Page 19 - Timeliness of Infectious Disease Notification & Response Systems - Corien Swaan

General introduction 17
regional, and national levels, as well as reduction of underreporting of notifi- able diseases (7-9). According to current WHO standards, a surveillance system is equipped with a fully secure, interoperable, electronic tool for public health surveillance that is: 1) connected to other relevant electronic tools (e.g. animal health); and 2) can easily share data with other electronic tools used either at regional or international levels (10). Another contribution to early identification of causative pathogens and outbreaks is made by the development of labora- tory techniques as multiplex reverse transcription polymerase chain reaction (PCR), and more recently the next generation sequencing technology (2, 11).
Besides health based sources, also animal health data, or nowadays meteo- rological and entomological data can be included to predict epidemiological de- velopments. Warm and wet weather for example is associated with an increase in Legionella pneumonia incidence (12, 13). Information on vector abundance and seasonality contributes to the assessment of vector borne disease risks (14). Other examples of IBS are sentinel surveillance, mortality data and data retrieved from surveys and research. The objective of IBS is not only early warning, but data are also used to achieve other objectives, such as measuring the impact of installed prevention programs, and the identification of priority health problems.
Next to IBS, Event-Based Surveillance (EBS) has been embedded into sur- veillance systems over the recent decades. EBS is defined as the organized col- lection, monitoring, assessment, and interpretation of mainly unstructured ad hoc information regarding health events or risks, which may represent an acute risk to human health (6). Sources for EBS include (social) media, informal net- works, websites of (inter)national authorities and non-governmental organi- zations (NGO’s). Although innovative, defining the appropriate algorithms for outbreak detection through social media can be difficult. Google Flu Trends (GFT) for example missed the first wave of the influenza A(H1N1) pandemic in the United States in New York in 2009, and needed to adjust its algorithm (2, 15). IBS and EBS are complementary sources for Early Warning and Response (EWAR), and data are integrated in ‘Epidemic Intelligence (EI)’ in order to detect acute public health events and/or risks (6).
After controlling or eliminating major infectious diseases in the 20th cen- tury, the world has been increasingly confronted with (re-)emerging disease outbreaks and threats over the last two decades, as SARS (2003), avian influ- enza A(H5N1) (2004), MERS-CoV (2014) and Ebola (2014-2015 and from 2018 onwards). This led to renewed attention for emergency preparedness and in- ternational standardization of requirements for capacities for surveillance and response. First, following the SARS outbreak in 2003 and the growth in interna-
1