The EI Niño Southern Oscillation and the historic malaria epidemics on the Indian subcontinent and Sri Lanka: an early warning system for future epidemics?

The recurrent great malaria epidemics which occurred in the Punjab province of former British India and Ceylon before the introduction of residual insecticides have been related to excessive and failing monsoon rains respectively. In the arid Punjab, rainfall facilitated breeding and increased the lifespan of the mosquito vector and, in the wet part of Ceylon, failing monsoon rains caused rivers to pool, creating more favourable breeding conditions. The periodic fluctuations in monsoon rainfall and epidemic malaria are here explained in relation to the El Niño Southern Oscillation. In the Punjab, epidemic malaria between 1868 and 1943 correlates significantly ( r =0.34, P <0.005) with the sea surface temperature anomalies in the Eastern Equatorial Pacific, a parameter of the oscillation, and epidemics were significantly more prevalent in a year with a wet monsoon following a dry El Niño year than in other years. In Ceylon, epidemics were significantly more prevalent during El Niño years, when the same south-west monsoon tends to fail. With the reduced reliance on residual insecticides and the recurrence of epidemic malaria on the Indian subcontinent, advances made in predicting El Niño events may be used to forecast high and low risk years for future malaria epidemics.     

The periodicity of Plasmodium vivax and Plasmodium falciparum in Venezuela

We investigated the periodicity of Plasmodium vivax and P. falciparum incidence in time-series of malaria data (1990–2010) from three endemic regions in Venezuela. In particular, we determined whether disease epidemics were related to local climate variability and regional climate anomalies such as the El Niño Southern Oscillation (ENSO). Malaria periodicity was found to exhibit unique features in each studied region. Significant multi-annual cycles of 2- to about 6-year periods were identified. The inter-annual variability of malaria cases was coherent with that of SSTs (ENSO), mainly at temporal scales within the 3–6 year periods. Additionally, malaria cases were intensified approximately 1 year after an El Niño event, a pattern that highlights the role of climate inter-annual variability in the epidemic patterns. Rainfall mediated the effect of ENSO on malaria locally. Particularly, rains from the last phase of the season had a critical role in the temporal dynamics of Plasmodium. The malaria–climate relationship was complex and transient, varying in strength with the region and species. By identifying temporal cycles of malaria we have made a first step in predicting high-risk years in Venezuela. Our findings emphasize the importance of analyzing high-resolution spatial–temporal data to better understand malaria transmission dynamics.     

El Niño and health

El Ni?o Southern Oscillation (ENSO) is a climate event that originates in the Pacific Ocean but has wide-ranging consequences for weather around the world, and is especially associated with droughts and floods. The irregular occurrence of El Ni?o and La Ni?a events has implications for public health. On a global scale, the human effect of natural disasters increases during El Ni?o. The effect of ENSO on cholera risk in Bangladesh, and malaria epidemics in parts of South Asia and South America has been well established. The strongest evidence for an association between ENSO and disease is provided by time-series analysis with data series that include more than one event. Evidence for ENSO's effect on other mosquito-borne and rodent-borne diseases is weaker than that for malaria and cholera. Health planners are used to dealing with spatial risk concepts but have little experience with temporal risk management. ENSO and seasonal climate forecasts might offer the opportunity to target scarce resources for epidemic control and disaster preparedness.     

Impact of climate variability in the occurrence of leishmaniasis in northeastern Colombia

Previous studies have shown that variation in the distribution of vectors associated to the transmission of Leishmania species may be related to climatic changes. However, the potential implications of these ecological changes in human health need to be further defined in various endemic populations where leishmaniasis carries a substantial burden of disease such as in Northeastern Colombia. Herein, we report the impact of El Ni?o Southern Oscillation climatic fluctuations during 1985-2002 in the occurrence of cases of leishmaniasis in two northeastern provinces of Colombia. During this period, we identified that during El Ni?o, cases of leishmaniasis increased, whereas during La Ni?a phases, leishmaniasis cases decreased. This preliminary data show how climatic changes influence the occurrence of leishmaniasis in northeastern Colombia and contributes to the growing body of evidence that shows that the incidence of vector-borne diseases is associated with annual changes in weather conditions.     

Patterns of rainfall and malaria in Madhya Pradesh,central India

Some recent outbreaks of Plasmodium falciparum malaria have been attributed, at least in part, to increases in the intensity and duration of rainfall caused by the El Ni?o southern oscillation (ENSO), a periodic climatic phenomenon. Since it takes time for unusually heavy rainfall to translate into unusually high densities of the vector mosquitoes, it has been suggested that data on recent rainfall might be used to predict climate-related epidemics of malaria. This possibility was explored by comparing the patterns in the incidence of malaria in (1) Dungaria, a highly malarious village in the central-Indian district of Mandla, and (2) Mandla district as a whole, for the periods 1986-2000 and 1967-2000, respectively, with data on rainfall for the same areas and periods. Unfortunately, no clear relationship was observed between rainfall and malaria incidence, although a major development project to improve water resources in the study area (which resulted in local villages being partially or completely submerged in water) may have masked any significant association. A useful method for predicting which years are going to be high- or low-risk years for malaria epidemics, in the present and other epidemiological settings, remains a future goal.     

The Indian Ocean Dipole and malaria risk in the highlands of western Kenya

Epidemics of malaria in the East African highlands in the last 2 decades have often been associated with climate variability, particularly the El Niño-Southern Oscillation (ENSO). However, there are other factors associated with malaria risk and there is increased interest in the influences of the Indian Ocean Dipole (IOD), a climate mode of coupled ocean–atmosphere variability, on East African rainfall. This study explores the relationship between IOD and the number of malaria patients in 7 hospitals from 2 districts in the western Kenyan highlands, controlling for the effects of ENSO. We examined temporal patterns (1982–2001) in the number of malaria cases in relation to the dipole mode index (DMI), defined as the difference in sea surface temperature anomaly between the western (10°S-10°N, 50°-70°E) and eastern (10°S-0°, 90°-110°E) tropical Indian Ocean. We used Poisson regression models, adjusted for ENSO index Niño 3 region (NINO3), seasonal and interannual variations. The number of malaria patients per month increased by 3.4%–17.9% for each 0.1 increase above a DMI threshold (3–4 months lag). Malaria cases increased by 1.4%–10.7% per month, for each 10 mm increase in monthly rainfall (2–3 months lag). In 6 of 7 places, there was no evidence of an association between NINO3 and the number of malaria cases after adjusting for the effect of DMI. This study suggests that the number of malaria cases in the western Kenyan highlands increases with high DMI in the months preceding hospital visits.     

Climate Anomalies and Spillover of Bat-Borne Viral Diseases in the Asia–Pacific Region and the Arabian Peninsula

Climate variability and anomalies are known drivers of the emergence and outbreaks of infectious diseases. In this study, we investigated the potential association between climate factors and anomalies, including El Niño Southern Oscillation (ENSO) and land surface temperature anomalies, as well as the emergence and spillover events of bat-borne viral diseases in humans and livestock in the Asia–Pacific region and the Arabian Peninsula. Our findings from time series analyses, logistic regression models, and structural equation modelling revealed that the spillover patterns of the Nipah virus in Bangladesh and the Hendra virus in Australia were differently impacted by climate variability and with different time lags. We also used event coincidence analysis to show that the emergence events of most bat-borne viral diseases in the Asia–Pacific region and the Arabian Peninsula were statistically associated with ENSO climate anomalies. Spillover patterns of the Nipah virus in Bangladesh and the Hendra virus in Australia were also significantly associated with these events, although the pattern and co-influence of other climate factors differed. Our results suggest that climate factors and anomalies may create opportunities for virus spillover from bats to livestock and humans. Ongoing climate change and the future intensification of El Niño events will therefore potentially increase the emergence and spillover of bat-borne viral diseases in the Asia–Pacific region and the Arabian Peninsula.     

Association of early annual peak influenza activity with El Niño southern oscillation in Japan

Background Seasonality characterizing influenza epidemics suggests susceptibility to climate variation. El Niño southern oscillation (ENSO), which involves two extreme events, El Niño and La Niña, is well‐known for its large effects on inter‐annual climate variability. The influence of ENSO on several diseases has been described. Objectives In this study, we attempt to analyze the possible influence of ENSO on the timing of the annual influenza activity peak using influenza‐like illness report data in Japan during 1983–2007. Materials Influenza surveillance data for 25 influenza epidemics, available under the National Epidemiological Surveillance of the Infectious Diseases, was used in this study. ENSO data were obtained from the Japan Meteorological Agency. Results Influenza‐like illness peak week varied largely during the study period, ranging between 4th and 11th weeks (middle of winter to early spring). The average of peak week during ENSO cycles (n = 11, average = 4·5 ± 0·9) was significantly earlier than in non‐ENSO years (n = 14, average = 7·6 ± 2·9; P = 0·01), but there was no significant difference in the peak timing between hot (El Niño) and cold (La Niña) phases. Earlier peaks of influenza activity were observed in 16, out of 25, epidemics. These coincided with 10 (90·9%) out of 11 ENSO and 6 (85·7%) out of seven large‐scale epidemics. Conclusion Influenza activity peak occurred earlier in years associated with ENSO and/or large scale epidemics.     

El Ni?o/Southern Oscillation response to global warming

The El Niño/Southern Oscillation (ENSO) phenomenon, originating in the Tropical Pacific, is the strongest natural interannual climate signal and has widespread effects on the global climate system and the ecology of the Tropical Pacific. Any strong change in ENSO statistics will therefore have serious climatic and ecological consequences. Most global climate models do simulate ENSO, although large biases exist with respect to its characteristics. The ENSO response to global warming differs strongly from model to model and is thus highly uncertain. Some models simulate an increase in ENSO amplitude, others a decrease, and others virtually no change. Extremely strong changes constituting tipping point behavior are not simulated by any of the models. Nevertheless, some interesting changes in ENSO dynamics can be inferred from observations and model integrations. Although no tipping point behavior is envisaged in the physical climate system, smooth transitions in it may give rise to tipping point behavior in the biological, chemical, and even socioeconomic systems. For example, the simulated weakening of the Pacific zonal sea surface temperature gradient in the Hadley Centre model (with dynamic vegetation included) caused rapid Amazon forest die-back in the mid-twenty-first century, which in turn drove a nonlinear increase in atmospheric CO₂, accelerating global warming.One of the main characteristics of the earth''s climate is its strong natural variability on a wide range of timescales from seasonal to millennial. Climate variability can be generated, on the one hand, internally through interactions within and between the different climate subsystems (atmosphere, ocean, land, sea ice, glaciers, biogeochemistry). The internal nonlinear (chaotic) dynamics of the atmosphere and the oceans, for instance, and ocean–atmosphere interactions generate a large amount of variability on seasonal to decadal timescales. However, the climate system can be externally forced. The annual cycle is a prominent example. On the very long millennial timescales, changes in the orbital parameters are the most important drivers of climate change. They are the pacemakers of the ice age cycles. Anthropogenic climate change is also considered as externally driven in this context.     

Application of Advanced Very High Resolution Radiometer (AVHRR)-based Vegetation Health Indices for Estimation of Malaria Cases

Satellite data may be used to map climatic conditions conducive to malaria outbreaks, assisting in the targeting of public health interventions to mitigate the worldwide increase in incidence of the mosquito-transmitted disease. This work analyzes correlation between malaria cases and vegetation health (VH) indices derived from satellite remote sensing for each week over a period of 14 years for Bandarban, Bangladesh. Correlation analysis showed that years with a high summer temperature condition index (TCI) tended to be those with high malaria incidence. Principal components regression was performed on patterns of weekly TCI during each of the two annual malaria seasons to construct a model as a function of the TCI. These models reduced the malaria estimation error variance by 57% if first-peak (June–July) TCI was used as the estimator and 74% if second-peak (August–September) was used, compared with an estimation of average number of malaria cases for each year.     

ENSO and cholera: a nonstationary link related to climate change?

We present here quantitative evidence for an increased role of interannual climate variability on the temporal dynamics of an infectious disease. The evidence is based on time-series analyses of the relationship between El Ni?o/Southern Oscillation (ENSO) and cholera prevalence in Bangladesh (formerly Bengal) during two different time periods. A strong and consistent signature of ENSO is apparent in the last two decades (1980-2001), while it is weaker and eventually uncorrelated during the first parts of the last century (1893-1920 and 1920-1940, respectively). Concomitant with these changes, the Southern Oscillation Index (SOI) undergoes shifts in its frequency spectrum. These changes include an intensification of the approximately 4-yr cycle during the recent interval as a response to the well documented Pacific basin regime shift of 1976. This change in remote ENSO modulation alone can only partially serve to substantiate the differences observed in cholera. Regional or basin-wide changes possibly linked to global warming must be invoked that seem to facilitate ENSO transmission. For the recent cholera series and during specific time intervals corresponding to local maxima in ENSO, this climate phenomenon accounts for over 70% of disease variance. This strong association is discontinuous in time and can only be captured with a technique designed to isolate transient couplings.     

1,500 year quantitative reconstruction of winter precipitation in the Pacific Northwest

Multiple paleoclimate proxies are required for robust assessment of past hydroclimatic conditions. Currently, estimates of drought variability over the past several thousand years are based largely on tree-ring records. We produced a 1,500-y record of winter precipitation in the Pacific Northwest using a physical model-based analysis of lake sediment oxygen isotope data. Our results indicate that during the Medieval Climate Anomaly (MCA) (900–1300 AD) the Pacific Northwest experienced exceptional wetness in winter and that during the Little Ice Age (LIA) (1450–1850 AD) conditions were drier, contrasting with hydroclimatic anomalies in the desert Southwest and consistent with climate dynamics related to the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). These findings are somewhat discordant with drought records from tree rings, suggesting that differences in seasonal sensitivity between the two proxies allow a more compete understanding of the climate system and likely explain disparities in inferred climate trends over centennial timescales.     

Strong influence of El Ni?o Southern Oscillation on flood risk around the world

El Niño Southern Oscillation (ENSO) is the most dominant interannual signal of climate variability and has a strong influence on climate over large parts of the world. In turn, it strongly influences many natural hazards (such as hurricanes and droughts) and their resulting socioeconomic impacts, including economic damage and loss of life. However, although ENSO is known to influence hydrology in many regions of the world, little is known about its influence on the socioeconomic impacts of floods (i.e., flood risk). To address this, we developed a modeling framework to assess ENSO’s influence on flood risk at the global scale, expressed in terms of affected population and gross domestic product and economic damages. We show that ENSO exerts strong and widespread influences on both flood hazard and risk. Reliable anomalies of flood risk exist during El Niño or La Niña years, or both, in basins spanning almost half (44%) of Earth’s land surface. Our results show that climate variability, especially from ENSO, should be incorporated into disaster-risk analyses and policies. Because ENSO has some predictive skill with lead times of several seasons, the findings suggest the possibility to develop probabilistic flood-risk projections, which could be used for improved disaster planning. The findings are also relevant in the context of climate change. If the frequency and/or magnitude of ENSO events were to change in the future, this finding could imply changes in flood-risk variations across almost half of the world’s terrestrial regions.El Niño Southern Oscillation (ENSO) is the most dominant interannual signal of climate variability on Earth (1) and influences climate over large parts of the Earth’s surface. In turn, ENSO is known to strongly influence many physical processes and societal risks, including droughts, food production, hurricane damage, and tropical tree cover (2–4). For decision makers it is essential to have information on the possible impacts of this climate variability on society. Such information can be particularly useful when the climate variability can be anticipated in advance, thus allowing for early warning and disaster planning (5). For example, projections carried out in September 2013 already suggested a 75% likelihood that El Niño conditions would develop in late 2014 (6). According to the ENSO forecast of the International Research Institute for Climate and Society and the Climate Prediction Center/NCEP/NWS, dated 9 October 2014, observed ENSO conditions did indeed move to those of a borderline El Niño during September and October 2014, with indications of weak El Niño conditions during the northern hemisphere winter 2014–2015 (iri.columbia.edu/our-expertise/climate/forecasts/enso/current/).However, to date little is known on ENSO’s influence on flood risk, whereby risk is defined as a function of hazard, exposure, and vulnerability (7) and is expressed in terms of socioeconomic indicators such as economic damage or affected people. Although global-scale flood-risk assessments have recently become a hot topic in both the scientific and policy communities, assessments to date have focused on current risks (7–11) or future risks under long-term mean climate change (12, 13). Meanwhile, other recent research suggests that ENSO-related variations of precipitation are likely to intensify in the future (14, 15) and that extreme El Niño events may increase in frequency (16). Hence, an understanding of ENSO’s influence on flood risk is vital in understanding both the possible impacts of upcoming ENSO events as well as planning for the potential socioeconomic impacts of changes in future ENSO frequency.In this paper, we show for the first time to our knowledge that ENSO has a very strong influence on flood risk in large parts of the world. These findings build on previous studies, especially in Australia and the United States, which show that ENSO and other forms of climate variability are strongly related to flood hazard in some regions (17–25). To do this, we developed a modeling framework to specifically assess ENSO’s influence on global flood risk. The modeling framework involves using a cascade of hydrological, hydraulic, and impact models (10, 11). Using this model cascade, we assessed flood impacts in terms of three indicators: (i) exposed population, (ii) exposed gross domestic product (GDP), and (iii) urban damage (Materials and Methods). A novel aspect of the framework is that we are able to calculate flood risk conditioned on the climatology of all years, El Niño years only, and La Niña years only. This allows us, for the first time to our knowledge, to simulate the impacts of ENSO on flood risk. The hydrological and impact models have previously been validated for the period 1958–2000 (11). Here, we carried out further validation to assess the specific ability of the model cascade to simulate year-to-year fluctuations in peak river flows and flood impacts and anomalies in peak flows and impacts during El Niño and La Niña years (SI Discussion, Validation of Hydrological and Hydraulic Models).     

Climatic variables and malaria incidence in Dehradun, Uttaranchal, India

BACKGROUND & OBJECTIVES: Mosquito-borne diseases particularly malaria and Japanese encephalitis (JE) are becoming most dreaded health problems in Dehradun district. Keeping in view that the climatic factors particularly temperature and rainfall may alter the distribution of vector species--increasing or decreasing the ranges, depending on weather conditions that are favourable or unfavourable for mosquito breeding, it is aimed to find out the effect of climatic factors on malaria incidence with particular emphasis to capture the essential events as a result of climatic variability. METHODS: Mosquito sampling and identification was done using WHO entomological methods and follow-up of recognised keys and catalogues. Data on malaria incidence and meteorological information were gathered in a collaborative study with the District Malaria Office, and the Forest Research Institute, Dehradun respectively. Pearson's correlation analysis was applied for establishing relationship between climate variables and malaria transmission. RESULTS: Higher positive correlation of association was found between monthly parasite incidence and climatic variables (temperature, rainfall and humidity). However, highest significant correlation was found between rainfall and malaria incidence (r = 0.718, p < 0.0001) when the data were staggered to allow a lag of one-month. INTERPRETATION & CONCLUSION: Climatic variables that predict the presence or absence of malaria are likely to be the best suited for forecasting the distribution of this disease at the edges of its range.     

El Niño and the related phenomenon Southern Oscillation (ENSO): The largest signal in interannual climate variation

El Ni?o and the related phenomenon Southern Oscillation (ENSO) is the strongest signal in the interannual variation of ocean-atmosphere system. It is mainly a tropical event but its impact is global. ENSO has been drawing great scientific attention in many international research programs. There has been an observational system for the tropical ocean, and scientists have known the climatologies of the upper ocean, developed some theories about the ENSO cycle, and established coupled ocean-atmosphere models to give encouraging predictions of ENSO for a 1-year lead. However, questions remain about the physical mechanisms for the ENSO cycle and its irregularity, ENSO-monsoon interactions, long-term variation of ENSO, and increasing the predictive skill of ENSO and its related climate variations.     

On the influence of ENSO complexity on Pan-Pacific coastal wave extremes

Wind-generated waves are dominant drivers of coastal dynamics and vulnerability, which have considerable impacts on littoral ecosystems and socioeconomic activities. It is therefore paramount to improve coastal hazards predictions through the better understanding of connections between wave activity and climate variability. In the Pacific, the dominant climate mode is El Niño Southern Oscillation (ENSO), which has known a renaissance of scientific interest leading to great theoretical advances in the past decade. Yet studies on ENSO’s coastal impacts still rely on the oversimplified picture of the canonical dipole across the Pacific. Here, we consider the full ENSO variety to delineate its essential teleconnection pathways to tropical and extratropical storminess. These robust seasonally modulated relationships allow us to develop a mathematical model of coastal wave modulation essentially driven by ENSO’s complex temporal and spatial behavior. Accounting for this nonlinear climate control on Pan-Pacific wave activity leads to a much better characterization of waves’ seasonal to interannual variability (+25% in explained variance) and intensity of extremes (+60% for strong ENSO events), therefore paving the way for significantly more accurate forecasts than formerly possible with the previous baseline understanding of ENSO’s influence on coastal hazards.

As coastal breaking waves represent the ultimate dissipation of the energy generated by local and remote storms through large increases in surface wind over the ocean, their activity is modulated by the large-scale ocean–atmosphere coupled variability. This emphasizes the importance of better understanding the connections between coastal dynamics and modes of climate variability in order to improve their prediction at subseasonal timescales and beyond (1, 2). In particular, the Pacific basin is under the siege of El Niño Southern Oscillation (ENSO), the strongest interannual climate fluctuation, which has widespread effects on weather, climate, and societies (3). Recently, the littoral community has started to identify the role of ENSO as a major driver of coastal vulnerability across the Pacific (4, 5). The alternating coastal conditions, with shifts in wave activity and water-level anomalies between the northeastern and northwestern Pacific, were noted to mimic the well-known oscillations of ENSO phases. However, the Pacific wave climate and coastal variability associated with ENSO remains only understood at a basic reconnaissance level (6). As a matter of fact, even the most recent studies on the connection between wave climate and coastal extremes have only relied on a simplified view of ENSO (7, 8), omitting the existence of different regimes with distinct teleconnections and dynamics, recently coined “ENSO diversity and/or complexity” (9, 10).“ENSO diversity” originates from the concept that Sea Surface Temperature (SST) anomaly patterns exhibit wide variations. In particular, the repeated occurrence of SST patterns in the central Pacific (CP) in the 2000s suggested that ENSO events may be grouped into two flavors: the conventional El Niño, with SST anomalies concentrated in the eastern Pacific (EP El Niño), and the CP El Niño, with SST anomalies located around the dateline (11, 12). The “complexity” or sometimes “diversity and complexity” further refers to ENSO’s irregular temporal behavior characterized by large variations in amplitude and duration. In particular, recent progresses demonstrated that ENSO’s seasonal phase locking (i.e., its tendency to peak in boreal winter) (13) can produce a low-frequency instability known as the “Annual cycle-ENSO combination mode” and generate a deterministic variability at near-annual timescales, which significantly broadens the ENSO continuum (14). Such diversity and complexity translate to pronounced differences in remote ENSO climate impacts on the climate system through atmospheric and oceanic teleconnections (15, 16). In particular, one of ENSO’s most significant influences is its modulation of Tropical Cyclone (TC) activity, one of the most severe natural hazards (17). Because the large-scale air–sea environment mostly drives these storms, TC activity is substantially modified by ENSO through atmospheric and oceanic pathways (18). Similarly, ENSO also strongly affects extratropical storms and related coastal wave activity (19, 20).A variety of oceanic and atmospheric wave reanalysis products and TC databases were examined to capture comprehensively the regional and large-scale climate variability in the Pacific associated with ENSO diversity and complexity and how it affects coastal waves’ variability. More specifically and unlike any previous studies, the focus is not solely directed toward the direct influence on extratropical wave patterns of El Niño at its winter peak but also on its delayed and early effects on summer TC storminess considered as an integral wave regime potentially affecting coastlines far from the storms’ generation (21). In particular, since ENSO’s influences on tropical and extratropical storm activity are subject to a strong seasonal synchronization, the combined ENSO–Annual cycle influence on teleconnections patterns and coastal wave variability is considered. Insights from these unraveled seasonally modulated ENSO teleconnections allow us to develop a simple mathematical model that points toward a strong predictability of the Pacific coastal wave variability over a range of timescales much broader than just the interannual band and therefore opens up the door for predictions of coastal hazards significantly more accurate than current state-of-the-art seasonal forecasts.     

Linking global climate and temperature variability to widespread amphibian declines putatively caused by disease

The role of global climate change in the decline of biodiversity and the emergence of infectious diseases remains controversial, and the effect of climatic variability, in particular, has largely been ignored. For instance, it was recently revealed that the proposed link between climate change and widespread amphibian declines, putatively caused by the chytrid fungus Batrachochytrium dendrobatidis (Bd), was tenuous because it was based on a temporally confounded correlation. Here we provide temporally unconfounded evidence that global El Niño climatic events drive widespread amphibian losses in genus Atelopus via increased regional temperature variability, which can reduce amphibian defenses against pathogens. Of 26 climate variables tested, only factors associated with temperature variability could account for the spatiotemporal patterns of declines thought to be associated with Bd. Climatic predictors of declines became significant only after controlling for a pattern consistent with epidemic spread (by temporally detrending the data). This presumed spread accounted for 59% of the temporal variation in amphibian losses, whereas El Niño accounted for 59% of the remaining variation. Hence, we could account for 83% of the variation in declines with these two variables alone. Given that global climate change seems to increase temperature variability, extreme climatic events, and the strength of Central Pacific El Niño episodes, climate change might exacerbate worldwide enigmatic declines of amphibians, presumably by increasing susceptibility to disease. These results suggest that changes to temperature variability associated with climate change might be as significant to biodiversity losses and disease emergence as changes to mean temperature.     

Exploring 30 years of malaria case data in KwaZulu-Natal, South Africa: part I. The impact of climatic factors

Large parts of Africa are prone to malaria epidemics. Advance epidemic warning would give health services an opportunity to prepare. Because malaria transmission is largely limited by climate, climate-based epidemic warning systems are a real possibility. To develop and test such a system, good long-term malaria and climate data are needed. In KwaZulu-Natal (KZN), South Africa, 30 years of confirmed malaria case data provide a unique opportunity to examine short- and long-term trends. We analysed seasonal case totals and seasonal changes in cases (both log-transformed) against a range of climatic indicators obtained from three weather stations in the highest malaria incidence districts, using linear regression analysis. Seasonal changes in case numbers (delta log cases, dlc) were significantly associated with several climate variables. The two most significant ones were mean maximum daily temperatures from January to October of the preceding season (n=30, r2=0.364, P=0.0004) and total rainfall during the current summer months of November-March (n=30, r2=0.282, P=0.003). These two variables, when entered into the same regression model, together explained 49.7% of the total variation in dlc. We found no evidence of association between case totals and climate. In KZN, where malaria control operations are intense, climate appears to drive the interannual variation of malaria incidence, but not its overall level. The accompanying paper provides evidence that overall levels are associated with non-climatic factors such as drug resistance and possibly HIV prevalence.     

Effect of El Ni?o Southern Oscillations on the incidence of enteric fever in Ahmedabad,India from 1985 to 2017

Objective: To explore the relationship between climate variables and enteric fever in the city of Ahmedabad and report preliminary findings regarding the influence of El Ni?o Southern Oscillations and Indian Ocean Dipole over enteric fever incidence.Method: A total of 29 808 Widal positive enteric fever cases reported by the Ahmedabad Municipal Corporation and local climate data in 1985-2017 from Ahmedabad Meteorology Department were analysed. El Ni?o, La Ni?a, neutral and Indian Ocean Dipole years as reported by the National Oceanic and Atmospheric Administration for the same period were compared for the incidence of enteric fever. Results: Population-normalized average monthly enteric fever case rates were the highest for El Ni?o years(25.5), lower for La Ni?a years(20.5) and lowest for neutral years(17.6). A repeated measures ANOVA analysis showed no significant difference in case rates during the three yearly El Ni?o Southern Oscillations categories. However, visual profile plot of estimated marginal monthly means showed two distinct characteristics: an early rise and peaking of cases in the El Ni?o and La Ni?a years, and a much more restrained rise without conspicuous peaks in neutral years. Further analysis based on monthly El Ni?o Southern Oscillations categories was conducted to detect differences in median monthly case rates. Median case rates in strong and moderate El Ni?o months and strong La Ni?a months were significantly dissimilar from that during neutral months(P0.001). Conclusions: El Ni?o Southern Oscillations events influence the incidence of enteric fever cases in Ahmedabad, and further investigation from more cities and towns is required.     

Spatial synchrony of malaria outbreaks in a highland region of Ethiopia

To understand the drivers and consequences of malaria in epidemic‐prone regions, it is important to know whether epidemics emerge independently in different areas as a consequence of local contingencies, or whether they are synchronised across larger regions as a result of climatic fluctuations and other broad‐scale drivers. To address this question, we collected historical malaria surveillance data for the Amhara region of Ethiopia and analysed them to assess the consistency of various indicators of malaria risk and determine the dominant spatial and temporal patterns of malaria within the region. We collected data from a total of 49 districts from 1999–2010. Data availability was better for more recent years and more data were available for clinically diagnosed outpatient malaria cases than confirmed malaria cases. Temporal patterns of outpatient malaria case counts were correlated with the proportion of outpatients diagnosed with malaria and confirmed malaria case counts. The proportion of outpatients diagnosed with malaria was spatially clustered, and these cluster locations were generally consistent from year to year. Outpatient malaria cases exhibited spatial synchrony at distances up to 300 km, supporting the hypothesis that regional climatic variability is an important driver of epidemics. Our results suggest that decomposing malaria risk into separate spatial and temporal components may be an effective strategy for modelling and forecasting malaria risk across large areas. They also emphasise both the value and limitations of working with historical surveillance datasets and highlight the importance of enhancing existing surveillance efforts.