Welcome to the global digital health catalog for health adaptation to climate change!
This catalog currently publishes the digital health tools and models that are used for predictive modeling, environmental monitoring, risk stratification, and health program planning, implementation, and monitoring for climate-sensitive infectious diseases. We have categorized the functionality of these tools into three major containers for the ease of understanding of our readers.
Analytics and Decision Support
These tools utilize advanced data analytics, modeling, and machine learning techniques to predict disease scenarios, evaluate the effectiveness of interventions, and assist in policymaking. They analyze large datasets to identify patterns, correlations, and trends, enabling policymakers to make informed decisions and allocate resources effectively. These tools aid in forecasting disease outbreaks, estimating the impact of interventions, and prioritizing resource allocation based on projected health needs.
Disaster Management
These tools provide operational support during disaster and outbreak preparedness and response efforts. They are designed to facilitate efficient coordination, communication, and resource allocation in emergency situations. Examples include communication platforms, real-time mapping systems, early warning systems, and emergency response coordination tools. They help streamline outbreak and emergency response efforts, ensure timely communication among stakeholders, and enable effective allocation of resources.
Frontline Worker Support
These tools are developed to assist frontline workers in delivering services and implementing public health interventions. Examples include mobile applications for data collection, patient management systems, decision support systems for diagnosis and treatment, and training platforms for skill development. These tools empower frontline health workers by providing them with accessible and actionable information, enabling them to deliver quality healthcare services and implement interventions effectively.
| Name | Target | Type | Analytics and decision support | Disaster and outbreak management | Frontline health worker support | Method | GitHub | Publication | Presentation |
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Malaria Early Warning Systems Assessment
Malaria Early Warning Systems (MEWS) provide timely information on malaria transmission patterns, aiding early detection and proactive management. First developed in 1913, MEWS is one of the earliest known digital health solutions to aid the health programming to address climate-sensitive infectious diseases. These systems integrate data on cases, weather, and other factors to detect outbreaks, target interventions, and optimize resource allocation.
IMACS conducted a comprehensive assessment of Malaria Early Warning Systems (MEWS) in 2022-23. It analyzed MEWS models and tools worldwide, using synthesized evidence from a scoping review and expert insights from 15+ countries. The assessment covered aspects like scale, data inputs, regulations, technical barriers, and best practices in MEWS development and implementation.
Report Summary
Scale & outreach
MEWS initiatives are deployed globally across different scales and regions, addressing unique challenges. A regional, multi-country approach is seen in the Amazon and East Africa, while national and sub-national efforts are observed in various countries targeting malaria outbreaks. Communication capabilities vary, with some systems incorporating local community and health authority input. The population coverage ranges from regional to provincial and subdistrict levels, with high-resolution systems implemented in a few countries, such as India, Ethiopia, Swaziland, and Zimbabwe.
Data & models
Developers used malaria surveillance data available from both public and private healthcare facilities, with limited private facility data in select African countries and India. Modeling incorporates weather data, vegetation indices, entomological information, and geotagged data. Weather information is sourced from local offices, weather stations, and global organizations. Multivariate systems use centralized databases, with predictive models including time series, anomaly detection, and regression-based methods. Implementation employs R packages, Python, HTML/CSS, and some systems use C++.
Challenges
Limited digital data collection (36%), data validation (75%), uniform data (75%), availability of data entry staff (46%), lack of real-time notification (14%), limited case notification (71%), low presence of real-time and digital dashboards (9% and 55% respectively) impeded the developmental progresses. Regulatory challenges include varied government involvement (46%), participation of non-government entities (30%), and the need for innovative approaches (18%). Implementation challenges vary across countries, with most common ones being inadequate technical capacity, data quality issues, funding scarcity, and political conflicts. Lack of understanding, data sharing, capacity, and interdepartmental coordination were also reported.
Best Practices
According to interviewed experts, high prediction accuracy is crucial for a successful Multivariate Early Warning Systems (MEWS) (90%). Collaborative efforts across sectors (45%) and comprehensive cross-sectoral training (81%) are important. A robust pilot and open-source data were highlighted (72%), while real-time alerts and low licensing costs were less significant (9%). Complex designs were preferred by the majority (63%). Acceptance enablers include innovation buy-in, proof of concept, advanced technology demand, and developer commitment. Noteworthy collaborations include Welcome & Gates Venture, WHO TDR Aedes Warning System, World Bank Pandemic Fund, IMACS expert network, World Bank Digital Earth partnership, and NASA Support.
Policy, financing & technical
Recommendations for policymakers, donors, financial institutions, and international health organizations include establishing financial incentives for early warning systems, integrating them into global health security and Universal Health Coverage, comprehensive funding strategies, and forming working groups for global data infrastructure. Technologists, data scientists, and model developers are advised to publish robust proof of concepts, promote shared data platforms, and ensure interpretability and scalability of models for non-technical audiences.
Local & global collaborations
To enhance the effectiveness of early warning systems, it is vital to foster holistic collaboration involving government agencies, local communities, universities, NGOs, and international organizations. This collaboration should encourage cross-sectoral cooperation, data sharing, and capacity building. Co-development of models with local expertise and knowledge is crucial, involving communities, local modelers, and universities. Cross-cutting coordination among donors, organizations, and governments is essential to address various diseases and focus areas. A needs-driven approach should be adopted, allowing countries to define their specific requirements and providing best practices to address those needs effectively.
Thank you for visiting the catalog of digital health tools for climate change!
We are still accepting submissions for our MEWS Assessment. If you have developed a MEWS and have not already contributed to the survey, please visit the survey page.
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