SUMMARYThe existence of change point in the Turkish earthquake data is investigated. For this purpose, the 218 earthquake data of magnitude 5 and higher, between the north (39.00-42.008) and the east (26.00-45.008) coordinates in Turkey from 12 July 1900 to 28 February 2007 are used. The characteristic function of the magnitude is derived. Poisson distribution is used to describe the recurrence times. In addition, using the compound Poisson process, the expected value and variance are estimated and computed for the loss of life and damaged buildings after the change point.

Objectives The COVID-19 pandemic caused by the novel SARS-CoV-2 coronavirus has drastically altered the global realities. Harnessing national scale data from the COVID-19 pandemic may better inform policy makers in decision making surrounding the reopening of society. We examined country-level, daily-confirmed, COVID-19 case data from the World Health Organization (WHO) to better understand the comparative dynamics associated with the ongoing global pandemic at a national scale. Study design Observational study. Methods We included data from 20 countries in Europe, the Americas, Africa, Eastern Mediterranean and West Pacific regions, and obtained the aggregated daily new case data for the European Union including 27 countries. We utilized an innovative analytic approach by applying statistical change point models, which have been previously employed to model volatility in stock markets, changes in genomic data, and data dynamics in other scientific disciplines, to segment the transformed case data. This allowed us to identify possible change or turning points as indicated by the dynamics of daily COVID-19 incidences. We also employed B-spline regression models to express the estimated (predicted) trend of daily new incidences for each country’s COVID-19 disease burden with the identified key change points in the model. Results We identified subtle, yet different change points (translated to actual calendar days) by either the mean and variance change point model with small p-values or by a Bayesian online change point algorithm with large posterior probability in the trend of COVID-19 incidences for different countries. We correlated these statistically identified change points with evidence from the literature surrounding these countries’ policies regarding opening and closing of their societies in an effort to slow the spread of COVID-19. The days when change points were detected were ahead of the actual policy implementation days, and in most of the countries included in this study the decision lagged the change point days too long to prevent potential widespread extension of the pandemic. Conclusions Our models describe the behavior of COVID-19 prevalence at a national scale and identify changes in national disease burden as relating to chronological changes in restrictive societal activity. Globally, social distancing measures may have been most effective in smaller countries with single governmental and public health organizational structures. Further research examining the impact of heterogeneous governmental responses to pandemic management appears warranted.

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In this paper, we study the change-point inference problem motivated by the genomic data that were collected for the purpose of monitoring DNA copy number changes. DNA copy number changes or copy number variations (CNVs) correspond to chromosomal aberrations and signify abnormality of a cell. Cancer development or other related diseases are usually relevant to DNA copy number changes on the genome. There are inherited random noises in such data, therefore, there is a need to employ an appropriate statistical model for identifying statistically significant DNA copy number changes. This type of statistical inference is evidently crucial in cancer researches, clinical diagnostic applications, and other related genomic researches. For the high-throughput genomic data resulting from DNA copy number experiments, a mean and variance change point model (MVCM) for detecting the CNVs is appropriate. We propose to use a Bayesian approach to study the MVCM for the cases of one change and propose to use a sliding window to search for all CNVs on a given chromosome. We carry out simulation studies to evaluate the estimate of the locus of the DNA copy number change using the derived posterior probability. These simulation results show that the approach is suitable for identifying copy number changes. The approach is also illustrated on several chromosomes from nine fibroblast cancer cell line data (array-based comparative genomic hybridization data). All DNA copy number aberrations that have been identified and verified by karyotyping are detected by our approach on these cell lines.

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