Abstract. An Enterprise Information System (EIS) software has three main aspects: data, which are processed to generate business information; application functions, which transform data into information; and business rules, which control and restrict the manipulation of data by functions. Traditional approaches to EIS software development consider data and application functions. Rules are second class citizens, embedded on the specification of either data (as database integrity constraints) or on the EIS functions (as a part of the application software). This work presents a new, integrated approach for the development and maintenance of EIS software. The main ideas are to focus on the conceptual modeling of the three aspects of the EIS software -application functions, business rules, and database schema -and to automatically generate code for each of these software aspects. This improves software quality, reducing redundancies by centralizing EIS definitions on a single conceptual model. Due to automatic generation of code, this approach increases the software engineering staff productivity, making it possible to respond to the continuous changes in the business domain.
Background: Influenza is a disease under surveillance worldwide with different seasonal patterns in temperate and tropical regions. Previous studies have conducted modeling of influenza seasonality using climate variables. This study aimed to identify potential meteorological factors that are associated with influenza seasonality in Jinan, China.Methods: Data from three influenza sentinel hospitals and respective climate factors (average temperature, relatively humidity (RH), absolute humidity (AH), sunshine duration, accumulated rainfall and speed of wind), from 2013 to 2016, were collected. Statistical and wavelet analyses were used to explore the epidemiological characteristics of influenza virus and its potential association with climate factors.Results: The dynamic of influenza was characterized by annual cycle, with remarkable winter epidemic peaks from December to February. Spearman's correlation and wavelet coherence analysis illuminated that temperature, AH and atmospheric pressure were main influencing factors. Multiple wavelet coherence analysis showed that temperature and atmospheric pressure might be the main influencing factors of influenza virus A(H3N2) and influenza virus B, whereas temperature and AH might best shape the seasonality of influenza virus A(H1N1)pdm09. During the epidemic season, the prevalence of influenza virus lagged behind the change of temperature by 1-8 weeks and atmospheric pressure by 0.5-3 weeks for different influenza viruses.Conclusion: Climate factors were significantly associated with influenza seasonality in Jinan during the influenza epidemic season, and the optional time for influenza vaccination is before November. These finding should be considered in influenza planning of control and prevention.
Background: Influenza is a disease under surveillance worldwide with different seasonal patterns in temperate and tropical regions. Previous studies have conducted modeling of influenza seasonality using climate variables. This study aimed to identify potential meteorological factors that are associated with influenza seasonality in Jinan, China.Methods: Data from three influenza sentinel hospitals and respective climate factors (average temperature, relatively humidity (RH), absolute humidity (AH), sunshine duration, accumulated rainfall and speed of wind), from 2013 to 2016, were collected. Statistical and wavelet analyses were used to explore the epidemiological characteristics of influenza virus and its potential association with climate factors.Results: The dynamic of influenza was characterized by annual cycle, with remarkable winter epidemic peaks from December to February. Spearman's correlation and wavelet coherence analysis illuminated that temperature, AH and atmospheric pressure were main influencing factors. Multiple wavelet coherence analysis showed that temperature and atmospheric pressure might be the main influencing factors of influenza virus A(H3N2) and influenza virus B, whereas temperature and AH might best shape the seasonality of influenza virus A(H1N1)pdm09. During the epidemic season, the prevalence of influenza virus lagged behind the change of temperature by 1-8 weeks and atmospheric pressure by 0.5-3 weeks for different influenza viruses.Conclusion: Climate factors were significantly associated with influenza seasonality in Jinan during the influenza epidemic season, and the optional time for influenza vaccination is before November. These finding should be considered in influenza planning of control and prevention.
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