Climate Models Could Help Predict Future Disease Outbreaks

Climate Models Could Help Predict Future Disease Outbreaks

Numerous studies over the past two decades have shown a strong relationship between climate and human diseases like cholera and malaria. Climate changes, including long-term warming trends as well as short-term climate variability could have an impact on patterns of disease. Xavier Rodo is a climate dynamics specialist and computational ecologist at the Barcelona Institute for Global Health and Catalan Institution for Research and Advanced Studies, Spain. He spoke to about climate modeling and the challenges he faced in implementing such systems.

How does climate influence disease transmission?

Climate has many impacts on the emergence and spread disease. Some are quite complex. Some are quite complex. We see, for example, that changes in temperature in the Brazilian Atlantic Forest drive waves of yellow fever in howler monkeys (Alouatta species) that precede human epidemics in a predictable manner1.

As the climate changes, so will the intensity and spread of disease outbreaks. Although the effects won’t be the same in all places, changes in temperature and rainfall will lead to significant changes in the distribution and dynamics zoonotic or vector-borne diseases. For example, we are seeing record numbers in New York City of mosquitoes carrying West Nilevirus, which is more common in the west.

What evidence is there to support climate change’s influence on disease outbreaks?

The first study2 I was part of that demonstrated this was published in 2002, in collaboration with Mercedes Pascual, a theoretical ecologist now at the University of Chicago, Illinois. In a previous study3, we had shown that the incidence of cholera in Bangladesh was affected by short-term climate patterns. Six months later, cases rose due to increased temperatures caused by the El Nino Southern Oscillation(ENSO). This is a recurring climate pattern that occurs irregularly every 3-7 year in the Pacific Ocean. But since the 1980s, there has been a marked intensification of ENSO, and we thought that this long-term trend might also be affecting cholera incidence. We looked at historical cholera data spanning a 70-year period, and saw that, between 1980 and 2001, incidence was strongly correlated with ENSO2. However, data from before the intensification showed no such correlation. The long-term trend in ENSO intensification driven by a warming environment seems to be affecting cholera dynamics.

Trends in the warm water current of the El Nio Southern Oscillation in the Pacific Ocean (red band at centre).
Xavier Rodo, a computational ecologist and climate dynamics specialist at the Barcelona Institute for Global Health and the Catalan Institution for Research and Advanced Studies in Spain. Credit: Xavier Rodo

How can climate modeling be used to prepare for and predict disease outbreaks?

With current tools, it’s possible to forecast the climatic conditions in certain regions. Some El Nino events can even be predicted up to two decades in advance. Public-health authorities can anticipate and plan their response months in advance if there is an unusually wet season in a country. They could stockpile medicines or spray insecticides in specific areas to reduce the number of mosquitoes hatching.

What are the obstacles to developing these predictive model?

Both infectious-disease epidemiology and climate change are complex systems. We need to bring together scientists of different disciplines to tackle this problem. Interdisciplinarity is often talked about more than it actually is. It can also be difficult to attract funding for these types of projects, and publishing opportunities in established journals can be limited.

We don’t have enough epidemiological data to train and test our models. We have more historical data on cholera than we do recent data. It is similar for COVID-19–reporting has dropped off, so we have much better data for the first two years of the pandemic than we do for now. If we want to be prepared for future dangers, it is important to understand the importance of long-term data collection.

What is the current state of development and implementation of such tools in your country?

I have collaborated with an international team to create a model that uses El Nino predictions in order to forecast dengue outbreaks for Ecuador. The model correctly predicted that in 2016, warmer temperatures and excess rainfall would lead to an outbreak in the city of Machala in March–three months earlier than would be expected. It also predicted that there was a 90% chance that incidence would exceed the average for the previous five years, and that a weak El Nino in 2019 would result in a low probability of a dengue outbreak during the typical peak season4,5.

This model and others have been adapted for use in other regions6. These models have not been adopted by public-health authorities. People say they are interesting, but they don’t see the immediate economic benefit–unfortunately, saving lives is not valued as it should be. Our cholera prediction model has been tried in India and Bangladesh many times–Pascual has tried more than me–without success. I’ve also tried to set up a malaria forecast service in Madagascar, Senegal and Ethiopia, because there is a wealth of data the model can rely on there7. We have not been able to convince the stakeholders.

This article is part of Nature Outlook: Pandemic Preparedness, an editorially independent supplement produced with the financial support of third parties. About this content.

References

  1. Rodo, X. et al. Nature Med. 27, 576-579 (2021).

  2. Rodo, X. et al. Proc. Natl Acad. Sci. USA 99, 12901-12906 (2002).

  3. Pascual, M. et al. Science 289, 1766-1769 (2000).

  4. Lowe, R. et al. Lancet Planet. Health 1, e142-e151 (2017).

  5. Petrova, D. et al. Int. J. Climatol. 41, 3813-3823 (2021).

  6. Lowe, R. et al. eLife 5, e11285 (2016).

  7. Laneri, K. et al. Proc. Natl Acad. Sci. USA 112, 8786-8791 (2015).

ABOUT THE AUTHOR(S)

    Laura Vargas-Parada is a freelance science writer whose work has appeared in Nature as well as numerous publications in Mexico where she lives. She holds a Ph.D. from biomedicine.

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