Trend Analysis
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Public Health ◽  
2022 ◽  
Vol 203 ◽  
pp. 31-35
C. Nie ◽  
T. Yang ◽  
L. Liu ◽  
F. Hong

Gilaad G Kaplan ◽  
Fox E Underwood ◽  
Stephanie Coward ◽  
Manasi Agrawal ◽  
Ryan C Ungaro ◽  

Abstract Background Cases of coronavirus disease 2019 (COVID-19) have emerged in discrete waves. We explored temporal trends in the reporting of COVID-19 in inflammatory bowel disease (IBD) patients. Methods The Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease (SECURE-IBD) is an international registry of IBD patients diagnosed with COVID-19. The average percent changes (APCs) were calculated in weekly reported cases of COVID-19 during the periods of March 22 to September 12, September 13 to December 12, 2020, and December 13 to July 31, 2021. Results Across 73 countries, 6404 cases of COVID-19 were reported in IBD patients. COVID-19 reporting decreased globally by 4.2% per week (95% CI, −5.3% to −3.0%) from March 22 to September 12, 2020, then climbed by 10.2% per week (95% CI, 8.1%-12.3%) from September 13 to December 12, 2020, and then declined by 6.3% per week (95% CI, −7.8% to −4.7%). In the fall of 2020, weekly reporting climbed in North America (APC, 11.3%; 95% CI, 8.8-13.8) and Europe (APC, 17.7%; 95% CI, 12.1%-23.5%), whereas reporting was stable in Asia (APC, −8.1%; 95% CI, −15.6-0.1). From December 13, 2020, to July 31, 2021, reporting of COVID-19 in those with IBD declined in North America (APC, −8.5%; 95% CI, −10.2 to −6.7) and Europe (APC, −5.4%; 95% CI, −7.2 to −3.6) and was stable in Latin America (APC, −1.5%; 95% CI, −3.5% to 0.6%). Conclusions Temporal trends in reporting of COVID-19 in those with IBD are consistent with the epidemiological patterns COVID-19 globally.

2022 ◽  
Nitin Prajapati

The Aim of this research is to identify influence, usage, and the benefits of AI (Artificial Intelligence) and ML (Machine learning) using big data analytics in Insurance sector. Insurance sector is the most volatile industry since multiple natural influences like Brexit, pandemic, covid 19, Climate changes, Volcano interruptions. This research paper will be used to explore potential scope and use cases for AI, ML and Big data processing in Insurance sector for Automate claim processing, fraud prevention, predictive analytics, and trend analysis towards possible cause for business losses or benefits. Empirical quantitative research method is used to verify the model with the sample of UK insurance sector analysis. This research will conclude some practical insights for Insurance companies using AI, ML, Big data processing and Cloud computing for the better client satisfaction, predictive analysis, and trending.

2022 ◽  
Vol Publish Ahead of Print ◽  
Atsuya Miki ◽  
Tomoyuki Okazaki ◽  
Robert N. Weinreb ◽  
Misa Morota ◽  
Aki Tanimura ◽  

2022 ◽  
Bruno César dos Santos ◽  
Rafael Grecco Sanches ◽  
Talyson de Melo Bolleli ◽  
Paulo Henrique de Souza ◽  
Vandoir Bourscheidt

Abstract With the advance of remote sensing technologies, meteorological satellites have become an alternative in the process of monitoring and measuring meteorological variables, both spatially and temporally. The present study brings some additional elements to the validation of satellite-based precipitation estimates by evaluating the CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station) monthly product for the central region of the state of São Paulo, Brazil, in the period 1981-2019. Initially, the general relationship between satellite estimates and surface rainfall data is assessed using the linear adjustment and error analysis in both temporal and spatial perspectives, followed by a trend analysis using Laplace test. The monthly map analysis showed a better performance of CHIRPS during the dry period (April to August) than for the wet period (October to March). Finally, monthly trends showed, in general, the same pattern of variability in rainfall over 38 years and a prevalence toward the reduction of rainfall. In summary, CHIRPS product seems a reasonable alternative for regions that lack historical rainfall information.

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