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The impact of climate change continues to manifest itself daily in the form of extreme events and conditions such as droughts, floods, heatwaves, and storms. Better forecasting tools are mandatory to calibrate our response to these hazards and help adapt to the planet’s dynamic environment. Here, we present a deep convolutional residual regressive neural network (dcrrnn) platform called Flux to Flow (F2F) for discerning the response of watersheds to water-cycle fluxes and their extremes. We examine four United States drainage basins of varying acreage from smaller to very large (Bear, Colorado, Connecticut, and Mississippi). F2F combines model and ground observations of water-cycle fluxes in the form of surface runoff, subsurface baseflow, and gauged streamflow. We use these time series datasets to simulate, visualize, and analyze the watershed basin response to the varying climates and magnitudes of hydroclimatic fluxes in each river basin. Experiments modulating the time lag between remotely sensed and ground-truth measurements are performed to assess the metrological limits of forecasting with this platform. The resultant mean Nash–Sutcliffe and Kling–Gupta efficiency values are both greater than 90%. Our results show that a hydrological machine learning platform such as F2F can become a powerful resource to simulate and forecast hydroclimatic extremes and the resulting watershed responses and natural hazards in a changing global climate.

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...

Background: Across low-income and middle-income countries (LMICs), one in ten deaths in children younger than 5 years is attributable to diarrhoea. The substantial between-country variation in both diarrhoea incidence and mortality is attributable to interventions that protect children, prevent infection, and treat disease. Identifying subnational regions with the highest burden and mapping associated risk factors can aid in reducing preventable childhood diarrhoea. Methods: We used Bayesian model-based geostatistics and a geolocated dataset comprising 15 072 746 children younger than 5 years from 466 surveys in 94 LMICs, in combination with findings of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, to estimate posterior distributions of diarrhoea prevalence, incidence, and mortality from 2000 to 2017. From these data, we estimated the burden of diarrhoea at varying subnational levels (termed units) by spatially aggregating draws, and we investigated the drivers of subnational patterns by creating aggregated risk factor estimates. Findings: The greatest declines in diarrhoeal mortality were seen in south and southeast Asia and South America, where 54·0% (95% uncertainty interval [UI] 38·1-65·8), 17·4% (7·7-28·4), and 59·5% (34·2-86·9) of units, respectively, recorded decreases in deaths from diarrhoea greater than 10%. Although children in much of Africa remain at high risk of death due to diarrhoea, regions with the most deaths were outside Africa, with the highest mortality units located in Pakistan. Indonesia showed the greatest within-country geographical inequality; some regions had mortality rates nearly four times the average country rate. Reductions in mortality were correlated to improvements in water, sanitation, and hygiene (WASH) or reductions in child growth failure (CGF). Similarly, most high-risk areas had poor WASH, high CGF, or low oral rehydration therapy coverage. Interpretation: By co-analysing geospatial trends in diarrhoeal burden and its key risk factors, we could assess candidate drivers of subnational death reduction. Further, by doing a counterfactual analysis of the remaining disease burden using key risk factors, we identified potential intervention strategies for vulnerable populations. In view of the demands for limited resources in LMICs, accurately quantifying the burden of diarrhoea and its drivers is important for precision public health. This work was primarily supported by a grant from the Bill & Melinda Gates Foundation (OPP1132415). S Aljunid reports additional funding from the International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia and Department of Health Policy and Management, Faculty of Public Health, Kuwait University for the approval and support to participate in this research project outside of the study. A Awasthi is supported by the Department of Science and Technology, Government of India, New Delhi, through the INSPIRE Faculty Program outside of the study. A Badawi reports additional funding from the Public Health Agency of Canada outside of the study. A Barac reports additional funding from the Project of Ministry of Education, Science and Technology of the Republic of Serbia (no III45005) outside of the study. T Bärnighausen reports additional funding by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research outside of the study. F Carvalho and E Fernandez report additional funding from the Portuguese national funds (UID/MULTI/04378/2019 and UID/QUI/50006/2019) outside of the study. V M Costa reports additional funding from Fundação da Ciência e Tecnologia (FCT) for her grant (SFRH/BPD/110001/2015), which was funded by national funds through FCT – Fundação para a Ciência e a Tecnologia, IP, under the Norma Transitória – DL57/2016/CP1334/CT0006 outside of the study. J De Neve reports additional funding from the Alexander von Humboldt Foundation outside of the study. K Deribe reports additional funding from the Wellcome Trust (grant number 201900) as part of his International Intermediate Fellowship outside of the study. D Endalew and M Moradi report additional funding from Wolkite University. M Ausloos, C Herteliu, and A Pana report additional funding from the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI (project number PN-III-P4-ID-PCCF-2016-0084) outside of the study. C Herteliu reports additional funding from the European Fund for Regional Development through Operational Program for Competitiveness (Project ID P_40_382) and the European Fund for Regional Development, through InterReg Romania-Hungary (project code EMS ROHU 217) outside of the study. P Hoogar reports additional funding from the Centre for Holistic Development and Research (CHDR), Kalaghatagi and The Department of Studies in Anthropology, Karnatak University, D S Islam reports additional funding from the National Heart Foundation of Australia and the Institute for Physical Activity and Nutrition, Deakin University outside of the study. A Khatony reports additional funding from the Clinical Research Development Center of Imam Reza Hospital in Kermanshah outside of the study. J Khubchandani reports additional funding from Merck Research Laboratories outside of the study. K Krishan reports additional funding from the UGC Center of Advanced Study (CAS II) awarded to the Department of Anthropology, Panjab University, Chandigarh, India outside of the study. M Kumar reports additional funding from the Fogarty Foundation/NIH through a K43 award (TW010716-01A1) outside of the study. B Lacey reports additional funding from the National Institute for Health Research Oxford Biomedical Research Centre and the British Heart Foundation Centre of Research Excellence (Oxford, UK) outside of the study. A M Samy reports additional funding from the Egyptian Fulbright Mission Program (EFMP) outside of the study. S Seyedmousavi reports additional funding from the Intramural Program of National Institute of Health Clinical Center, Bethesda, MD, USA outside of the study. M Shey reports additional funding from the Wellcome Trust Kenji Shibuya reports additional funding from Japan's Ministry of Health, Labour and Welfare and Japan's Ministry of Education, Culture, Sport, Science and Technology outside of the study. M Sobhiyeh reports additional funding from the Clinical Research Development Center of Imam Reza Hospital, Kermanshah University of Medical Sciences outside of the study. J Soriano reports additional funding from Centro de Investigación en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III (ISCIII), Madrid, Spain outside of the study. N Taveira reports additional funding from the LIFE study (RIA2016MC-1615) of the European and Developing Countries Clinical Trials Partnership (EDCTP) program supported by the European Union outside of the study. B Unnikrishnan reports additional funding from the Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, India outside of the study. T Wijeratne reports additional funding from the Department of Medicine, Faculty of Medicine, University of Rajarata, Saliyapura, Sri Lanka outside of the study. C S Wiysonge reports additional funding from the South African Medical Research Council and the National Research Foundation of South Africa outside of the study.

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.

Changes in human water use, along with temperature and rainfall patterns, are facilitating habitat spread and distribution of Aedes aegypti and Aedes albopictus mosquitoes, the primary vectors for the transmission of Dengue, Chikungunya, and Zika viruses in the Americas. Artificial containers and wet spots provide major sources of mosquito larval habitat in residential areas. Mosquito abatement and control strategies remain the most effective public health interventions for minimizing the impact of these vector‐borne diseases. Understanding how water insecurity is conducive to the establishment and elimination of endemic mosquito populations, particularly in arid or semiarid regions, is a vital component in shaping these intervention strategies., Key Points Water insecurity facilitated vector habitats are on the rise in arid and semiarid regions of Mexico and the AmericasTemperature and rainfall changes are facilitating habitat spread and distribution of the Aedes aegypti and the Aedes albopictus mosquitoesFocus should be on improved water management, vector surveillance, and disease awareness among the most vulnerable and migrant populations

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

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.

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.

Outbreaks of diarrheal diseases, including cholera, are related to floods and droughts in regions where water and sanitation infrastructure are inadequate or insufficient. However, availability of data on water scarcity and abundance in transnational basins, are a prerequisite for developing cholera forecasting systems. With more than a decade of terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment, conditions favorable for predicting cholera occurrence may now be determined. We explored lead-lag relationships between TWS in the Ganges-Brahmaputra-Meghna basin and endemic cholera in Bangladesh. Since bimodal seasonal peaks in cholera in Bangladesh occur during spring and autumn seasons, two separate logistical models between TWS and disease time series (2002-2010) were developed. TWS representing water availability showed an asymmetrical, strong association with cholera prevalence in the spring (τ = -0.53; P < 0.001) and autumn (τ = 0.45; P < 0.001) up to 6 months in advance. One unit (centimeter of water) decrease in water availability in the basin increased odds of above normal cholera by 24% (confidence interval [CI] = 20-31%; P < 0.05) in the spring, while an increase in regional water by 1 unit, through floods, increased odds of above average cholera in the autumn by 29% (CI = 22-33%; P < 0.05).