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- W3020302015 startingPage "109828" @default.
- W3020302015 abstract "A worldwide multi-scale interplay among a plethora of factors, ranging from micro-pathogens and individual or population interactions to macro-scale environmental, socio-economic and demographic conditions, entails the development of highly sophisticated mathematical models for robust representation of the contagious disease dynamics that would lead to the improvement of current outbreak control strategies and vaccination and prevention policies. Due to the complexity of the underlying interactions, both deterministic and stochastic epidemiological models are built upon incomplete information regarding the infectious network. Hence, rigorous mathematical epidemiology models can be utilized to combat epidemic outbreaks. We introduce a new spatiotemporal approach (SBDiEM) for modeling, forecasting and nowcasting infectious dynamics, particularly in light of recent efforts to establish a global surveillance network for combating pandemics with the use of artificial intelligence. This model can be adjusted to describe past outbreaks as well as COVID-19. Our novel methodology may have important implications for national health systems, international stakeholders and policy makers." @default.
- W3020302015 created "2020-05-01" @default.
- W3020302015 creator A5038937667 @default.
- W3020302015 creator A5077190128 @default.
- W3020302015 date "2020-07-01" @default.
- W3020302015 modified "2023-10-10" @default.
- W3020302015 title "SBDiEM: A new mathematical model of infectious disease dynamics" @default.
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- W3020302015 doi "https://doi.org/10.1016/j.chaos.2020.109828" @default.
- W3020302015 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7177179" @default.
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