Enhancing Epidemic Detection Using Syndromic Surveillance and Early Notification Methods

ObjectiveThis paper presents an investigation using early notification methods to enhancing epidemic detection in syndromic surveillance data from royal Thai army in Thailand.IntroductionEarly Notification Detection Systems have taken a critical role in providing early notice of disease outbreaks. To improve the detection methods for disease outbreaks, many detection methods have been created and implemented. However, there is limited information on the effectively of syndromic surveillance in Thailand. Knowing the performance, strengths and weakness of these surveillance systems in providing early warning for outbreaks will increase disease outbreak detection capacity in Thailand.MethodsThis study describes and compares the capabilities of various outbreak detection algorithms using 37,043 unique syndromic daily reports based on medical information from both civilian and military personnel from the Unit Base Surveillance of Royal Thai Army (RTA) along the Thai-Myanmar and Thai-Cambodia boarder areas. Traditional epidemic detection method: mean plus two SD were compared with algorithms for early notification methods and which included regression, regression/EWMA/Poisson, CDC-C1, CDC-C2 and CDC-C3. Early notification and epidemic detection methods were compared according to their ability to generate alert notifications. Sensitivity, specificity, positive predictive value (PPV), negative predictive value and overall accuracy to detect or predict disease outbreaks were estimated.ResultsThis study shows that the preliminary results are promising for epidemic detection by early notification methods in syndromic surveillance in Thailand. The majority of syndromic records were categorized into 12 symptoms. The three most common symptoms were respiratory, fever and gastrointestinal illness (11,501; 9,549 and 4,498 respectively). The results from the early notification systems were analyzed and their performances were compared with traditional epidemic detection method according to their ability to generate early warning alerts for the 3 symptoms. In our study regression/EWMA/Poisson method had higher specificity across the 3 symptoms (94.5%, 94.7% and 95.9% respectively), but generated lower sensitivity (22.6%, 40.4% and 23.1%). CDC-C1, CDC-C2 and CDC-C3 algorithms are easy to understand and are widely used. CDC-C3 had higher sensitivity to detect gradual disease outbreak effects (64.2%, 70.2% and 57.7%), but it is known to produce higher alarm rates/false positive signals.ConclusionsWithin the syndromic surveillance data of RTA, the CDC algorithm is the best chosen to use in the syndromic system due to being easy to understand and implement in a system with high sensitivity. CDC-C2 is the best early notification detection method due to its high sensitivity and PPV. However, CDC-C3 is shows the highest sensitivity, but exhibits the lowest specificity and PPV for all symptoms including a high alarm rates. To be useful, early notification detection methods must have acceptable operating characteristics. Consequently, we should select the most appropriate algorithm method to explain the data well and in order to improve detection of outbreaks. The comparison methods used in this study may be useful for testing other proposed alert threshold methods and may have further applications for other populations and other diseases.References1. Chretien JP, Burkom HS, Sedyaningsih ER, Larasati RP, et al. Syndromic Surveillance: Adapting Innovations to Developing Settings. PLoS Medicine 2008; vol 5: page 1-6.2. Burkom HS, Elbert Y, Magruder SF, Najmi AH, Peter W, Thompson MW. Developments in the roles, features, and evaluation of alerting algorithms for disease outbreak monitoring. Johns Hopkins APL Technical Digest 2008; vol 27: page 313

Human Sentinel Surveillance of Influenza and Other Respiratory Viral Pathogens in Border Areas of Western Cambodia

<div><p>Little is known about circulation of influenza and other respiratory viruses in remote populations along the Thai-Cambodia border in western Cambodia. We screened 586 outpatients (median age 5, range 1–77) presenting with influenza-like-illness (ILI) at 4 sentinel sites in western Cambodia between May 2010 and December 2012. Real-time reverse transcriptase (rRT) PCR for influenza was performed on combined nasal and throat specimens followed by viral culture, antigenic analysis, antiviral susceptibility testing and full genome sequencing for phylogenetic analysis. ILI-specimens negative for influenza were cultured, followed by rRT-PCR for enterovirus and rhinovirus (EV/RV) and EV71. Influenza was found in 168 cases (29%) and occurred almost exclusively in the rainy season from June to November. Isolated influenza strains had close antigenic and phylogenetic relationships, matching vaccine and circulating strains found elsewhere in Cambodia. Influenza vaccination coverage was low (<20%). Western Cambodian H1N1(2009) isolate genomes were more closely related to 10 earlier Cambodia isolates (94.4% genome conservation) than to 13 Thai isolates (75.9% genome conservation), despite sharing the majority of the amino acid changes with the Thai references. Most genes showed signatures of purifying selection. Viral culture detected only adenovirus (5.7%) and parainfluenza virus (3.8%), while non-polio enteroviruses (10.3%) were detected among 164 culture-negative samples including coxsackievirus A4, A6, A8, A9, A12, B3, B4 and echovirus E6 and E9 using nested RT-PCR methods. A single specimen of EV71 was found. Despite proximity to Thailand, influenza epidemiology of these western Cambodian isolates followed patterns observed elsewhere in Cambodia, continuing to support current vaccine and treatment recommendations from the Cambodian National Influenza Center. Amino acid mutations at non-epitope sites, particularly hemagglutinin genes, require further investigation in light of an increasingly important role of permissive mutations in influenza virus evolution. Further research about the burden of adenovirus and non-polio enteroviruses as etiologic agents in acute respiratory infections in Cambodia is also needed.</p></div

H3N2 maximum likelihood phylogenetic trees (aLRT support >70 for all vaccine clades and major nodes) for all segments analyzed in this study for 10 samples from Cambodia (colored in blue) and references in Genbank collected in 2011 and 2012.

<p>Vaccine strains are highlighted in red and clades defined by brackets. Unique sample that did not fall within vaccine clades highlighted in yellow with percent nucleotide identity indicated in bottom right table.</p

IC₅₀-values (nM) of influenza A and B isolates from 2011 to oseltamivir and zanamivir [Range(s) = Range for sensitive control isolates; Range(r) = Range for resistant control isolates).

<p>IC₅₀-values (nM) of influenza A and B isolates from 2011 to oseltamivir and zanamivir [Range(s) = Range for sensitive control isolates; Range(r) = Range for resistant control isolates).</p

Influenza and other respiratory viruses detected between 2010–2012.

<p>(A) Number of ILI and influenza subtypes, May 2010-Dec 2012. (B) Number of other respiratory viruses among influenza-negative ILI specimens May 2010-Dec 2012.</p

General characteristics of the study population.

<p>General characteristics of the study population.</p

Changes in glycosylation, antigenic and polymorphic sites linked to amino acid (AA) substitutions and sub-antigenic/catalytic sites (epitopes) where AA substitutions were found for hemagglutinin (HA) and neuraminidase (NA) segments of H3N2.

<p>Changes in glycosylation, antigenic and polymorphic sites linked to amino acid (AA) substitutions and sub-antigenic/catalytic sites (epitopes) where AA substitutions were found for hemagglutinin (HA) and neuraminidase (NA) segments of H3N2.</p

Dataset descriptions and models used in maximum likelihood phylogenetic analysis.

<p>Dataset descriptions and models used in maximum likelihood phylogenetic analysis.</p

Laboratory testing flowchart for influenza-like-illness (ILI).

<p>Laboratory testing flowchart for influenza-like-illness (ILI).</p