El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers

El Niño and other climatic drivers of epidemic malaria in Ethiopia: new tools for national health adaptation plans

BACKGROUND

Ethiopia has a history of climate related malaria epidemics. An improved understanding of malaria-climate interactions is needed to inform malaria control and national adaptation plans.

METHODS

Malaria-climate associations in Ethiopia were assessed using (a) monthly climate data (1981-2016) from the Ethiopian National Meteorological Agency (NMA), (b) sea surface temperatures (SSTs) from the eastern Pacific, Indian Ocean and Tropical Atlantic and (c) historical malaria epidemic information obtained from the literature. Data analysed spanned 1950-2016. Individual analyses were undertaken over relevant time periods. The impact of the El Niño Southern Oscillation (ENSO) on seasonal and spatial patterns of rainfall and minimum temperature (Tmin) and maximum temperature (Tmax) was explored using NMA online Maprooms. The relationship of historic malaria epidemics (local or widespread) and concurrent ENSO phases (El Niño, Neutral, La Niña) and climate conditions (including drought) was explored in various ways. The relationships between SSTs (ENSO, Indian Ocean Dipole and Tropical Atlantic), rainfall, Tmin, Tmax and malaria epidemics in Amhara region were also explored.

RESULTS

El Niño events are strongly related to higher Tmax across the country, drought in north-west Ethiopia during the July-August-September (JAS) rainy season and unusually heavy rain in the semi-arid south-east during the October-November-December (OND) season. La Niña conditions approximate the reverse. At the national level malaria epidemics mostly occur following the JAS rainy season and widespread epidemics are commonly associated with El Niño events when Tmax is high, and drought is common. In the Amhara region, malaria epidemics were not associated with ENSO, but with warm Tropical Atlantic SSTs and higher rainfall.

CONCLUSION

Malaria-climate relationships in Ethiopia are complex, unravelling them requires good climate and malaria data (as well as data on potential confounders) and an understanding of the regional and local climate system. The development of climate informed early warning systems must, therefore, target a specific region and season when predictability is high and where the climate drivers of malaria are sufficiently well understood. An El Niño event is likely in the coming years. Warming temperatures, political instability in some regions, and declining investments from international donors, implies an increasing risk of climate-related malaria epidemics.

A new generation of real-time environmental monitoring systems to study the impact of El Niño on disease dynamics

Global warming is drastically altering weather patterns, accentuating the frequency and strength of global events such as the El Niño Southern Oscillation. This alteration is driving the spread of diseases sensitive to climate such as diarrheal diseases. Environmental monitoring through remote sensing, in combination with data from epidemiological surveillance programs, is facilitating the study of infectious disease dynamics associated with El Niño. This integrative approach can inform the development of strategies for mitigating the impact of these diseases on public health. Here, we discuss some of the achievements of this approach in the management, control, and prevention of infectious diseases linked to El Niño.

Climate variability and change are drivers of salmonellosis in Australia: 1991 to 2019

Salmonellosis is a climate-sensitive gastroenteritis with over 92 million cases and over 50,000 deaths a year globally. Australia has high rates of salmonellosis compared with other industrialised nations. This study used a negative binomial time-series regression model to investigate the association between Australian salmonellosis notifications and monthly climate variables including El Niño Southern Oscillation (ENSO) and mean temperature anomaly from 1991 to 2019. Between 1991 and 2019 in Australia there were 275,753 salmonellosis notifications and the median annual rate for salmonellosis was 40.1 per 100,000 population. Salmonellosis notifications exhibited strong seasonality, reaching a peak in summer and a minimum in winter. There was an estimated increase of 3.4 % in salmonellosis cases nationally per 1 °C increase in monthly mean temperature anomaly (incidence rate ratio [IRR] of 1.034, 95 % confidence interval [CI]: 1.009, 1.059). Similar associations between salmonellosis and mean temperature anomaly were found for some states. Mean temperature anomaly exhibited an upward trend of 0.9 °C over the period 1991 to 2019. Additionally, a positive association was found between salmonellosis in Australia and ENSO whereby El Niño periods were associated with 7.9 % more salmonellosis cases compared to neutral periods (IRR 1.079, 95 % CI: 1.019, 1.143). A similar ENSO association was detected in the two eastern states of New South Wales and Queensland. This study suggests public health preventative measures to reduce salmonellosis could be enhanced in some regions during El Niño as well as during times of increased temperatures.

Influence of Vegetation on Outdoor Thermal Comfort in a High-Altitude Tropical Megacity: Climate Change and Variability Scenarios

The objective of this paper is to show a study on the influence of vegetation on the outdoor thermal comfort (OTC) of a high-altitude tropical megacity. The OTC is evaluated by the PET (Physiological Equivalent Temperature) index and by establishing three simulation scenarios: (i) Current OTC, (ii) OTC under RCPs 4.5 and 8.5 (Representative Concentration Pathway), and (iii) OTC under RCPs and ENSO (El Niño–Southern Oscillation). The results show that the hourly variation range of the current OTC in urban areas with vegetation is greater (+3.15 °C) compared to impermeable areas. Outdoor thermal stress due to cold in vegetated areas is 1.29 °C lower compared to impervious areas. The effect of vegetated coverage on the improvement of urban OTC increases as the phenomenon of global warming intensifies. On average, in the current, RCP4.5, and RCP8.5 scenarios for each 10% increase in urban vegetation coverage, an increase of 0.22, 0.24, and 0.28 °C in OTC is obtained, respectively. The hourly variation range of the PET index increases during the ENSO scenario (vegetated areas: +16.7%; impervious areas: +22.7%). In the context of climate change and variability, this study provides a reference point for decision-makers to assess possible planning options for improving OTC in megacities.

Can El Niño-Southern Oscillation Increase Respiratory Infectious Diseases in China? An Empirical Study of 31 Provinces

Respiratory infectious diseases (RID) are the major form of infectious diseases in China, and are highly susceptible to climatic conditions. Current research mainly focuses on the impact of weather on RID, but there is a lack of research on the effect of El Niño–Southern Oscillation (ENSO) on RID. Therefore, this paper uses the system generalized method of moments (SYS-GMM) and the data of 31 provinces in China from 2007 to 2018 to construct a dynamic panel model to empirically test the causality between ENSO and RID morbidity. Moreover, this paper considers the moderating effects of per capita disposable income and average years of education on this causality. The results show that ENSO can positively and significantly impact RID morbidity, which is 5.842% higher during El Niño years than normal years. In addition, per capita disposable income and average years of education can effectively weaken the relationship between ENSO and RID morbidity. Thus, this paper is of great significance for improving the RID early climate warning system in China and effectively controlling the spread of RID.

Spatiotemporal Variation in Rainfall Erosivity and Correlation with the ENSO on the Tibetan Plateau since 1971

Soil erosion is a serious ecological problem in the fragile ecological environment of the Tibetan Plateau (TP). Rainfall erosivity is one of the most important factors controlling soil erosion and is associated with the El Niño southern oscillation (ENSO). However, there is a lack of studies related to the spatial distribution and temporal trends of rainfall erosivity on the TP as a whole. Additionally, the understanding of the general influence of ENSO on rainfall erosivity across the TP remains to be developed. In this study, long-term (1971-2020) daily precipitation data from 91 meteorological stations were selected to calculate rainfall erosivity. The analysis combines co-kriging interpolation, Sen's slope estimator, and the Mann-Kendall trend test to investigate the spatiotemporal patten of rainfall erosivity across the TP. The Oceanic Niño Index (ONI) and multivariate ENSO Index (MEI) were chosen as ENSO phenomenon characterization indices, and the relationship between ENSO and rainfall erosivity was explored by employing a continuous wavelet transform. The results showed that an increasing trend in annual rainfall erosivity was detected on the TP from 1971 to 2020. The seasonal and monthly rainfall erosivity was highly uneven, with the summer erosivity accounting for 60.36%. The heterogeneous spatial distribution of rainfall erosivity was observed with an increasing trend from southeast to northwest. At the regional level, rainfall erosivity in the southeastern TP was mainly featured by a slow increase, while in the northwest was more destabilizing and mostly showed no significant trend. The rainfall erosivity on the whole TP was relatively high during non-ENSO periods and relatively low during El Niño/La Niña periods. It is worth noting that rainfall erosivity in the northwest TP appears to be more serious during the La Niña event. Furthermore, there were obvious resonance cycles between the rainfall erosivity and ENSO in different regions of the plateau, but the cycles had pronounced discrepancies in the occurrence time, direction of action and intensity. These findings contribute to providing references for soil erosion control on the TP and the formulation of future soil conservation strategies.

The correlation between three teleconnections and leptospirosis incidence in the Kandy District, Sri Lanka, 2004-2019

Background

Leptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals excrete leptospires into the environment via their urine. Survival of leptospires in the environment until they enter into a person and several other factors that influence leptospirosis transmission are dependent upon local weather. Past studies show that rainfall and other weather parameters are correlated with the LI in the Kandy district, Sri Lanka. El Niño Southern Oscillation (ENSO), ENSO Modoki, and the Indian Ocean Dipole (IOD) are teleconnections known to be modulating rainfall in Sri Lanka. There is a severe dearth of published studies on the correlations between indices of these teleconnections and LI.

Methods

We acquired the counts of leptospirosis cases notified and midyear estimated population data of the Kandy district from 2004 to 2019, respectively, from weekly epidemiology reports of the Ministry of Health and Department of Census and Statistics of Sri Lanka. We estimated weekly and monthly LI of Kandy. We obtained weekly and monthly teleconnection indices data for the same period from the National Oceanic and Atmospheric Administration (NOAA) of the USA and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We performed wavelet time series analysis to determine correlations with lag periods between teleconnection indices and LI time series. Then, we did time-lagged detrended cross-correlation analysis (DCCA) to verify wavelet analysis results and to find the magnitudes of the correlations detected.

Results

Wavelet analysis displayed indices of ENSO, IOD, and ENSO Modoki were correlated with the LI of Kandy with 1.9–11.5-month lags. Indices of ENSO showed two correlation patterns with Kandy LI. Time-lagged DCCA results show all indices of the three teleconnections studied were significantly correlated with the LI of Kandy with 2–5-month lag periods.

Conclusions

Results of the two analysis methods generally agree indicating that ENSO and IOD modulate LI in Kandy by modulating local rainfall and probably other weather parameters. We recommend further studies about the ENSO Modoki and LI correlation in Sri Lanka. Monitoring for extreme teleconnection events and enhancing preventive measures during lag periods can blunt LI peaks that may follow.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41182-021-00325-z.

COVID-19 and Ecosyndemic Vulnerability: Implications for El Niño-Sensitive Countries in Latin America

Latin America has emerged as an epicenter of the COVID-19 pandemic. Brazil, Peru, and Ecuador report some of the highest COVID-19 rates of incidence and deaths in the region. These countries also face synergistic threats from multiple infectious diseases (that is, ecosyndemic) and quasi-periodic El Niño-related hazards every few years. For example, Peru, which is highly sensitive to El Niño, already copes with an ecosyndemic health burden that heightens during and following weather and climate extreme events. Using an ecosyndemic lens, which draws on a multi-disease hazard context of place, this commentary highlights the importance of El Niño as a major factor that not only may aggravate COVID-19 incidence in the future, but also the broader health problem of ecosyndemic vulnerability in Latin America.

Projection of winter NPO-following winter ENSO connection in a warming climate: uncertainty due to internal climate variability

Previous observational and modeling studies indicate that the wintertime North Pacific Oscillation (NPO) could significantly impact the following winter El Niño-Southern Oscillation (ENSO) variability via the seasonal footprinting mechanism (SFM). This study explores climate projections of this winter NPO-ENSO relation in a warming climate based on a 50-member large ensemble of climate simulations conducted with the second-generation Canadian Earth System Model (CanESM2). The ensemble mean of the 50 members can well reproduce the observed winter NPO pattern, the NPO-ENSO relationship, and the SFM process over the historical period 1950–2003. These 50 members are then employed to examine climate projections of the NPO-ENSO connection over the anthropogenic forced period 2020–2073. Results indicate that there exists a large spread of projected NPO-ENSO connections across these 50 ensemble members due to internal climate variability. Internal climate variability brings uncertainties in the projection of the winter NPO-ENSO connection originally seen in projected changes of the subtropical center of the winter NPO. The spread of projections of winter NPO-associated atmospheric anomalies over the subtropical North Pacific further results in various responses in the projections of winter and spring precipitation anomalies over the tropical North Pacific, as well as spring zonal wind anomalies over the tropical western Pacific, which eventually lead to uncertainties in the projection of the sea surface temperature anomalies in the tropical central-eastern Pacific from the following summer to winter.

The Role of El Niño in Driving Drought Conditions over the Last 2000 Years in Thailand

Irregular climate events frequently occur in Southeast Asia due to the numerous climate patterns combining. Thailand sits at the confluence of these interactions, and consequently experiences major hydrological events, such as droughts. Proxy data, speleothem records, lake sediment sequences and tree ring chronologies were used to reconstruct paleo drought conditions. These trends were compared with modelled and historic El Niño Southern Oscillation (ENSO) data to assess if the ENSO climate phenomena is causing droughts in Thailand. Drought periods were found to occur both during El Niño events and ENSO neutral conditions. This indicates droughts are not a product of one climate pattern, but likely the result of numerous patterns interacting. There is uncertainty regarding how climate patterns will evolve under climate change, but changes in amplitude and variability could potentially lead to more frequent and wider reaching hydrological disasters. It is vital that policies are implemented to cope with the resulting social and economic repercussions, including diversification of crops and reorganisation of water consumption behaviour in Thailand.

Spatiotemporal Distribution of Malaria in Spain in a Global Change Context

Malaria is one of the most cited vector-borne infectious diseases by climate change expert panels. Malaria vectors often need water sheets or wetlands to complete the disease life cycle. The current context of population mobility and global change requires detailed monitoring and surveillance of malaria in all countries. This study analysed the spatiotemporal distribution of death and illness cases caused by autochthonous and imported malaria in Spain during the 20th and 21st centuries using multidisciplinary sources, Geographic Information System (GIS) and geovisualisation. The results obtained reveal that, in the 20th and 21st centuries, malaria has not had a homogeneous spatial distribution. Between 1916 and 1930, 77% of deaths from autochthonous malaria were concentrated in only 20% of Spanish provinces; in 1932, 88% of patients treated in anti-malarial dispensaries were concentrated in these same provinces. These last data reveal the huge potential that anti-malarial dispensaries could have as a tool to reconstruct historical epidemiology. Spanish autochthonous malaria has presented epidemic upsurge episodes, especially those of 1917–1922 and 1939–1944, influenced by armed conflict, population movement and damaged health and hygiene conditions. Although meteorological variables have not played a key role in these epidemic episodes, they contributed by providing suitable conditions for their intensification. After the eradication of autochthonous malaria in 1961, imported malaria cases began to be detected in 1973, reaching more than 700 cases per year at the end of the second decade of the 21st century. Therefore, consistent and detailed historical studies are necessary to better understand the drivers that have led to the decline and elimination of malaria in Europe and other temperate countries.

A multi-scalar climatological analysis in preparation for extreme heat at the Tokyo 2020 Olympic and Paralympic Games

Extreme heat can be harmful to human health and negatively affect athletic performance. The Tokyo Olympic and Paralympic Games are predicted to be the most oppressively hot Olympics on record. An interdisciplinary multi-scale perspective is provided concerning extreme heat in Tokyo-from planetary atmospheric dynamics, including El Niño Southern Oscillation (ENSO), to fine-scale urban temperatures-as relevant for heat preparedness efforts by sport, time of day, and venue. We utilize stochastic methods to link daytime average wet bulb globe temperature (WBGT) levels in Tokyo in August (from meteorological reanalysis data) with large-scale atmospheric dynamics and regional flows from 1981 to 2016. Further, we employ a mesonet of Tokyo weather stations (2009-2018) to interpolate the spatiotemporal variability in near-surface air temperatures at outdoor venues. Using principal component analysis, two planetary (ENSO) regions in the Pacific Ocean explain 70% of the variance in Tokyo's August daytime WBGT across 35 years, varying by 3.95°C WGBT from the coolest to warmest quartile. The 10-year average daytime and maximum intra-urban air temperatures vary minimally across Tokyo (<1.2°C and 1.7°C, respectively), and less between venues (0.6-0.7°C), with numerous events planned for the hottest daytime period (1200-1500 hr). For instance, 45% and 38% of the Olympic and Paralympic road cycling events (long duration and intense) occur midday. Climatologically, Tokyo will present oppressive weather conditions, and March-May 2020 is the critical observation period to predict potential anomalous late-summer WBGT in Tokyo. Proactive climate assessment of expected conditions can be leveraged for heat preparedness across the Game's period.

El Niño-Southern oscillation and under-5 diarrhea in Botswana

Childhood diarrheal disease causes significant morbidity and mortality in low and middle-income countries, yet our ability to accurately predict diarrhea incidence remains limited. El Niño-Southern Oscillation (ENSO) has been shown to affect diarrhea dynamics in South America and Asia. However, understanding of its effects in sub-Saharan Africa, where the burden of under-5 diarrhea is high, remains inadequate. Here we investigate the connections between ENSO, local environmental conditions, and childhood diarrheal disease in Chobe District, Botswana. Our results demonstrate that La Niña conditions are associated with cooler temperatures, increased rainfall, and higher flooding in the Chobe region during the rainy season. In turn, La Niña conditions lagged 0–5 months are associated with higher than average incidence of under-5 diarrhea in the early rainy season. These findings demonstrate the potential use of ENSO as a long-lead prediction tool for childhood diarrhea in southern Africa.

Here, Heaney et al. show that La Niña conditions are associated with higher than average incidence of childhood diarrheal disease in Botswana in the early rainy season. This finding could help to predict childhood diarrhea outbreaks in southern Africa.

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya

The United Nations' Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used-Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino-southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.

The cholera epidemic in Yemen - How did it start? The role of El Niño conditions followed by regional winds

The largest cholera epidemic of modern times began during the autumn of 2016 in Yemen, under ongoing war conditions. What exactly caused the epidemic to emerge is unclear. It is suggested that a combination of the impact of the strong El Niño of 2015-16 on cholera incidence in Somalia, followed by southwestern winds over the Gulf of Aden throughout the summer of 2016, contributed to the disease spreading through dissemination of cholera-contaminated flying insects (chironomids) from the Horn of Africa to Yemen.

Time-Series Study of Associations between Rates of People Affected by Disasters and the El Niño Southern Oscillation (ENSO) Cycle

The El Niño Southern Oscillation (ENSO) is a major driver of climatic variability that can have far reaching consequences for public health globally. We explored whether global, regional and country-level rates of people affected by natural disasters (PAD) are linked to ENSO. Annual numbers of PAD between 1964–2017 recorded on the EM-DAT disaster database were combined with UN population data to create PAD rates. Time-series regression was used to assess de-trended associations between PAD and 2 ENSO indices: Oceanic Niño Index (ONI) and multivariate El Niño Index (MEI). Over 95% of PAD were caused by floods, droughts or storms, with over 75% of people affected by these three disasters residing in Asia. Globally, drought-related PAD rate increased sharply in El Niño years (versus neutral years). Flood events were the disaster type most strongly associated with El Niño regionally: in South Asia, flood-related PAD increased by 40.5% (95% CI 19.3% to 65.6%) for each boundary point increase in ONI (p = 0.002). India was found to be the country with the largest increase in flood-related PAD rates following an El Niño event, with the Philippines experiencing the largest increase following La Niña. Multivariate ENSO Index (MEI)-analyses showed consistent results. These findings can be used to inform disaster preparedness strategies.

Hydrological Modelling and Water Resources Assessment of Chongwe River Catchment using WEAP Model

The Chongwe River Catchment (CRC) is located in Zambia. It receives a mean annual precipitation of 889 mm. The catchment is facing growing anthropogenic and socio-economic activities leading to severe water shortages in recent years, particularly from July to October. The objective of this study was to assess the available water resources by investigating the important hydrological components and estimating the catchment water balance using the Water Evaluation and Planning (WEAP) model. The average precipitation over a 52 year period and a 34 year period of streamflow measurement data for four stations were used in the hydrological balance model. The results revealed that the catchment received an estimated mean annual precipitation of 4603.12 Mm3. It also released an estimated mean annual runoff and evapotranspiration of 321.94 Mm3 and 4063.69 Mm3, respectively. The estimated mean annual total abstractions in the catchment was 119.87 Mm3. The average annual change in the catchment storage was 120.18 Mm3. The study also determined an external inflow of 22.55 Mm3 from the Kafue River catchment. The simulated mean monthly streamflow at the outlet of the CRC was 10.32 m3/s. The estimated minimum and maximum streamflow volume of the Chongwe River was about 1.01 Mm3 in September and 79.7 Mm3 in February, respectively. The performance of the WEAP model simulation was assessed statistically using the coefficient of determination (R2 = 0.97) and the Nash–Sutcliffe model efficiency coefficient (NSE = 0.64). The R2 and NSE values indicated a satisfactory model fit and result. Meeting the water demand of the growing population and associated socio-economic development activities in the CRC is possible but requires appropriate water resource management options.

Improving Entropy Estimates of Complex Network Topology for the Characterization of Coupling in Dynamical Systems

A new measure for the characterization of interconnected dynamical systems coupling is proposed. The method is based on the representation of time series as weighted cross-visibility networks. The weights are introduced as the metric distance between connected nodes. The structure of the networks, depending on the coupling strength, is quantified via the entropy of the weighted adjacency matrix. The method has been tested on several coupled model systems with different individual properties. The results show that the proposed measure is able to distinguish the degree of coupling of the studied dynamical systems. The original use of the geodesic distance on Gaussian manifolds as a metric distance, which is able to take into account the noise inherently superimposed on the experimental data, provides significantly better results in the calculation of the entropy, improving the reliability of the coupling estimates. The application to the interaction between the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole and to the influence of ENSO on influenza pandemic occurrence illustrates the potential of the method for real-life problems.