Old tools, new applications: Use of environmental bacteriophages for typhoid surveillance and evaluating vaccine impact

Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel

Background: Typhoid fever remains a significant public health challenge, especially in developing countries where diagnostic resources are limited. Accurate and timely diagnosis is crucial for effective treatment and disease containment. Traditional diagnostic methods, while effective, can be time-consuming and resource-intensive. This study aims to develop a lightweight machine learning-based diagnostic tool for the early and efficient detection of typhoid fever using clinical data. Methods: A custom dataset comprising 14 clinical and demographic parameters-including age, gender, headache, muscle pain, nausea, diarrhea, cough, fever range (°F), hemoglobin (g/dL), platelet count, urine culture bacteria, calcium (mg/dL), and potassium (mg/dL)-was analyzed. A machine learning metamodel, integrating Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Decision Tree classifiers with a Light Gradient Boosting Machine (LGBM), was trained and evaluated using k-fold cross-validation. Performance was assessed using precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results: The proposed metamodel demonstrated superior diagnostic performance, achieving a precision of 99%, recall of 100%, and an AUC of 1.00. It outperformed traditional diagnostic methods and other standalone machine learning algorithms, offering high accuracy and generalizability. Conclusions: The lightweight machine learning metamodel provides a cost-effective, non-invasive, and rapid diagnostic alternative for typhoid fever, particularly suited for resource-limited settings. Its reliance on accessible clinical parameters ensures practical applicability and scalability, potentially improving patient outcomes and aiding in disease control. Future work will focus on broader validation and integration into clinical workflows to further enhance its utility.

Genome sequences of bacteriophages that infect Salmonella Typhi from Bangladesh

This report presents near-complete genome sequences of 14 bacteriophages that infect Salmonella Typhi, identified through environmental surveillance in Bangladesh between August 2021 and June 2022. The bacteriophages, belonging to the genera Kayfunavirus, Macdonaldcampvirus, and Teseptimavirus, exhibit high degrees of sequence similarity and conserved genetic features with previously reported Typhi bacteriophages.

Longitudinal wastewater-based surveillance of SARS-CoV-2 during 2023 in Ethiopia

Introduction

Although wastewater-based epidemiology (WBE) successfully functioned as a tool for monitoring the coronavirus disease 2019 (COVID-19) pandemic globally, relatively little is known about its utility in low-income countries. This study aimed to quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater, estimate the number of infected individuals in the catchment areas, and correlate the results with the clinically reported COVID-19 cases in Addis Ababa, Ethiopia.

Methods

A total of 323 influent and 33 effluent wastewater samples were collected from three Wastewater Treatment Plants (WWTPs) using a 24-h composite Moore swab sampling method from February to November 2023. The virus was captured using Ceres Nanotrap® Enhancement Reagent 2 and Nanotrap® Microbiome A Particles, and then nucleic acids were extracted using the Qiagen QIAamp Viral RNA Mini Kit. The ThermoFisher TaqPath™ COVID-19 kit was applied to perform real-time reverse transcriptase polymerase chain reaction (qRT-PCR) to quantify the SARS-CoV-2 RNA. Wastewater viral concentrations were normalized using flow rate and number of people served. In the sampling period, spearman correlation was used to compare the SARS-CoV-2 target gene concentration to the reported COVID-19 cases. The numbers of infected individuals under each treatment plant were calculated considering the target genes' concentration, the flow rate of treatment plants, a gram of feces per person-day, and RNA copies per gram of feces.

Results

SARS-CoV-2 was detected in 94% of untreated wastewater samples. All effluent wastewater samples (n = 22) from the upflow anaerobic sludge blanket (UASB) reactor and membrane bioreactor (MBR) technology were SARS-COV-2 RNA negative. In contrast, two out of 11 effluents from Waste Stabilization Pond were found positive. Positive correlations were observed between the weekly average SARS-CoV-2 concentration and the cumulative weekly reported COVID-19 cases in Addis Ababa. The estimated number of infected people in the Kality Treatment catchment area was 330 times the number of COVID-19 cases reported during the study period in Addis Ababa.

Discussion

This study revealed that SARS-CoV-2 was circulating in the community and confirmed previous reports of more asymptomatic COVID-19 cases in Ethiopia. Additionally, this study provides further evidence of the importance of wastewater-based surveillance in general to monitor infectious diseases in low-income settings.

Conclusion

Wastewater-based surveillance of SARS-CoV-2 can be a useful method for tracking the increment of COVID-19 cases before it spreads widely throughout the community.

Tailored wastewater surveillance framework uncovered the epidemics of key pathogens in a Northwestern city of China

Wastewater surveillance enables rapid pathogen monitoring and community prevalence estimation. However, how to design an integrated and tailored wastewater surveillance framework to monitor major health threats in metropolises remains a major challenge. In this study, we first analyzed the historical clinical data of Xi'an city and designed a wastewater surveillance framework covering five key endemic viruses, namely, SARS-CoV-2, norovirus, influenza A virus (IAV), influenza B virus (IBV), respiratory syncytial virus (RSV), and hantavirus. Amplicon sequencing of SARS-CoV-2, norovirus and hantavirus was conducted biweekly to determine the prevalent community genotypes circulating in this region. The results showed that from April 2023 to August 2023, Xi'an experienced two waves of SARS-CoV-2 infection, which peaked in the middle of May-2023 and late August-2023. The sewage concentrations of IAV and RSV peaked in early March and early May 2023, respectively, while the sewage concentrations of norovirus fluctuated throughout the study period and peaked in late August. The dynamics of the sewage concentrations of SARS-CoV-2, norovirus, IAV, RSV, and hantavirus were in line with the trends in the sentinel hospital percent positivity data, indicating the role of wastewater surveillance in enhancing the understanding of epidemic trends. Amplicon sequencing of SARS-CoV-2 revealed a transition in the predominant genotype, which changed from DY.1 and FR.1.4 to the XBB and EG.5 subvariants. Amplicon sequencing also revealed that there was only one predominant hantavirus genotype in the local population, while highly diverse genotypes of norovirus GI and GII were found in the wastewater. In conclusion, this study provided valuable insights into the dynamics of infection trends and predominant genotypes of key pathogens in a city without sufficient clinical surveillance, highlighting the role of a tailored wastewater surveillance framework in addressing public health priorities. More importantly, our study provides the first evidence demonstrating the applicability of wastewater surveillance for hantavirus, which is a major health threat locally.