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Cholera, an acute diarrheal disease spread by lack of hygiene and contaminated water, is a major public health risk in many countries. As cholera is triggered by environmental conditions influenced by climatic variables, establishing a correlation between cholera incidence and climatic variables would provide an opportunity to develop a cholera forecasting model. Considering the auto-regressive nature and the seasonal behavioral patterns of cholera, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was used for time-series analysis during 2000–2013. As both rainfall (r = 0.43) and maximum temperature (r = 0.56) have the strongest influence on the occurrence of cholera incidence, single-variable (SVMs) and multi-variable SARIMA models (MVMs) were developed, compared and tested for evaluating their relationship with cholera incidence. A low relationship was found with relative humidity (r = 0.28), ENSO (r = 0.21) and SOI (r = −0.23). Using SVM for a 1 °C increase in maximum temperature at one-month lead time showed a 7% increase of cholera incidence (p < 0.001). However, MVM (AIC = 15, BIC = 36) showed better performance than SVM (AIC = 21, BIC = 39). An MVM using rainfall and monthly mean daily maximum temperature with a one-month lead time showed a better fit (RMSE = 14.7, MAE = 11) than the MVM with no lead time (RMSE = 16.2, MAE = 13.2) in forecasting. This result will assist in predicting cholera risks and better preparedness for public health management in the future.

Water is the basis of all life on this planet. Yet, approximately one in seven people in the world do not have access to safe water. Water can become unsafe due to contamination by various organic and inorganic compounds due to various natural and anthropogenic processes. Identifying and monitoring water quality changes in space and time remains a challenge, especially when contamination events occur over large geographic areas. This study investigates recent advances in remote sensing that allow us to detect and monitor the unique spectral characteristics of water quality events over large areas. Based on an extensive literature review, we focus on three critical water quality problems as part of this study: algal blooms, acid mine drainage, and suspended solids. We review the advances made in applications of remote sensing in each of these issues, identify the knowledge gaps and limitations of current studies, analyze the existing approaches in the context of global environmental changes, and discuss potential ways to combine multi-sensor methods and different wavelengths to develop improved approaches. Synthesizing the findings of these studies in the context of the three specific tracks will help stakeholders to utilize, share, and embed satellite-derived earth observations for monitoring and tracking the ever-evolving water quality in the earth’s limited freshwater reserves.

As the world's longest running pandemic, cholera poses a substantial public health burden in Bangladesh, where human vulnerability intersects with climatic variability. Barriers to safe water and sanitation place the health of millions of Bangladeshis in jeopardy – especially those who have highly constrained choices in preventing and responding to cholera. In this paper we investigate demand for cholera prevention among residents in the Mirpur and Karail slum areas of urban Dhaka. Using survey data from 2023 households in two slum areas, we analyze responses from a contingent valuation questionnaire that elicited willingness to pay (WTP) for cholera vaccines across household members and under varying disease risk scenarios, finding higher valuation for cholera prevention for children and under scenarios of greater epidemic risk. We estimate the average WTP for a cholera vaccine for a child ranges from TK 134–167 (US$ 1.58–1.96). Consistently, respondents with prior knowledge of the cholera vaccine reported lower WTP valuations, providing suggestive evidence of concerns about vaccine effectiveness and preferences for cholera treatment over prevention. We supplement the contingent valuation analysis with cost of illness estimates from both our household sample as well as from administrative hospital records of over 34,000 cholera patients. We estimate that a household incurs costs of TK 801–922 (US$ 9.43–10.50) per episode of cholera that requires medical treatment. Taken together, these findings indicate higher WTP for cholera treatment compared to prevention, but increased interest in prevention under early warning system scenarios of high disease risk.

Occurrence and growth of Vibrio cholerae, the causative agent of cholera, is linked to modalities of elevated temperatures and heavy precipitation. Previous studies have employed temperature- and s...

The arid and semi-arid regions of the world are facing limited freshwater resources, minimal amounts of rainfall, and increasing population pressure and water demands. These resources, often groundwater, are vulnerable to both natural variability and anthropogenic interventions. Here, we develop and apply an integrated approach using geophysical, geochemical, and remote sensing observations to quantify the recharge rates of arid region aquifers that are witnessing rapid groundwater depletion. Focusing on the Saq aquifer system in the Arabian Peninsula, our study was three-fold: (1) to examine the areal extent of aquifer recharge domains using geologic, climatic, and remote sensing data; (2) to investigate the origin of, and modern contributions to, the aquifer system by examining the isotopic composition of groundwater samples; and (3) to estimate the magnitude of modern recharge to the aquifer utilizing estimates from the Gravity Recovery and Climate Experiment (GRACE) data and a continuous rainfall-runoff model, the Soil and Water Assessment Tool (SWAT) model. Isotopic examination of the groundwater from Saq revealed that deeper aquifers (fossil water) are more depleted compared to shallow reserves (alluvial aquifer; mixed waters). Analysis of GRACE-derived terrestrial and groundwater storage estimates indicates that the aquifer system received an annual recharge rate of 5.21 ± 0.10 km3/yr (11.85 ± 0.22 mm/yr), equal to about half of the withdrawal rate from wells during the investigation period (2002–2016). Analysis of SWAT results indicates that, from 1998 to 2014, the investigated watersheds received an average annual precipitation of 19.20 km3, of which approximately 51% is partitioned as potential recharge. The temporal variations in storage, depletion and recharge are related to the changes in regional groundwater extraction, rainfall, and extent of irrigated areas.

Responding to infrastructural damage in the aftermath of natural disasters at a national, regional, and local level poses a significant challenge. Damage to road networks, clean water supply, and sanitation infrastructures, as well as social amenities like schools and hospitals, exacerbates the circumstances. As safe water sources are destroyed or mixed with contaminated water during a disaster, the risk of a waterborne disease outbreak is elevated in those disaster-affected locations. A country such as Haiti, where a large quantity of the population is deprived of safe water and basic sanitation facilities, would suffer more in post-disaster scenarios. Early warning of waterborne diseases like cholera would be of great help for humanitarian aid, and the management of disease outbreak perspectives. The challenging task in disease forecasting is to identify the suitable variables that would better predict a potential outbreak. In this study, we developed five (5) models including a machine learning approach, to identify and determine the impact of the environmental and social variables that play a significant role in post-disaster cholera outbreaks. We implemented the model setup with cholera outbreak data in Haiti after the landfall of Hurricane Matthew in October 2016. Our results demonstrate that adding high-resolution data in combination with appropriate social and environmental variables is helpful for better cholera forecasting in a post-disaster scenario. In addition, using a machine learning approach in combination with existing statistical or mechanistic models provides important insights into the selection of variables and identification of cholera risk hotspots, which can address the shortcomings of existing approaches.

A forecasting lead time of 5–10 days is desired to increase the flood response and preparedness for large river basins. Large uncertainty in observed and forecasted rainfall appears to be a key bottleneck in providing reliable flood forecasting. Significant efforts continue to be devoted to developing mechanistic hydrological models and statistical and satellite-driven methods to increase the forecasting lead time without exploring the functional utility of these complicated methods. This paper examines the utility of a data-based modeling framework with requisite simplicity that identifies key variables and processes and develops ways to track their evolution and performance. Findings suggest that models with requisite simplicity—relying on flow persistence, aggregated upstream rainfall, and travel time—can provide reliable flood forecasts comparable to relatively more complicated methods for up to 10 days lead time for the Ganges, Brahmaputra, and upper Meghna (GBM) gauging locations inside Bangladesh. Forecasting accuracy improves further by including weather-model-generated forecasted rainfall into the forecasting scheme. The use of water level in the model provides equally good forecasting accuracy for these rivers. The findings of the study also suggest that large-scale rainfall patterns captured by the satellites or weather models and their “predictive ability” of future rainfall are useful in a data-driven model to obtain skillful flood forecasts up to 10 days for the GBM basins. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge-measured rainfall and flow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective.

In the era of global warning, the insight of future climate and their changing extremes is critical for climate-vulnerable regions of the world. In this study, we have conducted a robust assessment of Regional Climate Model (RCM) results in a monsoon-dominated region within the new Coupled Model Intercomparison Project Phase 5 (CMIP5) and the latest Representative Concentration Pathways (RCP) scenarios. We have applied an advanced bias correction approach to five RCM simulations in order to project future climate and associated extremes over Bangladesh, a critically climate-vulnerable country with a complex monsoon system. We have also generated a new gridded product that performed better in capturing observed climatic extremes than existing products. The bias-correction approach provided a notable improvement in capturing the precipitation extremes as well as mean climate. The majority of projected multi-model RCMs indicate an increase of rainfall, where one model shows contrary results during the 2080s (2071–2100) era. The multi-model mean shows that nighttime temperatures will increase much faster than daytime temperatures and the average annual temperatures are projected to be as hot as present-day summer temperatures. The expected increase of precipitation and temperature over the hilly areas are higher compared to other parts of the country. Overall, the projected extremities of future rainfall are more variable than temperature. According to the majority of the models, the number of the heavy rainy days will increase in future years. The severity of summer-day temperatures will be alarming, especially over hilly regions, where winters are relatively warm. The projected rise of both precipitation and temperature extremes over the intense rainfall-prone northeastern region of the country creates a possibility of devastating flash floods with harmful impacts on agriculture. Moreover, the effect of bias-correction, as presented in probable changes of both bias-corrected and uncorrected extremes, can be considered in future policy making.

The association of cholera and climate has been extensively documented. However, determining the effects of changing climate on the occurrence of disease remains a challenge. Bimodal peaks of cholera in Bengal Delta are hypothesized to be linked to asymmetric flow of the Ganges and Brahmaputra rivers. Spring cholera is related to intrusion of bacteria-laden coastal seawater during low flow seasons, while autumn cholera results from cross-contamination of water resources when high flows in the rivers cause massive inundation. Coarse resolution of General Circulation Model (GCM) output (usually at 100 – 300 km)cannot be used to evaluate variability at the local scale(10–20 km),hence the goal of this study was to develop a framework that could be used to understand impacts of climate change on occurrence of cholera. Instead of a traditional approach of downscaling precipitation, streamflow of the two rivers was directly linked to GCM outputs, achieving reasonable accuracy (R2 = 0.89 for the Ganges and R2 = 0.91 for the Brahmaputra)using machine learning algorithms (Support Vector Regression-Particle Swarm Optimization). Copula methods were used to determine probabilistic risks of cholera under several discharge conditions. Key results, using model outputs from ECHAM5, GFDL, andHadCM3for A1B and A2 scenarios, suggest that the combined low flow of the two rivers may increase in the future, with high flows increasing for first half of this century, decreasing thereafter. Spring and autumn cholera, assuming societal conditions remain constant e.g., at the current rate, may decrease. However significant shifts were noted in the magnitude of river discharge suggesting that cholera dynamics of the delta may well demonstrate an uncertain predictable pattern of occurrence over the next century.

Diarrheal diseases, notably cholera, have been shown to be related to episodic seasonal variability in river discharge, predominantly low flows, in regions where water and sanitation infras...

Capacity building using Earth observing (EO) systems and data (i.e., from orbital and nonorbital platforms) to enable societal applications includes the network of human, nonhuman, technical, nontechnical, hardware, and software dimensions that are necessary to successfully cross the valley [of death; see NRC (2001)] between science and research (port of departure) and societal application (port of arrival). In many parts of the world (especially where ground-based measurements are scarce or insufficient), applications of EO data still struggle for longevity or continuity for a variety of reasons, foremost among them being the lack of resilient capacity. An organization is said to have resilient capacity when it can retain and continue to build capacity in the face of unexpected shocks or stresses. Stresses can include intermittent power and limited Internet bandwidth, constant need for education on ever-increasing complexity of EO systems and data, communication challenges between the ports of departure and arrival (especially across time zones), and financial limitations and instability. Shocks may also include extreme events such as disasters and losing key staff with technical and institutional knowledge.

Dengue Fever (DF) is a mosquito vector transmitted flavivirus and a reemerging global public health threat. Although several studies have addressed the relation between climatic and environmental factors and the epidemiology of DF, or looked at purely spatial or time series analysis, this article presents a joint spatio-temporal epidemiological analysis. Our approach accounts for both temporal and spatial autocorrelation in DF incidence and the effect of temperatures and precipitation by using a hierarchical Bayesian approach. We fitted several space-time areal models to predict relative risk at the municipality level and for each month from 1990 to 2014. Model selection was performed according to several criteria: the preferred models detected significant effects for temperature at time lags of up to four months and for precipitation up to three months. A boundary detection analysis is incorporated in the modeling approach, and it was successful in detecting municipalities with historically anomalous risk.

In August 2017, South Asia was affected by devastating floods that caused widespread death and destruction in Nepal, India, and Bangladesh. The Brahmaputra River experienced a record flow in Bangladesh. However, the prediction of a 200-year flood from an international forecasting agency did not materialize. In fact, the evolution and dynamics of 2017 flood was very different from previous floods in the region—localized in nature and short-lived in duration. We examine the hydrometeorology of the Ganges, Brahmaputra, and Meghna River basins to show how the 2017 August flood emerged differently from past major floods in the region. Assessment of available rainfall datasets reveals the usefulness of satellite sources, such as TMPA, GPM, ERA-Interim, CPC Global Precipitation, and WRF to capture the extreme events and highlights the poor spatial coverage of in-situ measurements. We then present a data-driven Requisitely Simple (ReqSim) flood forecast model. Findings from the real-time application of ReqSim during the 2017 flood provide encouraging results with accurate predictions of peak flood timing and magnitude for several locations. For example, the ReqSim provides 10-day forecasts with Nash Sutcliffe efficiency (NSE) values 0.89–0.97 and Mean Absolute Errors (MAE) 0.33–0.54 m for the Ganges River system inside Bangladesh. For the Brahmaputra River system, NSE value ranges 0.49–0.68 and MAE 0.32–0.48 m for 10-day forecasts and NSE 0.62–0.78 and MAE 0.36–0.68 m for 7-day forecasts. Comparison of 2017 flood with past major regional floods suggests that—it is not the intensity of rainfall or severity of flood in either the Ganges or the Brahmaputra basin—but the lack of peak flow synchronization of these two rivers may help explain why 2017 flood did not become as severe as the floods of 1998 and 2007 in Bangladesh. Ease of operationalization and reliable forecasting accuracy of ReqSim for 2017 flood is of particular relevance for large rivers, where access to upstream gauge-measured rainfall and flow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective. Plain language summary Vast areas of South Asia—across the border regions of Nepal, India, and Bangladesh—were flooded by intense rainfall and overflowing rivers in August 2017. This record flood event killed over a thousand people and affected over 40 million people. However, forecasts of a 200-year flood event, issued by an international agency and covered widely in local media outlets, did not materialize. It was found that the evolution and dynamics of 2017 flood was very different from previous major floods in the region—localized in nature and short-lived in duration. We examine the rainfall and flood generating processes before and during the floods and the performance of a simple flood forecast model in detail to identify the mechanisms that led to these apparent false forecasts including the nature and timing of the peak rainfall events and subsequent floods.

Rotavirus is the most common cause of diarrheal disease among children under 5. Especially in South Asia, rotavirus remains the leading cause of mortality in children due to diarrhea. As climatic extremes and safe water availability significantly influence diarrheal disease impacts in human populations, hydroclimatic information can be a potential tool for disease preparedness. In this study, we conducted a multivariate temporal and spatial assessment of 34 climate indices calculated from ground and satellite Earth observations to examine the role of temperature and rainfall extremes on the seasonality of rotavirus transmission in Bangladesh. We extracted rainfall data from the Global Precipitation Measurement and temperature data from the Moderate Resolution Imaging Spectroradiometer sensors to validate the analyses and explore the potential of a satellite‐based seasonal forecasting model. Our analyses found that the number of rainy days and nighttime temperature range from 16°C to 21°C are particularly influential on the winter transmission cycle of rotavirus. The lower number of wet days with suitable cold temperatures for an extended time accelerates the onset and intensity of the outbreaks. Temporal analysis over Dhaka also suggested that water logging during monsoon precipitation influences rotavirus outbreaks during a summer transmission cycle. The proposed model shows lag components, which allowed us to forecast the disease outbreaks 1 to 2 months in advance. The satellite data‐driven forecasts also effectively captured the increased vulnerability of dry‐cold regions of the country, compared to the wet‐warm regions., Key Points Rotavirus shows strong mortality and morbidity, as well as strong spatial and temporal variability in South AsiaStrong winter and weak monsoon transmission cycles dominate South Asia, modulated by regional climatic extremesSatellite‐derived hydroclimatic information has potential to help forecasting of rotavirus risk over Bengal Delta

Letter to the Editor Environmental Factors Influencing Epidemic Cholera Dear Sir: We have concerns with the recent publication by Jutla and colleagues, which aims to describe the environmental factors influencing epidemic cholera. 1 Regarding cholera in Haiti, the authors challenged the findings of many studies showing that the epidemic likely originated from the importation of toxi-genic Vibrio cholerae by Nepalese peacekeepers in October 2010. 2–5 Instead, they attempted to show that environmental conditions conducive to rapid growth and transmission of V. cholerae played a substantial role in epidemic onset. Their hypothesis is based on the claim that increased temperatures and rainfall during the months preceding the epidemic favored the proliferation of V. cholerae in the Haitian waters and its subsequent transmission to the local population 1 ; we believe their claims are based on misinterpretations of our published data and statistical correlations that fail to establish causality. Our field investigation of the Haitian cholera epidemic has clearly indicated that outbreaks started in Meye, near Mirebalais, before subsequently spreading downstream, following the Artibonite River. 2 Using a Spearman's rank statistical test, Jutla and colleagues stated that the correlation between cholera cases in Mirebalais and the Lower Artibonite was " very high, " thereby inferring that the epidemic started simultaneously in the two locations. 1 According to our report, each of the Lower Artibonite communes displayed a markedly higher correlation with the other Lower Artibonite communes than with Mirebalais. 2 Therefore, we did not report a strong correlation between Mirebalais and the Lower Artibonite communes , as claimed by Jutla and colleagues. 1 Nevertheless, correlation analyses are irrelevant to question the chronological progression of the epidemic. Indeed, our field investigation revealed that no suspected cases of cholera or severe diarrhea were reported in the Lower Artibonite before October 19, although the epidemic began on October 14 near Mirebalais. 2

Cholera remains one of the most prevalent water-related infections in many tropical regions of the world. Macro-environmental processes provide a natural ecological niche for Vibrio cholerae and because powerful evidence of new biotypes is emerging, it is unlikely that the bacteria will be fully eradicated. Consequently, to develop effective intervention and mitigation strategies, it is necessary to develop cholera prediction models with several months' lead time. Almost all cholera outbreaks originate near the coastal areas and cholera bacteria exhibit a strong relationship with coastal plankton. Using chlorophyll as a surrogate for plankton bloom in coastal areas, recent studies have postulated a relationship between chlorophyll and cholera incidence. Here, we show that seasonal cholera outbreaks in the Bengal Delta can be predicted two to three months in advance with an overall prediction accuracy of over 75% by using satellite-derived chlorophyll and air temperature data. Such high prediction accuracy is achievable because the two seasonal peaks of cholera are predicted using two separate models representing distinctive macro-scale environmental processes. We have shown that interannual variability of pre-monsoon cholera outbreaks can be satisfactorily explained with coastal plankton blooms and a cascade of hydro-coastal processes. Post-monsoon cholera outbreaks, on the other hand, are related to macro-scale monsoon processes and subsequent breakdown of sanitary conditions. Our results demonstrate that satellite data over a range of space and time scales are effective in developing a cholera prediction model for the Bengal Delta with several months' lead time. We anticipate our modeling framework and findings will provide the impetus to explore the utility of satellite derived macro-scale variables for cholera prediction in other cholera endemic regions. Two seasonal peaks of Bengal cholera can be predicted using two separate models.Two to three months prediction lead time achievable.Prediction accuracy exceeds 75% using combinations of chlorophyll and air temperature data.Cascade of hydro-coastal processes may result in cholera outbreaks.

The ability to predict an occurrence of cholera, a water-related disease, offers a significant public health advantage. Satellite based estimates of chlorophyll, a surrogate for plankton abundance, have been linked to cholera incidence. However, cholera bacteria can survive under a variety of coastal ecological conditions, thus constraining the predictive ability of the chlorophyll, since it provides only an estimate of greenness of seawater. Here, a new remote sensing based index is proposed: Satellite Water Marker (SWM), which estimates condition of coastal water, based on observed variability in the difference between blue (412 nm) and green (555 nm) wavelengths that can be related to seasonal cholera incidence. The index is bounded between physically separable wavelengths for relatively clear (blue) and turbid (green) water. Using SWM, prediction of cholera with reasonable accuracy, with at least two month in advance, can potentially be achieved in the endemic coastal regions.

The Eastern Mediterranean Region (EMR) is witnessing an increase in chronic disorders, including mental illness. With ongoing unrest, this is expected to rise. This is the first study to quantify the burden of mental disorders in the EMR. We used data from the Global Burden of Disease study (GBD) 2013. DALYs (disability-adjusted life years) allow assessment of both premature mortality (years of life lost-YLLs) and nonfatal outcomes (years lived with disability-YLDs). DALYs are computed by adding YLLs and YLDs for each age-sex-country group. In 2013, mental disorders contributed to 5.6% of the total disease burden in the EMR (1894 DALYS/100,000 population): 2519 DALYS/100,000 (2590/100,000 males, 2426/100,000 females) in high-income countries, 1884 DALYS/100,000 (1618/100,000 males, 2157/100,000 females) in middle-income countries, 1607 DALYS/100,000 (1500/100,000 males, 1717/100,000 females) in low-income countries. Females had a greater proportion of burden due to mental disorders than did males of equivalent ages, except for those under 15 years of age. The highest proportion of DALYs occurred in the 25-49 age group, with a peak in the 35-39 years age group (5344 DALYs/100,000). The burden of mental disorders in EMR increased from 1726 DALYs/100,000 in 1990 to 1912 DALYs/100,000 in 2013 (10.8% increase). Within the mental disorders group in EMR, depressive disorders accounted for most DALYs, followed by anxiety disorders. Among EMR countries, Palestine had the largest burden of mental disorders. Nearly all EMR countries had a higher mental disorder burden compared to the global level. Our findings call for EMR ministries of health to increase provision of mental health services and to address the stigma of mental illness. Moreover, our results showing the accelerating burden of mental health are alarming as the region is seeing an increased level of instability. Indeed, mental health problems, if not properly addressed, will lead to an increased burden of diseases in the region.

Few regions in the world are nearing a large-scale water crisis as rapidly as South Asia, especially the basins in the Eastern Himalayas. Despite being known to be a water-rich part of the world, lack of integrated planning, and unfavourable natural and political settings have pushed this populous region towards basin-wide water scarcity, flood hazards, water quality and ecosystem degradation, and public health problems. Rapid growth of population and an unforgiving future climate complicate the situation and put millions more at risk. Meanwhile, the riparian nations continue unilateral efforts to develop their water resources without addressing the ecological integrity of the complex transboundary river system. This article provides an overview of the asymmetry in hydroclimatic conditions and unhelpful political realities that have pushed the region towards a looming large-scale water conflict.

Cholera bacteria exhibit strong association with coastal plankton. Characterization of space–time variability of chlorophyll, a surrogate for plankton abundance, in the northern Bay of Bengal is an essential first step to develop any methodology for predicting cholera outbreaks in the Bengal Delta region using remote sensing. This study quantifies the space–time distribution of chlorophyll, using the data from SeaWiFS, in the Bay of Bengal region using 10 years of satellite data. Variability of chlorophyll at daily scale, irrespective of spatial averaging, resembles white noise. At a monthly scale, chlorophyll shows distinct seasonality and chlorophyll values are significantly higher close to the coast than in the offshore regions. At pixel level (9 km) on monthly scale, on the other hand, chlorophyll does not exhibit much persistence in time. With increased spatial averaging, temporal persistence of chlorophyll increases and lag 1 autocorrelation stabilizes around 0.60 for 1296 km 2 or larger areal averages. In contrast to the offshore regions, spatial analyses of chlorophyll suggest that only coastal region has a stable correlation length of 100 km. Presence (absence) of correlation length in the coastal (offshore) regions indicate that the two regions may have two separate processes controlling the production of phytoplankton. This study puts a lower limit on space–time averaging of satellite measured plankton at 1296 km 2 monthly scale to establish relationships with cholera incidence in Bengal Delta.

20 and the Delaware, 21 Po, 22 Abstract. Phytoplankton abundance is inversely related to sea surface temperature (SST). However, a positive rela- tionship is observed between SST and phytoplankton abundance in coastal waters of Bay of Bengal. This has led to an assertion that in a warming climate, rise in SST may increase phytoplankton blooms and, therefore, cholera outbreaks. Here, we explain why a positive SST-phytoplankton relationship exists in the Bay of Bengal and the implications of such a relationship on cholera dynamics. We found clear evidence of two independent physical drivers for phytoplankton abun- dance. The first one is the widely accepted phytoplankton blooming produced by the upwelling of cold, nutrient-rich deep ocean waters. The second, which explains the Bay of Bengal findings, is coastal phytoplankton blooming during high river discharges with terrestrial nutrients. Causal mechanisms should be understood when associating SST with phytoplankton and subsequent cholera outbreaks in regions where freshwater discharge are a predominant mechanism for phytoplank- ton production.

Reiner RC Jr, Hay SI. Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality in low-income and middle-income countries, 2000–17: analysis for the Global Burden of Disease Study 2017. Lancet 2020; 395: 1779–801—In this Article, the author byline has been amended to Local Burden of Disease Diarrhoea Collaborators. This correction has been made to the online version as of June 4, 2020, and the printed version is correct. © 2020 Elsevier Ltd

To increase preparedness and improve response to floods in Bangladesh, a streamflow forecasting lead time of 7–10 days is desired at the international border entry points of the major rivers. The accuracy of available streamflow forecast products is currently limited to 3–5 days, which is much shorter than the desired lead time for floods. Several initiatives continue to develop and refine hydrologic and flood forecasting models without adequately exploring the utility of precipitation forecasts to obtain longer flood forecasting lead time. For large basins with a strong seasonality, such as the Ganges, simple but carefully designed linear regression models may provide great value to operational flood forecasting. Here, a simple linear model with observed streamflow and gridded precipitation data for the upstream basin areas is introduced to provide satisfactory forecasts for the Ganges flow at the Hardinge Bridge point with up to 10-day lead time. The proposed linear model can provide 3- to 5-day forecasting lead time using only lagged values of flow with skill comparable to published results of detailed hydrological or statistical models. The results also suggest that 5–6 days forecasted rainfall and observed streamflow are able to enhance streamflow forecasting accuracy for a lead time up to 10 days. The proposed framework can be of great value to operationalize flood forecasting in large basins, where upstream data are scarce or inaccessible due to political boundaries, and detailed forecasting model setups are prohibitively expensive.

IntroductionCholera, an infectious diarrheal disease, has been shown to be associated with large scale hydroclimatic processes. The sudden and sporadic occurrence of epidemic cholera is linked with high mortality rates, in part, due to uncertainty in timing and location of outbreaks. Improved understanding of the relationship between pathogenic abundance and climatic processes allows prediction of disease outbreak to be an achievable goal. In this study, we show association of large scale hydroclimatic processes with the cholera epidemic in Zimbabwe reported to have begun in Chitungwiza, a city in Mashonaland East province, in August, 2008.Principal findingsClimatic factors in the region were found to be associated with triggering cholera outbreak and are shown to be related to anomalies of temperature and precipitation, validating the hypothesis that poor conditions of sanitation, coupled with elevated temperatures, and followed by heavy rainfall can initiate outbreaks of cholera. Spatial estimation by satellite of precipitation and global gridded air temperature captured sensitivities in hydroclimatic conditions that permitted identification of the location in the region where the disease outbreak began.DiscussionSatellite derived hydroclimatic processes can be used to capture environmental conditions related to epidemic cholera, as occurred in Zimbabwe, thereby providing an early warning system. Since cholera cannot be eradicated because the causative agent, Vibrio cholerae, is autochthonous to the aquatic environment, prediction of conditions favorable for its growth and estimation of risks of triggering the disease in a given population can be used to alert responders, potentially decreasing infection and saving lives.

The highly populated floodplains of the Bengal Delta have a long history of endemic and epidemic cholera outbreaks, both coastal and inland. Previous studies have not addressed the spatio-temporal dynamics of population vulnerability related to the influence of underlying large-scale processes. We analyzed spatial and temporal variability of cholera incidence across six surveillance sites in the Bengal Delta and their association with regional hydroclimatic and environmental drivers. More specifically, we use salinity and flood inundation modeling across the vulnerable districts of Bangladesh to test earlier proposed hypotheses on the role of these environmental variables. Our results show strong influence of seasonal and interannual variability in estuarine salinity on spring outbreaks and inland flooding on fall outbreaks. A large segment of the population in the Bengal Delta floodplains remain vulnerable to these biannual cholera transmission mechanisms that provide ecologic and environmental conditions for outbreaks over large geographic regions.

In this chapter, we provide a physical understanding based on the regional large-scale environmental and hydroclimatic processes of how the first outbreaks of cholera may be related to low flow discharge of the Ganges, Brahmaputra, and Meghna rivers and subsequent and salinity intrusion during spring. Cholera incidence values in this season are inversely related to streamflow volumes, that is, bigger spring cholera peaks are seen in strong drought years. On the other hand, autumn cholera outbreaks are positively correlated to peak streamflow volumes, that is, bigger autumn peaks are seen in high flood years. Evidence points to large-scale population vulnerability to biannual cholera transmission mechanisms that provide ecological and environmental “conditions” for cholera outbreaks in the Bengal Delta floodplains.

[1] Cholera remains a major public health threat in many developing countries around the world. The striking seasonality and annual recurrence of this infectious disease in endemic areas remain of considerable interest to scientists and public health workers. Despite major advances in the ecological and microbiological understanding of Vibrio cholerae, the causative agent of the disease, the role of underlying large-scale hydroclimatic processes in propagating the disease for different seasons and spatial locations is not well understood. Here we show that the cholera outbreaks in the Bengal Delta region are propagated from the coastal to the inland areas and from spring to fall by two distinctly different transmission cycles, premonsoon and postmonsoon, influenced by coastal and terrestrial hydroclimatic processes, respectively. A coupled analysis of the regional hydroclimate and cholera incidence reveals a strong association of the space-time variability of incidence peaks with seasonal processes and extreme climatic events. We explain how the asymmetric seasonal hydroclimatology affects regional cholera dynamics by providing a coastal growth environment for bacteria in spring, while propagating the disease to fall by monsoon flooding. Our findings may serve as the basis for “climate-informed” early warnings and for prompting effective means for intervention and preempting epidemic cholera outbreaks in vulnerable regions.

Cholera remains a significant health threat across the globe. The pattern and magnitude of the seven global pandemics suggest that cholera outbreaks primarily originate in coastal regions and then spread inland through secondary means. Cholera bacteria show strong association with plankton abundance in coastal ecosystems. This review study investigates relationship(s) between cholera incidence and coastal processes and explores utility of using remote sensing data to track coastal plankton blooms, using chlorophyll as a surrogate variable for plankton abundance, and subsequent cholera outbreaks. Most studies over the last several decades have primarily focused on the microbiological and epidemiological understanding of cholera outbreaks. Accurate identification and mechanistic understanding of large scale climatic, geophysical and oceanic processes governing cholera-chlorophyll relationship is important for developing cholera prediction models. Development of a holistic understanding of these processes requires long and reliable chlorophyll dataset(s), which are beginning to be available through satellites. We have presented a schematic pathway and a modeling framework that relate cholera with various hydroclimatic and oceanic variables for understanding disease dynamics using latest advances in remote sensing. Satellite data, with its unprecedented spatial and temporal coverage, have potentials to monitor coastal processes and track cholera outbreaks in endemic regions.

[1] Cholera has reemerged as a global killer with the world witnessing an unprecedented rise in cholera infection and transmission since the 1990s. Cholera outbreaks across most affected areas show infection patterns with a single annual peak. However, cholera incidences in the Bengal Delta region, the native homeland of cholera, show bi-annual peaks. The mechanisms behind this unique seasonal dual peak phenomenon in cholera dynamics, especially the role of climatic and hydrologic variables, are not fully understood. Here, we show that low flow in the Brahmaputra and the Ganges during spring is associated with the first outbreaks of cholera in Bangladesh; elevated spring cholera outbreaks are seen in low discharge years. Peak streamflow of these rivers, on the other hand, create a different cholera transmission environment; peak flood volumes and extent of flood-affected areas during monsoon are responsible for autumn cholera outbreaks. Our results demonstrate how regional hydroclimatology may explain the seasonality and dual peaks of cholera incidence in the Bengal Delta region. A quantitative understanding of the relationships among the hydroclimatological drivers and seasonal cholera outbreaks will help early cholera detection and prevention efforts.

Today a handful of developing nations (India, China, Nigeria, Brazil, Bangladesh, Indonesia, and Pakistan) account for more than half of the world's population. A majority of these nations are rapidly urbanizing, as people from remote regions migrate in search of economic prosperity [Cohen, 2003]. Such a rural-to-urban migratory trend has been the leading cause of cities expanding to megacities, which are defined by the United Nations as cities with a population greater than 10 million. By 2025 a greater proportion of the world's megacities, most of them situated in low-lying coastal areas, will be in developing nations in Asia and Africa (Figure 1a) [Asian Development Bank, 2012].

This article was published in the Bulletin of the World Health Organization [ © 2012 Bulletin of the World Health Organization ] and the definite version is available at : http://dx.doi.org/10.2471/BLT.11.092189 The Journal's website is at: http://www.who.int/bulletin/volumes/90/3/11-092189.pdf

Vibrio cholerae is an estuarine bacterium associated with a single peak of cholera (March-May) in coastal villages of Bangladesh.For anunknownreason, however, cholera occurs in a unique dual peak (March-May and September-November) pattern in the city of Dhakathat is bordered by a heavily polluted freshwater river system and flood embankment. In August 2007, extreme flooding was accompanied by an unusually severe diarrhea outbreak in Dhaka that resulted in a record high illness. This study was aimed to understand the unusual outbreak and if it was related to the circulation of a new V. cholerae clone. Nineteen V. cholerae isolated during the peak of the 2007 outbreak were subjected to extensive phenotypic and molecular analyses, including multilocus genetic screening by polymerase chain reaction (PCR), sequence-typing of the ctxB gene, and pulsed-field gel electrophoresis (PFGE). Factors associated with the unusual incidence of cholera were determined and analysis of the disease severity was done. Overall, microbiological and molecular data confirmed that the hypervirulent V. cholerae was O1 biotype El Tor (ET) that possessed cholera toxin (CT) of the classical biotype. The PFGE ( Not I) and dendrogram clustering confirmed that the strains were clonal and related to the pre-2007 variant ET from Dhaka and Matlab and resembled one of two distinct clones of the variant ETconfirmed to be present in the estuarine ecosystem of Bangladesh. Results of the analyses of both diarrheal case data for three consecutive years (2006-2008) and regional hydroclimatology over three decades (1980-2009) clearly indicate that the pattern of cholera occurring in Dhaka, and not seen at other endemic sites, was associated with flood waters transmitting the infectious clone circulating via the fecal-oral route during and between the dual seasonal cholera peaks in Dhaka. Circular river systems and flood embankment likely facilitate transmission of infectious V. cholerae throughout the year that leads to both sudden and off-season outbreaks in the densely populated urban ecosystem of Dhaka. Clonal recycling of hybrid ElTor with increasing virulence in a changing climate and in a region with a growing urban population represents a serious public health concern for Bangladesh. Keywords: Cholera; Vibrio cholerae; dual peak; flood; off-season; fecal-oral; clonal transmission (Published: 8 August 2011) Citation: Infection Ecology and Epidemiology 2011, 1 : 7273 - DOI: 10.3402/iee.v1i0.7273