Opportunities and challenges for cholera control in India

Antibiotic resistance and virulence genes profiling of Vibrio cholerae and Vibrio mimicus isolates from some seafood collected at the aquatic environment and wet markets in Eastern Cape Province, South Africa

The current study determines the density of Vibrio spp. and isolates V. cholerae and Vibrio mimicus from fish-anatomical-sites, prawn, crab and mussel samples recovered from fish markets, freshwater and brackish water. Virulence and antibiotic resistance profiling of isolates were carried out using standard molecular and microbiology techniques. Vibrio spp. was detected in more than 90% of samples [134/144] and its density was significantly more in fish than in other samples. Vibrio. cholerae and V. mimicus were isolated in at least one sample of each sample type with higher isolation frequency in fish samples. All the V. cholerae isolates belong to non-O1/non-O139 serogroup. One or more V. cholerae isolates exhibited intermediate or resistance against each of the eighteen panels of antibiotics used but 100% of the V. mimicus were susceptible to amikacin, gentamycin and chloramphenicol. Vibrio cholerae exhibited relatively high resistance against polymyxin, ampicillin and amoxicillin/clavulanate while V. mimicus isolates exhibited relatively high resistance against nitrofurantoin, ampicillin and polymixin. The multiple-antibiotic-resistance-index [MARI] for isolates ranges between 0 and 0.67 and 48% of the isolates have MARI that is >0.2 while 55% of the isolates exhibit MultiDrug Resistance Phenotypes. The percentage detection of acc, ant, drf18, sul1, mcr-1, blasvh, blaoxa, blatem, blaoxa48, gyrA, gyrB and parC resistance-associated genes were 2%, 9%, 14%, 7%, 2%, 25%, 7%, 2%, 2%, 32%, 25% and 27% respectively while that for virulence-associated genes in increasing other was ace [2%], tcp [11%], vpi [16%], ompU [34%], toxR [43%], rtxC [70%], rtxA [73%] and hyla [77%]. The study confirmed the potential of environmental non-O1/non-O139 V. cholerae and V. mimicus to cause cholera-like infection and other vibriosis which could be difficult to manage with commonly recommended antibiotics. Thus, regular monitoring of the environment to create necessary awareness for this kind of pathogens is important in the interest of public health.

Challenges for Programmatic Implementation of Oral Cholera Vaccine in India

Cholera remains a major contributor of diarrheal diseases and leads to substantial morbidity and mortality, particularly in low socioeconomic settings. Nonavailability of a national cholera control plan in India, compounded by underreporting of cholera cases and deficient accurate cholera hotspot estimates, has made cholera control a challenge. Obstacles in the programmatic introduction of oral cholera vaccine (OCV) lie within the infrastructure—stockpile, costing, distribution system, cold-chain mechanism, vaccine logistics, and lack of strengthened surveillance systems for adverse events following immunization. Sustained political commitment along with collaboration of people working in the media will also determine the policy outcome of OCV introduction in India.

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.