BACKGROUND: Temperature and precipitation are known to affect Vibrio cholerae outbreaks. Despite this, the impact of drought on outbreaks has been largely understudied. Africa is both drought and cholera prone and more research is needed in Africa to understand cholera dynamics in relation to drought. METHODS: Here, we analyse a range of environmental and socioeconomic covariates and fit generalised linear models to publicly available national data, to test for associations with several indices of drought and make cholera outbreak projections to 2070 under three scenarios of global change, reflecting varying trajectories of CO(2) emissions, socio-economic development, and population growth. RESULTS: The best-fit model implies that drought is a significant risk factor for African cholera outbreaks, alongside positive effects of population, temperature and poverty and a negative effect of freshwater withdrawal. The projections show that following stringent emissions pathways and expanding sustainable development may reduce cholera outbreak occurrence in Africa, although these changes were spatially heterogeneous. CONCLUSIONS: Despite an effect of drought in explaining recent cholera outbreaks, future projections highlighted the potential for sustainable development gains to offset drought-related impacts on cholera risk. Future work should build on this research investigating the impacts of drought on cholera on a finer spatial scale and potential non-linear relationships, especially in high-burden countries which saw little cholera change in the scenario analysis.
In this article we draw on an interdisciplinary study on drinking water quality in Maputo, the capital of Mozambique, to examine the nature, scale, and politics of waterborne diseases. We show how water contamination and related diseases are discursively framed as household risks, thereby concealing the politics of uneven exposure to contaminated water and placing the burden of being healthy on individuals. In contrast, we propose that uneven geographies of waterborne diseases are best understood as the product of Maputo’s urban metabolism, in which attempts at being sanitary and healthy are caught up in relations of power, class, and variegated citizenship. Waterborne diseases are the result of complex and fragmented circulations and intersections of (waste)waters, generated by uneven urban development, heterogeneous infrastructure configurations, and everyday practices to cope with basic service deficits, in conjunction with increasing climatic variability. The latrine-from which ultimately contamination and diseases spread-is an outcome of these processes, rather than the site to be blamed. This article also advances an interdisciplinary framework for analyzing urban metabolism and deepening its explanatory potential. It serves as a demonstration of how interdisciplinary approaches might be taken forward to generate new readings of more-than-human metabolic processes at distinct temporal and spatial scales.
Africa has historically seen several periods of prolonged and extreme droughts across the continent, causing food insecurity, exacerbating social inequity and frequent mortality. A known consequence of droughts and their associated risk factors are infectious disease outbreaks, which are worsened by malnutrition, poor access to water, sanitation and hygiene and population displacement. Cholera is a potential causative agent of such outbreaks. Africa has the highest global cholera burden, several drought-prone regions and high levels of inequity. Despite this, research on cholera and drought in Africa is lacking. Here, we review available research on drought-related cholera outbreaks in Africa and identify a variety of potential mechanisms through which these outbreaks occurred, including poor access to water, marginalization of refugees and nomadic populations, expansion of informal urban settlements and demographic risks. Future climate change may alter precipitation, temperature and drought patterns, resulting in more extremes, although these changes are likely to be spatially heterogeneous. Despite high uncertainty in future drought projections, increases in drought frequency and/or durations have the potential to alter these related outbreaks into the future, potentially increasing cholera burden in the absence of countermeasures (e.g. improved sanitation infrastructure). To enable effective planning for a potentially more drought-prone Africa, inequity must be addressed, research on the health implications of drought should be enhanced, and better drought diplomacy is required to improve drought resilience under climate change.
Cholera is a water-borne infectious disease that affects 1.3 to 4 million people, with 21,000 to 143,000 reported fatalities each year worldwide. Outbreaks are devastating to affected communities and their prospects for development. The key to support preparedness and public health response is the ability to forecast cholera outbreaks with sufficient lead time. How Vibrio cholerae survives in the environment outside a human host is an important route of disease transmission. Thus, identifying the environmental and climate drivers of these pathogens is highly desirable. Here, we elucidate for the first time a mechanistic link between climate variability and cholera (Satellite Water Marker; SWM) index in the Bengal Delta, which allows us to predict cholera outbreaks up to two seasons earlier. High values of the SWM index in fall were associated with above-normal summer monsoon rainfalls over northern India. In turn, these correlated with the La Niña climate pattern that was traced back to the summer monsoon and previous spring seasons. We present a new multi-linear regression model that can explain 50% of the SWM variability over the Bengal Delta based on the relationship with climatic indices of the El Niño Southern Oscillation, Indian Ocean Dipole, and summer monsoon rainfall during the decades 1997-2016. Interestingly, we further found that these relationships were non-stationary over the multi-decadal period 1948-2018. These results bear novel implications for developing outbreak-risk forecasts, demonstrating a crucial need to account for multi-decadal variations in climate interactions and underscoring to better understand how the south Asian summer monsoon responds to climate variability.
BACKGROUND: Pakistan has been experiencing intervals of sporadic cases and localized outbreaks in the last two decades. No proper study has been carried out in order to find out the environmental burden of toxigenic V. cholerae as well as how temporal and environmental factors associated in driving cholera across the country. METHODS: We tested waste water samples from designated national environment surveillance sites in Pakistan with RT-PCR assay. Multistage sampling technique were utilized for samples collection and for effective sample processing Bag-Mediated Filtration system, were employed. Results were analysed by district and month wise to understand the geographic distribution and identify the seasonal pattern of V. cholera detection in Pakistan. RESULTS: Between May 2019, and February 2020, we obtained and screened 160 samples in 12 districts across Pakistan. Out of 16 sentinel environmental surveillance sites, 15 sites showed positive results against cholera toxigenic gene with mostly lower CT value (mean, 34??2) and have significant difference (p < 0.05). The highest number of positive samples were collected from Sindh in month of November, then in June it is circulating in different districts of Pakistan including four Provinces respectively. CONCLUSION: V. cholera detection do not follow a clear seasonal pattern. However, the poor sanitation problems or temperature and rainfall may potentially influence the frequency and duration of cholera across the country. Occurrence of toxigenic V. cholerae in the environment samples showed that cholera is endemic, which is an alarming for a potential future cholera outbreaks in the country.
BACKGROUND: Infections caused by non-cholera Vibrio species have undergone a global expansion over the past few decades reaching new areas of the world that were previously considered adverse for these organisms. The geographical extent of the expansion has not been uniform, and some areas have shown a rapid increase in infections. METHODS: We applied a new generation of models combining climate, population, and socioeconomic projections to map future scenarios of distribution and season suitability for pathogenic Vibrio. We used the Coupled Model Intercomparison Project 6 framework. Three datasets were used: Geophysical Fluid Dynamics Laboratory’s CM4.0 sea surface temperature and sea surface salinity; the coastline length dataset from the World Resources Institute; and Inter-Sectoral Impact Model Intercomparison Project 2b annual global population data. Future projections were used up to the year 2100 and historical simulations from 1850 to 2014. We also project human population at risk under different shared socioeconomic pathways worldwide. FINDINGS: Projections showed that coastal areas suitable for Vibrio could cover 38000 km of new coastal areas by 2100 under the most unfavourable scenario with an expansion rate of season suitability in these regions of around 1 month every 30 years. Population at risk in suitable regions almost doubled from 1980 to 2020 (from 610 million to 1100 million under the scenario of medium challenges to mitigation and adaptation, shared socioeconomic pathway 2-4.5), although the increment will be more moderate in the future and stabilises after 2050 at 1300 million. Finally, we provide the first global estimate for Vibrio infections, with values around half a million of cases worldwide in 2020. INTERPRETATION: Our projections anticipated an expansion of both the temporal and spatial disease burden for Vibrio infections, in particular at high latitudes of the northern hemisphere. However, the largest extent occurred from 1980 to 2020 and a more moderate increase is expected for the future. The most positive outcome is that the projections showed that Vibrio morbidity will remain relatively stable over the coming decades.
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.
This study introduces a new approach for the investigation of infections after an accidental ingestion of contaminated floodwater. The concept of Expected Annual Probability of Infection (EAPI) is introduced and implemented in an infection risk-model approach, by combining a Quantitative Microbial Risk Assessment (QMRA) with the four steps in flood risk assessment. Two groups and exposure paths are considered: adults wading in floodwater and small children swimming/playing in floodwater. The study area is located in Ghana, West Africa. Even though Ghana is one of the most urbanized countries in Africa it has significant problems with water resources management and public health. While cholera is classified as endemic in Accra, the natural and human-made characteristics of the capital makes it prone to flooding. The results of the EAPI approach show that on one hand the concentration of pathogens in floodwater, and thus the risk of infection, decreases with the increase of the flood magnitude. On the other hand, larger floods can spread the pathogens further from the point source, threatening populations previously not identified as at risk by small-scale floods. The concept of EAPI is demonstrated for cholera but it can be extended to other waterborne diseases and also different pathways of exposure, requiring minimal adaptations. For future applications, better estimation of EAPI key components and improvement points are discussed and recommendations given for all the assessment steps.
Climate variables influence the occurrence, growth, and distribution of Vibrio cholerae in the aquatic environment. Together with socio-economic factors, these variables affect the incidence and intensity of cholera outbreaks. The current pandemic of cholera began in the 1960s, and millions of cholera cases are reported each year globally. Hence, cholera remains a significant health challenge, notably where human vulnerability intersects with changes in hydrological and environmental processes. Cholera outbreaks may be epidemic or endemic, the mode of which is governed by trigger and transmission components that control the outbreak and spread of the disease, respectively. Traditional cholera risk assessment models, namely compartmental susceptible-exposed-infected-recovered (SEIR) type models, have been used to determine the predictive spread of cholera through the fecal-oral route in human populations. However, these models often fail to capture modes of infection via indirect routes, such as pathogen movement in the environment and heterogeneities relevant to disease transmission. Conversely, other models that rely solely on variability of selected environmental factors (i.e., examine only triggers) have accomplished real-time outbreak prediction but fail to capture the transmission of cholera within impacted populations. Since the mode of cholera outbreaks can transition from epidemic to endemic, a comprehensive transmission model is needed to achieve timely and reliable prediction with respect to quantitative environmental risk. Here, we discuss progression of the trigger module associated with both epidemic and endemic cholera, in the context of the autochthonous aquatic nature of the causative agent of cholera, V. cholerae, as well as disease prediction.
Cholera is a severe diarrhoeal disease affecting vulnerable communities. A long-term solution to cholera transmission is improved access to and uptake of water, sanitation and hygiene (WASH). Climate change threatens WASH. A systematic review and meta-analysis determined five overarching WASH factors incorporating 17 specific WASH factors associated with cholera transmission, focussing upon community cases. Eight WASH factors showed lower odds and six showed higher odds for cholera transmission. These results were combined with findings in the climate change and WASH literature, to propose a health impact pathway illustrating potential routes through which climate change dynamics (e.g. drought, flooding) impact on WASH and cholera transmission. A causal process diagram visualising links between climate change dynamics, WASH factors, and cholera transmission was developed. Climate change dynamics can potentially affect multiple WASH factors (e.g. drought-induced reductions in handwashing and rainwater use). Multiple climate change dynamics can influence WASH factors (e.g. flooding and sea-level rise affect piped water usage). The influence of climate change dynamics on WASH factors can be negative or positive for cholera transmission (e.g. drought could increase pathogen desiccation but reduce rainwater harvesting). Identifying risk pathways helps policymakers focus on cholera risk mitigation, now and in the future.
Water ecosystems can be rather sensitive to evolving or sudden changes in weather parameters. These changes can result in alterations in the natural habitat of pathogens, vectors, and human hosts, as well as in the transmission dynamics and geographic distribution of infectious agents. However, the interaction between climate change and infectious disease is rather complicated and not deeply understood. In this narrative review, we discuss climate-driven changes in the epidemiology of Vibrio species-associated diseases with an emphasis on cholera. Changes in environmental parameters do shape the epidemiology of Vibrio cholerae. Outbreaks of cholera cause significant disease burden, especially in developing countries. Improved sanitation systems, access to clean water, educational strategies, and vaccination campaigns can help control vibriosis. In addition, real-time assessment of climatic parameters with remote-sensing technologies in combination with robust surveillance systems could help detect environmental changes in high-risk areas and result in early public health interventions that can mitigate potential outbreaks.
Although the number of cholera infection decreased universally, climate change can potentially affect both incidence and prevalence rates of disease in endemic regions. There is considerable consistent evidence, explaining the associations between cholera and climatic variables. However, it is essentially required to compare and interpret these relationships globally. The aim of the present study was to carry out a systematic review in order to identify and appraise the literature concerning the relationship between nonanthropogenic climatic variabilities such as extreme weather- and ocean-related variables and cholera infection rates. The systematic literature review of studies was conducted by using determined search terms via four major electronic databases (PubMed, Web of Science, Embase, and Scopus) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. This search focused on published articles in English-language up to December 31, 2018. A total of 43 full-text studies that met our criteria have been identified and included in our analysis. The reviewed studies demonstrated that cholera incidence is highly attributed to climatic variables, especially rainfall, temperature, sea surface temperature (SST) and El Niño Southern Oscillation (ENSO). The association between cholera incidence and climatic variables has been investigated by a variety of data analysis methodologies, most commonly time series analysis, generalized linear model (GLM), regression analysis, and spatial/GIS. The results of this study assist the policy-makers who provide the efforts for planning and prevention actions in the face of changing global climatic variables.
Introduction: Altered weather patterns and changes in precipitation, temperature and humidity resulting from climate change could affect the distribution and incidence of cholera. This study is to quantify climate-induced increase in morbidity rates of cholera. Material and Methods: Monthly cholera cases and monthly temperature, precipitation, and relative humidity data from 2004 to 2014 were obtained from the Malaysian Ministry of Health and Malaysian Meteorological Department, respectively. Poisson generalized linear models were developed to quantify the relationship between meteorological parameters and the number of reported cholera cases. Results: The findings revealed that the total number of cholera cases in Malaysia during the 11 year study period was 3841 cases with 32 deaths. Out of these, 45.1% of the cases were among children below 12 years old and 75% of the cases were from Sabah. Temperature and precipitation gave significant impact on the cholera cases in Sabah, (p<0.001) while precipitation were significant in Terengganu (p<0.001), and Sarawak (p=0.013). Monthly lag temperature data at Lag 0, 1, and 2 months were associated with the cholera cases in Sabah (p<0.001). The change in odds of having cholera cases were by the factor of 3.5 for every 1 degrees C increase in temperature. However, the contribution of rainfall was very mild, whereby an increase of 1 mm in precipitation will increase the excess risk of cholera by up to 0.8%. Conclusion: This study concludes that climate does influence the number of cholera cases in Malaysia.
BACKGROUND: Between 2014 and 2017, successive cholera epidemics occurred in South Sudan within the context of civil war, population displacement, flooding, and drought. We aim to describe the spatiotemporal and molecular features of the three distinct epidemic waves and explore the role of vaccination campaigns, precipitation, and population movement in shaping cholera spread in this complex setting. METHODS: In this descriptive epidemiological study, we analysed cholera linelist data to describe the spatiotemporal progression of the epidemics. We placed whole-genome sequence data from pandemic Vibrio cholerae collected throughout these epidemics into the global phylogenetic context. Using whole-genome sequence data in combination with other molecular attributes, we characterise the relatedness of strains circulating in each wave and the region. We investigated the association of rainfall and the instantaneous basic reproduction number using distributed lag non-linear models, compared county-level attack rates between those with early and late reactive vaccination campaigns, and explored the consistency of the spatial patterns of displacement and suspected cholera case reports. FINDINGS: The 2014 (6389 cases) and 2015 (1818 cases) cholera epidemics in South Sudan remained spatially limited whereas the 2016-17 epidemic (20?438 cases) spread among settlements along the Nile river. Initial cases of each epidemic were reported in or around Juba soon after the start of the rainy season, but we found no evidence that rainfall modulated transmission during each epidemic. All isolates analysed had similar genotypic and phenotypic characteristics, closely related to sequences from Uganda and Democratic Republic of the Congo. Large-scale population movements between counties of South Sudan with cholera outbreaks were consistent with the spatial distribution of cases. 21 of 26 vaccination campaigns occurred during or after the county-level epidemic peak. Counties vaccinated on or after the peak incidence week had 2·2 times (95% CI 2·1-2·3) higher attack rates than those where vaccination occurred before the peak. INTERPRETATION: Pandemic V cholerae of the same clonal origin was isolated throughout the study period despite interepidemic periods of no reported cases. Although the complex emergency in South Sudan probably shaped some of the observed spatial and temporal patterns of cases, the full scope of transmission determinants remains unclear. Timely and well targeted use of vaccines can reduce the burden of cholera; however, rapid vaccine deployment in complex emergencies remains challenging. FUNDING: The Bill & Melinda Gates Foundation.
In the freshwater environment of north India, cholera appears seasonally in form of clusters as well as sporadically, accounting for a significant piece of the puzzle of cholera epidemiology. We describe a number of cholera outbreaks with an average attack rate of 96.5/1000 but an overall low case fatality (0.17). Clinical cholera cases coincided with high rainfall and elevated temperatures, whereas isolation of V. cholerae non-O1 non-O139 from water was dependent on temperature (p??0.05). However, isolation from plankton samples correlated with increased temperature and pH (p?
Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010-2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model’s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model’s transferability potential and applicative value for cholera forecasting systems.
