Forecasting tuberculosis using diabetes-related google trends data

Highly Fluorescent ZnO Composite of N-doped Carbon Dots From Dregea Volubilis for Fluorometric Determination of Glucose in Biological Samples

A nano-sensor based on N-doped carbon dots (NCDs)@ZnO (NCZ) composite was fabricated and efficacy for detecting glucose from human blood and urine samples in a straightforward manner was examined. The composite was prepared following a green hydrothermal method under ambient condition using a novel plant material, Dregea volubilis fruit and structural and optical properties were evaluated using standard techniques. The composite exhibited excellent characteristics including good photostability, biocompatibility, low toxicity, and strong fluorescence, with a decent quantum yield of up to 59%. The NCZ composite has been very sensitive and could selectively detect glucose in urine and blood samples. Selective glucose quenching was efficacious at different concentrations of glucose (1-6 mM) and in the pH range of 7-8, limit of detection was 0.25 mM. The potential uses of carbon-based materials have grown, thanks to the excellent sensing/detection capabilities of the NCZ composite as well as the capacity to prevent nanoparticle aggregation, opening up new possibilities for the development of environmentally benign nano-sensors.

Forecasting tuberculosis incidence: a review of time series and machine learning models for prediction and eradication strategies

Despite efforts by the World Health Organization (WHO), tuberculosis (TB) remains a leading cause of fatalities globally. This study reviews time series and machine learning models for TB incidence prediction, identifies popular algorithms, and highlights the need for further research to improve accuracy and global scope. SCOPUS, PUBMED, IEEE, Web of Science, and PRISMA were used for search and article selection from 2012 to 2023. The results revealed that ARIMA, SARIMA, ETS, GRNN, BPNN, NARNN, NNAR, and RNN are popular time series and ML algorithms adopted for TB incidence rate predictions. The inaccurate TB incidence prediction and limited global scope of prior studies suggest a need for further research. This review serves as a roadmap for the WHO to focus on regions that require more attention for TB prevention and the need for more sophisticated models for TB incidence predictions.

Decreased online hepatitis information seeking during the COVID-19 pandemic: an Infodemiology study

Introduction

Viral hepatitis remains a public health concern worldwide, mainly in developing countries. The public's awareness and interest in viral hepatitis information are essential in preventing and controlling this disease. Infodemiology has been used as a surrogate to assess the general understanding of disease and measure public awareness of health topics. However, this analysis has not been applied to viral hepatitis. Thus, this study investigated the online global search interest for viral hepatitis in the last decade, focusing on the period before and during the COVID-19 pandemic.

Methods

Global online search interest for hepatitis was measured using the Google Trends™ database. Spearman's rank-order correlation correlated country-specific characteristics and prevalence data with search volume index.

Results

There was a significant reduction in online search interest for hepatitis during the COVID-19 pandemic (2020). People searching for hepatitis are also interested in hepatitis vaccination. Search volume index is positively correlated with viral hepatitis and HIV prevalence and negatively correlated with GDP. This correlation mirrors the high burden of viral hepatitis in developing countries and their citizens' desire to be informed about this disease.

Conclusions

Our study found decreased global online interest in viral hepatitis during the pandemic. Moreover, higher online interest in hepatitis was observed in countries with a lower gross domestic product and high viral hepatitis and HIV prevalence. We demonstrated that global online interest toward viral hepatitis could be assessed through the infodemiologic approach using Google Trends™.

A Co-Doped Carbon Dot/Silver Nanoparticle Nanocomposite-Based Fluorescence Sensor for Metformin Hydrochloride Detection

In this study, a fluorescence sensor based on nitrogen and phosphorus co-doped carbon dot/silver nanoparticle (NPCD/AgNP) nanocomposites was developed for metformin hydrochloride (MFH) detection. We first utilized the reducing nature of the NPCDs to prepare AgNPs from Ag⁺ and subsequently prepare NPCD/AgNP nanocomposites. The nanocomposite material was characterized by various methods, including electron microscopic methods (SEM and TEM), spectroscopic methods (UV-Vis, PL, FTIR, and XPS spectroscopy), light scattering (ELS), and XRD. Further, we utilized the enhanced fluorescence of the NPCDs as well as the overlap between the fluorescence emission spectrum of the NPCDs and the absorption spectrum of the AgNPs to use the NPCD/AgNP nanocomposites as an effective inner filter effect (IFE) pair for sensing MFH. The IFE between NPCDs and AgNPs in the nanocomposite material resulted in a significant quenching of the fluorescence intensity of the nanocomposites compared to that of the pure NPCDs. However, the fluorescence was recovered when MFH was introduced into the nanocomposite solution. The fluorescence intensity of the nanocomposites increased linearly as the MFH concentration increased from 2 to 100 µg/L. This detection method showed good sensitivity compared to other methods. It also showed high selectivity and high sensing potential for MFH in human serum and yielded acceptable results.

Forecasting the future number of pertussis cases using data from Google Trends

Background

Alternative methods could be used to enhance the monitoring and forecasting of re-emerging conditions such as pertussis. Here, whether data on the volume of Internet searching on pertussis could complement traditional modeling based solely on reported case numbers was assessed.

Methods

SARIMA models were fitted to describe reported weekly pertussis case numbers over a four-year period in Germany. Pertussis-related Google Trends data (GTD) was added as an external regressor. Predictions were made by the models, both with and without GTD, and compared with values within the validation dataset over a one-year and for a two-weeks period.

Results

Predictions of the traditional model using solely reported case numbers resulted in an RMSE (residual mean squared error) of 192.65 and 207.8, a mean absolute percentage error (MAPE) of 58.59 and 72.1, and a mean absolute error (MAE) 169.53 and 190.53 for the one-year and for the two-weeks period, respectively. The GTD expanded model achieved better forecasting accuracy (RMSE: 144.22 and 201.78), a MAPE 43.86, and 68.54 and a MAE of 124.46 and 178.96. Corrected Akaike Information Criteria also favored the GTD expanded model (1750.98 vs. 1746.73). The difference between the predictive performances was significant when using a two-sided Diebold-Mariano test (DM value: 6.86, p < 0.001) for the one-year period.

Conclusion

Internet-based surveillance data enhanced the predictive ability of a traditionally based model and should be considered as a method to enhance future disease modeling.

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Pertussis-related Google Trends Data (GTD) showed a weak but significant correlation with the reported weekly number of pertussis cases.

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We fitted a SARIMA models to estimate reported weekly pertussis case numbers

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The GTD-expanded models achieved significantly better predictive accuracy than the traditional model over a one-year-period.

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Corrected Akaike Information Criteria also favored the GTD-Expanded SARIMA model.

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The use of GTD should be considered as a method to enhance pertussis forecasting.

Impact of the World Inflammatory Bowel Disease Day and Crohn's and Colitis Awareness Week on Population Interest Between 2016 and 2020: Google Trends Analysis

Background

More than 6 million people are affected by inflammatory bowel disease (IBD) globally. The World IBD Day (WID, May 19) and Crohn's and Colitis Awareness Week (CCAW, December 1-7) occur yearly as national health observances to raise public awareness of IBD, but their effects are unclear.

Objective

The aim of this study was to analyze the relationship between WID or CCAW and the public health awareness on IBD represented by the Google search engine query data.

Methods

This study evaluates the impact of WID and CCAW on the public awareness of IBD in the United States and worldwide from 2016 to 2020 by using the relative search volume of "IBD," "ulcerative colitis," and "Crohn's disease" in Google Trends. To identify significant time points of trend changes (joinpoints), we performed joinpoint regression analysis.

Results

No joinpoints were noted around the time of WID or CCAW during the study period in the search results of the United States. Worldwide, joinpoints were noted around WID in 2020 with the search for "IBD" and around CCAW in 2017 and 2019 with the search for "ulcerative colitis." However, the extents of trend changes were modest without statistically significant increases.

Conclusions

These results posed a question that WID and CCAW might not have worked as expected to raise public awareness of IBD. Additional studies are needed to precisely estimate the impact of health observances to raise the awareness of IBD.

Google Trends Data and COVID-19 in Europe: Correlations and model enhancement are European wide

The current COVID-19 pandemic offers a unique opportunity to examine the utility of Internet search data in disease modelling across multiple countries. Most such studies typically examine trends within only a single country, with few going beyond describing the relationship between search data patterns and disease occurrence. Google Trends data (GTD) indicating the volume of Internet searching on 'coronavirus' were obtained for a range of European countries along with corresponding incident case numbers. Significant positive correlations between GTD with incident case numbers occurred across European countries, with the strongest correlations being obtained using contemporaneous data for most countries. GTD was then integrated into a distributed lag model; this improved model quality for both the increasing and decreasing epidemic phases. These results show the utility of Internet search data in disease modelling, with possible implications for cross country analysis.