Multiple change point estimation of trends in Covid-19 infections and deaths in India as compared with WHO regions

Development of maximum relevant prior feature ensemble (MRPFE) index to characterize future drought using global climate models

Drought is one of the foremost outcomes of global warming and global climate change. It is a serious threat to humans and other living beings. To reduce the adverse impact of drought, mitigation strategies as well as sound projections of extreme events are essential. This research aims to strengthen the robustness of anticipated twenty-first century drought by combining different Global Climate Models (GCMs). In this article, we develop a new drought index, named Maximum Relevant Prior Feature Ensemble index that is based on the newly proposed weighting scheme, called weighted ensemble (WE). In the application, this study considers 32 randomly scattered grid points within the Tibetan Plateau region and 18 GCMs of Coupled Model Intercomparison Project Phase 6 (CMIP6) of precipitation. In this study, the comparative inferences of the WE scheme are made with the traditional simple model averaging (SMA). To investigate the trend and long-term probability of various classes, this research employs Markov chain steady states probability, Mann-Kendall trend test, and Sen's Slope estimator. The outcomes of this research are twofold. Firstly, the comparative inference shows that the proposed weighting scheme has greater efficiency than SMA to conflate GCMs. Secondly, the research indicates that the Tibetan Plateau is projected to experience "moderate drought (MD)" in the twenty-first century.

Multiple change point analysis of hepatitis B reports in Xinjiang, China from 2006 to 2021

Objective

Hepatitis B (HB) is a major global challenge, but there has been a lack of epidemiological studies on HB incidence in Xinjiang from a change-point perspective. This study aims to bridge this gap by identifying significant change points and trends.

Method

The datasets were obtained from the Xinjiang Information System for Disease Control and Prevention. Change points were identified using binary segmentation for full datasets and a segmented regression model for five age groups.

Results

The results showed four change points for the quarterly HB time series, with the period between the first change point (March 2007) and the second change point (March 2010) having the highest mean number of HB reports. In the subsequent segments, there was a clear downward trend in reported cases. The segmented regression model showed different numbers of change points for each age group, with the 30-50, 51-80, and 15-29 age groups having higher growth rates.

Conclusion

Change point analysis has valuable applications in epidemiology. These findings provide important information for future epidemiological studies and early warning systems for HB.

Clustering spatio-temporal series of confirmed COVID-19 deaths in Europe

The impact of the COVID-19 pandemic varied significantly across different countries, with important consequences in the definition of control and response strategies. In this work, to investigate the heterogeneity of this crisis, we analyse the spatial patterns of deaths attributed to COVID-19 in several European countries. To this end, we propose a Bayesian nonparametric approach, based on mixture of Gaussian processes coupled with Dirichlet process, to group the COVID-19 mortality curves. The model provides a flexible framework for the analysis of time series data, allowing the inclusion in the clustering procedure of different features of the series, such as spatial correlations, time varying parameters and measurement errors. We evaluate the proposed methodology on the death counts recorded at NUTS-2 regional level for several European countries in the period from March 2020 to February 2021.

eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis

Understanding social media networks and group interactions is crucial to the advancement of linguistic and cultural behavior. This includes how people accessed advice on health during COVID-19 lockdown. Some people turned to social media to access information on health when other routes were curtailed by isolation rules, particularly among older generations. Facebook public pages, groups and verified profiles using keywords “senior citizen health”, “older generations”, and “healthy living” were analyzed over a 12-month period to examine engagement with social media promoting good mental health. CrowdTangle was used to source status updates, photo and video sharing information in the English language, which resulted in an initial 116,321 posts and 6,462,065 interactions. Data analysis and visualization were used to explore large datasets, including natural language processing for “message” content discovery, word frequency and correlational analysis as well as co-word clustering. Preliminary results indicate strong links to healthy aging information shared on social media, which showed correlations to global daily confirmed cases and daily deaths. The results can identify public concerns early on and address mental health issues among senior citizens on Facebook.