Communicable disease epidemiology is closely linked to pathogen ecology, environmental and social determinants, economic factors, access to care, as well as the state of country development. This has historically been mirrored in the different epidemics and new threats that have challenged humanity over time. In today’s world the development of our societies and the changes of environmental and global systems are happening at such an unprecedented scale and rapid rate that they will pose new challenges to the surveillance of infectious disease threats and the development of adaptive measures. Climate change has been shown to have and to continue to have both direct and indirect effects on communicable diseases, often in combination with other drivers, such as increased global travel and trade. It will therefore become more and more important to prepare for projected climate change impacts, both internationally and in Europe, as some novel infections have the potential to spread widely and cause substantial morbidity and mortality. Public health actions are needed to prepare for the health impacts of climate change, particularly the infectious diseases ones. Although the impacts are predicted to be higher in developing countries than in developed ones, it is thought that there will still be significant impacts in Europe. Mapping is important in the investigation and measurement of these changes, and a variety of analytical approaches are possible. The impacts of climate change on infectious diseases are particularly focused on vulnerable groups, but intervening on these groups has proven to be difficult at best.
Climate change manifests itself locally, regionally and globally, with altered patterns of temperature, precipitation, storms and winds reflecting the complex changes resulting from the slow increase in global temperatures that reflect the impact of increased greenhouse gases. The frequency, duration, and intensity of heat waves have increased across Europe, and the last decade was the warmest ever recorded. Climate change may impact infectious diseases in different ways. Some of these impacts include an upward movement of tick vectors into higher latitude and altitude and a shift in the transmission of other vector-borne diseases. Food and water borne diseases are also susceptible to climate change because dispersion, transport, fate and environmental exposure pathways of these pathogens are intricately linked to local climate and weather conditions, although interventions may contribute more to change in the future than climate change.
Surveillance is the on-going collection, validation, analysis and interpretation of health and disease data needed to inform key stakeholders and enable them to take action through planning and implementing effective, evidence-based public health policies and strategies for the control and prevention of diseases and epidemics. Reported cases based on positive test results are often only the top of the surveillance pyramid (Figure 1). The degradation of information through the surveillance hierarchy remains a challenge, with detailed records that are somewhat unstructured at the individual physician level and highly structured surveillance records with limited data fields, less detail and, for some countries with a poor ability to examine the original records at national and, thus, at EU level. Surveillance data need to be timely and distributed to those who need it for the early detection and control of outbreaks, for measuring the impact of interventions, or for undertaking research. Surveillance may be compulsory or voluntary, active or passive, case-based or aggregated (although aggregated data is usually less useful). Some environmental surveillance data can also contribute to disease surveillance processes.
Ensuring that public health infrastructures are adequate is the best preparation for the coming changes in infectious diseases that will result from climate change and other drivers. It is therefore important to review existing surveillance systems and the data they provide as part of a response to these future risks. The purpose of this assessment is to review the current status of appropriate European monitoring systems. Here we examine the datasets they produce and assess their ability to monitor changes in infectious disease transmission and to pick up signals of new threats due to climatic and environmental change, as well as to identify potential weaknesses in their ability to detect climate change-related impacts.
This paper describes the infectious disease surveillance systems in place in the EU, cross surveillance initiatives and environmental surveillance data that can be used for investigating the environmental determinants of ID. The information sources are documented so that local, national and European public health practitioners and scientists can access these data to examine infectious disease epidemiology, evaluate intervention efficacy to look for impacts of climate and other change and to provide an evidence base for examining disease shifts and climate change adaptation initiatives.
The organisations, agencies, and networks involved in infectious disease surveillance in Europe were examined, along with their underlying legal framework, regulations, mandate and surveillance scope. Information on these organisations and networks, their current collaborations, the different surveillance systems and the environmental datasets was collected from surveillance experts, the peer-reviewed literature, grey literature and web sites of respective organisations, agencies and networks. Scientific and medical experts at the European Centre for Disease Prevention and Control were interviewed about the different surveillance systems maintained by the agency. Interviews were also held with a number of technical experts at other international agencies, including the European Food Safety Authority (EFSA). The methodology was predominantly descriptive, and designed to identify as many of the systems as possible. The ability of the different European surveillance systems to detect potential climate change signals was assessed, along with evaluations of how to best adapt these systems to identify new threats and changes in disease risks.
ECDC has developed the European Environment and Epidemiology (E3) Network with the goal of monitoring environmental precursors of epidemics and providing predictions that can be used for intervention. The E3 Network has a group of experts in environmental epidemiology and a distributed, secure, web-based hub called the E3 geoportal; that provides access to environmental datasets for assessing determinants of infectious and modelling outputs. European public health agencies and researchers can use this platform in preparedness and response to infectious disease spread in the short and long term such as environmental and social changes. The initial building-block of the E3 data repository was the data acquisition from the Emerging Diseases in a Changing European Environment project (EDEN), an FP-6 funded initiative. Further collaborations are on-going with several FP7 project in order to enrich the data repository. The repository is also used as a secure place to store project-specific geo-spatial data, such as the TigerMaps, DengueMaps or V-Bornet which generate novel geospatial data. The data files are re-classified into themes and categories and amendments to the metadata files are done to make the data more suitable for storage and maintenance in a database. Metadata standard for E3 data are formulated based on the mandatory elements of the INSPIRE metadata standards to the requirements of E3 on one hand, and ECDC-core metadata on the other hand. A set of metadata translation and compilation tools were developed to facilitate the authoring of metadata that complies with E3 standards. Contributors of data to the E3 Network can use these tools to author a compliant metadata file to accompany the data resources that they wish to submit to the E3 service. This tool is fully integrated into the E3 Geoportal. The environmental datasets cover a range of potential determinants of communicable diseases in the broadest sense: from past, current and future climatic parameters, landscape features, remote sensing information and socio-economic determinants that are known to have a key in human epidemiology (e.g., climate change datasets, land cover information, vegetation, hydrology, soil data, elevation, biota, wind speed; socio-economic data including population, economic, education, healthcare, hospitals, transport networks and statistics, migrant populations, demographic profiles, agriculture and livestock). Environmental datasets were identified and documented during the process of establishing the E3 Geoportal. Datasets from many sources were examined, and where relevant were stored for public access in the E3 Geoportal along with associated metadata. The data can be used in incident response, as a resource for investigation and to build understanding.
3.1. Infectious Disease Surveillance Systems
Main infectious disease surveillance organisations, agencies and departments operating at the European level are documented (Table 1). Several of these are essential data sources for surveillance streams that can be used for examining the impacts of climate and environmental changes on geographical distribution, morbidity and mortality. Surveillance systems include both indicator and event based systems and include data from many sources that can include mortality data, morbidity reports, laboratory data, outbreak data and field reports, vaccine and drug utilization, primary care surveillance (including sentinel systems), sickness absence data, syndromic surveillance etc. (Table 2).
European Union Member States (EUMS) have national surveillance systems using data from clinical (seldom used alone) and/or laboratory (e.g., salmonellosis) based systems, sentinel surveillance systems, in which only a proportion of practitioners or microbiologists report cases (e.g., influenza) or enhanced surveillance systems in which additional demographic and risk related data is collected (e.g., STEC/VTEC infection). The quality of data differs between EUMS, often by pathogen, due to differences in case definitions, the level of participation of data providers at different levels of the reporting systems (physician, hospital, laboratory diagnosis or laboratory reporting), technical equipment, and country-specific differences in health care systems organisation, surveillance infrastructure and public health capacity. ECDC has addressed these differences and is working to harmonize discrepancies through promoting disease networks and a common central health information system (TESSy).
Prior to the establishment of ECDC there were 17 dedicated (active) surveillance networks for various pathogens and some standardised case definitions. Historically, some of the surveillance data from different EUMS were not equivalent, representing as they were diverse diagnostic, laboratory and surveillance infrastructures as well as differences in prior exposure and infection rates within the EU community. Since European Centre for Disease Prevention and Control (ECDC) came into operation in 2005 region-wide surveillance data have been collected for over 52 notifiable diseases. For each notifiable disease a common standardized case definition has been agreed upon by the EUMS and ECDC, sometimes resulting in countries reporting data to ECDC that is different from that used at a national level. There is a central system for reporting notifiable disease and the case definitions and list of diseases is updated periodically.
Mandatory notification and laboratory surveillance are very effective in monitoring threats related to known risks. Such indicator-based surveillance will be able to show trends over time as well as changes in geographical distribution within the EU region, for example a spread of leishmaniasis or of tick-borne diseases and their vectors towards higher latitudes and altitudes due to a changing climate.
Event-based surveillance, on the other hand, focuses on recognizing new signals and emerging threats through the collection and study of unstructured data such as news releases, internet-based information and other epidemic intelligence sources. Outbreaks of non-notifiable diseases in an area will be observed through this type of surveillance as well as new threats. The emergence of wound infections in the northern countries around the Baltic Sea in the early/mid 2000s when several deaths occurred due to higher concentrations of non-toxigenic Vibrio cholerae in bathing waters after periods of unusually high water temperatures.
3.2. Laboratory Surveillance
Most of the common surveillance systems are based on laboratory surveillance, while mandatory reporting of some diseases by physicians occurs in some countries. Routine laboratory based surveillance may not be sufficient to detect emerging, re-emerging and new diseases and other types of surveillance are necessary, such as syndromic or sentinel surveillance, as well as surveillance of animal diseases, animal infections, environmental changes, drinking water and bathing water quality, food contamination etc. with increased collaboration between these reporting systems at a European level.
Both food and animal data are sometimes collected in a less systematic way than human disease data. Some EUMS have mandatory reporting (i.e., notification) for some or all reportable diseases both from laboratories and physicians and the number of physician reported cases are often not comparable with the number of confirmed laboratory reports for the same disease.
Pathogen specific surveillance is important for some pathogens that might be climate change related, and the pathogens that are most likely to be sensitive to climate change have been proposed. Molecular surveillance uses the laboratory typing of pathogens to focus on a subset of pathogens and take action where there is an increase. ECDC initiatives on molecular surveillance are currently focusing on Salmonella and Listeria infections. An examination of long term trends in the impact of temperature on salmonellosis showed that this had changed over time and suggested that the impacts of climate change on different serotypes as a result of raised temperature have declined more recently. There are also sequence databases focusing on organism phylogeny that can contribute to the understanding of human and animal diseases, but this paper has not reviewed these. There are also publications relating to climate change indicators, but these are not reviewed here.
There are a number of areas where classical surveillance may not capture all human infections. Some pathogens are only commonly detected through cytology, histology, parasitology or haematology departments and reporting of infectious diseases from these may not be as complete as from diagnostic microbiology laboratories (e.g., Pneumocystis jirovecii; Tropheryma whipplei; Enterocytozoon bieneusi, Plasmodium spp. respectively).
3.3. Syndromic Surveillance
Syndromic surveillance uses health-related information as a tool to monitor trends for any unexpected health outcomes and to detect outbreaks. This can sometimes be better for early detection of outbreaks such as seasonal influenza and some environmental/climate related outbreaks. For example for the early detection of water-borne outbreaks after flooding events, by collecting data on over-the-counter sales of drugs, or calls made to telephone help lines. An EU project called “TRIPLES” made an inventory of syndromic surveillance systems in place in Europe as well as proposing the development of a European platform for monitoring threats using syndromic surveillance data. One of the well-known limitations of syndromic surveillance is that it is unspecific and can give false positive signals.
3.4. Sentinel Surveillance
The sensitivity of disease ID monitoring can be enhanced through sentinel surveillance where a rapid assessment of the incidence in certain area and during a certain season can be achieved. Designated sites are selected as sentinel institutions to represent a random sample of the population, in a certain area. Sentinel surveillance is useful for answering specific epidemiologic questions in a certain region, but may not represent the general population or the general incidence of disease, and may have limited usefulness in analysing national disease patterns and trends. Sentinel surveillance has been used for a long time to predict and follow increasing/decreasing trends during the influenza season. A European system of sentinel dengue surveillance has been implemented in the Mediterranean region to monitor the emergence of autochthonous transmission. Sentinel surveillance could be used to answer research questions such as the current distribution and incidence of a disease in a specific area, with follow-up studies examining changes over time and in space due environmental and/or climate change. For example, the incidence of Tick Borne Encephalitis (TBE) in an area could be studied by an on-going cross sectional sero-survey of all encephalitis patients that are admitted to a specific numbers of hospitals during a year, or by testing the blood of blood donors from a specific area, or by following annual seroconversion in a specified population. If this is only done for a short period this would be classed as a cross-sectional study. Positive serological results should be followed-up from an epidemiologic point of view, and could then be studied in relation to different determinants and drivers.
3.5. Cross-sectoral Surveillance
Surveillance collaboration between different sectors is useful for early detection of potential threats, or to assess changes in risk area distribution and in seasonal incidence. Collaboration between human case reporting systems at the national levels and within ECDC and other agencies/organisation (like EFSA, FAO, WHO, see Table 1) could be further strengthened. In addition, human infectious disease surveillance (Figure 2) benefits from collaborations with other sectors, such as the veterinary investigation of agricultural, domestic and wild animals, vector surveillance (e.g., VBORNET, see Table 3), water monitoring (drinking and bathing waters), food safety (“From the Farm to the Fork”), tourist industry and trade, travel information, health systems (including vaccination coverage), etc.
The Nomenclature of Territorial Units for Statistics (NUTS) is a hierarchical geocoding standard system for recording the geography and statistics of EU Member States. NUTS 0 are the member states; NUTS 1 are major socio-economic regions; NUTS 2 are basic regions for the application of regional policies and NUTS 3 are small regions.
Collaboration on surveillance between human and veterinary sectors occurs at local, national and international level (e.g., investigation of STEC/VTEC outbreak in Germany; reporting of Highly Pathogenic Avian Influenza poultry outbreaks to the Commission) to detect changes in zoonotic disease risk, in combination with vector surveillance in areas where land cover/land use and climatic conditions are, or will become favorable for disease transmission. To ensure cross-sectoral surveillance activities, information to local stakeholders and actors are important to initiate and increase active participation.
Data on food, water and the environment that derive from disease control programs and process monitoring are important in preventing human health threats. This includes official controls, surveillance and other monitoring that are used to covering all stages of production, processing and distribution of food, together with information to the public of any risks to health. Regulation 178/2002/EC states that food and animal feed on sale in EUMS should be safe. Food businesses are responsible for ensuring that their food and animal feed fulfils legal requirements and are checked by food authorities related to the producer or at import from non-EU states. There is free movement of foods within Europe and food checked by local food authorities does not normally need to be re-checked. Unsafe food is withdrawn from the market and public warnings issued, with information reported to the Rapid Alert System for Food and Feed (RASFF) which provides food control authorities with a means for exchanging information about serious risks from food or feed.
Incidents where there is microbiological contamination of foods with Salmonella, Campylobacter, Listeria monocytogenes, verotoxigenic E. coli, and Yersinia, are also reported. The specific nature of molecular typing systems (e.g., Salmonella) can mean that the isolation of a pathogen from a food product can be used to link to identical isolates detected in a number of patients. The increasing use of typing based on whole genome sequencing may make raw food monitoring more productive in attributing pathogens detected to source food animals and transmission pathways. EUMS use different types of surveillance and monitoring to detect food-borne outbreaks. It is crucial that a food-borne outbreak is detected immediately in order to protect members of the public from preventable diseases. EUMS with suspected international outbreaks can communicate through the EPIS secured network at ECDC to report food-borne outbreaks on both mandatory and optional bases to EFSA. The aim is to follow trends, detect deviation from trend and examine emerging public health risks from new agents and food items. EFSA and ECDC are collecting this information and present it annually in the European Union Summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in the European Union.
3.6. Environmental Surveillance
Environmental surveillance has been widely used for the detection of disease outbreaks, in disease reduction and as indicators for early warning systems and for source attribution. The institution and data source platform are summarized into the Table 3.
The European Drinking Water Directive on the quality of water intended for human consumption state that EU Member States must monitor potable water and take action if contaminated. EUMS can decide themselves if they want to include monitoring of private water sources as well. The monitoring of potable waters for Cryptosporidium oocysts in the UK has, for example, resulted in the early detection of outbreaks of cryptosporidiosis, in some cases with oocyst detection in water before the start of the outbreak. Monitoring and control of oocysts in potable waters has resulted in significant reductions in cases of human cryptosporidiosis.
The E3 network aims to facilitate collaborative initiatives through the compilation and processing of environmental datasets, correlation and advanced analysis, supporting risk assessments and the rapid detection of emerging public health threats related to environmental factors. Previous work has included malaria, tick borne encephalitis (TBE) and vibriosis. Europe has since 2006 also had a Bathing Water Directive that obliges EUMS to monitor bathing waters. The directive covers all types of surface waters (coastal and inland areas) where a large number of people are bathing.
4.1. Use of Surveillance Data to Detect Changes in Threats, Studying Causes and Drivers, Project Changes in Risks, and Develop Adaptation Tools
Surveillance is, as described above, primarily used to detect changes in threats; either an increase in outbreak frequency, changes in seasonal incidence, changes in geographical risk distribution, or the introduction of new pathogens and/or disease vectors into new areas.
Surveillance data can also be used to study relationships with different determinants and drivers, such as climatic, environmental, socio-economic or demographic factors to better understand causes of observed changes. This is often done either by analyzing times series of reported cases, (detecting outbreaks retrospectively or prospectively) and comparing them to times series of exposure parameters or in seasonal incidence over time in an area, by in-depth studies of a specific outbreak, or by studying current differences between geographical areas if reliable historical data is not available. Outbreak surveillance is for example useful in the area of waterborne diseases, and the relations between rainfall and outbreaks has already been examined. Both heavy rainfall and periods of sustained low rainfall appear to be associated with outbreaks. Similarly, cholera outbreaks have been analysed to examine global differences in seasonality.
Satellite and other remote data sources are useful tools when studying links between environmental and disease datasets over a larger geographic area, like the whole of Europe for example (Table 3). Satellite and remote sensing have been used to monitor and develop early warning systems for disease outbreaks and are being tested for some diseases world-wide. Cholera outbreaks around the Gulf of Bengal can for example be projected by combining satellite monitoring of sea water surface temperatures and chlorophyll concentrations near the coast (indicating algal blooms in nutrient waters). Early-warning systems at a local level are, on the other hand, usually based on observed local data instead of satellite data. The Czech Republic has developed an early warning system for the risk of tick-borne diseases (TBE and Lyme borreliosis) based on a combination of known vector distribution (based on data from continuous vector surveillance), the ecology of tick activity, and weather forecasts over the coming week.
Disease vector models, for example, are often based on a combination of satellite data or local land cover/land use and climatic data, vector surveillance data, known vector ecology, and outcomes of climate change scenarios for the region. A country-based model on the northern spread of Lyme borreliosis in Sweden over the coming decades has been constructed based on a combination of vector surveillance data, tick ecology, local land cover and local climate change scenarios. Accordingly, satellite data was the basis for models on possible distribution changes in Europe due to climate change of the Asian tiger mosquito, Aedes albopictus, the main vector of dengue fever and chikungunya fever in Europe. Projections about future changes in disease risks due to environmental or climate changes can be made based on surveillance data in combination with temporal and geographic models derived from known epidemiological and ecological factors related to certain diseases. Such projections can either be made on a local scale or for a whole region. Projections that are applicable across countries are often based on satellite images and remote sensing in addition to other data.
Risk assessments of possible future changes in infectious disease risk from climate change in combination with other disease drivers can be made either based on mathematical scenario models like the ones described above, or through theoretical models based on surveillance data and projections. Adaptation measures and tools can then be developed in collaboration with local stakeholders and policymakers.
4.2. Access to Data
The examination of human disease against environmental data has a number of limitations that can make it difficult to conduct useful analysis. Not all the sources described in the tables provide easy on-line access to data, and some are covered by legal stipulations or commercial limitations. Human infectious disease data is subject to confidentiality and data security rules. Environmental data can be subject to problems including format, temporal and geographic resolution, completeness, and period covered, while human disease data can also be limited by temporal and geographic resolution as well as lack of demographic identifiers, risk markers and molecular typing data. There are also mapping issues in linking vector and raster based data. The development of geoportals to facilitate easy access to environmental data should improve access, and the ECDC E3 geoportal developed for use with infectious diseases should improve this. For TESSy data there have been standards for reporting to provide comparable datasets and access rules to share data, but there is still diversity in the temporal basis of the report (e.g., onset, specimen, lab report, reporting date).
4.3. Completeness and Consistency of Human Disease Data
The human disease datasets are subject to variations in quality, and results can differ substantially between countries, both with regard to how the data is collected, what temporal and geographic markers are reported and the ascertainment level, that reflects differences in the whole chain from patient to physician to laboratory to surveillance reporting. Diagnostic and typing methodology can differ between laboratories and countries. Large studies using data from across Europe can allow interesting approaches to analysis, but TESSy data may be available for only a few years, and for most countries only at country level (NUTS 0). While national datasets can be accessed that extend over longer timescales these can require effort and agreement to establish for many countries.
4.4. Completeness and Consistency of Environmental Data
Many of the environmental datasets are obtained from remote satellite observation of the earth and their outputs are based on algorithms that are subject checked by ground observation data. Data can be missing because of cloud cover and many of the datasets are corrected for this using interpolation from adjacent geographic and temporal readings. Some datasets are more readily accessed than others, and there can be considerable differences in the temporal and geographic resolution of different types of data. The satellites providing datasets change over time and this may affect data quality, but the quality across Europe is thought to be good.
There are a range of organisations, institutions, systems and other tools involved in infectious disease surveillance in Europe at both national and EU regional levels. The quality and consistency of the data that these produce could in many cases be further improved. Increased collaborations between systems and across sectors as well as standardized definitions and methodologies would allow data to be analysed between locations and over time. Early signals of changes in disease burden and in geographical distribution as well as the introduction of new threats into the EU region due to environmental and climatic changes would be easier to pick-up and analyse in order that EUMS can develop adequate response measures.
Linking geographic information with infectious disease surveillance data can in many cases lead to a better understanding of the disease epidemiology in general and the impact of climate change in particular. This will require a more detailed understanding of the infectious disease drivers and how they interact. Infectious disease data from national, expert and EU reference and surveillance systems such as TESSY data should provide the evidence base. Human case data need to be collected in a consistent way, while keeping the confidentiality of patient data. It should include parameters e.g., date of onset/specimen/ reporting/outbreak, and geographic location of infection. This should allow better linkage to different satellite derived variables, e.g. climate variables, land cover/land use data, vegetation index, and demographic data, as well as observed data on other relevant variables and drivers (Figure 2) depending on the eco-epidemiology of the specific infectious disease that is under study. The EU Member States will benefit from the results of such regional analyses by increased information about changes in geographical distributions, seasonality, disease burden, risk populations and possible new threats in different parts of the EU region. This can inform policy makers and intervention strategies.