Indian Ocean Dipole drives malaria resurgence in East African highlands

Impacts of climate change on water-related mosquito-borne diseases in temperate regions: A systematic review of literature and meta-analysis

Mosquito-borne diseases are a known tropical phenomenon. This review was conducted to assess the mecha-nisms through which climate change impacts mosquito-borne diseases in temperate regions. Articles were searched from PubMed, Scopus, Web of Science, and Embase databases. Identification criteria were scope (climate change and mosquito-borne diseases), region (temperate), article type (peer-reviewed), publication language (English), and publication years (since 2015). The WWH (who, what, how) framework was applied to develop the research question and thematic analyses identified the mechanisms through which climate change affects mosquito-borne diseases. While temperature ranges for disease transmission vary per mosquito species, all are viable for temperate regions, particularly given projected temperature increases. Zika, chikungunya, and dengue transmission occurs between 18-34 °C (peak at 26-29 °C). West Nile virus establishment occurs at monthly average temperatures between 14-34.3 °C (peak at 23.7-25 °C). Malaria establishment occurs when the consecutive average daily temperatures are above 16 °C until the sum is above 210 °C. The identified mechanisms through which climate change affects the transmission of mosquito-borne diseases in temperate regions include: changes in the development of vectors and pathogens; changes in mosquito habitats; extended transmission seasons; changes in geographic spread; changes in abundance and behaviors of hosts; reduced abundance of mosquito predators; interruptions to control operations; and influence on other non-climate factors. Process and stochastic approaches as well as dynamic and spatial models exist to predict mosquito population dynamics, disease transmission, and climate favorability. Future projections based on the observed relations between climate factors and mosquito-borne diseases suggest that mosquito-borne disease expansion is likely to occur in temperate regions due to climate change. While West Nile virus is already established in some temperate regions, Zika, dengue, chikungunya, and malaria are also likely to become established over time. Moving forward, more research is required to model future risks by incorporating climate, environmental, sociodemographic, and mosquito-related factors under changing climates.

Nonlinear country-heterogenous impact of the Indian Ocean Dipole on global economies

A positive Indian Ocean Dipole features an anomalously high west-minus-east sea surface temperature gradient along the equatorial Indian Ocean, affecting global extreme weathers. Whether the associated impact spills over to global economies is unknown. Here, we develop a nonlinear and country-heterogenous econometric model, and find that a typical positive event causes a global economic loss that increases for further two years after an initial shock, inducing a global loss of hundreds of billion US dollars, disproportionally greater to the developing and emerging economies. The loss from the 2019 positive event amounted to US$558B, or 0.67% in global economic growth. Benefit from a negative dipole event is far smaller. Under a high-emission scenario, a projected intensification in Dipole amplitude causes a median additional loss of US$5.6 T at a 3% discount rate, but likely as large as US$24.5 T. The additional loss decreases by 64% under the target of the Paris Agreement.

Weakened western Indian Ocean dominance on Antarctic sea ice variability in a changing climate

Patterns of sea surface temperature (SST) anomalies of the Indian Ocean Dipole (IOD) exhibit strong diversity, ranging from being dominated by the western tropical Indian Ocean (WTIO) to the eastern tropical Indian Ocean (ETIO). Whether and how the different types of IOD variability patterns affect the variability of Antarctic sea ice is not known, nor is how the impact may change in a warming climate. Here, we find that the leading mode of austral spring Antarctic sea ice variability is dominated by WTIO SST variability rather than ETIO SST or El Niño-Southern Oscillation. WTIO warm SST anomalies excite a poleward-propagating Rossby wave, inducing a tri-polar anomaly pattern characterized by a decrease in sea ice near the Amundsen Sea but an increase in regions on both sides. Such impact has been weakening in the two decades post-2000, accompanied by weakened WTIO SST variability. Under greenhouse warming, climate models project a decrease in WTIO SST variability, suggesting that the reduced impact on Antarctic sea ice from the IOD will likely to continue, facilitating a fast decline of Antarctic sea ice.

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.

Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning

Malaria is the cause of nearly half a million deaths worldwide each year, posing a great socioeconomic burden. Despite recent progress in understanding the influence of climate on malaria infection rates, climatic sources of predictability remain poorly understood and underexploited. Local weather variability alone provides predictive power at short lead times of 1-2 months, too short to adequately plan intervention measures. Here, we show that tropical climatic variability and associated sea surface temperature over the Pacific and Indian Oceans are valuable for predicting malaria in Limpopo, South Africa, up to three seasons ahead. Climatic precursors of malaria outbreaks are first identified via lag-regression analysis of climate data obtained from reanalysis and observational datasets with respect to the monthly malaria case count data provided from 1998-2020 by the Malaria Institute in Tzaneen, South Africa. Out of 11 sea surface temperature sectors analyzed, two regions, the Indian Ocean and western Pacific Ocean regions, emerge as the most robust precursors. The predictive value of these precursors is demonstrated by training a suite of machine-learning classification models to predict whether malaria case counts are above or below the median historical levels and assessing their skills in providing early warning predictions of malaria incidence with lead times ranging from 1 month to a year. Through the development of this prediction system, we find that past information about SST over the western Pacific Ocean offers impressive prediction skills (~80% accuracy) for up to three seasons (9 months) ahead. SST variability over the tropical Indian Ocean is also found to provide good skills up to two seasons (6 months) ahead. This outcome represents an extension of the effective prediction lead time by about one to two seasons compared to previous prediction systems that were more computationally costly compared to the machine learning techniques used in the current study. It also demonstrates the value of climatic information and the prediction framework developed herein for the early planning of interventions against malaria outbreaks.

Impact of the Strong Downwelling (Upwelling) on Small Pelagic Fish Production during the 2016 (2019) Negative (Positive) Indian Ocean Dipole Events in the Eastern Indian Ocean off Java

Although researchers have investigated the impact of Indian Ocean Dipole (IOD) phases on human lives, only a few have examined such impacts on fisheries. In this study, we analyzed the influence of negative (positive) IOD phases on chlorophyll a (Chl-a) concentrations as an indicator of phytoplankton biomass and small pelagic fish production in the eastern Indian Ocean (EIO) off Java. We also conducted field surveys in the EIO off Palabuhanratu Bay at the peak (October) and the end (December) of the 2019 positive IOD phase. Our findings show that the Chl-a concentration had a strong and robust association with the 2016 (2019) negative (positive) IOD phases. The negative (positive) anomalous Chl-a concentration in the EIO off Java associated with the negative (positive) IOD phase induced strong downwelling (upwelling), leading to the preponderant decrease (increase) in small pelagic fish production in the EIO off Java.

Coupling of Indo-Pacific climate variability over the last millennium

The Indian Ocean Dipole (IOD) affects climate and rainfall across the world, and most severely in nations surrounding the Indian Ocean^1-4. The frequency and intensity of positive IOD events increased during the twentieth century⁵ and may continue to intensify in a warming world⁶. However, confidence in predictions of future IOD change is limited by known biases in IOD models⁷ and the lack of information on natural IOD variability before anthropogenic climate change. Here we use precisely dated and highly resolved coral records from the eastern equatorial Indian Ocean, where the signature of IOD variability is strong and unambiguous, to produce a semi-continuous reconstruction of IOD variability that covers five centuries of the last millennium. Our reconstruction demonstrates that extreme positive IOD events were rare before 1960. However, the most extreme event on record (1997) is not unprecedented, because at least one event that was approximately 27 to 42 per cent larger occurred naturally during the seventeenth century. We further show that a persistent, tight coupling existed between the variability of the IOD and the El Niño/Southern Oscillation during the last millennium. Indo-Pacific coupling was characterized by weak interannual variability before approximately 1590, which probably altered teleconnection patterns, and by anomalously strong variability during the seventeenth century, which was associated with societal upheaval in tropical Asia. A tendency towards clustering of positive IOD events is evident in our reconstruction, which-together with the identification of extreme IOD variability and persistent tropical Indo-Pacific climate coupling-may have implications for improving seasonal and decadal predictions and managing the climate risks of future IOD variability.

Differences of Rainfall-Malaria Associations in Lowland and Highland in Western Kenya

Many studies have reported a relationship between climate factors and malaria. However, results were inconsistent across the areas. We examined associations between climate factors and malaria in two geographically different areas: lowland (lakeside area) and highland in Western Kenya. Associations between climate factors (rainfall, land surface temperature (LST), and lake water level (LWL)) and monthly malaria cases from 2000 to 2013 in six hospitals (two in lowland and four in highland) were analyzed using time-series regression analysis with a distributed lag nonlinear model (DLNM) and multivariate meta-analysis. We found positive rainfall–malaria overall associations in lowland with a peak at 120 mm of monthly rainfall with a relative risk (RR) of 7.32 (95% CI: 2.74, 19.56) (reference 0 mm), whereas similar associations were not found in highland. Positive associations were observed at lags of 2 to 4 months at rainfall around 100–200 mm in both lowland and highland. The RRs at 150 mm rainfall were 1.42 (95% CI: 1.18, 1.71) in lowland and 1.20 (95% CI: 1.07, 1.33) in highland (at a lag of 3 months). LST and LWL did not show significant association with malaria. The results suggest that geographical characteristics can influence climate–malaria relationships.

Oman coral δ18O seawater record suggests that Western Indian Ocean upwelling uncouples from the Indian Ocean Dipole during the global-warming hiatus

The Indian Ocean Dipole (IOD) is an interannual mode of climate variability in the Indian Ocean that has intensified with 20^th century global-warming. However, instrumental data shows a global-warming hiatus between the late-1990s and 2015. It is presently not clear how the global-warming hiatus affects modes of climate variability such as the IOD, and their basin-wide ocean-atmosphere teleconnections. Here, we present a 26-year long, biweekly record of Sr/Ca and δ¹⁸O from a Porites coral drilled in the Gulf of Oman. Sea surface temperature (SST_anom) is calculated from Sr/Ca ratios, and seawater δ¹⁸O (δ¹⁸O_sw-anom) is estimated by subtracting the temperature component from coral δ¹⁸O. Our δ¹⁸O_sw-anom record reveals a significant regime shift in 1999, towards lower mean δ¹⁸O_sw values, reflecting intensified upwelling in the western Indian Ocean. Prior to the 1999 regime shift, our SST_anom and δ¹⁸O_sw-anom show a clear IOD signature, with higher values in the summer of positive-IOD years due to weakened upwelling. The IOD signature in SST_anom and δ¹⁸O_sw-anom disappears with the overall intensification of upwelling after the 1999 regime shift. The inferred increase in upwelling is likely driven by an intensified Walker circulation during the global-warming hiatus. Upwelling in the Western Indian Ocean uncouples from the IOD.

Influence of internal climate variability on Indian Ocean Dipole properties

The Indian Ocean Dipole (IOD) is the dominant mode of interannual variability over the tropical Indian Ocean (IO) and its future changes are projected to impact the climate and weather of Australia, East Africa, and Indonesia. Understanding the response of the IOD to a warmer climate has been largely limited to studies of individual coupled general circulation models or multi-model ensembles. This has provided valuable insight into the IOD’s projected response to increasing greenhouse gases but has limitations in accounting for the role of internal climate variability. Using the Community Earth System Model Large Ensemble (CESM-LE), the IOD is examined in thirty-five present-day and future simulations to determine how internal variability influences properties of the IOD and their response to a warmer climate. Despite small perturbations in the initial conditions as the only difference between ensemble members, significant relationships between the mean state of the IO and the IOD arise, leading to a spread in the projected IOD responses to increasing greenhouse gases. This is driven by the positive Bjerknes feedback, where small differences in mean thermocline depth, which are caused by internal climate variability, generate significant variations in IOD amplitude, skewness, and the climatological zonal sea surface temperature gradient.

Stabilised frequency of extreme positive Indian Ocean Dipole under 1.5 °C warming

Extreme positive Indian Ocean Dipole (pIOD) affects weather, agriculture, ecosystems, and public health worldwide, particularly when exacerbated by an extreme El Niño. The Paris Agreement aims to limit warming below 2 °C and ideally below 1.5 °C in global mean temperature (GMT), but how extreme pIOD will respond to this target is unclear. Here we show that the frequency increases linearly as the warming proceeds, and doubles at 1.5 °C warming from the pre-industrial level (statistically significant above the 90% confidence level), underscored by a strong intermodel agreement with 11 out of 13 models producing an increase. However, in sharp contrast to a continuous increase in extreme El Niño frequency long after GMT stabilisation, the extreme pIOD frequency peaks as the GMT stabilises. The contrasting response corresponds to a 50% reduction in frequency of an extreme El Niño preceded by an extreme pIOD from that projected under a business-as-usual scenario.

It is unclear how extreme positive Indian Ocean Dipole will respond to 1.5 °C of warming. Here the authors show that the frequency of these events increases linearly with warming, doubling at 1.5 °C from the pre-industrial level, but plateaus thereafter.

Changes in Ugandan Climate Rainfall at the Village and Forest Level

In 2013, the US National Oceanographic and Atmospheric Administration (NOAA) refined the historical rainfall estimates over the African Continent and produced the African Rainfall Climate version 2.0 (ARC2) estimator. ARC2 offers a nearly complete record of daily rainfall estimates since 1983 at 0.1° × 0.1° resolution. Despite short-term anomalies, we identify an overall decrease in average rainfall of about 12% during the past 34 years in Uganda. Spatiotemporally, these decreases are greatest in agricultural regions of central and western Uganda, but similar rainfall decreases are also reflected in the gorilla habitat within the Bwindi Forest in Southwest Uganda. The findings carry significant implications for agriculture production, food security, wildlife habitat, and economic impact at the community and societal level.

Development of an empirical model to predict malaria outbreaks based on monthly case reports and climate variables in Hefei, China, 1990-2011

Malaria remains a significant public health concern in developing countries. Drivers of malaria transmission vary across different geographical regions. Climatic variables are major risk factor in seasonal and secular patterns of P. vivax malaria transmission along Anhui province. The study aims to forecast malaria outbreaks using empirical model developed in Hefei, China. Data on the monthly numbers of notified malaria cases and climatic factors were obtained for the period of January 1st 1990 to December 31st 2011 from the Hefei CDC and Anhui Institute of Meteorological Sciences, respectively. Two logistic regression models with time series seasonal decomposition were used to explore the impact of climatic and seasonal factors on malaria outbreaks. Sensitivity and specificity statistics were used for evaluating the predictive power. The results showed that relative humidity (OR = 1.171, 95% CI = 1.090-1.257), sunshine (OR = 1.076, 95% CI = 1.043-1.110) and barometric pressure (OR = 1.051, 95% CI = 1.003-1.100) were significantly associated with malaria outbreaks after adjustment for seasonality in Hefei area. The validation analyses indicated the overall agreement of 70.42% (sensitivity: 70.52%; specificity: 70.30%). The research suggested that the empirical model developed based on disease surveillance and climatic conditions may have applications in malaria control and prevention activities.

Assessment of climate-driven variations in malaria incidence in Swaziland: toward malaria elimination

Background

Swaziland aims to eliminate malaria by 2020. However, imported cases from neighbouring endemic countries continue to sustain local parasite reservoirs and initiate transmission. As certain weather and climatic conditions may trigger or intensify malaria outbreaks, identification of areas prone to these conditions may aid decision-makers in deploying targeted malaria interventions more effectively.

Methods

Malaria case-surveillance data for Swaziland were provided by Swaziland’s National Malaria Control Programme. Climate data were derived from local weather stations and remote sensing images. Climate parameters and malaria cases between 2001 and 2015 were then analysed using seasonal autoregressive integrated moving average models and distributed lag non-linear models (DLNM).

Results

The incidence of malaria in Swaziland increased between 2005 and 2010, especially in the Lubombo and Hhohho regions. A time-series analysis indicated that warmer temperatures and higher precipitation in the Lubombo and Hhohho administrative regions are conducive to malaria transmission. DLNM showed that the risk of malaria increased in Lubombo when the maximum temperature was above 30 °C or monthly precipitation was above 5 in. In Hhohho, the minimum temperature remaining above 15 °C or precipitation being greater than 10 in. might be associated with malaria transmission.

Conclusions

This study provides a preliminary assessment of the impact of short-term climate variations on malaria transmission in Swaziland. The geographic separation of imported and locally acquired malaria, as well as population behaviour, highlight the varying modes of transmission, part of which may be relevant to climate conditions. Thus, the impact of changing climate conditions should be noted as Swaziland moves toward malaria elimination.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-017-1874-0) contains supplementary material, which is available to authorized users.

Seasonally lagged effects of climatic factors on malaria incidence in South Africa

Globally, malaria cases have drastically dropped in recent years. However, a high incidence of malaria remains in some sub-Saharan African countries. South Africa is mostly malaria-free, but northeastern provinces continue to experience seasonal outbreaks. Here we investigate the association between malaria incidence and spatio-temporal climate variations in Limpopo. First, dominant spatial patterns in malaria incidence anomalies were identified using self-organizing maps. Composite analysis found significant associations among incidence anomalies and climate patterns. A high incidence of malaria during the pre-peak season (Sep-Nov) was associated with the climate phenomenon La Niña and cool air temperatures over southern Africa. There was also high precipitation over neighbouring countries two to six months prior to malaria incidence. During the peak season (Dec-Feb), high incidence was associated with positive phase of Indian Ocean Subtropical Dipole. Warm temperatures and high precipitation in neighbouring countries were also observed two months prior to increased malaria incidence. This lagged association between regional climate and malaria incidence suggests that in areas at high risk for malaria, such as Limpopo, management plans should consider not only local climate patterns but those of neighbouring countries as well. These findings highlight the need to strengthen cross-border control of malaria to minimize its spread.

Associations between Meteorological Factors and Aseptic Meningitis in Six Metropolitan Provinces of the Republic of Korea

We assessed the association between climate factors and a number of aseptic meningitis cases in six metropolitan provinces of the Republic of Korea using a weekly number of cases from January 2002 to December 2012. Generalized linear quasi-Poisson models were applied to estimate the effects of climate factors on the weekly number of aseptic meningitis cases. We used generalized additive and generalized additive mixed models to assess dose-response relationships. A 1 °C increase in mean temperature was associated with an 11.4% (95% confidence interval (CI): 9.6%-13.3%) increase in aseptic meningitis with a 0-week lag; a 10 mm rise in rainfall was associated with an 8.0% (95% CI: 7.2%-8.8%) increase in aseptic meningitis with a 7-week lag; and a 1 mJ/m² increase of solar radiation was associated with a 5.8% (95% CI: 3.0%-8.7%) increase in aseptic meningitis with a 10-week lag. Nino3 showed positive effects in all lags, and its one unit increase was associated with an 18.9% (95% CI: 15.3%-22.6%) increase of aseptic meningitis at lag 9. The variability in the relationship between climate factors and aseptic meningitis could be used to initiate preventive measures for climate determinants of aseptic meningitis.

A new dipole index of the salinity anomalies of the tropical Indian Ocean

With the increased interest in studying the sea surface salinity anomaly (SSSA) of the tropical Indian Ocean during the Indian Ocean Dipole (IOD), an index describing the dipole variability of the SSSA has been pursued recently. In this study, we first use a regional ocean model with a high spatial resolution to produce a high-quality salinity simulation during the period from 1982 to 2014, from which the SSSA dipole structure is identified for boreal autumn. On this basis, by further analysing the observed data, we define a dipole index of the SSSA between the central equatorial Indian Ocean (CEIO: 70°E-90°E, 5°S-5°N) and the region off the Sumatra-Java coast (SJC: 100°E-110°E, 13°S-3°S). Compared with previous SSSA dipole indices, this index has advantages in detecting the dipole signals and in characterizing their relationship to the sea surface temperature anomaly (SSTA) dipole variability. Finally, the mechanism of the SSSA dipole is investigated by dynamical diagnosis. It is found that anomalous zonal advection dominates the SSSA in the CEIO region, whereas the SSSA in the SJC region are mainly influenced by the anomalous surface freshwater flux. This SSSA dipole provides a positive feedback to the formation of the IOD events.

Nonlinear processes reinforce extreme Indian Ocean Dipole events

Under global warming, climate models show an almost three-fold increase in extreme positive Indian Ocean Dipole (pIOD) events by 2100. These extreme pIODs are characterised by a westward extension of cold sea surface temperature anomalies (SSTAs) which push the downstream atmospheric convergence further west. This induces severe drought and flooding in the surrounding countries, but the processes involved in this projected increase have not been fully examined. Here we conduct a detailed heat budget analysis of 19 models from phase 5 of the Coupled Model Intercomparison Project and show that nonlinear zonal and vertical heat advection are important for reinforcing extreme pIODs. Under greenhouse warming, these nonlinear processes do not change significantly in amplitude, but the frequency of occurrences surpassing a threshold increases. This is due to the projected weakening of the Walker circulation, which leads to the western tropical Indian Ocean warming faster than the east. As such, the magnitude of SSTAs required to shift convection westward is relatively smaller, allowing these convection shifts to occur more frequently in the future. The associated changes in wind and ocean current anomalies support the zonal and vertical advection terms in a positive feedback process and consequently, moderate pIODs become more extreme-like.

The role of the SST-thermocline relationship in Indian Ocean Dipole skewness and its response to global warming

A positive Indian Ocean Dipole (IOD) tends to have stronger cold sea surface temperature anomalies (SSTAs) over the eastern Indian Ocean with greater impacts than warm SSTAs that occur during its negative phase. Two feedbacks have been suggested as the cause of positive IOD skewness, a positive Bjerknes feedback and a negative SST-cloud-radiation (SCR) feedback, but their relative importance is debated. Using inter-model statistics, we show that the most important process for IOD skewness is an asymmetry in the thermocline feedback, whereby SSTAs respond to thermocline depth anomalies more strongly during the positive phase than negative phase. This asymmetric thermocline feedback drives IOD skewness despite positive IODs receiving greater damping from the SCR feedback. In response to global warming, although the thermocline feedback strengthens, its asymmetry between positive and negative IODs weakens. This behaviour change explains the reduction in IOD skewness that many models display under global warming.

Increased frequency of extreme Indian Ocean Dipole events due to greenhouse warming

The Indian Ocean dipole is a prominent mode of coupled ocean-atmosphere variability, affecting the lives of millions of people in Indian Ocean rim countries. In its positive phase, sea surface temperatures are lower than normal off the Sumatra-Java coast, but higher in the western tropical Indian Ocean. During the extreme positive-IOD (pIOD) events of 1961, 1994 and 1997, the eastern cooling strengthened and extended westward along the equatorial Indian Ocean through strong reversal of both the mean westerly winds and the associated eastward-flowing upper ocean currents. This created anomalously dry conditions from the eastern to the central Indian Ocean along the Equator and atmospheric convergence farther west, leading to catastrophic floods in eastern tropical African countries but devastating droughts in eastern Indian Ocean rim countries. Despite these serious consequences, the response of pIOD events to greenhouse warming is unknown. Here, using an ensemble of climate models forced by a scenario of high greenhouse gas emissions (Representative Concentration Pathway 8.5), we project that the frequency of extreme pIOD events will increase by almost a factor of three, from one event every 17.3 years over the twentieth century to one event every 6.3 years over the twenty-first century. We find that a mean state change--with weakening of both equatorial westerly winds and eastward oceanic currents in association with a faster warming in the western than the eastern equatorial Indian Ocean--facilitates more frequent occurrences of wind and oceanic current reversal. This leads to more frequent extreme pIOD events, suggesting an increasing frequency of extreme climate and weather events in regions affected by the pIOD.

Effect of non-stationary climate on infectious gastroenteritis transmission in Japan

Local weather factors are widely considered to influence the transmission of infectious gastroenteritis. Few studies, however, have examined the non-stationary relationships between global climatic factors and transmission of infectious gastroenteritis. We analyzed monthly data for cases of infectious gastroenteritis in Fukuoka, Japan from 2000 to 2012 using cross-wavelet coherency analysis to assess the pattern of associations between indices for the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO). Infectious gastroenteritis cases were non-stationary and significantly associated with the IOD and ENSO (Multivariate ENSO Index [MEI], Niño 1 + 2, Niño 3, Niño 4, and Niño 3.4) for a period of approximately 1 to 2 years. This association was non-stationary and appeared to have a major influence on the synchrony of infectious gastroenteritis transmission. Our results suggest that non-stationary patterns of association between global climate factors and incidence of infectious gastroenteritis should be considered when developing early warning systems for epidemics of infectious gastroenteritis.

Meridional variability of atmospheric convection associated with the Indian Ocean Dipole Mode

The Indian Ocean Dipole Mode (IODM) impacts many surrounding and remote regions of the Indian Ocean, with devastating floods over East Africa but severe droughts in countries surrounding Indonesia during a positive IODM event. Understanding the dynamics is important for seasonal prediction and climate projections, but the role of meridional temperature and circulation anomalies remains unclear. Here, we show that in combination with the zonal structure of temperature and rainfall anomalies, northward contraction of the warm water pool over the eastern equatorial Indian Ocean region (EEIO) also generates an anomalous meridional cross-equatorial temperature gradient in the east. This meridional temperature gradient controls northward retreat of the atmospheric convection in association with northward cross-equatorial winds, and hence declining rainfall over the EEIO. Our results have important implications for the mean state change under greenhouse warming.

Medical entomology--back to the future?

Some of problems and challenges facing Medical/Veterinary Entomology are presented from my perspective, focusing on the current millennium. Topics include anthropogenic environmental changes created by population growth, administrative problems hindering science's response to these changes, and some of the scientific discoveries potentially providing solutions. As the title implies, many recent research discoveries have yet to be translated into major changes in control approaches for the major vectorborne public health problems, thereby providing an interesting mix of modern surveillance technology used to track problems and direct historical intervention solutions.

Improving the modeling of disease data from the government surveillance system: a case study on malaria in the Brazilian Amazon

The study of the effect of large-scale drivers (e.g., climate) of human diseases typically relies on aggregate disease data collected by the government surveillance network. The usual approach to analyze these data, however, often ignores a) changes in the total number of individuals examined, b) the bias towards symptomatic individuals in routine government surveillance, and; c) the influence that observations can have on disease dynamics. Here, we highlight the consequences of ignoring the problems listed above and develop a novel modeling framework to circumvent them, which is illustrated using simulations and real malaria data. Our simulations reveal that trends in the number of disease cases do not necessarily imply similar trends in infection prevalence or incidence, due to the strong influence of concurrent changes in sampling effort. We also show that ignoring decreases in the pool of infected individuals due to the treatment of part of these individuals can hamper reliable inference on infection incidence. We propose a model that avoids these problems, being a compromise between phenomenological statistical models and mechanistic disease dynamics models; in particular, a cross-validation exercise reveals that it has better out-of-sample predictive performance than both of these alternative models. Our case study in the Brazilian Amazon reveals that infection prevalence was high in 2004–2008 (prevalence of 4% with 95% CI of 3–5%), with outbreaks (prevalence up to 18%) occurring during the dry season of the year. After this period, infection prevalence decreased substantially (0.9% with 95% CI of 0.8–1.1%), which is due to a large reduction in infection incidence (i.e., incidence in 2008–2010 was approximately one fifth of the incidence in 2004–2008).We believe that our approach to modeling government surveillance disease data will be useful to advance current understanding of large-scale drivers of several diseases.

Disease data collected by the government surveillance system are frequently used to understand the influence of large-scale phenomena (e.g., climate) on human health because these data often have a large temporal and/or geographical span. The down side is that a) these data are often biased towards individuals that come to the health facilities (i.e., symptomatic individuals); and b) the number of individuals examined can vary substantially regardless of concurrent changes in prevalence or incidence (e.g., due to shortage of personnel or supplies in health facilities), directly impacting the number of disease cases detected. Current modeling approaches typically ignore these peculiarities of the government data. Furthermore, current approaches do not take into account that observations directly influence disease dynamics since individuals with a positive diagnosis are often subsequently treated for the disease. In this article, we develop a novel model to circumvent these shortcomings and apply it to simulated data, highlighting how inference on infection incidence and prevalence might be misleading when some of the issues mentioned above are ignored. Finally, we illustrate this model using malaria data from the Brazilian Amazon, revealing the strong role of precipitation on infection prevalence seasonality and striking patterns in infection incidence.

Spatio-temporal dynamics of cholera during the first year of the epidemic in Haiti

BACKGROUND

In October 2010, cholera importation in Haiti triggered an epidemic that rapidly proved to be the world's largest epidemic of the seventh cholera pandemic. To establish effective control and elimination policies, strategies rely on the analysis of cholera dynamics. In this report, we describe the spatio-temporal dynamics of cholera and the associated environmental factors.

METHODOLOGY/PRINCIPAL FINDINGS

Cholera-associated morbidity and mortality data were prospectively collected at the commune level according to the World Health Organization standard definition. Attack and mortality rates were estimated and mapped to assess epidemic clusters and trends. The relationships between environmental factors were assessed at the commune level using multivariate analysis. The global attack and mortality rates were 488.9 cases/10,000 inhabitants and 6.24 deaths/10,000 inhabitants, respectively. Attack rates displayed a significantly high level of spatial heterogeneity (varying from 64.7 to 3070.9 per 10,000 inhabitants), thereby suggesting disparate outbreak processes. The epidemic course exhibited two principal outbreaks. The first outbreak (October 16, 2010-January 30, 2011) displayed a centrifugal spread of a damping wave that suddenly emerged from Mirebalais. The second outbreak began at the end of May 2011, concomitant with the onset of the rainy season, and displayed a highly fragmented epidemic pattern. Environmental factors (river and rice fields: p<0.003) played a role in disease dynamics exclusively during the early phases of the epidemic.

CONCLUSION

Our findings demonstrate that the epidemic is still evolving, with a changing transmission pattern as time passes. Such an evolution could have hardly been anticipated, especially in a country struck by cholera for the first time. These results argue for the need for control measures involving intense efforts in rapid and exhaustive case tracking.

A differential effect of Indian ocean dipole and El Niño on cholera dynamics in Bangladesh

Background

A stationary (i.e., constant through time) association between El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and epidemics of cholera in Bangladesh has been widely assumed. However, whether or not elements of the local climate that are relevant for cholera transmission have stationary signatures of the IOD on their dynamics over different time scales is still not clear. Here we report results on the time-varying relationships between the various remote and local environmental drivers and cholera incidence in Bangladesh.

Methodology/Principal Findings

We performed a cross wavelet coherency analysis to examine patterns of association between monthly cholera cases in the hospitals in Dhaka and Matlab (1983–2008) and indices for both IOD and ENSO. Our results showed that the strength of both the IOD and ENSO associations with cholera hospitalizations changed across time scales during the study period. In Dhaka, 4-year long coherent cycles were observed between cholera and the index of IOD in 1988–1997. In Matlab, the effect of ENSO was more dominant while there was no evidence for an IOD effect on cholera hospitalizations.

Conclusions/Significance

Our results call for the consideration of non-stationary, possibly non-linear, patterns of association between cholera hospitalizations and climatic factors in cholera epidemic early warning systems.

Rainfall drives hydrocephalus in East Africa

OBJECT

Hydrocephalus is one of the most common brain disorders in children throughout the world. The majority of infant hydrocephalus cases in East Africa appear to be postinfectious, related to preceding neonatal infections, and are thus preventable if the microbial origins and routes of infection can be characterized. In prior microbiological work, the authors noted evidence of seasonality in postinfectious hydrocephalus (PIH) cases.

METHODS

The geographical address of 696 consecutive children with PIH who were treated over 6 years was fused with satellite rainfall data for the same time period. A comprehensive time series and spatiotemporal analysis of cases and rainfall was performed.

RESULTS

Four infection-onset peaks were found to straddle the twice-yearly rainy season peaks, demonstrating that the infections occurred at intermediate levels of rainfall.

CONCLUSIONS

The findings in this study reveal a previously unknown link between climate and a neurosurgical condition. Satellite-derived rainfall dynamics are an important factor in driving the infections that lead to PIH. Given prior microbial analysis, these findings point to the importance of environmental factors with respect to preventing the newborn infections that lead to PIH.