Environmental signatures associated with cholera epidemics

Mechanisms of cholera transmission via environment in India and Bangladesh: state of the science review

OBJECTIVES

Cholera has a long history in India and Bangladesh, the region where six out of the past seven global pandemics have been seeded. The changing climate and growing population have led to global cholera cases remaining high despite a consistent improvement in the access to clean water and sanitation. We aim to provide a holistic overview of variables influencing environmental cholera transmission within the context of India and Bangladesh, with a focus on the mechanisms by which they act.

CONTENT

We identified 56 relevant texts (Bangladesh n = 40, India n = 7, Other n = 5). The results of the review found that cholera transmission is associated with several socio-economic and environmental factors, each associated variable is suggested to have at least one mediating mechanism. Increases in ambient temperature and coastal sea surface temperature support cholera transmission via increases in plankton and a preference of Vibrio cholerae for warmer waters. Increased rainfall can potentially support or reduce transmission via several mechanisms.

SUMMARY AND OUTLOOK

Common issues in the literature are co-variance of seasonal factors, limited access to high quality cholera data, high research bias towards research in Dhaka and Matlab (Bangladesh). A specific and detailed understanding of the relationship between SST and cholera incidence remains unclear.

Analysis of environmental factors influencing endemic cholera risks in sub-Saharan Africa

The recurring cholera outbreaks in sub-Saharan Africa are of growing concern, especially considering the potential acceleration in the global trend of larger and more lethal cholera outbreaks due to the impacts of climate change. However, there is a scarcity of evidence-based research addressing the environmental and infrastructure factors that sustain cholera recurrence in Africa. This study adopts a statistical approach to investigate over two decades of endemic cholera outbreaks and their relationship with five environmental factors: water provision, sanitation provision, raising temperatures, increased rainfall and GDP. The analysis covers thirteen of the forty-two countries in the mainland sub-Saharan region, collectively representing one-third of the region's territory and half of its population. This breadth enables the findings to be generalised at a regional level. Results from all analyses consistently associate water provision with cholera reduction. The stratified model links increased water provision with a reduction in cholera risk that ranged from 4.2 % to 84.1 % among eight countries (out of 13 countries) as well as a reduction of such risk that ranged from 9.8 % to 68.9 % when there is increased sanitation provision, which was observed in nine countries (out of 13). These results indicate that the population's limited access to water and sanitation, as well as the rise in temperatures, are critical infrastructure and environmental factors contributing to endemic cholera and the heightened risk of outbreaks across the sub-Saharan region. Therefore, these are key areas for targeted interventions and cross-border collaboration to enhance resilience to outbreaks and lead to the end of endemic cholera in the region. However, it is important to interpret the results of this study with caution; hence, further investigation is recommended to conduct a more detailed analysis of the impact of infrastructure and environmental factors on reducing cholera risk.

Tracking Vibrio: population dynamics and ecology of Vibrio parahaemolyticus and V. vulnificus in an Alabama estuary

Vibrio is a genus of halophilic, gram-negative bacteria found in estuaries around the globe. Integral parts of coastal cultures often involve contact with vectors of pathogenic Vibrio spp. (e.g., consuming raw shellfish). High rates of mortality from certain Vibrio spp. infections demonstrate the need for an improved understanding of Vibrio spp. dynamics in estuarine regions. Our study assessed meteorological, hydrographic, and biological correlates of Vibrio parahaemolyticus and V. vulnificus at 10 sites in the Eastern Mississippi Sound System (EMSS) from April to October 2019. During the sampling period, median abundances of V. parahaemolyticus and V. vulnificus were 2.31 log MPN/L and 2.90 log MPN/L, respectively. Vibrio spp. dynamics were largely driven by site-based variation, with sites closest to freshwater inputs having the highest abundances. The E-W wind scalar, which affects Ekman transport, was a novel Vibrio spp. correlate observed. A potential salinity effect on bacterial-particle associations was identified, where V. vulnificus was associated with larger particles in conditions outside of their optimal salinity. Additionally, V. vulnificus abundances were correlated to those of harmful algal species that did not dominate community chlorophyll. Correlates from this study may be used to inform the next iteration of regionally predictive Vibrio models and may lend additional insight to Vibrio spp. ecology in similar systems.

IMPORTANCE

Vibrio spp. are bacteria found in estuaries worldwide; some species can cause illness and infections in humans. Relationships between Vibrio spp. abundance, salinity, and temperature are well documented, but correlations to other environmental parameters are less understood. This study identifies unique correlates (e.g., E-W wind scalar and harmful algal species) that could potentially inform the next iteration of predictive Vibrio models for the EMSS region. Additionally, these correlates may allow existing environmental monitoring efforts to be leveraged in providing data inputs for future Vibrio risk models. An observed correlation between salinity and V. vulnificus/particle-size associations suggests that predicted environmental changes may affect the abundance of Vibrio spp. in certain reservoirs, which may alter which vectors present the greatest vibrio risk.

Genomic diversity of Vibrio spp. and metagenomic analysis of pathogens in Florida Gulf coastal waters following Hurricane Ian

IMPORTANCE

Evidence suggests warming temperatures are associated with the spread of potentially pathogenic Vibrio spp. and the emergence of human disease globally. Following Hurricane Ian, the State of Florida reported a sharp increase in the number of reported Vibrio spp. infections and deaths. Hence, monitoring of pathogens, including vibrios, and environmental parameters influencing their occurrence is critical to public health. Here, DNA sequencing was used to investigate the genomic diversity of Vibrio parahaemolyticus and Vibrio vulnificus, both potential human pathogens, in Florida coastal waters post Hurricane Ian, in October 2022. Additionally, the microbial community of water samples was profiled to detect the presence of Vibrio spp. and other microorganisms (bacteria, fungi, protists, and viruses) present in the samples. Long-term environmental data analysis showed changes in environmental parameters during and after Ian were optimal for the growth of Vibrio spp. and related pathogens. Collectively, results will be used to develop predictive risk models during climate change.

Anticipatory decision-making for cholera in Malawi

Climate change raises an old disease to a new level of public health threat. The causative agent, Vibrio cholerae, native to aquatic ecosystems, is influenced by climate and weather processes. The risk of cholera is elevated in vulnerable populations lacking access to safe water and sanitation infrastructure. Predictive intelligence, employing mathematical algorithms that integrate earth observations and heuristics derived from microbiological, sociological, and weather data, can provide anticipatory decision-making capabilities to reduce the burden of cholera and save human lives. An example offered here is the recent outbreak of cholera in Malawi, predicted in advance by such algorithms.

Cholera: An Overview with Reference to the Syrian Outbreak

Cholera is an acute type of diarrheal disease caused by intestinal infection with the toxin-producing bacteria Vibrio cholerae. The disease is still endemic in almost 69 countries, accounting for around 2.86 million cases and 95,000 deaths annually. Cholera is associated with poor infrastructure, and lack of access to sanitation and clean drinking water. The current cholera outbreak in Syria is associated with more than 10 years of conflict, which has devastated infrastructures and health services. There were 132,782 suspected cases reported between August 25, 2022 and May 20, 2023 in all 14 governorates, including 104 associated deaths. The recent earthquake in the region has complicated the situation, with an increase in cholera cases, and hindrance to a response to the disease. Climate change has driven a number of large cholera outbreaks around the world this year. The World Health Organization prequalifies three oral cholera vaccines. Cholera treatment mainly depends on rehydration, with the use of antibiotics in more severe infections. This review gives an overview of cholera bacteriology, pathogenesis, epidemiology, clinical manifestations, diagnosis, management, and prevention in light of global climate change and the ongoing outbreak in Syria, which poses a significant public health threat that requires urgent attention.

Seasonality of cholera in Kolkata and the influence of climate

BACKGROUND

Cholera in Kolkata remains endemic and the Indian city is burdened with a high number of annual cases. Climate change is widely considered to exacerbate cholera, however the precise relationship between climate and cholera is highly heterogeneous in space and considerable variation can be observed even within the Indian subcontinent. To date, relatively few studies have been conducted regarding the influence of climate on cholera in Kolkata.

METHODS

We considered 21 years of confirmed cholera cases from the Infectious Disease Hospital in Kolkata during the period of 1999-2019. We used Generalised Additive Modelling (GAM) to extract the non-linear relationship between cholera and different climatic factors; temperature, rainfall and sea surface temperature (SST). Peak associated lag times were identified using cross-correlation lag analysis.

RESULTS

Our findings revealed a bi-annual pattern of cholera cases with two peaks coinciding with the increase in temperature in summer and the onset of monsoon rains. Variables selected as explanatory variables in the GAM model were temperature and rainfall. Temperature was the only significant factor associated with summer cholera (mean temperature of 30.3 °C associated with RR of 3.8) while rainfall was found to be the main driver of monsoon cholera (550 mm total monthly rainfall associated with RR of 3.38). Lag time analysis revealed that the association between temperature and cholera cases in the summer had a longer peak lag time compared to that between rainfall and cholera during the monsoon. We propose several mechanisms by which these relationships are mediated.

CONCLUSIONS

Kolkata exhibits a dual-peak phenomenon with independent mediating factors. We suggest that the summer peak is due to increased bacterial concentration in urban water bodies, while the monsoon peak is driven by contaminated flood waters. Our results underscore the potential utility of preventative strategies tailored to these seasonal and climatic patterns, including efforts to reduce direct contact with urban water bodies in summer and to protect residents from flood waters during monsoon.

Simulation of the potential impact of climate change on malaria incidence using artificial neural networks (ANNs)

Climate change can increase the spread of infectious diseases and public health concerns. Malaria is one of the endemic infectious diseases of Iran, whose transmission is strongly influenced by climatic conditions. The effect of climate change on malaria in the southeastern Iran from 2021 to 2050 was simulated by using artificial neural networks (ANNs). Gamma test (GT) and general circulation models (GCMs) were used to determine the best delay time and to generate the future climate model under two distinct scenarios (RCP2.6 and RCP8.5). To simulate the various impacts of climate change on malaria infection, ANNs were applied using daily collected data for 12 years (from 2003 to 2014). The future climate of the study area will be hotter by 2050. The simulation of malaria cases elucidated that there is an intense increasing trend in malaria cases under the RCP8.5 scenario until 2050, with the highest number of infections occurring in the warmer months. Rainfall and maximum temperature were identified as the most influential input variables. Optimum temperatures and increased rainfall provide a suitable environment for the transmission of parasites and cause an intense increase in the number of infection cases with a delay of approximately 90 days. ANNs were introduced as a practical tool for simulating the impact of climate change on the prevalence, geographic distribution, and biological activity of malaria and for estimating the future trend of the disease in order to adopt protective measures in endemic areas.

Sporadic type VI secretion in seventh pandemic Vibrio cholerae

Vibrio cholerae is a pathogen that causes disease in millions of people every year by colonizing the small intestine and then secreting the potent cholera toxin. How the pathogen overcomes the colonization barrier created by the host's natural microbiota is, however, still not well understood. In this context, the type VI secretion system (T6SS) has gained considerable attention given its ability to mediate interbacterial killing. Interestingly, and in contrast to non-pandemic or environmental V. cholerae isolates, strains that are causing the ongoing cholera pandemic (7PET clade) are considered T6SS-silent under laboratory conditions. Since this idea was recently challenged, we performed a comparative in vitro study on T6SS activity using diverse strains or regulatory mutants. We show that modest T6SS activity is detectable in most of the tested strains under interbacterial competition conditions. The system's activity was also observed through immunodetection of the T6SS tube protein Hcp in culture supernatants, a phenotype that can be masked by the strains' haemagglutinin/protease. We further investigated the low T6SS activity within the bacterial populations by imaging 7PET V. cholerae at the single-cell level. The micrographs showed the production of the machinery in only a small fraction of cells within the population. This sporadic T6SS production was higher at 30 °C than at 37 °C and occurred independently of the known regulators TfoX and TfoY but was dependent on the VxrAB two-component system. Overall, our work provides new insight into the heterogeneity of T6SS production in populations of 7PET V. cholerae strains in vitro and provides a possible explanation of the system's low activity in bulk measurements.

Combating cholera by building predictive capabilities for pathogenic Vibrio cholerae in Yemen

Cholera remains a global public health threat in regions where social vulnerabilities intersect with climate and weather processes that impact infectious Vibrio cholerae. While access to safe drinking water and sanitation facilities limit cholera outbreaks, sheer cost of building such infrastructure limits the ability to safeguard the population. Here, using Yemen as an example where cholera outbreak was reported in 2016, we show how predictive abilities for forecasting risk, employing sociodemographical, microbiological, and climate information of cholera, can aid in combating disease outbreak. An epidemiological analysis using Bradford Hill Criteria was employed in near-real-time to understand a predictive model's outputs and cholera cases in Yemen. We note that the model predicted cholera risk at least four weeks in advance for all governorates of Yemen with overall 72% accuracy (varies with the year). We argue the development of anticipatory decision-making frameworks for climate modulated diseases to design intervention activities and limit exposure of pathogens preemptively.

Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density

Seasonal variations (SVs) affect the population density (PD), fate, and fitness of pathogens in environmental water resources and the public health impacts. Therefore, this study is aimed at applying machine learning intelligence (MLI) to predict the impacts of SVs on P. shigelloides population density (PDP) in the aquatic milieu. Physicochemical events (PEs) and PDP from three rivers acquired via standard microbiological and instrumental techniques across seasons were fitted to MLI algorithms (linear regression (LR), multiple linear regression (MR), random forest (RF), gradient boosted machine (GBM), neural network (NN), K-nearest neighbour (KNN), boosted regression tree (BRT), extreme gradient boosting (XGB) regression, support vector regression (SVR), decision tree regression (DTR), M5 pruned regression (M5P), artificial neural network (ANN) regression (with one 10-node hidden layer (ANN10), two 6- and 4-node hidden layers (ANN64), and two 5- and 5-node hidden layers (ANN55)), and elastic net regression (ENR)) to assess the implications of the SVs of PEs on aquatic PDP. The results showed that SVs significantly influenced PDP and PEs in the water (p < 0.0001), exhibiting a site-specific pattern. While MLI algorithms predicted PDP with differing absolute flux magnitudes for the contributing variables, DTR predicted the highest PDP value of 1.707 log unit, followed by XGB (1.637 log unit), but XGB (mean-squared-error (MSE) = 0.0025; root-mean-squared-error (RMSE) = 0.0501; R² =0.998; medium absolute deviation (MAD) = 0.0275) outperformed other models in terms of regression metrics. Temperature and total suspended solids (TSS) ranked first and second as significant factors in predicting PDP in 53.3% (8/15) and 40% (6/15), respectively, of the models, based on the RMSE loss after permutations. Additionally, season ranked third among the 7 models, and turbidity (TBS) ranked fourth at 26.7% (4/15), as the primary significant factor for predicting PDP in the aquatic milieu. The results of this investigation demonstrated that MLI predictive modelling techniques can promisingly be exploited to complement the repetitive laboratory-based monitoring of PDP and other pathogens, especially in low-resource settings, in response to seasonal fluxes and can provide insights into the potential public health risks of emerging pathogens and TSS pollution (e.g., nanoparticles and micro- and nanoplastics) in the aquatic milieu. The model outputs provide low-cost and effective early warning information to assist watershed managers and fish farmers in making appropriate decisions about water resource protection, aquaculture management, and sustainable public health protection.

Predictive Intelligence for Cholera in Ukraine?

Cholera, an ancient waterborne diarrheal disease, remains a threat to public health, especially when climate/weather processes, microbiological parameters, and sociological determinants intersect with population vulnerabilities of loss of access to safe drinking water and sanitation infrastructure. The ongoing war in Ukraine has either damaged or severely crippled civil infrastructure, following which the human population is at risk of health disasters. This editorial highlights a perspective on using predictive intelligence to combat potential (and perhaps impending) cholera outbreaks in various regions of Ukraine. Reliable and judicious use of existing earth observations inspired mathematical algorithms integrating heuristic understanding of microbiological, sociological, and weather parameters have the potential to save or reduce the disease burden.

Dynamic mapping of cholera outbreak during the Yemeni Civil War, 2016-2019

Widespread destruction from the Yemeni Civil War (2014-present) triggered the world's largest cholera outbreak. We compiled a comprehensive health dataset and created dynamic maps to demonstrate spatiotemporal changes in cholera infections and war conflicts. We aligned and merged daily, weekly, and monthly epidemiological bulletins of confirmed cholera infections and daily conflict events and fatality records to create a dataset of weekly time series for Yemen at the governorate level (subnational regions administered by governors) from 4 January 2016 through 29 December 2019. We demonstrated the use of dynamic mapping for tracing the onset and spread of infection and manmade factors that amplify the outbreak. We report curated data and visualization techniques to further uncover associations between infectious disease outbreaks and risk factors and to better coordinate humanitarian aid and relief efforts during complex emergencies.

The seasonality of cholera in sub-Saharan Africa: a statistical modelling study

Background

Cholera remains a major threat in sub-Saharan Africa (SSA), where some of the highest case-fatality rates are reported. Knowing in what months and where cholera tends to occur across the continent could aid in improving efforts to eliminate cholera as a public health concern. However, largely due to the absence of unified large-scale datasets, no continent-wide estimates exist. In this study, we aimed to estimate cholera seasonality across SSA and explore the correlation between hydroclimatic variables and cholera seasonality.

Methods

Using the global cholera database of the Global Task Force on Cholera Control, we developed statistical models to synthesise data across spatial and temporal scales to infer the seasonality of excess (defined as incidence higher than the 2010–16 mean incidence rate) suspected cholera occurrence in SSA. We developed a Bayesian statistical model to infer the monthly risk of excess cholera at the first and second administrative levels. Seasonality patterns were then grouped into spatial clusters. Finally, we studied the association between seasonality estimates and hydroclimatic variables (mean monthly fraction of area flooded, mean monthly air temperature, and cumulative monthly precipitation).

Findings

24 (71%) of the 34 countries studied had seasonal patterns of excess cholera risk, corresponding to approximately 86% of the SSA population. 12 (50%) of these 24 countries also had subnational differences in seasonality patterns, with strong differences in seasonality strength between regions. Seasonality patterns clustered into two macroregions (west Africa and the Sahel vs eastern and southern Africa), which were composed of subregional clusters with varying degrees of seasonality. Exploratory association analysis found most consistent and positive correlations between cholera seasonality and precipitation and, to a lesser extent, between cholera seasonality and temperature and flooding.

Interpretation

Widespread cholera seasonality in SSA offers opportunities for intervention planning. Further studies are needed to study the association between cholera and climate.

Funding

US National Aeronautics and Space Administration Applied Sciences Program and the Bill & Melinda Gates Foundation.

Modalities and preferred routes of geographic spread of cholera from endemic areas in eastern Democratic Republic of the Congo

Cholera is endemic along the Great Lakes Region, in eastern Democratic Republic of the Congo (DRC). From these endemic areas, also under perpetual conflicts, outbreaks spread to other areas. However, the main routes of propagation remain unclear. This research aimed to explore the modalities and likely main routes of geographic spread of cholera from endemic areas in eastern DRC. We used historical reconstruction of major outbreak expansions of cholera since its introduction in eastern DRC, maps of distribution and spatiotemporal cluster detection analyses of cholera data from passive surveillance (2000–2017) to describe the spread dynamics of cholera from eastern DRC. Four modalities of geographic spread and their likely main routes from the source areas of epidemics to other areas were identified: in endemic eastern provinces, and in non-endemic provinces of eastern, central and western DRC. Using non-parametric statistics, we found that the higher the number of conflict events reported in eastern DRC, the greater the geographic spread of cholera across the country. The present study revealed that the dynamics of the spread of cholera follow a fairly well-defined spatial logic and can therefore be predicted.

Atypical and dual biotypes variant of virulent SA-NAG-Vibrio cholerae: an evidence of emerging/evolving patho-significant strain in municipal domestic water sources

Introduction and purpose

The recent cholera spread, new cases, and fatality continue to arouse concern in public health systems; however, interventions on control is at its peak yet statistics show continuous report. This study characterized atypical and patho-significant environmental Vibrio cholerae retrieved from ground/surface/domestic water in rural-urban-sub-urban locations of Amathole District municipality and Chris Hani District municipality, Eastern Cape Province, South Africa.

Methods

Domestic/surface water was sampled and 759 presumptive V. cholerae isolates were retrieved using standard microbiological methods. Virulence phenotypic test: toxin co-regulated pili (tcp), choleragen red, protease production, lecithinase production, and lipase test were conducted. Serotyping using polyvalent antisera (Bengal and Ogawa/Inaba/Hikojima) and molecular typing: 16SrRNA, OmpW, serogroup (Vc-O1/O139), biotype (tcpAClas/El Tor, HlyAClas/El Tor, rstRClas/El Tor, RS1, rtxA, rtxC), and virulence (ctxA, ctxB, zot, ace, cep, prt, toxR, hlyA) genes were targeted.

Result

Result of 16SrRNA typing confirmed 508 (66.9%) while OmpW detected/confirmed 61 (12.01%) V. cholerae strains. Phenotypic-biotyping scheme showed positive test to polymyxin B (68.9%), Voges proskauer (6.6%), and Bengal serology (11.5%). Whereas Vc-O1/O139 was negative, yet two of the isolates harbored the cholera toxin with a gene-type ctxB and hlyAClas: 2/61, revealing atypical/unusual/dual biotype phenotypic/genotypic features. Other potential atypical genotypes detected include rstR: 7/61, Cep: 15/61, ace: 20/61, hlyAElTor: 53/61, rtxA: 30/61, rtxC: 11/61, and prtV: 15/61 respectively.

Conclusion

Although additional patho-significant/virulent genotypes associated with epidemic/sporadic cholera cases were detected, an advanced, bioinformatics, and post-molecular evaluation is necessary. Such stride possesses potential to adequately minimize future cholera cases associated with dynamic/atypical environmental V. cholerae strains.

Rethinking Disease Preparedness: Incertitude and the Politics of Knowledge

This paper argues for a rethinking of disease preparedness that puts incertitude and the politics of knowledge at the centre. Through examining the experiences of Ebola, Nipah, cholera and COVID-19 across multiple settings, the limitations of current approaches are highlighted. Conventional approaches assume a controllable, predictable future, which is responded to by a range of standard interventions. Such emergency preparedness planning approaches assume risk - where future outcomes can be predicted - and fail to address uncertainty, ambiguity and ignorance - where outcomes or their probabilities are unknown. Through examining the experiences of outbreak planning and response across the four cases, the paper argues for an approach that highlights the politics of knowledge, the constructions of time and space, the requirements for institutions and administrations and the challenges of ethics and justice. Embracing incertitude in disease preparedness responses therefore means making contextual social, political and cultural dimensions central.

Major Stressors Favoring Cholera Trigger and Dissemination in Guinea-Bissau (West Africa)

Cholera remains a heavy burden worldwide, especially in Sub-Saharan African countries, which account for the majority of the reported cases on the continent. In this study, a 27-year retrospective analysis of cholera epidemics in Guinea-Bissau was performed in order to highlight major stressors fueling the trigger and dissemination of the disease. Although the role of environmental factors did not always have the same degree of importance for the onset of epidemics, a cholera seasonal pattern was clearly perceived, with most of the reported cases occurring during the wet season. The generated theoretical hypothesis indicated rainfall above climatological average, associated with a lack of WASH (water, sanitation and hygiene) infrastructure, and the occurrence of concomitant epidemics in neighboring countries as the key indicators for optimal conditions for cholera to thrive in Guinea-Bissau. Warmer air temperature, the increase in sea surface temperature, and the decrease in salinity in the coastal areas may also contribute to the emergence and/or aggravation of cholera events. Prediction of the conditions favorable for cholera growth and identification of risk pathways will allow the timely allocation of resources, and support the development of alert tools and mitigation strategies.

The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review

Background: Climate change poses a real challenge and has contributed to causing the emergence and re-emergence of many communicable diseases of public health importance. Here, we reviewed scientific studies on the relationship between meteorological factors and the occurrence of dengue, malaria, cholera, and leptospirosis, and synthesized the key findings on communicable disease projection in the event of global warming. Method: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow checklist. Four databases (Web of Science, Ovid MEDLINE, Scopus, EBSCOhost) were searched for articles published from 2005 to 2020. The eligible articles were evaluated using a modified scale of a checklist designed for assessing the quality of ecological studies. Results: A total of 38 studies were included in the review. Precipitation and temperature were most frequently associated with the selected climate-sensitive communicable diseases. A climate change scenario simulation projected that dengue, malaria, and cholera incidence would increase based on regional climate responses. Conclusion: Precipitation and temperature are important meteorological factors that influence the incidence of climate-sensitive communicable diseases. Future studies need to consider more determinants affecting precipitation and temperature fluctuations for better simulation and prediction of the incidence of climate-sensitive communicable diseases.

Spatial comparison of herald and main waves in London's nineteenth-century cholera epidemics

Nineteenth-century London experienced four extraordinarily severe summertime cholera epidemics. Three were preceded by less severe non-summer outbreaks. Twenty-first-century research hypothesizes them as herald waves of potentially new cholera strains. This study examined the geographical characteristics of these herald waves and compared them to their subsequent main waves to determine if there was a geographical component to the significant difference in wave severity. Cholera mortality data for London's parishes and registration districts were extracted from contemporaneous records. The data were normalized and scaled. Each epidemic wave was divided into two segments for analysis. A Spearman's rank correlation was used to assess the relationship between a herald and its subsequent main wave. Geospatial analytical tools were used to determine and display each segment's geographic distribution pattern using autocorrelation techniques to determine its central point. Results show that the herald wave of each epidemic shared characteristics similar to its following main wave. Central-point locations were similar and Spearman's rank coefficients showed high degrees of correlation. Autocorrelation results were similar, with one exception reflecting an appalling anomalous cholera outbreak at an institution for children. Because of the demonstrated similarity of each epidemic's herald and main waves, this study did not detect a spatial characteristic that could explain the observed difference in severity between the studied heralds and mains.

Climate Precursors of Satellite Water Marker Index for Spring Cholera Outbreak in Northern Bay of Bengal Coastal Regions

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.

Environmental parameters associated with incidence and transmission of pathogenic Vibrio spp

Vibrio spp. thrive in warm water and moderate salinity, and they are associated with aquatic invertebrates, notably crustaceans and zooplankton. At least 12 Vibrio spp. are known to cause infection in humans, and Vibrio cholerae is well documented as the etiological agent of pandemic cholera. Pathogenic non-cholera Vibrio spp., e.g., Vibrio parahaemolyticus and Vibrio vulnificus, cause gastroenteritis, septicemia, and other extra-intestinal infections. Incidence of vibriosis is rising globally, with evidence that anthropogenic factors, primarily emissions of carbon dioxide associated with atmospheric warming and more frequent and intense heatwaves, significantly influence environmental parameters, e.g., temperature, salinity, and nutrients, all of which can enhance growth of Vibrio spp. in aquatic ecosystems. It is not possible to eliminate Vibrio spp., as they are autochthonous to the aquatic environment and many play a critical role in carbon and nitrogen cycling. Risk prediction models provide an early warning that is essential for safeguarding public health. This is especially important for regions of the world vulnerable to infrastructure instability, including lack of 'water, sanitation, and hygiene' (WASH), and a less resilient infrastructure that is vulnerable to natural calamity, e.g., hurricanes, floods, and earthquakes, and/or social disruption and civil unrest, arising from war, coups, political crisis, and economic recession. Incorporating environmental, social, and behavioural parameters into such models allows improved prediction, particularly of cholera epidemics. We have reported that damage to WASH infrastructure, coupled with elevated air temperatures and followed by above average rainfall, promotes exposure of a population to contaminated water and increases the risk of an outbreak of cholera. Interestingly, global predictive risk models successful for cholera have the potential, with modification, to predict diseases caused by other clinically relevant Vibrio spp. In the research reported here, the focus was on environmental parameters associated with incidence and distribution of clinically relevant Vibrio spp. and their role in disease transmission. In addition, molecular methods designed for detection and enumeration proved useful for predictive modelling and are described, namely in the context of prediction of environmental conditions favourable to Vibrio spp., hence human health risk.

The salmon louse genome: Copepod features and parasitic adaptations

Copepods encompass numerous ecological roles including parasites, detrivores and phytoplankton grazers. Nonetheless, copepod genome assemblies remain scarce. Lepeophtheirus salmonis is an economically and ecologically important ectoparasitic copepod found on salmonid fish. We present the 695.4 Mbp L. salmonis genome assembly containing ≈60% repetitive regions and 13,081 annotated protein-coding genes. The genome comprises 14 autosomes and a ZZ-ZW sex chromosome system. Assembly assessment identified 92.4% of the expected arthropod genes. Transcriptomics supported annotation and indicated a marked shift in gene expression after host attachment, including apparent downregulation of genes related to circadian rhythm coinciding with abandoning diurnal migration. The genome shows evolutionary signatures including loss of genes needed for peroxisome biogenesis, presence of numerous FNII domains, and an incomplete heme homeostasis pathway suggesting heme proteins to be obtained from the host. Despite repeated development of resistance against chemical treatments L. salmonis exhibits low numbers of many genes involved in detoxification.

A Review of the Environmental Trigger and Transmission Components for Prediction of Cholera

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.

Update on Environmental and Host Factors Impacting the Risk of Vibrio cholerae Infection

Vibrio cholerae is the causative agent of cholera, a diarrheal disease that kills tens of thousands of people each year. Cholera is transmitted primarily by the ingestion of drinking water contaminated with fecal matter, and a safe water supply remains out of reach in many areas of the world. In this Review, we discuss host and environmental factors that impact the susceptibility to V. cholerae infection and the severity of disease.

Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables

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.

How climate change can affect cholera incidence and prevalence? A systematic review

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.

Synergistic role of abiotic factors driving viable but non-culturable Vibrio cholerae

Vibrio cholerae O1, a natural inhabitant of estuarine environments, is found in a dormant, viable but non-culturable (VBNC) state during interepidemic periods. Although the individual roles of abiotic factors affecting VBNC formation have been extensively studied, their interplay in driving this phenomenon remains largely unaddressed. Here, we identified that major abiotic factors synergize with low nutrient conditions governing entry of cells into the VBNC state. Specifically, V. cholerae cells exposed to a combination of alkaline pH and high salinity under aeration at low temperatures (VBNC-inducing conditions) synergize to facilitate rapid entry into VBNC, whereas the opposite conditions prevented entry into the state. The major virulence regulator ToxR, and the stringent response protein RelA played opposing roles, repressing and facilitating VBNC entry respectively. Further, VBNC-inducing conditions negated the effects of ToxR and RelA, facilitating rapid formation of VBNC cells. In summary, this study highlights the synergy between critical abiotic factors and identified ToxR and RelA as two associated regulators, allowing for the persistence of V. cholerae in aquatic environments. Insights obtained in this study will help better understand environmental survival non-sporulating bacteria and transmission of facultative bacterial pathogens.

Spatial dynamics and the basic reproduction number of the 1991-1997 Cholera epidemic in Peru

After being cholera free for over 100 years, Peru experienced an unprecedented epidemic of Vibrio cholerae O1 that began in 1991 and generated multiple waves of disease over several years. We developed a mechanistic transmission model that accounts for seasonal variation in temperature to estimate spatial variability in the basic reproduction number ([Formula: see text]), the initial concentration of vibrios in the environment, and cholera reporting rates. From 1991-1997, cholera spread following a multi-wave pattern, with weekly incidence concentrated during warm seasons. The epidemic first hit the coastal departments of Peru and subsequently spread through the highlands and jungle regions. The correlation between model predictions and observations was high (range in R2: 58% to 97%). Department-level population size and elevation explained significant variation in spatial-temporal transmission patterns. The overall R0 across departments was estimated at 2.1 (95% CI: 0.8,7.3), high enough for sustained transmission. Geographic-region level [Formula: see text] varied substantially from 2.4 (95% CI: 1.1, 7.3) for the coastal region, 1.9 (0.7, 6.4) for the jungle region, and 1.5 (0.9, 2.2) for the highlands region. At the department level, mean [Formula: see text] ranged from 0.8 to 6.9. Department-level [Formula: see text] were correlated with overall observed attack rates (Spearman ρ = 0.59, P = 0.002), elevation (ρ = -0.4, P = 0.04), and longitude (ρ = -0.6, P = 0.004). We find that both [Formula: see text] and the initial concentration of vibrios were higher in coastal departments than other departments. Reporting rates were low, consistent with a substantial fraction of asymptomatic or mild cases associated with the El Tor cholera biotype. Our results suggest that cholera vibrios, autochthonous to plankton in the natural aquatic environment, may have triggered outbreaks in multiple coastal locations along the Pacific coast of Peru. Our methodology could be useful to investigate multi-wave epidemics of cholera and could be extended to conduct near real-time forecasts and investigate the impact of vaccination strategies.

Vibrio and Bacterial Communities Across a Pollution Gradient in the Bay of Bengal: Unraveling Their Biogeochemical Drivers

The highly populated coasts of the Bay of Bengal are particularly vulnerable to water-borne diseases, pollution and climatic extremes. The environmental factors behind bacterial community composition and Vibrio distribution were investigated in an estuarine system of a cholera-endemic region in the coastline of Bangladesh. Higher temperatures and sewage pollution were important drivers of the abundance of toxigenic Vibrio cholerae. A closer relation between non-culturable Vibrio and particulate organic matter (POM) was inferred during the post-monsoon. The distribution of operational taxonomic units (OTUs) of Vibrio genus was likely driven by salinity and temperature. The resuspension of sediments increased Vibrio abundance and organic nutrient concentrations. The δ¹³C dynamic in POM followed an increasing gradient from freshwater to marine stations; nevertheless, it was not a marker of sewage pollution. Bacteroidales and culturable coliforms were reliable indicators of untreated wastewater during pre and post-monsoon seasons. The presumptive incorporation of depleted-ammonium derived from ammonification processes under the hypoxic conditions, by some microorganisms such as Cloacibacterium and particularly by Arcobacter nearby the sewage discharge, contributed to the drastic ¹⁵N depletion in the POM. The likely capacity of extracellular polymeric substances production of these taxa may facilitate the colonization of POM from anthropogenic origin and may signify important properties for wastewater bioremediation. Genera of potential pathogens other than Vibrio associated with sewage pollution were Acinetobacter, Aeromonas, Arcobacter, and Bergeyella. The changing environmental conditions of the estuary favored the abundance of early colonizers and the island biogeography theory explained the distribution of some bacterial groups. This multidisciplinary study evidenced clearly the eutrophic conditions of the Karnaphuli estuary and assessed comprehensively its current bacterial baseline and potential risks. The prevailing conditions together with human overpopulation and frequent natural disasters, transform the region in one of the most vulnerable to climate change. Adaptive management strategies are urgently needed to enhance ecosystem health.

What are the drivers of recurrent cholera transmission in Nigeria? Evidence from a scoping review

BACKGROUND

The 2018 cholera outbreak in Nigeria affected over half of the states in the country, and was characterised by high attack and case fatality rates. The country continues to record cholera cases and related deaths to date. However, there is a dearth of evidence on context-specific drivers and their operational mechanisms in mediating recurrent cholera transmission in Nigeria. This study therefore aimed to fill this important research gap, with a view to informing the design and implementation of appropriate preventive and control measures.

METHODS

Four bibliographic literature sources (CINAHL (Plus with full text), Web of Science, Google Scholar and PubMed), and one journal (African Journals Online) were searched to retrieve documents relating to cholera transmission in Nigeria. Titles and abstracts of the identified documents were screened according to a predefined study protocol. Data extraction and bibliometric analysis of all eligible documents were conducted, which was followed by thematic and systematic analyses.

RESULTS

Forty-five documents met the inclusion criteria and were included in the final analysis. The majority of the documents were peer-reviewed journal articles (89%) and conducted predominantly in the context of cholera epidemics (64%). The narrative analysis indicates that social, biological, environmental and climatic, health systems, and a combination of two or more factors appear to drive cholera transmission in Nigeria. Regarding operational dynamics, a substantial number of the identified drivers appear to be functionally interdependent of each other.

CONCLUSION

The drivers of recurring cholera transmission in Nigeria are diverse but functionally interdependent; thus, underlining the importance of adopting a multi-sectoral approach for cholera prevention and control.

Effects of large-scale oceanic phenomena on non-cholera vibriosis incidence in the United States: implications for climate change

Non-cholera Vibrio (NCV) species are important causes of disease. These pathogens are thermophilic and climate change could increase the risk of NCV infection. The El Niño Southern Oscillation (ENSO) is a 'natural experiment' that may presage ocean warming effects on disease incidence. In order to evaluate possible climatic contributions to observed increases in NCV infection, we obtained NCV case counts for the United States from publicly available surveillance data. Trends and impacts of large-scale oceanic phenomena, including ENSO, were evaluated using negative binomial and distributed non-linear lag models (DNLM). Associations between latitude and changing risk were evaluated with meta-regression. Trend models demonstrated expected seasonality (P &lt; 0.001) and a 7% (6.1%-8.1%) annual increase in incidence from 1999 to 2014. DNLM demonstrated increased vibriosis risk following ENSO conditions over the subsequent 12 months (relative risk 1.940, 95% confidence interval (CI) 1.298-2.901). The 'relative-relative risk' (RRR) of annual disease incidence increased with latitude (RRR per 10° increase 1.066, 95% CI 1.027-1.107). We conclude that NCV risk in the United States is impacted by ocean warming, which is likely to intensify with climate change, increasing NCV risk in vulnerable populations.

High abundance of Vibrio in tarball-contaminated seawater from Vagator beach, Goa, India

Tarballs are semi-solid remnants of crude oil and they are formed in marine environment after oil-spill incidents. They are composed of diverse hydrocarbons; some of which are recalcitrant in nature. Recent studies based on amplicon sequencing of 16S rRNA gene suggested that tarballs support hundreds of bacterial genera and provided insights into their role as hydrocarbon degraders and potential human pathogens. In this study, bacterial composition of tarball-contaminated seawater from Vagator beach, Goa, India was characterized by amplicon sequencing of V₃-V₄ regions of 16S rRNA gene. The DNA data revealed an unusual surge of Vibrio in sea-water contaminated with tarballs in May 2018 (16.16% OTUs), compared to tarball-free seawater samples collected in March 2018 (no detectable OTUs) and September 2018 (0.17% OTUs). Further studies are required to investigate if Vibrio species form biofilms on tarballs which may act as good reservoirs for their survival and transmission success.

Environmental Reservoirs of Vibrio cholerae: Challenges and Opportunities for Ocean-Color Remote Sensing

The World Health Organization has estimated the burden of the on-going pandemic of cholera at 1.3 to 4 million cases per year worldwide in 2016, and a doubling of case-fatality-rate to 1.8% in 2016 from 0.8% in 2015. The disease cholera is caused by the bacterium Vibrio cholerae that can be found in environmental reservoirs, living either in free planktonic form or in association with host organisms, non-living particulate matter or in the sediment, and participating in various biogeochemical cycles. An increasing number of epidemiological studies are using land- and water-based remote-sensing observations for monitoring, surveillance, or risk mapping of Vibrio pathogens and cholera outbreaks. Although the Vibrio pathogens cannot be sensed directly by satellite sensors, remotely-sensed data can be used to infer their presence. Here, we review the use of ocean-color remote-sensing data, in conjunction with information on the ecology of the pathogen, to map its distribution and forecast risk of disease occurrence. Finally, we assess how satellite-based information on cholera may help support the Sustainable Development Goals and targets on Health (Goal 3), Water Quality (Goal 6), Climate (Goal 13), and Life Below Water (Goal 14).

The effect of climate change on cholera disease: The road ahead using artificial neural network

Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2050. Daily data for cholera infection cases in Qom city, which is located in the center of Iran, were analyzed from 1998 to 2016. To determine the best lag time and combination of inputs, Gamma Test (GT) was applied. General circulation model outputs were utilized to project future climate pattern under two scenarios of Representative Concentration Pathway (RCP2.6 and RCP8.5). Statistical downscaling was done to produce high-resolution synthetic time series weather dataset. ANNs were applied for simulating the impact of climate change on cholera. The observed climate variables including maximum and minimum temperatures and precipitation were tagged as predictors in ANNs. Cholera cases were considered as the target outcome variable. Projected future (2020–2050) climate in previous step was carried out to assess future cholera incidence. A seasonal trend in cholera infection was seen. Our results elucidated that the best lag time was 21 days. According to the results of downscaling tool, future climate in the study area by 2050 will be warmer and wetter. Simulation of cholera cases indicated that there is a clear trend of increasing cholera cases under the worst scenario (RCP8.5) by the year 2050 and the highest cholera cases observe in warmer months. The precipitation was recognized as the most effective input variable by sensitivity analysis. We observed a significant correlation between low precipitation and cholera infection. There is a strong evidence to show that cholera disease is correlated with environment variables, as low precipitation and high temperatures in warmer months could provide the swifter bacterial replication. These conditions in Iran, especially in the central parts, may raise the cholera infection rates. Furthermore, ANNs is an executive tool to simulate the impact of climate change on cholera to estimate the future trend of cholera incidence for adopting protective measures in endemic areas.

Modelling the influence of environmental parameters over marine planktonic microbial communities using artificial neural networks

Guanabara Bay is a tropical estuarine ecosystem that receives massive anthropogenic impacts from the metropolitan region of Rio de Janeiro. This ecosystem suffers from an ongoing eutrophication process that has been shown to promote the emergence of potentially pathogenic bacteria, giving rise to public health concerns. Although previous studies have investigated how environmental parameters influence the microbial community of Guanabara Bay, they often have been limited to small spatial and temporal gradients and have not been integrated into predictive mathematical models. Our objective was to fill this knowledge gap by building models that could predict how temperature, salinity, phosphorus, nitrogen and transparency work together to regulate the abundance of bacteria, chlorophyll and Vibrio (a potential human pathogen) in Guanabara Bay. To that end, we built artificial neural networks to model the associations between these variables. These networks were carefully validated to ensure that they could provide accurate predictions without biases or overfitting. The estimated models displayed high predictive capacity (Pearson correlation coefficients ≥0.67 and root mean square error ≤ 0.55). Our findings showed that temperature and salinity were often the most important factors regulating the abundance of bacteria, chlorophyll and Vibrio (absolute importance ≥5) and that each of these has a unique level of dependence on nitrogen and phosphorus for their growth. These models allowed us to estimate the Guanabara Bay microbiome's response to changes in environmental conditions, which allowed us to propose strategies for the management and remediation of Guanabara Bay.

Modelling cholera transmission dynamics in the presence of limited resources

Objectives

We study the transmission dynamics of cholera in the presence of limited resources, a common feature of the developing world. The model is used to gain insight into the impact of available resources of the health care system on the spread and control of the disease. A deterministic model that includes a nonlinear recovery rate is formulated and rigorously analyzed. Limited treatment is described by inclusion of a special treatment function. Center manifold theory is used to show that the model exhibits the phenomenon of backward bifurcation. Matlab has been used to carry out numerical simulations to support theoretical findings.

Results

The model analysis shows that the disease free steady state is locally stable when the threshold \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {R}}_{0} < 1$$\end{document}R0<1. It is also shown that the model has multiple equilibria and the model exhibits the phenomenon of backward bifurcation whose implications to cholera infection are discussed. The results are useful for the public health planning in resource allocation for the control of cholera transmission.

Oral cholera vaccine delivery strategy in India: Routine or campaign?-A scoping review

Oral Cholera Vaccine (OCV) has been recognized as an adjunct tool for prevention and control of cholera. However, policy directions are currently unavailable in India to guide the vaccine delivery. We conducted a scoping review to inform the policy about the scopes and challenges of different strategic choices of OCV delivery in India in light of current evidences, highlighting the scope of new research.

METHODS

Adopting the Arksey and O'Malley Framework for review, we searched for literatures on "efficacy", "effectiveness", and "cost" of oral cholera vaccine delivery through different strategies in Pubmed and Scopus.

RESULTS

We found that the protective efficacy of OCV depends on its coverage. Evidence on effectiveness of OCV are available for both reactive and pre-vaccination campaigns. Reactive high-risk vaccination is more effective than reactive ring and mass vaccination. Pre-vaccination campaigns are more effective than reactive vaccination when vaccine availability is adequate. Pre-vaccination through school campaigns in 1-14 years age group have been cost effective in India. Vaccination campaigns in under-5 children are also cost effective in spite of low efficacy due to the scope of averting a higher number of cases. However, no evidence is available regarding efficacy and effectiveness of OCV in children <1 year as well as the effectiveness of delivering OCV through routine immunization.

CONCLUSION

Little evidence exist to depict mass-campaign as more economic and effective than routine expanded programme on immunization (EPI) session for delivery of OCV. Considering operational feasibility, it needs to be explored whether OCV delivery strategy is compatible with India's current EPI, if it can be introduced in routine immunization at measles containing vaccine age-schedule, optionally preceded by a campaign in targeted hot-spots in the 1-14 year age-group. Safety and efficacy data of OCV during infancy as well as hot-spot surveillance are pre-requisites for formulation of such EPI policy.

Machine Learning Model for Imbalanced Cholera Dataset in Tanzania

Cholera epidemic remains a public threat throughout history, affecting vulnerable population living with unreliable water and substandard sanitary conditions. Various studies have observed that the occurrence of cholera has strong linkage with environmental factors such as climate change and geographical location. Climate change has been strongly linked to the seasonal occurrence and widespread of cholera through the creation of weather patterns that favor the disease's transmission, infection, and the growth of Vibrio cholerae, which cause the disease. Over the past decades, there have been great achievements in developing epidemic models for the proper prediction of cholera. However, the integration of weather variables and use of machine learning techniques have not been explicitly deployed in modeling cholera epidemics in Tanzania due to the challenges that come with its datasets such as imbalanced data and missing information. This paper explores the use of machine learning techniques to model cholera epidemics with linkage to seasonal weather changes while overcoming the data imbalance problem. Adaptive Synthetic Sampling Approach (ADASYN) and Principal Component Analysis (PCA) were used to the restore sampling balance and dimensional of the dataset. In addition, sensitivity, specificity, and balanced-accuracy metrics were used to evaluate the performance of the seven models. Based on the results of the Wilcoxon sign-rank test and features of the models, XGBoost classifier was selected to be the best model for the study. Overall results improved our understanding of the significant roles of machine learning strategies in health-care data. However, the study could not be treated as a time series problem due to the data collection bias. The study recommends a review of health-care systems in order to facilitate quality data collection and deployment of machine learning techniques.

Spatiotemporal Error in Rainfall Data: Consequences for Epidemiologic Analysis of Waterborne Diseases

The relationship between rainfall, especially extreme rainfall, and increases in waterborne infectious diseases is widely reported in the literature. Most of this research, however, has not formally considered the impact of exposure measurement error contributed by the limited spatiotemporal fidelity of precipitation data. Here, we evaluate bias in effect estimates associated with exposure misclassification due to precipitation data fidelity, using extreme rainfall as an example. We accomplished this via a simulation study, followed by analysis of extreme rainfall and incident diarrheal disease in an epidemiologic study in Ecuador. We found that the limited fidelity typical of spatiotemporal rainfall data sets biases effect estimates towards the null. Use of spatial interpolations of rain-gauge data or satellite data biased estimated health effects due to extreme rainfall (occurrence) and wet conditions (accumulated totals) downwards by 35%-45%. Similar biases were evident in the Ecuadorian case study analysis, where spatial incompatibility between exposed populations and rain gauges resulted in the association between extreme rainfall and diarrheal disease incidence being approximately halved. These findings suggest that investigators should pay greater attention to limitations in using spatially heterogeneous environmental data sets to assign exposures in epidemiologic research.

Effect of seasonality on the population density of wetland aquatic insects: A case study of the Hawr Al Azim and Shadegan wetlands, Iran

Aim:

Wetlands are extremely suitable ecosystems to assess the effect of climate change on the density of aquatic insects. This study aimed to assess the effect of seasonality on populations of aquatic insects in the Hawr Al Azim and Shadegan wetlands.

Materials and Methods:

The insect samplings were conducted at a large area of the Hawr Al Azim and five different sites of the Shadegan wetlands. In total, 18,534 arthropods of different life stages, including 12 orders containing 51 families, were collected and identified from the selected sites of the Shadegan and Hawr Al Azim wetlands.

Results:

Results showed that the population density of wetland aquatic insects gradually increased as the average daily temperature decreased, positively increased with daily mean relative humidity and precipitation, and decreased with the mean daily evaporation between October and April. Conversely, the population density of wetland aquatic insects gradually decreased with increasing average daily temperature and reduction of the mean relative humidity and precipitation and increasing the average evaporation from April to September. When differences between the average daily and water temperatures reached minimum in April, the population density of wetland aquatic insects reached maximum and turned mainly to families that they have high level of biological indices, indicating that wetlands have clean waters around the spring. While around the autumn conversely, they mostly changed to families that they have low level of biological indices, indicating that wetlands have unclean waters.

Conclusion:

The present study showed an optimum condition for the growth of insects around spring. Seasonality affects the population density of wetland aquatic insects during a year.

Rainfall as a driver of epidemic cholera: Comparative model assessments of the effect of intra-seasonal precipitation events

The correlation between cholera epidemics and climatic drivers, in particular seasonal tropical rainfall, has been studied in a variety of contexts owing to its documented relevance. Several mechanistic models of cholera transmission have included rainfall as a driver by focusing on two possible transmission pathways: either by increasing exposure to contaminated water (e.g. due to worsening sanitary conditions during water excess), or water contamination by freshly excreted bacteria (e.g. due to washout of open-air defecation sites or overflows). Our study assesses the explanatory power of these different modeling structures by formal model comparison using deterministic and stochastic models of the type susceptible-infected-recovered-bacteria (SIRB). The incorporation of rainfall effects is generalized using a nonlinear function that can increase or decrease the relative importance of the large precipitation events. Our modelling framework is tested against the daily epidemiological data collected during the 2015 cholera outbreak within the urban context of Juba, South Sudan. This epidemic is characterized by a particular intra-seasonal double peak on the incidence in apparent relation with particularly strong rainfall events. Our results show that rainfall-based models in both their deterministic and stochastic formulations outperform models that do not account for rainfall. In fact, classical SIRB models are not able to reproduce the second epidemiological peak, thus suggesting that it was rainfall-driven. Moreover we found stronger support across model types for rainfall acting on increased exposure rather than on exacerbated water contamination. Although these results are context-specific, they stress the importance of a systematic and comprehensive appraisal of transmission pathways and their environmental forcings when embarking in the modelling of epidemic cholera.

New facet of non-O1/non-O139 Vibrio cholerae hemolysin A: a competitive factor in the ecological niche

Different serogroups of Vibrio cholerae may inhabit the same ecological niche. However, serogroup O1/O139 strains are rarely isolated from their ecological sources. Quite plausibly, the non-O1/non-O139 vibrios and other bacterial species suppress growth of O1/O139 strains that share the same niche. Our bacterial inhibition assay data indicated that certain non-O1/non-O139 strains used a contact-dependent type VI secretion system (T6SS) to suppress growth of the O1 El Tor, N16961 pandemic strain. Comparative proteomics of the O1 and the suppressive non-O1/non-O139 strains co-cultured in a simulated natural aquatic microcosm showed that SecB and HlyD were upregulated in the latter. The HlyD-related effective factor was subsequently found to be hemolysin A (HlyA). However, not all hlyA-positive non-O1/non-O139 strains mediated growth suppression of the N16961 V. cholerae; only strains harboring intact cluster I HlyA could exert this activity. The key feature of the HlyA is located in the ricin-like lectin domain (β-trefoil) that plays an important role in target cell binding. In conclusion, the results of this study indicated that non-O1/non-O139 V. cholerae suppressed the growth of the O1 pandemic strain by using contact-dependent T6SS as well as by secreting the O1-detrimental hemolysin A during their co-persistence in the aquatic habitat.

Environmental and hydroclimatic factors influencing Vibrio populations in the estuarine zone of the Bengal delta

The objective of this study was to determine environmental parameters driving Vibrio populations in the estuarine zone of the Bengal delta. Spatio-temporal data were collected at river estuary, mangrove, beach, pond, and canal sites. Effects of salinity, tidal amplitude, and a cyclone and tsunami were included in the study. Vibrio population shifts were found to be correlated with tide-driven salinity and suspended particulate matter (SPM). Increased abundance of Vibrio spp. in surface water was observed after a cyclone, attributed to re-suspension of benthic particulate organic carbon (POC), and increased availability of chitin and dissolved organic carbon (DOC). Approximately a two log₁₀ increase in the (p < 0.05) number of Vibrio spp. was observed in < 20 μm particulates, compared with microphytoplankton (20-60 μm) and zooplankton > 60 μm fractions. Benthic and suspended sediment comprised a major reservoir of Vibrio spp. Results of microcosm experiments showed enhanced growth of vibrios was related to concentration of organic matter in SPM. It is concluded that SPM, POC, chitin, and salinity significantly influence abundance and distribution of vibrios in the Bengal delta estuarine zone.

Natural Disasters and Cholera Outbreaks: Current Understanding and Future Outlook

PURPOSE OF REVIEW

Diarrheal diseases remain a serious global public health threat, especially for those populations lacking access to safe water and sanitation infrastructure. Although association of several diarrheal diseases, e.g., cholera, shigellosis, etc., with climatic processes has been documented, the global human population remains at heightened risk of outbreak of diseases after natural disasters, such as earthquakes, floods, or droughts. In this review, cholera was selected as a signature diarrheal disease and the role of natural disasters in triggering and transmitting cholera was analyzed.

RECENT FINDINGS

Key observations include identification of an inherent feedback loop that includes societal structure, prevailing climatic processes, and spatio-temporal seasonal variability of natural disasters. Data obtained from satellite-based remote sensing are concluded to have application, although limited, in predicting risks of a cholera outbreak(s). We argue that with the advent of new high spectral and spatial resolution data, earth observation systems should be seamlessly integrated in a decision support mechanism to be mobilize resources when a region suffers a natural disaster. A framework is proposed that can be used to assess the impact of natural disasters with response to outbreak of cholera, providing assessment of short- and long-term influence of climatic processes on disease outbreaks.