Internet Search Pattern of Nipah Outbreaks in Bangladesh (Jan 2018- Jul 2023): A Google Trend Analysis
Muntasir, I.; Rahman, M. S.
Show abstract
IntroductionNipah virus is a fatal bat-borne pathogen that follows a seasonality from December to May in Bangladesh. Since 2001, Nipah outbreaks have been reported annually in Bangladesh. However, in early 2023, there has been an occurrence of a series of outbreaks here. This study aims to investigate the infodemiological aspect of this series of Nipah outbreaks by analyzing the Google search interest in Bangladesh. MethodsThe "Explore" feature of Google Trends was utilized to analyze search behavior focusing on the topics "Nipah virus infection" and "Date Juice" in Bangladesh from January 2018 to July 2023. Data from Nipah outbreaks during the same period was obtained. Correlation analysis was done between Relative Search Volume (RSV) and outbreak frequency, and spatial analysis to compare heat maps showing RSV and outbreaks. ResultsA line graph depicting the relative search volume (RSV) of Nipah virus infection reveals fluctuations in public interest, with spikes following outbreak events and during the Nipah season. Similarly, the RSV of "Date Juice" showcases changing patterns, occasionally aligning with Nipah outbreaks. Pearson correlation analysis indicates moderate positive correlations between Nipah-related RSVs and outbreaks, with p-values < 0.01, underscoring the link between public interest and outbreak frequency. Heat maps depict regional variations, with higher RSV regions coinciding with reported outbreaks. ConclusionThe study found that RSV of both "Nipah Virus Infection" and "Date Juice" increased with the frequency of Nipah Outbreaks. We recommend continuous monitoring of health information regarding Nipah and other important public health issues.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The knowledge and practice towards COVID-19 pandemic prevention among residents of Ethiopia. An online cross-sectional study. 96%
- Community’s misconception about COVID-19 and its associated factors: Evidence from a cross-sectional study in Bangladesh 96%
- Generalized Linear Mixed Model Approach for Analyzing Water, Sanitation, and Hygiene Facilities in Bangladesh: Insights from BDHS 2022 Data 96%
Similar papers in this journal
- Experience from a COVID-19 screening centre of a tertiary care institution: A retrospective hospital-based study 95%
- Serological prevalence of SARS-CoV-2 antibody among children and young age (between age 2-17 years) group in India: An interim result from a large multi-centric population-based seroepidemiological study 95%
- Assessment of the knowledge, preferences and concern regarding the prospective COVID- 19 vaccine among adults residing in New Delhi, India-A cross sectional study 93%
Similar papers in this journal
- COVID-19 Vaccine hesitancy in Addis Ababa, Ethiopia: A mixed-methods study 94%
- Risk Factors for Non-Communicable Diseases among Bangladeshi Adults: An Application of Generalized Linear Mixed Model on Multilevel Demographic and Health Survey Data 93%
- Protocol for a prospective, hospital-based registry of pregnant women with SARS-CoV-2 infection in India: PregCovid Registry study 93%
Similar papers in this journal
- Prevalence of SARS-CoV-2 infection among COVID-19 RT-PCR laboratory workers in Bangladesh 94%
- Comparative study between first and second wave of COVID-19 deaths in India - a single center study 94%
- Data Driven Monitoring in Community Based Management of SAM children using Psychometric Techniques: An Operational Framework 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.