Numerous studies over more than two decades have demonstrated a strong relationship between climate and the dynamics of human diseases, such as cholera, malaria and dengue fever. Changes in climate, including long-term warming trends and short-term climate variability, could affect disease patterns. Xavier Rodó, computational ecologist and specialist in climate dynamics at the Institute of Global Health in Barcelona and the Catalan Institute for Research and Advanced Studies in Spain, spoke with Nature on how climate modeling could be used to help prepare for future outbreaks and the obstacles he faced in implementing such systems.
How does climate affect disease transmission?
Climate influences the emergence and spread of disease in multiple ways. Some are quite complex. Climatic conditions can have cascading effects on ecosystems that affect the likelihood of zoonotic spillovers, in which pathogens jump from an animal host to humans. We see, for example, that temperature changes in the Brazilian Atlantic Forest cause waves of yellow fever in howler monkeys (Alouatta species) that predictably precede human epidemics1.
As the climate changes, the spread and intensity of epidemics will also change. The effects will not be the same everywhere, but changes in temperature and precipitation will lead to huge changes in the distribution and dynamics of zoonotic and vector-borne diseases. We’re already seeing record numbers of West Nile virus-carrying mosquitoes in New York City, for example, when it’s usually further west.
What evidence is there that climate change influences epidemics?
The first study2 I was part of this demonstration that was published in 2002, in collaboration with Mercedes Pascual, a theoretical ecologist now at the University of Chicago, Illinois. In a previous study3, we showed that the incidence of cholera in Bangladesh was affected by short-term climate patterns. Cases increased about six months after periods of increased local temperatures caused by the El Niño Southern Oscillation (ENSO), a recurring climate pattern of warm (El Niño) and cold (La Niña) phases that occur repeatedly. irregular every 3 to 7 years 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 affect the incidence of cholera. We reviewed historical cholera data covering a period of 70 years and found that between 1980 and 2001, incidence was strongly correlated with ENSO2. Data from a period before the intensification, however, showed no such correlation. The long-term trend of ENSO intensification, driven by global warming, appears to affect the dynamics of cholera.
How could climate modeling be used to forecast and prepare for epidemics?
With current tools, it is possible in some regions to predict weather conditions next season, next summer or even further into the future – some El Niño events can be predicted up to two years ahead. ‘advance. Knowing months in advance that there will be an abnormal rainy season in a country, and how this is likely to affect disease incidence, allows public health authorities to anticipate and plan their response. . For example, they could stock up on medicines or spray insecticides in certain areas to limit the outbreak of mosquitoes.
What are the obstacles to the development of these predictive models?
Climate change and infectious disease epidemiology are complex systems, and we need to bring together scientists from these very different disciplines to work on this problem. At the moment, interdisciplinarity is spoken more than it is seen. We also experience difficulty in attracting funding for such projects, and opportunities to publish in recognized journals may be limited.
The availability of epidemiological data with which we can train and test our models is also an issue. For cholera, we have better historical data than recent data. The same goes for COVID-19: reports have declined, so we have much better data for the first two years of the pandemic than we have now. We need to understand that long-term data collection is fundamental if we are to prepare for future threats.
Where is the development and implementation of such tools?
I worked with an international team to develop a model that uses El Niño predictions to predict dengue outbreaks in Ecuador. The model correctly predicted that in 2016 warmer temperatures and excessive rainfall would lead to an outbreak in Machala town in March, three months earlier than expected. He also predicted that there was a 90% chance that the incidence would exceed the previous five-year average and that a weak El Niño in 2019 would result in a low probability of a dengue outbreak during the typical peak season.4.5.
This model and others have been adapted for use in other regions6. But these patterns have yet to be picked up 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. We have tried several times to implement our cholera prediction model in India and Bangladesh—Pascual more often than me—without success. I have also tried to set up a malaria forecasting service in Madagascar, Senegal and Ethiopia, as there is a wealth of data on which the model can draw.seven. But we failed to convince the stakeholders.
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