ASTMH 2024: A Discussion on How Malaria and Dengue Early Warning Systems Can Strengthen Adaptation and Response to Climate Change

The World Bank estimates that climate change could lead to at least 21 million additional deaths by 2050 from just five health risks (extreme heat, stunting, diarrhea, dengue and malaria). To explore new perspectives and actionable solutions in response to the impending health impacts of climate change, the Institute for Health Modeling and Climate Solutions (IMACS) hosted a symposium at the American Society of Tropical Medicine and Hygiene (ASTMH) 2024 annual meeting, featuring members of the IMACS expert network.


The panel discussion, entitled, “How Malaria and Dengue Early Warning Systems Can Strengthen Adaptation and Response to Climate Change,” included reflections and observations on how climate-informed early warning systems (EWS) have the potential to serve as powerful adaptation tools to the growing threats of climate change to health by increasing effectiveness of disease control and strengthening.


Here is what we heard:


“When fully operational, early warning systems can be helpful in building health systems resilient to climate change,” said James Colborn, Senior Malaria Advisor, Clinton Health Access Initiative (CHAI). “But to achieve full functionality, we need to address all different aspects of the ecosystem, and this involves the design of early warning systems, which must take into account many different factors, including availability of data, analytical tools, as well as stakeholders and communities that will be using the early warning system.”


“In terms of tools, there is no one silver bullet that can meet the purpose of early earning and response across countries,” said Dr. Kaushik Sarkar, Director, IMACS. “It takes a full toolbox of connected digital solutions, all coming together on a single, unified platform.”


“Population data is key and getting it from all relevant sources, even unconventional databases that are now becoming increasingly available,” said Francis Kimani, Research Scientist, KEMRI Centre for Biotechnology Research and Development. “By combining data from traditional and nontraditional sources, this will provide more comprehensive data that can better inform early warning systems, integral to understanding climate change patterns. Community-based organizations, for example, we sometimes think that they are only useful in giving social demographics, but there’s more that they can do to also inform our early warning systems.”


“Barriers to implementing early warning systems include identifying technical skills and capacity,” said Richard Maude, Head of Epidemiology Department, Mahidol-Oxford Tropical Medicine Research Unit. “There is also a lack of integration and data and the cost, including the need to pay for analysis, lack of knowledge and awareness, and a lack of protocols and policies to support. “

“I was against AI for a very long time, because I am a biostatistician, but we have embraced it in the last year,” said William Pan, Professor of Global Environmental Health, Duke University. “I found that it was actually quite useful, especially when used for forecasting and predictions. But when you have these different types of data coming from different places, and you’re using different methods that people aren’t used to, you need a tremendous amount of skill for the implementation and effectively make decisions, because ultimately what we’re trying to do is to improve the way public health is conducted.”


“Often, we heavily focus on technical metrics – like, how well is the system performing? How well can we predict the timing of excess cases, or the peak of cases, or what’s the lead time that these models can predict,” said Kate Zinszer, Assistant Professor, School of Public Health, University of Montreal. “But what I propose is that we go beyond these types of evaluations and include the practical utility of early warning systems in practice systems – including examining more closely how they are being used in practice and if or how they are useful…But the ‘if’ and ‘how’ should be grounded within the experiences and perspectives of the end users.”


“About data collection … there are three golden rules: quality, definition and frequency,” said Dr. Kaushik Sarkar. “The better the data, the sharper the details and the more frequently its updated, the more powerful your system will be.”

IMACS Expert Sheetal Silal on the Role of Mathematical Modeling in Malaria Elimination Efforts

In an interview with Forecasting Healthy Futures, mathematical modeler and statistician Dr. Sheetal Silal discusses the role of infectious disease modeling in malaria elimination efforts. Dr. Silal, a member of the Forecasting Healthy Futures Institute for Health Modeling and Climate Solutions (IMACS), is the Director the Modeling and Simulation Hub, Africa (MASHA) and associate Professor in the Department of Statistical Sciences at the University of Cape Town (UCT).

How does mathematical modeling assist with malaria elimination efforts? 

SILAL: Mathematical modeling for malaria has become an essential part of the country's strategic development plans, including funding applications. It's almost a given that modeling is being done now, whereas before it really was something new.

What mathematical modeling tries to do is incorporate that entire system by representing each element of the system through mathematical equations. It’s almost like a giant video game for malaria: a virtual world where we add in vector biology, rainfall, temperature and humidity – and determine its impacts on the spread of malaria.

These factors impact your larval development and the growth of mosquitoes. We can also factor in residual spraying and treated nets, because these impact the mortality of mosquitoes and biting rate.

So, we build in all these different aspects using mathematics and using computer programming. The goal is that this isn’t just about math and malaria and computer science. It’s about broadening into all types of vector biology, entomology, climate science, and economics. It's quite multidisciplinary, but our work is a model for bringing experts together.  And we bring their knowledge together in a system, in a mathematical system.

How have trends changed since you started doing this?

SILAL: Since I started working in the malaria space, which would have been about 15 years ago or so, back then countries were just starting to use modeling to put together national strategy plans for malaria elimination. You had countries close to elimination, using modeling to determine what it would take to reach elimination and what do they need to do. They were asking questions, like ‘How much will it cost?’ and ‘What happens if I have a higher prevalence neighbor?’

And then you had countries with higher levels of malaria that were still in the control phase, and they were leaning on modeling to help with that.  Modeling for malaria was just developing and decision makers weren't quite seeing its value.

But over the past 10 to 15 years, with the demonstration of modeling, statistical sciences have become a constant companion for decision makers and the formulation of national strategy plans.

For example, in 2019, malaria elimination efforts in South Africa had been thrown off course. There were some climatic events and operational events that happened that set the country off track for meeting goals. So, the question was, do we need a new goal for elimination? When will it be? What do we need? But many elimination programs are expensive. And it wasn’t clear where they would get the additional funding. So, malaria is domestically resourced and funded with taxpayers' money.

So, we did an investment case with the governments to work out the different pathways that we could take to actually achieve elimination, and looked at how much it would cost. Then we took that proposal to our national Treasury to request the money. And that's how our program was funded - a direct outcome of the modeling itself. So, once you start seeing that modeling can actually have monetary benefits, it becomes the constant companion of decision making going forward.

What has been the impact of climate change?

SILAL: We are seeing the impacts of climate change in a variety of ways. For one, changes in behavior and of vectors appearing in areas where they have really shouldn't be transmitting, where the altitude was previously not suitable for them. Let’s also not forget the impact that climate has on human behavior and the adaptation of human behavior in terms of increased urbanization and how it relates to malaria transmission.

When it comes to malaria programs themselves, one of the biggest impacts has been the interannual variability. So, between years, things are changing. For example, rain was traditionally expected in September, but now they see rains coming a month earlier or they come in months later, or you just don't know what's going to happen.

The problem with this is that it impacts planning. If you want to implement a net distribution program or a spray program, you need to place orders for these a year in advance. But, if your mosquito season suddenly starts earlier, you may not have the products in hand by the time the season starts because of that weather variability. That’s the challenge.

But this is where modeling comes in. We're trying to understand better in the region how we can better support decision making by modeling through different climatic timescales.

There’s your immediate decision-making timescale, which is a few weeks hence or a month. But then there's the longer scale, such as what's going to happen in the next year's season. At the same time, every few years countries are developing their national strategic plans, looking at a  3 to 5 year window or even a 5 to 10 year planning window.

Then we need to bring into political discussions, what might a country's climate look like in 40 years’ time? It’s really important to bring this discussion to the table so that we can understand the sustainability of elimination activities and malaria control into the future.

How does being a member of the IMACS network support your efforts?

SILAL: These groups play a vital role in several ways. One, it brings together experts, all of them from different parts of the world. This helps us to mutually learn, transfer knowledge and learn from each other's experiences across the region by bringing new topics of discussion to the table.

There's a common saying: alone we go faster but together we go further. I think that's exactly what these initiatives do. As each country is striving for its own malaria goals, by bringing our scientific knowledge together across countries, we are able to advance each other just a little bit further.

What would you say to those who may be interested in making a difference in the world but may not have considered the role of math modeling?

SILAL: I started my career in the world of finance and the world of quantitative finance and actuarial science, which is very far from this, from this world. But I was drawn to mathematics and using mathematics for problem solving. Then I moved into a field called Operations Research, focused on problem solving, using quantitative methods like mathematics and basically whatever tools you need to support better decision making. And it was then that I came across a malaria modeling paper and suddenly realized, you can use these methods to support public health.

My first job was then in health economics, focused on public health problems, including supporting maternal health comprehensive maternal delivery packages in in South Africa. I got an appreciation for impacting people and health outcomes.

Then, you pair that with using math to support decision making, and you suddenly realize ‘I can make a difference.’  I haven't left the world of public health, decision making and policy modeling since.

I would just add, there are many ways in which people can support public health. Malaria is one issue. Supporting public health modeling is a technique. It is a tool that can be applied across the breadth of diseases.  These same skills and technique can be applied across a breadth of diseases and there are many, many problems that need solving. So, I'd encourage anybody remotely interested in using math to problem solve to get into this field because there's plenty of work for everyone.

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IMACS Network Member Launches Ghana Malaria Modeling Project

As part of Forecasting Healthy Futures’ mission to support emerging researchers at the intersection of climate and health, we spoke to Ekuwa Adade, a doctoral researcher at Brunel University London, and a member of the IMACS Expert Network. Adade, who is researching climate sensitive diseases, recently launched a malaria modeling project as part of her doctoral research focused on her home country, Ghana. IMACS is mentoring Ekuwa in understanding and comparing different time series models and helping advance her skills in integrating climate and malaria surveillance information using advanced machine learning.

Experts Discuss Accelerating Support to Build Healthy & Climate-Resilient Cities During WHA77

On the sidelines of the 77th World Health Assembly, Forecasting Healthy Futures hosted an event entitled “Healthy & Resilient Cities Worldwide: Clearing the social and financial hurdles to integrate and scale sustainable urban innovations”, in partnership with Reaching the Last Mile.  The discussion examined the health impacts of climate threats as experienced in urban settings around the world, and the innovative efforts underway to build sustainable and resilient cities.

IMACS Expert James Colborn Discusses Malaria Elimination Efforts in Mozambique & Climate Health Solutions

Malaria is among the greatest public health threats in Mozambique, with more than 12 million cases in 2022. In a discussion with James Colborn, Senior Malaria Advisor at the Clinton Health Access Initiative and member of the Forecasting Healthy Futures Institute for Health Modeling and Climate Solutions (IMACS), Colborn discusses his malaria work in Mozambique, as well as his thoughts on addressing climate change and leveraging early warning systems.   

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