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

Assessing presence of per- and polyfluoroalkyl substances (PFAS) in the Indian River Lagoon: A Bayesian approach to understanding the impact of environmental stressors

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants, and their presence in aquatic environments, especially coastal waters, poses significant ecological and human health risks. This study investigates the occurrence and behavior of four PFAS compounds in the Indian River Lagoon, a biodiverse estuarine ecosystem located in Florida USA, by evaluating how ecological and hydroclimatic factors influence PFAS occurrence. A Bayesian Logistic Regression Model (BLRM) was employed to quantify the relationships between environmental stressors such as salinity, precipitation, river discharge, water temperature, and pH, and the presence of these PFAS compounds. The BLRM approach not only estimated the log odds of PFAS presence but also provided posterior estimates and odd ratios, making it a transparent and interpretable model compared to other machine learning techniques. The results indicate that salinity is a significant negative predictor for all PFAS compounds, showing a decrease in PFAS presence with increasing salinity. Precipitation exhibited a statistically significant positive association with PFBS, PFOA, and PFHxS, whereas river discharge negatively affected PFNA and PFOA. Model diagnostics confirmed BLRM's robustness, with posterior predictive checks showing strong alignment between observed PFAS presence and the model's predictions, validating its accuracy. The study highlights BLRM's advantages in environmental modeling, identifying key stressors and the direction of their effects on PFAS occurrence. It emphasizes the importance of ecological and hydroclimatic factors, such as salinity, precipitation, and river discharge, in understanding PFAS behavior in coastal ecosystems. These insights aid future risk assessments and management strategies to mitigate PFAS contamination in aquatic environments.

Modeling solutions for microbial water contamination in the global south for public health protection

Microbial contamination of water sources is a pressing global challenge, disproportionately affecting developing regions with inadequate infrastructure and limited access to safe drinking water. In the Global South, waterborne pathogens such as bacteria, viruses, protozoa, and helminths contribute to diseases like cholera, dysentery, and typhoid fever, resulting in severe public health burdens. Predictive modeling emerges as a pivotal tool in addressing these challenges, offering data-driven insights to anticipate contamination events and optimize mitigation strategies. This review highlights the application of predictive modeling techniques-including machine learning, hydrological simulations, and quantitative microbial risk assessment -to identify contamination hotspots, forecast pathogen dynamics, and inform water resource allocation in the Global South. Predictive models enable targeted actions to improve water safety and lower the prevalence of waterborne diseases by combining environmental, socioeconomic, and climatic factors. Water resources in the Global South are increasingly vulnerability to microbial contamination, and the challenge is exacerbated by rapid urbanization, climate variability, and insufficient sanitation infrastructure. This review underscores the importance of region-specific modeling approaches. Case studies from sub-Saharan Africa and South Asia demonstrated the efficacy of predictive modeling tools in guiding public health actions connected to environmental matrices, from prioritizing water treatment efforts to implementing early-warning systems during extreme weather events. Furthermore, the review explores integrating advanced technologies, such as remote sensing and artificial intelligence, into predictive frameworks, highlighting their potential to improve accuracy and scalability in resource-constrained settings. Increased funding for data collecting, predictive modeling tools, and cross-sectoral cooperation between local communities, non-governmental organizations, and governments are all recommended in the review. Such efforts are critical for developing resilient water systems capable of withstanding environmental stressors and ensuring sustainable access to safe drinking water. By leveraging predictive modeling as a core component of water management strategies, stakeholders can address microbial contamination challenges effectively, safeguard public health, and contribute to achieving the United Nations' Sustainable Development Goals.

Predictive modeling for infectious diarrheal disease in pediatric populations: A systematic review

Introduction

Diarrhea is still a significant global public health problem. There are currently no systematic evaluation of the modeling areas and approaches to predict diarrheal illness outcomes. This paper reviews existing research efforts in predictive modeling of infectious diarrheal illness in pediatric populations.

Methods

We conducted a systematic review via a PubMed search for the period 1990-2021. A comprehensive search query was developed through an iterative process and literature on predictive modeling of diarrhea was retrieved. The following filters were applied to the search results: human subjects, English language, and children (birth to 18 years). We carried out a narrative synthesis of the included publications.

Results

Our literature search returned 2671 articles. After manual evaluation, 38 of these articles were included in this review. The most common research topic among the studies were disease forecasts 14 (36.8%), vaccine-related predictions 9 (23.7%), and disease/pathogen detection 5 (13.2%). Majority of these studies were published between 2011 and 2020, 28 (73.7%). The most common technique used in the modeling was machine learning 12 (31.6%) with various algorithms used for the prediction tasks. With change in the landscape of diarrheal etiology after rotavirus vaccine introduction, many open areas (disease forecasts, disease detection, and strain dynamics) remain for pathogen-specific predictive models among etiological agents that have emerged as important. Additionally, the outcomes of diarrheal illness remain under researched. We also observed lack of consistency in the reporting of results of prediction models despite the available guidelines highlighting the need for common data standards and adherence to guidelines on reporting of predictive models for biomedical research.

Conclusions

Our review identified knowledge gaps and opportunities in predictive modeling for diarrheal illness, and limitations in existing attempts whilst advancing some precursory thoughts on how to address them, aiming to invigorate future research efforts in this sphere.

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.

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 forecast for Dhaka, Bangladesh, with the 2015-2016 El Niño: Lessons learned

A substantial body of work supports a teleconnection between the El Niño-Southern Oscillation (ENSO) and cholera incidence in Bangladesh. In particular, high positive anomalies during the winter (Dec-Feb) in sea surface temperatures (SST) in the tropical Pacific have been shown to exacerbate the seasonal outbreak of cholera following the monsoons from August to November. Climate studies have indicated a role of regional precipitation over Bangladesh in mediating this long-distance effect. Motivated by this previous evidence, we took advantage of the strong 2015-2016 El Niño event to evaluate the predictability of cholera dynamics for the city in recent times based on two transmission models that incorporate SST anomalies and are fitted to the earlier surveillance records starting in 1995. We implemented a mechanistic temporal model that incorporates both epidemiological processes and the effect of ENSO, as well as a previously published statistical model that resolves space at the level of districts (thanas). Prediction accuracy was evaluated with "out-of-fit" data from the same surveillance efforts (post 2008 and 2010 for the two models respectively), by comparing the total number of cholera cases observed for the season to those predicted by model simulations eight to twelve months ahead, starting in January each year. Although forecasts were accurate for the low cholera risk observed for the years preceding the 2015-2016 El Niño, the models also predicted a high probability of observing a large outbreak in fall 2016. Observed cholera cases up to Oct 2016 did not show evidence of an anomalous season. We discuss these predictions in the context of regional and local climate conditions, which show that despite positive regional rainfall anomalies, rainfall and inundation in Dhaka remained low. Possible explanations for these patterns are given together with future implications for cholera dynamics and directions to improve their prediction for the city.