clove and Severe-Acute-Respiratory-Syndrome

The rapid detection of ongoing outbreak - and the identification of causative pathogen - is pivotal for the early recognition of public health threats. The emergence and re-emergence of infectious diseases are linked to several determinants, both human factors - such as population density, travel, and trade - and ecological factors - like climate change and agricultural practices. Several technologies are available for the rapid molecular identification of pathogens [e.g. real-time polymerase chain reaction (PCR)], and together with on line monitoring tools of infectious disease activity and behaviour, they contribute to the surveillance system for infectious diseases. Web-based surveillance tools, infectious diseases modelling and epidemic intelligence methods represent crucial components for timely outbreak detection and rapid risk assessment. The study aims to integrate the current prevention and control system with a prediction tool for infectious diseases, based on regression analysis, to support decision makers, health care workers, and first responders to quickly and properly recognise an outbreak. This study has the intention to develop an infectious disease regressive prediction tool working with an off-line database built with specific epidemiological parameters of a set of infectious diseases of high consequences. The tool has been developed as a first prototype of a software solution called Infectious Diseases Seeker (IDS) and it had been established in two main steps, the database building stage and the software implementation stage (MATLAB

Topics: China; Communicable Diseases; Democratic Republic of the Congo; Disease Outbreaks; Female; Hemorrhagic Fever, Ebola; Humans; Madagascar; Male; Plague; Public Health Surveillance; Severe Acute Respiratory Syndrome; Software