TL;DR:
- Unicef and partners are using AI to combat dengue fever in impoverished communities worldwide.
- Dengue fever is a growing threat, affecting over 100 countries and disproportionately impacting children.
- AI model predicts and forecasts future dengue outbreaks by analyzing climate change and disease vectors.
- Collaboration with the European Space Agency facilitated the collection of climate data for the model.
- Success in Brazil and Peru showcases the potential for scalability and application to other diseases.
- The model empowers proactive measures to save lives and enhance resilience.
- UNICEF aims to engage governments and partners for further development and analysis of the model.
- The ultimate goal is to make public health systems better prepared and operationalize the model’s impact.
- AI offers the potential to address future climate crises and protect vulnerable communities.
Main AI News:
Unicef and its partners are harnessing the power of artificial intelligence (AI) in their relentless battle against dengue fever, a grave threat to impoverished communities worldwide. While climate change conjures images of rising shorelines and parched landscapes, it also harbors less visible perils, including the escalation of neglected tropical diseases. Among them, dengue fever is spreading at an alarming rate, with 2019 witnessing the highest number of reported cases in history. This debilitating disease has now entrenched itself in over 100 countries, posing a disproportionate risk to children who bear the brunt of its severe manifestations.
Dengue fever, an infectious ailment marked by excruciating joint pain, throbbing headaches, and distinctive rashes, is transmitted through bites from infected Aedes aegypti mosquitoes. As global temperatures rise and rainfall patterns undergo a transformation, these disease-carrying vectors find increasingly favorable conditions for proliferation.
Acknowledging this looming crisis, the visionary minds within UNICEF’s Offices of Innovation and Climate embarked on a groundbreaking mission—to create a predictive AI model that could unravel the intricate web of dengue outbreaks, unveiling where and when they would strike in the future.
The potential impact of this pioneering initiative is momentous. Armed with the ability to provide communities with advance warning, it has the power to redefine the rules of engagement against this relentless adversary. By decoding the complex interplay between climate change, disease vectors, and vulnerable populations, UNICEF aims to empower communities to take proactive measures, saving lives and mitigating the devastating consequences of dengue outbreaks. With the aid of AI, the battle against this pernicious disease takes a monumental leap forward, ushering in a new era of preparedness and resilience.
The creation of an AI ensemble learning machine to combat dengue fever was a collaborative endeavor, uniting the forces of UNICEF country offices, governments, and esteemed partners such as the European Space Agency (ESA). The initial strides towards developing a climate-based AI model unfolded within Peru’s Ucayali region, nestled in the verdant expanse of the Amazon rainforest.
Back in 2018, Do-Hyung Kim, a Data Science Lead at UNICEF’s Giga Initiative, and Hanoch Barlevi, a UNICEF Climate, Environment, and Disaster Risk Specialist, embarked on a mission to leverage the organization’s expertise for climate adaptation, with a particular focus on children’s health.
Recognizing the immense value of comprehensive climate data, the team forged a collaboration with the European Space Agency. However, the diverse topography of Peru, coupled with limited health records, presented formidable challenges in developing a predictive model for dengue outbreaks. Barlevi reflects, “Basically, we were lacking evidence. There was no coherent, consistent, and systematic way of understanding what was in the past, what the situation is now, and how we could project this so the health system could be better prepared in order to prevent an increase of cases.”
Amidst this daunting landscape, the team discovered renewed hope in Brazil. The South American nation not only boasted richer and more comprehensive public health records but also offered access to abundant socioeconomic data. Barlevi enthuses, “This is great because that is exactly what we’re looking at vulnerability, disparity, leaving no one behind.“
Harnessing the meteorological tracking data from the European Space Agency’s Copernicus program, the team embarked on constructing a robust AI ensemble machine learning model. The collaboration with ESA unlocked new horizons. As Barlevi notes, “They have all the technology. They have satellites orbiting in the sky. But building this project has really given them the opportunity to address new end-users with different targets and objectives.” The initial success of the tool prompted the team to return to Peru, where they achieved similarly promising results.
The outcome of this collective effort is an invaluable tool capable of accurately forecasting dengue epidemics, potentially saving countless lives. Kim highlights, “Currently, the model predicts a future trend two to three weeks in advance. Maybe we can even add more data to extend that to a month.”
Amidst the excitement surrounding this learning machine, UNICEF’s new global lead on malaria and Neglected Tropical Diseases (NTDs), Alexandre Boon, eagerly explores the possibilities it presents. Collaborating with the team, Boon envisions applying a similar approach to other NTDs, including malaria. Given its shared mosquito-borne nature, malaria shares common variables with dengue that could be harnessed.
Boon elaborates, “You could then take public health preventative measures if this tool ever gave you an alert. With malaria, for example, you can give seasonal chemoprophylaxis that helps, within a month, reduce the vulnerability of populations. You could increase indoor residual spraying and treated net distribution or clean up the water places where you’ve got mosquito larvae.”
The convergence of cutting-edge technology, interdisciplinary collaboration, and unwavering commitment to public health heralds a new chapter in the fight against dengue fever and other devastating diseases. UNICEF and its partners stand at the forefront, paving the way for innovative solutions that empower communities and foster a healthier, more resilient world.
With successful pilot deployments in two countries, the critical question now arises: What lies ahead for the ensemble machine learning model? Alexandre Boon, UNICEF’s global lead on malaria and NTDs, emphasizes the need to explore new horizons. He explains, “The idea is to indeed try to see whether or not this tool could be used for the same disease but in other settings. And then, in other settings with other diseases, and then we can mature the model.”
However, this ambitious endeavor requires time and meticulous planning. Do-Hyung Kim, the visionary Data Science Lead, acknowledges the challenges, stating, “We are planning to make it more scalable. It’s actually not really easy to gather the same kind of dengue case data from other country offices, but we could hopefully transfer the learnings that we have made from Brazil and Peru.”
In this quest for scalability, collaboration remains a linchpin. The team is steadfast in its pursuit of engaging governments, ensuring that they comprehend the value this transformative technology can bring. Moreover, they maintain close communication with numerous partners, seeking to incorporate new features into the model and scrutinize additional climate factors.
Hanoch Barlevi, the esteemed UNICEF Climate, Environment, and Disaster Risk Specialist, articulate their ultimate goal: “Our big test is how we’ll manage to operationalize all this work and make public health systems more prepared. And that’s what we really want to see.”
By harnessing the power of predictive AI to gain deeper insights into the past, we fortify our capacity to confront the impending climate crises that children and families will inevitably face in the future. This visionary initiative spearheaded by UNICEF and its partners signifies a resolute commitment to leveraging cutting-edge technology to safeguard the well-being of vulnerable communities.
As the journey towards operationalizing and expanding the model unfolds, it is underpinned by an unwavering dedication to building a more resilient world equipped to navigate the challenges posed by a rapidly changing climate.
Conlcusion:
The utilization of artificial intelligence (AI) in combating dengue fever, as spearheaded by Unicef and its partners, holds significant implications for the market. By harnessing the power of predictive AI models to forecast and mitigate disease outbreaks, a new paradigm of proactive healthcare strategies is emerging.
This technological advancement enables governments, healthcare organizations, and stakeholders in the market to better understand and respond to the complex interplay between climate change, disease vectors and vulnerable populations. The potential scalability and application of AI models to other diseases further expand the market’s horizons, fostering opportunities for innovative solutions and collaborations.
As the focus shifts towards operationalizing these AI-driven tools, the market can anticipate the growth of advanced analytics capabilities, partnerships, and investments in public health preparedness. Ultimately, this transformative use of AI signifies a substantial stride towards building a more resilient and adaptive market, empowered to confront the challenges of a rapidly changing world.
