Antarpreet Jutla

Dual peak cholera transmission in Bengal Delta: A hydroclimatological explanation

[1]xa0Cholera 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.

Warming Oceans, Phytoplankton, and River Discharge: Implications for Cholera Outbreaks

Phytoplankton abundance is inversely related to sea surface temperature (SST). However, a positive relationship 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 abundance. 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 phytoplankton production.

Hydroclimatic influences on seasonal and spatial cholera transmission cycles: Implications for public health intervention in the Bengal Delta

[1]xa0Cholera 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.

Simulation of the hydrological processes on reconstructed watersheds using system dynamics

Abstract Reconstruction of disturbed watersheds is a common practice by the oil sands industry in northern Alberta, Canada. The reconstruction and restoration of the watershed hydrology are required as part of the reclamation mandated by Alberta Environment for mine closure. Assessment of the hydrological performance of the reconstructed watersheds is essential to ensure a sustainable reclamation strategy. A conceptual lumped system dynamics watershed (SDW) model is developed and calibrated in this study. The model, built within an object-based simulation environment, is capable of simulating the various hydrological processes in the reconstructed watersheds with good accuracy. STELLA Software is used as an object-based simulation environment that allows visual computations. The SDW model developed combines both physically-based and empirical formulations to replicate the hydrological system mathematically. The system dynamics approach along with the visual simulation environment help in developing a simulation-for-learning model, not only simulation for prediction. The model is successfully calibrated and validated; the results show that the SDW model is capable of simulating the various hydrological processes (soil moisture, evapotranspiration and runoff) with good accuracy. The SDW model can help in the assessment of the short- and long-term performances of the reconstructed watersheds, thus providing a useful decision-aid tool for the mining industry.

A precipitation dipole in eastern North America

[1]xa0We have identified a dipole in annual precipitation across eastern North America (ENA) east of 100°W between 30°N and 60°N. This dipole appears to create spatially coherent opposing variations in precipitation with a separation of the two regions around 45°N. Annual average precipitation over ENA appears to be stable and unimodal, suggesting that the amount of overall precipitation variability is a small fraction of the mean and is largely determined by similar large scale processes. Analysis of regional average time series at interannual (3–7 year) and decadal (10–16 year) scales indicates that the dipole over the ENA region is most clearly discernible at the decadal scale. Linear regression analysis between global sea surface temperatures (SSTs) and precipitation over the two subregions in ENA suggests that SST variations in several areas of the oceans tend to be associated with opposite precipitation anomalies in the two subregions of ENA.

Predictive Time Series Analysis Linking Bengal Cholera with Terrestrial Water Storage Measured from Gravity Recovery and Climate Experiment Sensors

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).