ISS--an electronic syndromic surveillance system for infectious disease in rural China

Abstract

Background: Syndromic surveillance system has great advantages in promoting the early detection of epidemics and reducing the necessities of disease confirmation, and it is especially effective for surveillance in resource poor settings. However, most current syndromic surveillance systems are established in developed countries, and there are very few reports on the development of an electronic syndromic surveillance system in resource-constrained settings.

Objective: This study describes the design and pilot implementation of an electronic surveillance system (ISS) for the early detection of infectious disease epidemics in rural China, complementing the conventional case report surveillance system.

Methods: ISS was developed based on an existing platform 'Crisis Information Sharing Platform' (CRISP), combining with modern communication and GIS technology. ISS has four interconnected functions: 1) work group and communication group; 2) data source and collection; 3) data visualization; and 4) outbreak detection and alerting.

Results: As of Jan. 31(st) 2012, ISS has been installed and pilot tested for six months in four counties in rural China. 95 health facilities, 14 pharmacies and 24 primary schools participated in the pilot study, entering respectively 74,256, 79,701, and 2330 daily records into the central database. More than 90% of surveillance units at the study sites are able to send daily information into the system. In the paper, we also presented the pilot data from health facilities in the two counties, which showed the ISS system had the potential to identify the change of disease patterns at the community level.

Conclusions: The ISS platform may facilitate the early detection of infectious disease epidemic as it provides near real-time syndromic data collection, interactive visualization, and automated aberration detection. However, several constraints and challenges were encountered during the pilot implementation of ISS in rural China.

Figure 1. Structure of ISS platform.

ISS has four interconnected components: 1) work group and communication group; 2) data source and collection; 3) data visualization; and 4) outbreak detection and alerting.

Figure 2. Web-form for data collection in health center report.

The web-form for data collection in the ISS platform is very simple for users to fill in and it usually takes less than one minute for a familiar user to input one case. High-level users can also edit the web-form and define different checking rules for data input.

Figure 3. Chart examples in the dashboard.

Through the ISS platform, users have the ability to show the characteristics of data in the format of lines, bars and pies.

Figure 4. Running result of a detection model.

Each time when the user activates an alarm trigger, a running log will be generated and saved in the system, including data sources, model parameters, and detection time if the model threshold is exceeded to generate alerts.

Figure 5. Generated matrix of selected models using data source from health centers.

The generated matrix showed the running results of SheWhart and Cusum models when they were applied to the number of patients with four different symptoms in the four counties. Each point represents a model, and the red point represents there is one alert generated from this model based on the selected data source.

Figure 6. A system supervisor reminding a data collector to make corrections via QQ messenger in Chinese.

QQ messenger is popularly used for online communications in China. In the pilot study, we used QQ to create a communication platform for data quality control, like reminding a timely data reporting, correcting reporting mistakes, etc.

Figure 7. Time series plots of six main symptoms in Qianjiang County (Aug.1, 2011–Jan.31, 2012).

The time series plots of the main symptoms in Qianjiang County was a screenshot from the Chart of the Dashboard in the ISS system, which provided users a preliminary description and analysis of rough data on daily numbers of each symptom in the county.

Figure 8. Time series plots of six main symptoms in Shayang County (Aug.1, 2011–Jan.31, 2012).

The time series plots of the main symptoms in Shayang County was a screenshot from the Chart of the Dashboard in the ISS system, which provided users a preliminary description and analysis of rough data on daily numbers of each symptom in the county.