risk population and persons over 60 in the Netherlands.

Methods

We conducted a retrospective cohort study in a Dutch based GP medical record database between 30 November 2009 and 1 March 2010 to estimate the vaccine effectiveness against influenza-like illness. Within the cohort we nested a test negative case-control study to estimate the effectiveness against laboratory confirmed influenza.

Results

The crude effectiveness in preventing diagnosed or possible influenza-like illness was 17.3% (95%CI: −8.5%–36.9%). Of the measured covariates, age, the severity of disease and health seeking behaviour through devised proxies confounded the association between vaccination and influenza-like illness. The adjusted vaccine effectiveness was 20.8% (95%CI: −5.4%, 40.5%) and varied significantly by age, being highest in adults up to 50 years (59%, 95%CI: 23%, 78%), and non-detectable in adults over 50 years. The number of cases in the nested case control study was too limited to validly estimate the VE against confirmed influenza.

Conclusions

With our study we demonstrated that the approach of combining a cohort study in a primary health care database with field sampling is a feasible and useful option to monitor VE of influenza vaccines in the future.     

Correlation between National Influenza Surveillance Data and Google Trends in South Korea

Background

In South Korea, there is currently no syndromic surveillance system using internet search data, including Google Flu Trends. The purpose of this study was to investigate the correlation between national influenza surveillance data and Google Trends in South Korea.

Methods

Our study was based on a publicly available search engine database, Google Trends, using 12 influenza-related queries, from September 9, 2007 to September 8, 2012. National surveillance data were obtained from the Korea Centers for Disease Control and Prevention (KCDC) influenza-like illness (ILI) and virologic surveillance system. Pearson''s correlation coefficients were calculated to compare the national surveillance and the Google Trends data for the overall period and for 5 influenza seasons.

Results

The correlation coefficient between the KCDC ILI and virologic surveillance data was 0.72 (p<0.05). The highest correlation was between the Google Trends query of H1N1 and the ILI data, with a correlation coefficient of 0.53 (p<0.05), for the overall study period. When compared with the KCDC virologic data, the Google Trends query of bird flu had the highest correlation with a correlation coefficient of 0.93 (p<0.05) in the 2010-11 season. The following queries showed a statistically significant correlation coefficient compared with ILI data for three consecutive seasons: Tamiflu (r = 0.59, 0.86, 0.90, p<0.05), new flu (r = 0.64, 0.43, 0.70, p<0.05) and flu (r = 0.68, 0.43, 0.77, p<0.05).

Conclusions

In our study, we found that the Google Trends for certain queries using the survey on influenza correlated with national surveillance data in South Korea. The results of this study showed that Google Trends in the Korean language can be used as complementary data for influenza surveillance but was insufficient for the use of predictive models, such as Google Flu Trends.     

Influenza and Other Respiratory Viruses Detected by Influenza-Like Illness Surveillance in Leyte Island,the Philippines, 2010–2013

This study aimed to determine the role of influenza-like illness (ILI) surveillance conducted on Leyte Island, the Philippines, including involvement of other respiratory viruses, from 2010 to 2013. ILI surveillance was conducted from January 2010 to March 2013 with 3 sentinel sites located in Tacloban city, Palo and Tanauan of Leyte Island. ILI was defined as fever ≥38°C or feverish feeling and either cough or running nose in a patient of any age. Influenza virus and other 5 respiratory viruses were searched. A total of 5,550 ILI cases visited the 3 sites and specimens were collected from 2,031 (36.6%) cases. Among the cases sampled, 1,637 (75.6%) were children aged <5 years. 874 (43.0%) cases were positive for at least one of the respiratory viruses tested. Influenza virus and respiratory syncytial virus (RSV) were predominantly detected (both were 25.7%) followed by human rhinovirus (HRV) (17.5%). The age distributions were significantly different between those who were positive for influenza, HRV, and RSV. ILI cases were reported throughout the year and influenza virus was co-detected with those viruses on approximately half of the weeks of study period (RSV in 60.5% and HRV 47.4%). In terms of clinical manifestations, only the rates of headache and sore throat were significantly higher in influenza positive cases than cases positive to other viruses. In conclusion, syndromic ILI surveillance in this area is difficult to detect the start of influenza epidemic without laboratory confirmation which requires huge resources. Age was an important factor that affected positive rates of influenza and other respiratory viruses. Involvement of older age children may be useful to detect influenza more effectively.     

Evaluation of Outbreak Detection Performance Using Multi-Stream Syndromic Surveillance for Influenza-Like Illness in Rural Hubei Province,China: A Temporal Simulation Model Based on Healthcare-Seeking Behaviors

Background

Syndromic surveillance promotes the early detection of diseases outbreaks. Although syndromic surveillance has increased in developing countries, performance on outbreak detection, particularly in cases of multi-stream surveillance, has scarcely been evaluated in rural areas.

Objective

This study introduces a temporal simulation model based on healthcare-seeking behaviors to evaluate the performance of multi-stream syndromic surveillance for influenza-like illness.

Methods

Data were obtained in six towns of rural Hubei Province, China, from April 2012 to June 2013. A Susceptible-Exposed-Infectious-Recovered model generated 27 scenarios of simulated influenza A (H1N1) outbreaks, which were converted into corresponding simulated syndromic datasets through the healthcare-behaviors model. We then superimposed converted syndromic datasets onto the baselines obtained to create the testing datasets. Outbreak performance of single-stream surveillance of clinic visit, frequency of over the counter drug purchases, school absenteeism, and multi-stream surveillance of their combinations were evaluated using receiver operating characteristic curves and activity monitoring operation curves.

Results

In the six towns examined, clinic visit surveillance and school absenteeism surveillance exhibited superior performances of outbreak detection than over the counter drug purchase frequency surveillance; the performance of multi-stream surveillance was preferable to signal-stream surveillance, particularly at low specificity (Sp <90%).

Conclusions

The temporal simulation model based on healthcare-seeking behaviors offers an accessible method for evaluating the performance of multi-stream surveillance.     

On estimation of vaccine efficacy using validation samples with selection bias

Using validation sets for outcomes can greatly improve the estimation of vaccine efficacy (VE) in the field (Halloran and Longini, 2001; Halloran and others, 2003). Most statistical methods for using validation sets rely on the assumption that outcomes on those with no cultures are missing at random (MAR). However, often the validation sets will not be chosen at random. For example, confirmational cultures are often done on people with influenza-like illness as part of routine influenza surveillance. VE estimates based on such non-MAR validation sets could be biased. Here we propose frequentist and Bayesian approaches for estimating VE in the presence of validation bias. Our work builds on the ideas of Rotnitzky and others (1998, 2001), Scharfstein and others (1999, 2003), and Robins and others (2000). Our methods require expert opinion about the nature of the validation selection bias. In a re-analysis of an influenza vaccine study, we found, using the beliefs of a flu expert, that within any plausible range of selection bias the VE estimate based on the validation sets is much higher than the point estimate using just the non-specific case definition. Our approach is generally applicable to studies with missing binary outcomes with categorical covariates.     

Surveillance and Vaccine Effectiveness of an Influenza Epidemic Predominated by Vaccine-Mismatched Influenza B/Yamagata-Lineage Viruses in Taiwan, 2011?12 Season

Introduction

The 2011−12 trivalent influenza vaccine contains a strain of influenza B/Victoria-lineage viruses. Despite free provision of influenza vaccine among target populations, an epidemic predominated by influenza B/Yamagata-lineage viruses occurred during the 2011−12 season in Taiwan. We characterized this vaccine-mismatched epidemic and estimated influenza vaccine effectiveness (VE).

Methods

Influenza activity was monitored through sentinel viral surveillance, emergency department (ED) and outpatient influenza-like illness (ILI) syndromic surveillance, and case-based surveillance of influenza with complications and deaths. VE against laboratory-confirmed influenza was evaluated through a case-control study on ILI patients enrolled into sentinel viral surveillance. Logistic regression was used to estimate VE adjusted for confounding factors.

Results

During July 2011−June 2012, influenza B accounted for 2,382 (72.5%) of 3,285 influenza-positive respiratory specimens. Of 329 influenza B viral isolates with antigen characterization, 287 (87.2%) were B/Yamagata-lineage viruses. Proportions of ED and outpatient visits being ILI-related increased from November 2011 to January 2012. Of 1,704 confirmed cases of influenza with complications, including 154 (9.0%) deaths, influenza B accounted for 1,034 (60.7%) of the confirmed cases and 103 (66.9%) of the deaths. Reporting rates of confirmed influenza with complications and deaths were 73.5 and 6.6 per 1,000,000, respectively, highest among those aged ≥65 years, 50−64 years, 3−6 years, and 0−2 years. Adjusted VE was −31% (95% CI: −80, 4) against all influenza, 54% (95% CI: 3, 78) against influenza A, and −66% (95% CI: −132, −18) against influenza B.

Conclusions

This influenza epidemic in Taiwan was predominated by B/Yamagata-lineage viruses unprotected by the 2011−12 trivalent vaccine. The morbidity and mortality of this vaccine-mismatched epidemic warrants careful consideration of introducing a quadrivalent influenza vaccine that includes strains of both B lineages.     

Influenza-Like Illness Surveillance on Twitter through Automated Learning of Na?ve Language

Twitter has the potential to be a timely and cost-effective source of data for syndromic surveillance. When speaking of an illness, Twitter users often report a combination of symptoms, rather than a suspected or final diagnosis, using naïve, everyday language. We developed a minimally trained algorithm that exploits the abundance of health-related web pages to identify all jargon expressions related to a specific technical term. We then translated an influenza case definition into a Boolean query, each symptom being described by a technical term and all related jargon expressions, as identified by the algorithm. Subsequently, we monitored all tweets that reported a combination of symptoms satisfying the case definition query. In order to geolocalize messages, we defined 3 localization strategies based on codes associated with each tweet. We found a high correlation coefficient between the trend of our influenza-positive tweets and ILI trends identified by US traditional surveillance systems.     

Are School Absences Correlated with Influenza Surveillance Data in England? Results from Decipher My Data—A Research Project Conducted through Scientific Engagement with Schools

Background

School aged children are a key link in the transmission of influenza. Most cases have little or no interaction with health services and are therefore missed by the majority of existing surveillance systems. As part of a public engagement with science project, this study aimed to establish a web-based system for the collection of routine school absence data and determine if school absence prevalence was correlated with established surveillance measures for circulating influenza.

Methods

We collected data for two influenza seasons (2011/12 and 2012/13). The primary outcome was daily school absence prevalence (weighted to make it nationally representative) for children aged 11 to 16. School absence prevalence was triangulated graphically and through univariable linear regression to Royal College of General Practitioners (RCGP) influenza like illness (ILI) episode incidence rate, national microbiological surveillance data on the proportion of samples positive for influenza (A+B) and with Rhinovirus, RSV and laboratory confirmed cases of Norovirus.

Results

27 schools submitted data over two respiratory seasons. During the first season, levels of influenza measured by school absence prevalence and established surveillance were low. In the 2012/13 season, a peak of school absence prevalence occurred in week 51, and week 1 in RCGP ILI surveillance data. Linear regression showed a strong association between the school absence prevalence and RCGP ILI (All ages, and 5–14 year olds), laboratory confirmed cases of influenza A & B, and weak evidence for a linear association with Rhinovirus and Norovirus.

Interpretation

This study provides initial evidence for using routine school illness absence prevalence as a novel tool for influenza surveillance. The network of web-based data collection platforms we established through active engagement provides an innovative model of conducting scientific research and could be used for a wide range of infectious disease studies in the future.     

Case Fatality Rates Based on Population Estimates of Influenza-Like Illness Due to Novel H1N1 Influenza: New York City,May–June 2009

Background

The public health response to pandemic influenza is contingent on the pandemic strain''s severity. In late April 2009, a potentially pandemic novel H1N1 influenza strain (nH1N1) was recognized. New York City (NYC) experienced an intensive initial outbreak that peaked in late May, providing the need and opportunity to rapidly quantify the severity of nH1N1.

Methods and Findings

Telephone surveys using rapid polling methods of approximately 1,000 households each were conducted May 20–27 and June 15–19, 2009. Respondents were asked about the occurrence of influenza-like illness (ILI, fever with either cough or sore throat) for each household member from May 1–27 (survey 1) or the preceding 30 days (survey 2). For the overlap period, prevalence data were combined by weighting the survey-specific contribution based on a Serfling model using data from the NYC syndromic surveillance system. Total and age-specific prevalence of ILI attributed to nH1N1 were estimated using two approaches to adjust for background ILI: discounting by ILI prevalence in less affected NYC boroughs and by ILI measured in syndromic surveillance data from 2004–2008. Deaths, hospitalizations and intensive care unit (ICU) admissions were determined from enhanced surveillance including nH1N1-specific testing. Combined ILI prevalence for the 50-day period was 15.8% (95% CI:13.2%–19.0%). The two methods of adjustment yielded point estimates of nH1N1-associated ILI of 7.8% and 12.2%. Overall case-fatality (CFR) estimates ranged from 0.054–0.086 per 1000 persons with nH1N1-associated ILI and were highest for persons ≥65 years (0.094–0.147 per 1000) and lowest for those 0–17 (0.008–0.012). Hospitalization rates ranged from 0.84–1.34 and ICU admission rates from 0.21–0.34 per 1000, with little variation in either by age-group.

Conclusions

ILI prevalence can be quickly estimated using rapid telephone surveys, using syndromic surveillance data to determine expected “background” ILI proportion. Risk of severe illness due to nH1N1 was similar to seasonal influenza, enabling NYC to emphasize preventing severe morbidity rather than employing aggressive community mitigation measures.     

Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic

Background

Google Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. The 2009 influenza virus A (H1N1) pandemic [pH1N1] provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1.

Methodology/Principal Findings

We evaluated the accuracy of each U.S. GFT model by comparing weekly estimates of ILI (influenza-like illness) activity with the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). For each GFT model we calculated the correlation and RMSE (root mean square error) between model estimates and ILINet for four time periods: pre-H1N1, Summer H1N1, Winter H1N1, and H1N1 overall (Mar 2009–Dec 2009). We also compared the number of queries, query volume, and types of queries (e.g., influenza symptoms, influenza complications) in each model. Both models'' estimates were highly correlated with ILINet pre-H1N1 and over the entire surveillance period, although the original model underestimated the magnitude of ILI activity during pH1N1. The updated model was more correlated with ILINet than the original model during Summer H1N1 (r = 0.95 and 0.29, respectively). The updated model included more search query terms than the original model, with more queries directly related to influenza infection, whereas the original model contained more queries related to influenza complications.

Conclusions

Internet search behavior changed during pH1N1, particularly in the categories “influenza complications” and “term for influenza.” The complications associated with pH1N1, the fact that pH1N1 began in the summer rather than winter, and changes in health-seeking behavior each may have played a part. Both GFT models performed well prior to and during pH1N1, although the updated model performed better during pH1N1, especially during the summer months.     

Probabilistic Daily ILI Syndromic Surveillance with a Spatio-Temporal Bayesian Hierarchical Model

Background

For daily syndromic surveillance to be effective, an efficient and sensible algorithm would be expected to detect aberrations in influenza illness, and alert public health workers prior to any impending epidemic. This detection or alert surely contains uncertainty, and thus should be evaluated with a proper probabilistic measure. However, traditional monitoring mechanisms simply provide a binary alert, failing to adequately address this uncertainty.

Methods and Findings

Based on the Bayesian posterior probability of influenza-like illness (ILI) visits, the intensity of outbreak can be directly assessed. The numbers of daily emergency room ILI visits at five community hospitals in Taipei City during 2006–2007 were collected and fitted with a Bayesian hierarchical model containing meteorological factors such as temperature and vapor pressure, spatial interaction with conditional autoregressive structure, weekend and holiday effects, seasonality factors, and previous ILI visits. The proposed algorithm recommends an alert for action if the posterior probability is larger than 70%. External data from January to February of 2008 were retained for validation. The decision rule detects successfully the peak in the validation period. When comparing the posterior probability evaluation with the modified Cusum method, results show that the proposed method is able to detect the signals 1–2 days prior to the rise of ILI visits.

Conclusions

This Bayesian hierarchical model not only constitutes a dynamic surveillance system but also constructs a stochastic evaluation of the need to call for alert. The monitoring mechanism provides earlier detection as well as a complementary tool for current surveillance programs.     

Influenza Pneumonia Surveillance among Hospitalized Adults May Underestimate the Burden of Severe Influenza Disease

Background

Studies seeking to estimate the burden of influenza among hospitalized adults often use case definitions that require presence of pneumonia. The goal of this study was to assess the extent to which restricting influenza testing to adults hospitalized with pneumonia could underestimate the total burden of hospitalized influenza disease.

Methods

We conducted a modelling study using the complete State Inpatient Databases from Arizona, California, and Washington and regional influenza surveillance data acquired from CDC from January 2003 through March 2009. The exposures of interest were positive laboratory tests for influenza A (H1N1), influenza A (H3N2), and influenza B from two contiguous US Federal Regions encompassing the study area. We identified the two outcomes of interest by ICD-9-CM code: respiratory and circulatory hospitalizations, as well as critical illness hospitalizations (acute respiratory failure, severe sepsis, and in-hospital death). We linked the hospitalization datasets with the virus surveillance datasets by geographic region and month of hospitalization. We used negative binomial regression models to estimate the number of influenza-associated events for the outcomes of interest. We sub-categorized these events to include all outcomes with or without pneumonia diagnosis codes.

Results

We estimated that there were 80,834 (95% CI 29,214–174,033) influenza-associated respiratory and circulatory hospitalizations and 26,760 (95% CI 14,541–47,464) influenza-associated critical illness hospitalizations. When a pneumonia diagnosis was excluded, the estimated number of influenza-associated respiratory and circulatory hospitalizations was 24,816 (95% CI 6,342–92,624). The estimated number of influenza-associated critical illness hospitalizations was 8,213 (95% CI 3,764–20,799). Around 30% of both influenza-associated respiratory and circulatory hospitalizations, as well as influenza-associated critical illness hospitalizations did not have pneumonia diagnosis codes.

Conclusions

Surveillance studies which only consider hospitalizations that include a diagnosis of pneumonia may underestimate the total burden of influenza hospitalizations.     

Estimating Sensitivity of Laboratory Testing for Influenza in Canada through Modelling

Background

The weekly proportion of laboratory tests that are positive for influenza is used in public health surveillance systems to identify periods of influenza activity. We aimed to estimate the sensitivity of influenza testing in Canada based on results of a national respiratory virus surveillance system.

Methods and Findings

The weekly number of influenza-negative tests from 1999 to 2006 was modelled as a function of laboratory-confirmed positive tests for influenza, respiratory syncytial virus (RSV), adenovirus and parainfluenza viruses, seasonality, and trend using Poisson regression. Sensitivity was calculated as the number of influenza positive tests divided by the number of influenza positive tests plus the model-estimated number of false negative tests. The sensitivity of influenza testing was estimated to be 33% (95%CI 32–34%), varying from 30–40% depending on the season and region.

Conclusions

The estimated sensitivity of influenza tests reported to this national laboratory surveillance system is considerably less than reported test characteristics for most laboratory tests. A number of factors may explain this difference, including sample quality and specimen procurement issues as well as test characteristics. Improved diagnosis would permit better estimation of the burden of influenza.     

Influenza Vaccine Effectiveness in the Tropics: Moderate Protection in a Case Test-Negative Analysis of a Hospital-Based Surveillance Population in Bangkok between August 2009 and January 2013

Influenza in the tropics occurs year round with peaks that correspond variably to temperate regions. However, data on influenza vaccine effectiveness (VE) in the tropics is sparse. We report on the effectiveness of influenza vaccine to prevent medically attended laboratory confirmed influenza from sentinel surveillance conducted at a Thai military medical facility in Bangkok, Thailand from August 2009 to January 2013. Patients ≥6 months old presenting with influenza-like illness underwent combined nasal/throat swabs which were tested by influenza RT-PCR. A case test-negative study design was used to evaluate VE. Of 2999 samples available for analysis,1059 (35.3%) were PCR-positive (cases) and 1940 (64.6%) were PCR-negative (test-negative controls). Five hundred and seven (16.9%) of these patients reported being vaccinated within the previous 12 months. Periods of high and low influenza activity were defined based on publicly available Thai Ministry of Public Health data. Overall VE adjusted for age and epiweek was found to be 50.1% (95%CI: 35.0, 61.9%). The May to April adjusted VE for year 2010, 2011 and 2012 was 57.7% (95%CI: 33.7, 73.8%), 57.1% (95% CI: 35.2, 68.3%) and 37.6% (95% CI: 3.5, 62.9%).During high influenza activity in years with the same vaccine formulation, the adjusted VE was 54.9% (95%CI: 38.9, 66.9%). VE appeared to be much higher during high versus low influenza activity periods. The adjusted point estimate for VE was highest in the 18–49 year age group (76.6%) followed by 6–23 months (58.1%) and 2–17 years (52.5%). Adjusted estimates were not done for those ≥50 years of age due to small numbers. VE in patients with underlying disease was 75.5% compared to 48.0% in those without. Our findings demonstrate moderate protection by influenza vaccination and support the utility of influenza vaccination in the tropics including in very young children and those with underlying disease.     

Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time

Circulating levels of both seasonal and pandemic influenza require constant surveillance to ensure the health and safety of the population. While up-to-date information is critical, traditional surveillance systems can have data availability lags of up to two weeks. We introduce a novel method of estimating, in near-real time, the level of influenza-like illness (ILI) in the United States (US) by monitoring the rate of particular Wikipedia article views on a daily basis. We calculated the number of times certain influenza- or health-related Wikipedia articles were accessed each day between December 2007 and August 2013 and compared these data to official ILI activity levels provided by the Centers for Disease Control and Prevention (CDC). We developed a Poisson model that accurately estimates the level of ILI activity in the American population, up to two weeks ahead of the CDC, with an absolute average difference between the two estimates of just 0.27% over 294 weeks of data. Wikipedia-derived ILI models performed well through both abnormally high media coverage events (such as during the 2009 H1N1 pandemic) as well as unusually severe influenza seasons (such as the 2012–2013 influenza season). Wikipedia usage accurately estimated the week of peak ILI activity 17% more often than Google Flu Trends data and was often more accurate in its measure of ILI intensity. With further study, this method could potentially be implemented for continuous monitoring of ILI activity in the US and to provide support for traditional influenza surveillance tools.     

Influenza in Outpatient ILI Case-Patients in National Hospital-Based Surveillance,Bangladesh, 2007–2008

Background

Recent population-based estimates in a Dhaka low-income community suggest that influenza was prevalent among children. To explore the epidemiology and seasonality of influenza throughout the country and among all age groups, we established nationally representative hospital-based surveillance necessary to guide influenza prevention and control efforts.

Methodolgy/Principal Findings

We conducted influenza-like illness and severe acute respiratory illness sentinel surveillance in 12 hospitals across Bangladesh during May 2007–December 2008. We collected specimens from 3,699 patients, 385 (10%) which were influenza positive by real time RT-PCR. Among the sample-positive patients, 192 (51%) were type A and 188 (49%) were type B. Hemagglutinin subtyping of type A viruses detected 137 (71%) A/H1 and 55 (29%) A/H3, but no A/H5 or other novel influenza strains. The frequency of influenza cases was highest among children aged under 5 years (44%), while the proportions of laboratory confirmed cases was highest among participants aged 11–15 (18%). We applied kriging, a geo-statistical technique, to explore the spatial and temporal spread of influenza and found that, during 2008, influenza was first identified in large port cities and then gradually spread to other parts of the country. We identified a distinct influenza peak during the rainy season (May–September).

Conclusions/Significance

Our surveillance data confirms that influenza is prevalent throughout Bangladesh, affecting a wide range of ages and causing considerable morbidity and hospital care. A unimodal influenza seasonality may allow Bangladesh to time annual influenza prevention messages and vaccination campaigns to reduce the national influenza burden. To scale-up such national interventions, we need to quantify the national rates of influenza and the economic burden associated with this disease through further studies.     

Estimating the Effectiveness of Early Control Measures through School Absenteeism Surveillance in Observed Outbreaks at Rural Schools in Hubei,China

Background

School absenteeism is a common data source in syndromic surveillance, which allows for the detection of outbreaks at an early stage. Previous studies focused on its correlation with other data sources. In this study, we evaluated the effectiveness of control measures based on early warning signals from school absenteeism surveillance in rural Chinese schools.

Methods

A school absenteeism surveillance system was established in all 17 primary schools in 3 adjacent towns in the Chinese region of Hubei. Three outbreaks (varicella, mumps, and influenza-like illness) were detected and controlled successfully from April 1, 2012, to January 15, 2014. An impulse susceptible-exposed-infectious-recovered model was used to fit the epidemics of these three outbreaks. Moreover, it simulated the potential epidemics under interventions resulting from traditional surveillance signals. The effectiveness of the absenteeism-based control measures was evaluated by comparing the simulated datasets.

Results

The school absenteeism system generated 52 signals. Three outbreaks were verified through epidemiological investigation. Compared to traditional surveillance, the school absenteeism system generated simultaneous signals for the varicella outbreak, but 3 days in advance for the mumps outbreak and 2–4 days in advance for the influenza-like illness outbreak. The estimated excess protection rates of control measures based on early signals were 0.0%, 19.0–44.1%, and 29.0–37.0% for the three outbreaks, respectively.

Conclusions

Although not all outbreak control measures can benefit from early signals through school absenteeism surveillance, the effectiveness of early signal-based interventions is obvious. School absenteeism surveillance plays an important role in reducing outbreak spread.     

The feasibility of forecasting influenza epidemics in Cuba.

A large influenza epidemic took place in Havana during the winter of 1988. The epidemiologic surveillance unit of the Pedro Kourí Institute of Tropical Medicine detected the beginning of the epidemic wave. The Rvachev-Baroyan mathematical model of the geographic spread of an epidemic was used to forecast this epidemic under routine conditions of the public health system. The expected number of individuals who would attend outpatient services, because of influenza-like illness, was calculated and communicated to the health authorities within enough time to permit the introduction of available control measures. The approximate date of the epidemic peak, the daily expected number of individuals attending medical services, and the approximate time of the end of the epidemic wave were estimated. The prediction error was 12%. The model was sufficiently accurate to warrant its use as a practical forecasting tool in the Cuban public health system.     

A susceptible-infected model of early detection of respiratory infection outbreaks on a background of influenza

The threat of biological warfare and the emergence of new infectious agents spreading at a global scale have highlighted the need for major enhancements to the public health infrastructure. Early detection of epidemics of infectious diseases requires both real-time data and real-time interpretation of data. Despite moderate advancements in data acquisition, the state of the practice for real-time analysis of data remains inadequate. We present a nonlinear mathematical framework for modeling the transient dynamics of influenza, applied to historical data sets of patients with influenza-like illness. We estimate the vital time-varying epidemiological parameters of infections from historical data, representing normal epidemiological trends. We then introduce simulated outbreaks of different shapes and magnitudes into the historical data, and estimate the parameters representing the infection rates of anomalous deviations from normal trends. Finally, a dynamic threshold-based detection algorithm is devised to assess the timeliness and sensitivity of detecting the irregularities in the data, under a fixed low false-positive rate. We find that the detection algorithm can identify such designated abnormalities in the data with high sensitivity with specificity held at 97%, but more importantly, early during an outbreak. The proposed methodology can be applied to a broad range of influenza-like infectious diseases, whether naturally occurring or a result of bioterrorism, and thus can be an integral component of a real-time surveillance system.