Towards a leptospirosis early warning system in northeastern Argentina

Lotto-Batista, Martín; Rees, Eleanor M.; Gómez, Aarón; López, María Luisa García; Castell, Stefanie; Kucharski, Adam J.; Ghozzi, Stéphane; Müller, Gabriela V.; Lowe, Rachel

doi:10.1098/rsif.2023.0069

Cited by 2 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A study in New Caledonia found that La Niña phases (cool SST anomalies in the Pacific Ocean) were associated with leptospirosis outbreaks [ 24 ]. By contrast, another study in Argentina showed the El Nino phase (warm SST anomalies in the Pacific Ocean) were associated with leptospirosis incidence, and they demonstrated how the Nino 3.4 index could form part of an early warning system in this setting [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the relationship between climatic indicators and leptospirosis incidence in Fiji: A modelling study

Rees,

Lotto Batista,

Kama

et al. 2023

PLOS Glob Public Health

Self Cite

View full text Add to dashboard Cite

Leptospirosis, a global zoonotic disease, is prevalent in tropical and subtropical regions, including Fiji where it’s endemic with year-round cases and sporadic outbreaks coinciding with heavy rainfall. However, the relationship between climate and leptospirosis has not yet been well characterised in the South Pacific. In this study, we quantify the effects of different climatic indicators on leptospirosis incidence in Fiji, using a time series of weekly case data between 2006 and 2017. We used a Bayesian hierarchical mixed-model framework to explore the impact of different precipitation, temperature, and El Niño Southern Oscillation (ENSO) indicators on leptospirosis cases over a 12-year period. We found that total precipitation from the previous six weeks (lagged by one week) was the best precipitation indicator, with increased total precipitation leading to increased leptospirosis incidence (0.24 [95% CrI 0.15–0.33]). Negative values of the Niño 3.4 index (indicative of La Niña conditions) lagged by four weeks were associated with increased leptospirosis risk (-0.2 [95% CrI -0.29 –-0.11]). Finally, minimum temperature (lagged by one week) when included with the other variables was positively associated with leptospirosis risk (0.15 [95% CrI 0.01–0.30]). We found that the final model was better able to capture the outbreak peaks compared with the baseline model (which included seasonal and inter-annual random effects), particularly in the Western and Northern division, with climate indicators improving predictions 58.1% of the time. This study identified key climatic factors influencing leptospirosis risk in Fiji. Combining these results with demographic and spatial factors can support a precision public health framework allowing for more effective public health preparedness and response which targets interventions to the right population, place, and time. This study further highlights the need for enhanced surveillance data and is a necessary first step towards the development of a climate-based early warning system.

show abstract

Section: Introductionmentioning

confidence: 99%

Quantifying the relationship between climatic indicators and leptospirosis incidence in Fiji: A modelling study

Rees,

Lotto Batista,

Kama

et al. 2023

PLOS Glob Public Health

Self Cite

View full text Add to dashboard Cite

show abstract

Towards a leptospirosis early warning system in northeastern Argentina

Lotto-Batista

Rees

Gómez

et al. 2023

J. R. Soc. Interface.

Self Cite

View full text Add to dashboard Cite

Leptospirosis is a zoonotic disease with a high burden in Latin America, including northeastern Argentina, where flooding events linked to El Niño are associated with leptospirosis outbreaks. The aim of this study was to evaluate the value of using hydrometeorological indicators to predict leptospirosis outbreaks in this region. We quantified the effects of El Niño, precipitation, and river height on leptospirosis risk in Santa Fe and Entre Ríos provinces between 2009 and 2020, using a Bayesian modelling framework. Based on several goodness of fit statistics, we selected candidate models using a long-lead El Niño 3.4 index and shorter lead local climate variables. We then tested predictive performance to detect leptospirosis outbreaks using a two-stage early warning approach. Three-month lagged Niño 3.4 index and one-month lagged precipitation and river height were positively associated with an increase in leptospirosis cases in both provinces. El Niño models correctly detected 89% of outbreaks, while short-lead local models gave similar detection rates with a lower number of false positives. Our results show that climatic events are strong drivers of leptospirosis incidence in northeastern Argentina. Therefore, a leptospirosis outbreak prediction tool driven by hydrometeorological indicators could form part of an early warning and response system in the region.

show abstract

Towards a leptospirosis early warning system in northeastern Argentina

Cited by 2 publications

References 23 publications

Quantifying the relationship between climatic indicators and leptospirosis incidence in Fiji: A modelling study

Quantifying the relationship between climatic indicators and leptospirosis incidence in Fiji: A modelling study

Towards a leptospirosis early warning system in northeastern Argentina

Contact Info

Product

Resources

About