Towards a leptospirosis early warning system in northeastern Argentina.

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.

Relationship between Climate Variables and Dengue Incidence in Argentina.

BACKGROUND: Climate change is an important driver of the increased spread of dengue from tropical and subtropical regions to temperate areas around the world. Climate variables such as temperature and precipitation influence the dengue vector's biology, physiology, abundance, and life cycle. Thus, an analysis is needed of changes in climate change and their possible relationships with dengue incidence and the growing occurrence of epidemics recorded in recent decades. OBJECTIVES: This study aimed to assess the increasing incidence of dengue driven by climate change at the southern limits of dengue virus transmission in South America. METHODS: We analyzed the evolution of climatological, epidemiological, and biological variables by comparing a period of time without the presence of dengue cases (1976-1997) to a more recent period of time in which dengue cases and important outbreaks occurred (1998-2020). In our analysis, we consider climate variables associated with temperature and precipitation, epidemiological variables such as the number of reported dengue cases and incidence of dengue, and biological variables such as the optimal temperature ranges for transmission of dengue vector. RESULTS: The presence of dengue cases and epidemic outbreaks are observed to be consistent with positive trends in temperature and anomalies from long-term means. Dengue cases do not seem to be associated with precipitation trends and anomalies. The number of days with optimal temperatures for dengue transmission increased from the period without dengue cases to the period with occurrences of dengue cases. The number of months with optimal transmission temperatures also increased between periods but to a lesser extent. CONCLUSIONS: The higher incidence of dengue virus and its expansion to different regions of Argentina seem to be associated with temperature increases in the country during the past two decades. The active surveillance of both the vector and associated arboviruses, together with continued meteorological data collection, will facilitate the assessment and prediction of future epidemics that use trends in the accelerated changes in climate. Such surveillance should go hand in hand with efforts to improve the understanding of the mechanisms driving the geographic expansion of dengue and other arboviruses beyond the current limits. https://doi.org/10.1289/EHP11616.

Prediction of leptospirosis outbreaks by hydroclimatic covariates: a comparative study of statistical models.

Leptospirosis, the infectious disease caused by a spirochete bacteria, is a major public health problem worldwide. In Argentina, some regions have climatic and geographical characteristics that favor the habitat of bacteria of the Leptospira genus, whose survival strongly depends on climatic factors, enhanced by climate change, which increase the problems associated with people's health. In order to have a method to predict leptospirosis cases, in this paper, five time series forecasting methods are compared: two parametric (autoregressive integrated moving average and an alternative one that allows covariates, ARIMA and ARIMAX, respectively), two nonparametric (Nadaraya-Watson Kernel estimator, one and two kernels versions, NW-1 K and NW-2 K), and one semiparametric (semi-functional partial linear regression, SFPLR) method. For this, the number of cases of leptospirosis registered from 2009 to 2020 in three important cities of northeastern Argentina is used, as well as hydroclimatic covariates related to the presence of cases. According to the obtained results, there is no method that improves considerably the rest and can be recommended as a unique tool for leptospirosis prediction. However, in general, the NW-2 K method gets a better performance. This work, in addition to using a long-term high-quality time series, enriches the area of applications of statistical models to epidemiological leptospirosis data by the incorporation of hydroclimatic variables, and it is recommended directing further efforts in this line of research, under the context of current climate change.

Dengue emergence in the temperate Argentinian province of Santa Fe, 2009-2020.

Dengue virus (DENV) transmission occurs primarily in tropical and subtropical climates, but within the last decade it has extended to temperate regions. Santa Fe, a temperate province in Argentina, has experienced an increase in dengue cases and virus circulation since 2009, with the recent 2020 outbreak being the largest in the province to date. The aim of this work is to describe spatio-temporal fluctuations of dengue cases from 2009 to 2020 in Santa Fe Province. The data presented in this work provide a detailed description of DENV transmission for Santa Fe Province by department. These data are useful to assist in investigating drivers of dengue emergence in Santa Fe Province and for developing a better understanding of the drivers and the impacts of ongoing dengue emergence in temperate regions across the world. This work provides data useful for future studies including those investigating socio-ecological, climatic, and environmental factors associated with DENV transmission, as well as those investigating other variables related to the biology and the ecology of vector-borne diseases.

Spatio-temporal analysis of leptospirosis incidence and its relationship with hydroclimatic indicators in northeastern Argentina.

Leptospirosis is considered the most globally widespread zoonotic illness; it has been classified as an emerging or reemerging infectious disease by the World Health Organization. Leptospirosis is a disease caused by a pathogenic spirochete of the genus Leptospira. The infection occurs by contacting with the urine of animal reservoirs or contaminated environments. Leptospirosis can be controlled by vaccines used mainly in animals, antibiotics given to exposed humans, and flood-affected people moved to a safe place. Northeastern Argentina accounts for the highest annual number of cases and deaths due to leptospirosis of the country. This interdisciplinary study aims to analyze the spatial and temporal distribution of leptospirosis, and assesses the hydroclimatic factors that give rise to the outbreaks in northeastern Argentina. The main goal is to detect the hydroclimatic indicators that can influence leptospirosis outbreaks occurrence in the northeastern Argentina. We perform a spatio-temporal analysis of the leptospirosis in the provinces of Santa Fe and Entre Ríos to distinguish the regions, years and seasons with the highest incidence of this disease. This study analyzes confirmed cases of leptospirosis between 2009 and 2018 years. Hydroclimatic indicators (monthly total precipitation, monthly river hydrometric level and Oceanic Niño Index) associated with outbreaks of leptospirosis vary in different spatial scales (provincial, departmental and cities). In the last outbreak of leptospirosis in 2015-2016 the number of cases was lower than expected. This could have been a consequence of increased prophylaxis in that flood event. Therefore, this is a variable that should be incorporated in future studies.

Analysis of the spatial distribution of scientific publications regarding vector-borne diseases related to climate variability in South America.

Most vector-borne diseases exhibit a distinct seasonal pattern, which clearly suggests that they are weather sensitive. Rainfall, temperature, and other climate variables affect in many ways both the vectors and the pathogens they transmit. Likewise, climate can be determinant in outbreaks incidence. A growing number of studies have provided evidence indicating the effects of climate variability on vector-borne diseases. However, oftentimes, the different diseases and regions are not uniformly represented, scarcity or lack of publications in some countries is common. The objectives of this work were to analyze the distribution and abundance of publications on vector-borne diseases associated with climate variability in South America, identify those works that conducted a geographic analysis and detect the countries where outbreaks occurred and the climate variables with which they were associated. A systematic review of the literature published on vector-borne diseases linked to climate variability in South America was conducted, identifying, evaluating and summarizing scientific papers. The distribution of the study areas and disease type in the publications were represented on maps. Dengue and leishmaniasis were the most studied and widely represented diseases in South America. The country with the largest number of published papers and presence of all disease types was Brazil. Outbreaks of disease were related to different climate variables. Most diseases from the publications under study occurred in equatorial and tropical climates. The disease represented by the largest number of different types of climates was dengue. The technique used in this work allowed us to determine the status of knowledge of the main diseases associated with climate variability in South America. This methodology could be improved in the future by incorporating other bibliographic sources as well as other diseases related to climate variability.