What is a Health and Climate Early Warning System (EWS)?
According to the United Nations Office for Disaster Risk Reduction, an Early Warning System (EWS) is “an integrated system of hazard monitoring, forecasting and prediction, disaster risk assessment, communication and preparedness activities systems and processes that enables individuals, communities, governments, businesses and others to take timely action to reduce disaster risks in advance of hazardous events” (UNDRR, 2023). And, more concretely, a Health and Climate Early Warning System is a tool used to monitor and predict the impact of climate change on human health and to provide early warning alerts to communities, public health authorities, and healthcare providers.
This kind of system integrates data from multiple sources, including climate data, environmental data, and health data, to identify areas where climate-related health risks are high. This information can be used to inform public health interventions and to prepare communities for potential health impacts, such as heatwaves, droughts, floods, and the spread of vector-borne diseases like Leishmaniasis, malaria, dengue fever, or Lyme disease.
By using predictive models and analyzing historical trends, the system can provide early warning alerts for potential health threats, allowing public health authorities and healthcare providers to take preventive measures and respond quickly in case of a health emergency. Such early warning systems are becoming increasingly important in the context of climate change, as extreme weather events and environmental changes are expected to have significant impacts on human health.
Limitations of Health and Climate Early Warning System (EWS)?
Health and Climate Early Warning Systems (EWS) are designed to provide advance warning of environmental threats such as natural disasters, disease outbreaks, and climate-related events. While these systems are valuable tools for mitigating the impact of such events, they also have some limitations:
- Data Availability: the accuracy of EWS is highly dependent on the quality and quantity of data available to them. One of the most critical factors for accuracy is the time scale. CLIMOS EWS will consider short-range, seasonal and climate change scales. Each timescale has its own characteristics and limitations in terms of accuracy If there is insufficient data or if the data is outdated, the warnings generated by EWS may be inaccurate or insufficient.
- Technical Limitations: EWS systems rely on complex models and algorithms to analyze data and generate predictions. These weather and climate models that are taken as inputs for these complex algorithms may have technical limitations, such as insufficient computing power or inadequate algorithms, which can impact the accuracy and effectiveness of the system.
- Limited Scope: EWS systems are typically designed to address specific types of threats, such as natural disasters or disease outbreaks. This means that they may not be effective in predicting or addressing other types of hazards.
- Human Factors: the success of an EWS depends on human factors such as awareness, preparedness, and response capacity. Even if a warning is accurate and timely, it may not be effective if people do not have the resources or knowledge to respond appropriately.
- Communication: effective communication of warnings generated by EWS is critical. If the warnings are not communicated clearly or in a timely manner, people may not take appropriate actions to mitigate the impact of the hazard.
- Limited Resources: EWS systems require significant resources to operate effectively, including data collection and analysis, communication infrastructure, and human resources. This can limit the availability of EWS in certain regions or for certain types of hazards.
The creation of the Health and Climate sand fly borne-diseases (SFBDs) Early Warning System service in CLIMOS project
CLIMOS project aims to establish a platform to provide all the project-generated and collected data, research results and information that are critical for decision-making across sectors, applying advanced mathematical modelling techniques. The platform will use High-Performance Computing infrastructure, and trusted data sources like the Copernicus Climate Change Service to provide prediction, classification, recommendation systems and pattern recognition. In that respect two types of health and climate SFBDs services will be provided:
- Short-term daily predictions of both sand fly activity and probability of SFBDs incidence connected to the high-resolution weather forecasts and seasonal predictions connected to the climate anomalies for the next three months up to one year, one month in advance.
- Long-term projections of SFBDs spread, connected to regional climate change projections, to provide reliable data-based information for those working in adaptation and mitigation to climate change measures and decision-making. We will apply bias adjustment and downscaling techniques, to provide tailored simulations on how the climate is going to change in the upcoming decades and project the spread of sand flies and SFBDs with those. The application will be developed to be readily available for outside (on other platforms) use.
These services will be prepared to be used as parts of other information-providing systems, such as the local weather services, or European Health and Climate Observatory, and as stand-alone project products, so that those can be used by interested members of the general public (tourists travelling to the Mediterranean and the endemic areas, persons spending time outdoors for leisure and work, especially with children and pets, people living in endemic areas, support for vulnerable communities, migrants and refugees), public stakeholders, researchers and professionals working in health, tourism, public and occupational health, agriculture, field research, social services in support of vulnerable groups, NGOs working with migrants and refugees, veterinarians, risk managers and insurance companies, and many other applications.
These tailored applications will use Earth observation through satellites and in-situ sensors to facilitate their development, more concretely will be used as standards for data provision from the OGC partner. Where needed, CLIMOS will combine these data with our expertise in machine and deep learning to produce detailed, high-resolution land use maps for climate and SFBDs and for different specific public sectors.
