{"id":3950,"date":"2025-08-22T05:44:25","date_gmt":"2025-08-22T05:44:25","guid":{"rendered":"https:\/\/genedrivenetwork.org\/?p=3950"},"modified":"2025-08-22T07:15:08","modified_gmt":"2025-08-22T07:15:08","slug":"mosquito-borne-diseases-on-the-move-harnessing-data-and-new-tools-in-a-changing-climate","status":"publish","type":"post","link":"https:\/\/genedrivenetwork.org\/es\/blog\/mosquito-borne-diseases-on-the-move-harnessing-data-and-new-tools-in-a-changing-climate\/","title":{"rendered":"Mosquito-borne diseases on the move: Harnessing data and new tools in a changing climate"},"content":{"rendered":"

An interview with Dr. Kaushik Sarkar, Institute for Health Modeling and Climate Solutions (IMACS<\/a>)<\/p>\n\n\n\n

The Institute for Health Modeling and Climate Solutions (IMACS<\/a>) is an AI-first global center of excellence with the mission to empower the world\u2019s most climate-vulnerable countries with the tools, data, and expertise needed to predict, prevent, and respond to climate-driven health challenges. The institute was established with support from the Reaching the Last Mile<\/a>. Dr. Kaushik Sarkar, its founding leader, oversees the institute\u2019s strategy, innovation, and global initiatives. With over two decades of experience in computational epidemiology, public health and AI, he is known for creating and scaling new ventures at the intersection of science, technology, and impact. In this interview, he explores how climate change is altering disease landscapes, the promise of AI-driven forecasting, and the importance of proactive, predictive, and climate-integrated responses.<\/em><\/p>\n\n\n\n

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Aedes aegypti mosquito, a primary vector of dengue, zika, and yellow fever. These mosquitoes thrive in urban environments.<\/em> Photograph: Centers for Disease Control and Prevention (CDC<\/a>)<\/p>\n\n\n\n

How is climate change reshaping the patterns of mosquito-borne diseases globally?<\/strong><\/p>\n\n\n\n

Mosquito-borne diseases are among the most climate-sensitive health threats. Climate change is not only shifting where these diseases appear, but also when, how often, and how intensely outbreaks strike. Mosquitoes are highly sensitive to temperature, humidity, rainfall, and other climate variables. Even a small increase in global temperature can alter their breeding cycles, biting rates, and survival windows. With climate change, areas once too cool or dry for mosquitoes, such as parts of the Himalayas, southern Europe, or high-altitude regions of Africa, are now at risk for diseases like malaria, dengue, and chikungunya. Climate variability is also disrupting the seasonal predictability of these diseases. Erratic monsoon patterns, more frequent heatwaves, and extreme weather events are throwing public health systems off balance. So, outbreaks aren\u2019t just growing; they are becoming harder to anticipate and manage with the toolbox we have. We are now in an era of what I call \u201cclimate-amplified epidemics,\u201d which demands a new class of tools. This is where artificial intelligence, satellite data, and climate-integrated forecasting come in. Simply put: to prevent mosquito-borne diseases in this new climate reality, you can\u2019t just look at humans and mosquitoes. You also need to look at the sky, the soil, and the sensors and rely on data.<\/p>\n\n\n\n

What does this mean for vector control strategies and how can we prevent future outbreaks and epidemics?<\/strong><\/p>\n\n\n\n

I’m not an entomologist or a malaria specialist, so I can\u2019t prescribe the most appropriate mosquito control technologies. But from where I stand \u2013 someone shaping data-driven public systems, I can see a clear need to revisit and strengthen current vector control strategies. Traditionally, countries have often waited for outbreaks to flare up and then scrambled to respond as a reactive measure. With climate disruption altering disease dynamics, we cannot afford to be a step behind. However, moving from reactive to proactive is tricky when resources are finite and we don\u2019t know where to focus for prevention. Casting the net too wide means coming up with unsustainable cost curves. What we need now is a pivot from blanket approaches to precision programming, using predictive climate and environmental signals to plan dynamically and get ahead of outbreaks. That means flagging hotspots based on temperature, rainfall, and land-use patterns before cases appear on clinical radars. You zoom in, deploy resources fast, and pull out once the risk drops. During one of IMACS\u2019s earliest programs, we showed how precision programming with AI could reduce malaria by over 60% in two million population. We need hyper-local, time-bound, resource-smart strategies that work in sync. Additionally, vector control cannot operate in isolation anymore. It must plug into the same stack as urban planning, climatology, and emergency response. Climate and urban dynamics are lead indicators. The future of vector control must be proactive, predictive, and climate or environment-integrated.<\/p>\n\n\n\n

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ClimateSmart Indonesia<\/a>: An example of IMACS\u2019s country-level initiative to integrate climate-smart decision-making into national public health systems through a multisectoral approach<\/em>. Photograph: IMACS<\/em><\/p>\n\n\n\n

Why is modeling important to understand complex issues \u2013 such as the intersection between health and climate change \u2013 and what are the main opportunities and challenges that come with relying on modeling for decision-making?<\/strong><\/p>\n\n\n\n

When we are dealing with something as complex and nonlinear as the health\u2013climate nexus, whether it\u2019s vector-borne diseases or broader systemic risks, modeling is key. You are not looking at a single disease or weather event; you are dealing with cascading systems \u2013 temperature shifts influence mosquito habitats, changing rainfall patterns alter transmission windows, and urban heat amplifies vulnerability. And none of these happen in isolation. It\u2019s like aiming at a single target, except the target keeps moving. Modeling helps make sense of this chaos. It connects the dots across time, geography, and sectors, and gives policymakers what I call a \u201ctime machine\u201d \u2013 the ability to fast-forward into possible futures, whether that\u2019s outbreak trajectories, intervention impacts, or resource needs. That foresight allows them to act before crises escalate. Of course, there are challenges. Models are just data plus algorithms, so they are only as good as their inputs and assumptions. Climate and health data often sit in silos, in incompatible formats, and in many low- and middle-income countries, health data isn\u2019t updated in real time. There\u2019s also a trust gap \u2013 if models are too technical or opaque, decision-makers can\u2019t \u201csee under the hood,\u201d which makes them less likely to rely on them. That\u2019s why at IMACS, one of our top priorities is transparency and usability. A health official doesn\u2019t need to understand the mechanics of machine learning or interpret a complex chart \u2013 they need clear answers on when, where, and how to act. Data storytelling is just as important as the modeling itself.  The bottom line is that modeling is a leadership tool. Done right \u2013 with reliable inputs, a single source of truth, and clear, actionable outputs \u2013 it can help countries leapfrog from reactive systems and build future-ready public health systems. <\/p>\n\n\n\n

Can you tell us more about the Institute for Health Modeling and Climate Solutions (IMACS)? What are your top priorities in the next five years?<\/strong><\/p>\n\n\n\n

IMACS is something I\u2019m proud to have shaped from the ground up. If you scan today\u2019s risk landscape, climate volatility is not just creeping in \u2013 it\u2019s rewiring the entire global health ecosystem. Many diseases are worsening. Add to that the growing systemic strains on manufacturing, supply chains, and last-mile delivery. What we are seeing is the healthcare value chain under pressure at every node. One reason is that health and climate systems have spoken past each other for decades, missing each other\u2019s signals. IMACS exists to close that gap, purpose-built to inject combined health and environmental intelligence into every layer of the health stack. We use frontier models, frictionless data pipelines, and edge-to-core analytics to turn fragmented data into foresight, and foresight into actionable intelligence. Our recent models are lean, fast, and built to scale. With such tools in hand, we work shoulder to shoulder with governments, funders, and private-sector leaders to integrate scalable AI solutions directly into national infrastructure and critical choke points in the value chain. That means we are not just developing and shipping AI solutions \u2013 we are standing up institutions by training in-house data and AI talent in Ministries of Health, codifying model governance, and shifting the equation from \u201cData and AI for health\u201d to \u201cData and AI by health systems\u201d. Over the next five years, we are focused on three priorities:<\/p>\n\n\n\n