Outbreak detection algorithms for seasonal disease data : a case study using Ross River virus disease

Background Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. Methods We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. Results The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. Conclusions This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

Evaluation of the performance of a dengue outbreak detection tool for China

An outbreak detection and response system, using time series moving percentile method based on historical data, in China has been used for identifying dengue fever outbreaks since 2008. For dengue fever outbreaks reported from 2009 to 2012, this system achieved a sensitivity of 100%, a specificity of 99.8% and a median time to detection of 3 days, which indicated that the system was a useful decision tool for dengue fever control and risk-management programs in China.

A nationwide web-based automated system for early outbreak detection and rapid response in China

Timely reporting, effective analyses and rapid distribution of surveillance data can assist in detecting the aberration of disease occurrence and further facilitate a timely response. In China, a new nationwide web-based automated system for outbreak detection and rapid response was developed in 2008. The China Infectious Disease Automated-alert and Response System (CIDARS) was developed by the Chinese Center for Disease Control and Prevention based on the surveillance data from the existing electronic National Notifiable Infectious Diseases Reporting Information System (NIDRIS) started in 2004. NIDRIS greatly improved the timeliness and completeness of data reporting with real time reporting information via the Internet. CIDARS further facilitates the data analysis, aberration detection, signal dissemination, signal response and information communication needed by public health departments across the country. In CIDARS, three aberration detection methods are used to detect the unusual occurrence of 28 notifiable infectious diseases at the county level and to transmit that information either in real-time or on a daily basis. The Internet, computers and mobile phones are used to accomplish rapid signal generation and dissemination, timely reporting and reviewing of the signal response results. CIDARS has been used nationwide since 2008; all Centers for Disease Control and Prevention (CDC) in China at the county, prefecture, provincial and national levels are involved in the system. It assists with early outbreak detection at the local level and prompts reporting of unusual disease occurrences or potential outbreaks to CDCs throughout the country.

Detection of bacteriophage and respiratory viruses in droplets

Influenza is a widespread disease occurring in seasonal epidemics, and each year is responsible for up to 500,000 deaths worldwide. Influenza can develop into strains which cause severe symptoms and high mortality rates, and could potentially reach pandemic status if the virus’ properties allow easy transmission. Influenza is transmissible via contact with the virus, either directly (infected people) or indirectly (contaminated objects); via reception of large droplets over short distances (one metre or less); or through inhalation of aerosols containing the virus expelled by infected individuals during respiratory activities, that can remain suspended in the air and travel distances of more than one metre (the aerosol route). Aerosol transmission of viruses involves three stages: production of the droplets containing viruses; transport of the droplets and ability of a virus to remain intact and infectious; and reception of the droplets (via inhalation). Our understanding of the transmission of influenza viruses via the aerosol route is poor, and thus our ability to prevent a widespread outbreak is limited. This study explored the fate of viruses in droplets by investigating the effects of some physical factors on the recovery of both a bacteriophage model and influenza virus. Experiments simulating respiratory droplets were carried out using different types of droplets, generated from a commonly used water-like matrix, and also from an ‘artificial mucous’ matrix which was used to more closely resemble respiratory fluids. To detect viruses in droplets, we used the traditional plaque assay techniques, and also a sensitive, quantitative PCR assay specifically developed for this study. Our results showed that the artificial mucous suspension enhanced the recovery of infectious bacteriophage. We were able to report detection limits of infectious bacteriophage (no bacteriophage was detected by the plaque assay when aerosolised from a suspension of 103 PFU/mL, for three of the four droplet types tested), and that bacteriophage could remain infectious in suspended droplets for up to 20 minutes. We also showed that the nested real-time PCR assay was able to detect the presence of bacteriophage RNA where the plaque assay could not detect any intact particles. Finally, when applying knowledge from the bacteriophage experiments, we reported the quantitative recoveries of influenza viruses in droplets, which were more consistent and stable than we had anticipated. Influenza viruses can be detected up to 20 minutes (after aerosolisation) in suspended aerosols and possibly beyond. It also was detectable from nebulising suspensions with relatively low concentrations of viruses.

Web based surveillance systems could improve disease detection and the response to emerging disease events

The Chinese government should be commended for its open, concerted, and rapid response to the recent H7N9 influenza outbreak. However, the first known case was not reported until 48 days after disease onset.1 Although the difficulties in detecting the virus and the lack of suitable diagnostic methods have been the focus of discussion,2 systematic limitations that may have contributed to this delay have hardly been discussed. The detection speed of surveillance systems is limited by the highly structured nature of information flow and hierarchical organisation of these systems. Flu surveillance usually relies on notification to a central authority of laboratory confirmed cases or presentations to sentinel practices for flu-like illness. Each step in this pathway presents a bottleneck at which information and time can be lost; this limitation must be dealt with...

Hand, foot and mouth disease in China: Evaluating an automated system for the detection of outbreaks

Objective To evaluate the performance of China’s infectious disease automated alert and response system in the detection of outbreaks of hand, foot and mouth (HFM) disease. Methods We estimated size, duration and delay in reporting HFM disease outbreaks from cases notified between 1 May 2008 and 30 April 2010 and between 1 May 2010 and 30 April 2012, before and after automatic alert and response included HFM disease. Sensitivity, specificity and timeliness of detection of aberrations in the incidence of HFM disease outbreaks were estimated by comparing automated detections to observations of public health staff. Findings The alert and response system recorded 106 005 aberrations in the incidence of HFM disease between 1 May 2010 and 30 April 2012 – a mean of 5.6 aberrations per 100 days in each county that reported HFM disease. The response system had a sensitivity of 92.7% and a specificity of 95.0%. The mean delay between the reporting of the first case of an outbreak and detection of that outbreak by the response system was 2.1 days. Between the first and second study periods, the mean size of an HFM disease outbreak decreased from 19.4 to 15.8 cases and the mean interval between the onset and initial reporting of such an outbreak to the public health emergency reporting system decreased from 10.0 to 9.1 days. Conclusion The automated alert and response system shows good sensitivity in the detection of HFM disease outbreaks and appears to be relatively rapid. Continued use of this system should allow more effective prevention and limitation of such outbreaks in China.