Epidemic surveillance of novel coronavirus 2019 through probabilistic models - Ngerng, Sherilynn Siew Fong (2021)
This research paper explore s the fundamentals behind epidemic surveillance models and the characteristics that stand out towards the construction of modern made drop in surveill ance systems developed during the height of covid19 pandemic. It also allows a glimpse on creating a surveillance system for personal monitoring of disease outbreaks, whilst properties of the surveillance systems studied can be applied in personal monitor ing of similar unique events such as economic crisis. This research paper is typical in the application of ECDC covid' s publicly sourced surveillance data on 19 and this disease had little to no historical data prior to the pandemic. Amongst the epidemic s urveillance models discussed, Farrington ' s QuasiPoisson model predominantly works well with the aid of historical data to study previous trends and better predict incoming outbreaks while handling over Aberration Reporting Systemdispersed data. The Early (EARS) model was developed by CDC after the 9/11 incident to predict sudden terrorist attacks and sudden outbreaks. Temporal EndemicWhereas the SpatioEpidemic model monitors the disease outbreak in transitioning stages of Suspected covid19 'InfectedRemoved/Reco vered which allows the observation of s infection rate. These models could help us obtain essential information on the pandemic to possibly brace the next wave of disease outbreaks.