Leptospirosis is a climate-sensitive disease that poses a significant public health threat, affecting roughly one million people per year across the globe, with vulnerable populations being particularly at risk. Leptospirosis outbreaks are often associated with heavy rainfall and flooding events, leading to increased bacterial exposure. Many mammalian species are carriers of Leptospira bacteria, with rodents considered one of the main reservoirs of the disease for humans. Although symptoms are often mild, severe presentations can progress to kidney failure and pulmonary haemorrhage in roughly 10% of clinical cases. Despite its global distribution and high incidence, leptospirosis remains a neglected tropical disease.

One of the significant drivers of climatic events associated with leptospirosis outbreaks in tropical and subtropical countries is the El Niño-Southern Oscillation (ENSO or El Niño). This cyclic phenomenon, driven by sea surface temperature changes in the Pacific Ocean and atmospheric pressure differences, influences temperature and precipitation in the affected regions, leading to heavy rainfall and flooding events. As climate change continues, the frequency and intensity of extreme weather events are projected to rise, potentially resulting in an increased number and magnitude of leptospirosis outbreaks and other climate-sensitive infectious diseases.

In this context, the current response to leptospirosis outbreaks could benefit from an early warning system that would provide advanced warning of an episode, enabling prompt deployment of interventions. Building on previous mathematical and statistical models developed by local researchers, a collaborative effort between European and Argentinian institutions, including the Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS), aimed to investigate the influence of hydrometeorological processes on leptospirosis risk in northeastern Argentina.

The study

The study, recently published in the Journal of the Royal Society Interface, aimed to characterise the effect of hydrometeorological variables, including rainfall and river height, on leptospirosis cases in the Santa Fe and Entre Ríos provinces in northeastern Argentina. Led by researchers Martín Lotto Batista and Prof Rachel Lowe from the Global Health Resilience (GHR) group of the Earth Sciences Department at BSC and Dr Eleanor Rees from the London School of Hygiene and Tropical Medicine (LSHTM), the study involved collaboration with scientists from LSHTM, the Helmholtz Centre for Infection Research (HZI), and the National Council for Scientific and Technical Research (CONICET).

The researchers developed a two-stage forecasting approach to leverage the influence of the monthly anomalies in the sea surface temperature of the central Pacific Ocean, rainfall, and the Paraná River height on disease risk. This approach computed the probability of outbreaks using: 1) an initial model driven by an El Niño indicator, providing a forecast with a three-month lead time, and 2) a subsequently updated forecast of the outbreak probability one month in advance using a model driven by local climate (rainfall and river height).

The El Niño model successfully detected 89% of outbreaks, while the short-lead local model achieved similar detection rates. These results highlight the strong predictive power of climatic events in forecasting leptospirosis incidence in northeastern Argentina. Consequently, developing a leptospirosis outbreak prediction tool driven by hydrometeorological indicators could be part of the region's early warning and response system.

“The predictive models developed in this study could be delivered to public health users as executable packages or dashboards. This would enable them to calculate outbreak probabilities and, if necessary, deploy intervention strategies to prevent outbreaks, which is particularly relevant in the context of a changing climate,” stated Lotto Batista.

And Prof Rachel Lowe, ICREA research professor and leader of the GHR group at BSC, added: “El Niño conditions are developing and are likely to intensify later in the year. This is expected to aggregate extreme weather events across the globe. The modelling tool we have developed could help officials respond to the climate-sensitive disease risks posed by these events months in advance”.

These outcomes provide a foundation for further studies in the context of other projects, including EERIE, IDExtremes and IDAlert, to improve resilience to health threats linked to infectious diseases in climate change hotspots across the globe, including in Europe.

Reference: Lotto Batista, M., Rees, E. M., Gómez, A., López, S., Castell, S., Kucharski, A.J., Stéphane Ghozzi, S., Müller, G.V., and Lowe, R. (2023). Towards a leptospirosis early warning system in northeastern Argentina. J. R. Soc. Interface., Volume 20, Issue 202. http://doi.org/10.1098/rsif.2023.0069.