Abstract
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® environment). The IDS has been tested with the epidemiological data of three outbreaks occurred recently: severe acute respiratory syndrome epidemic in China (2002–2003), plague outbreak in Madagascar (2017) and the Ebola virus disease outbreak in the Democratic Republic of Congo (2018). The outcomes are promising and they reveal that the software has been able to recognize and characterize these outbreaks. The future perspective about this software regards the developing of that tool as a useful and user-friendly predictive tool appropriate for first responders, health care workers, and public health decision makers to help them in predicting, assessing and contrasting outbreaks.
Highlights
Infectious diseases represent a serious risk for public health; their spread can result in a large impact on people and populations [1]
This study aims to introduce and develop an infectious disease 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 tested in two different stages of the epidemic: at the beginning, when there is a lack of data, and during a more advanced stage, when more accurate and detailed data are available
Summary
Infectious diseases represent a serious risk for public health; their spread can result in a large impact on people and populations [1]. Thanks to greatly expanded trade and travel, infectious diseases – either naturally occurring or caused by accidental or intentional release of pathogens, can spread rapidly, resulting in a potentially significant impact on life, major economic crises, and political instability [3]. Information systems play a central role in developing a comprehensive and effective approach to prevent, detect, respond to, and manage infectious disease outbreaks [4,5]. Many agencies and institutions have developed information systems to access, analyse, and report outbreaks. The Center for Disease Control and Prevention (CDC) in the U.S.A. has developed web-based reporting systems for health departments
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