6533b831fe1ef96bd1298603

RESEARCH PRODUCT

Elasticity as a measure for online determination of remission points in ongoing epidemics.

Ernesto J. Veres-ferrerJose M. Pavía

subject

Statistics and Probability2019-20 coronavirus outbreakCoronavirus disease 2019 (COVID-19)Computer scienceEpidemiology01 natural sciencesTime010104 statistics & probability03 medical and health sciencesRemission induction0302 clinical medicinePandemicHealth careEconometricsHumansComputer Simulation030212 general & internal medicine0101 mathematicsElasticity (economics)EpidemicsPandemicsProportional Hazards Modelsbusiness.industryRemission InductionCOVID-19businessEpidemiologic MethodsRandom variableRate of growth

description

The correct identification of change-points during ongoing outbreak investigations of infectious diseases is a matter of paramount importance in epidemiology, with major implications for the management of health care resources, public health and, as the COVID-19 pandemic has shown, social live. Onsets, peaks, and inflexion points are some of them. An onset is the moment when the epidemic starts. A "peak" indicates a moment at which the incorporated values, both before and after, are lower: a maximum. The inflexion points identify moments in which the rate of growth of the incorporation of new cases changes intensity. In this study, after interpreting the concept of elasticity of a random variable in an innovative way, we propose using it as a new simpler tool for anticipating epidemic remission change-points. In particular, we propose that the "remission point of change" will occur just at the instant when the speed in the accumulation of new cases is lower than the average speed of accumulation of cases up to that moment. This gives stability and robustness to the estimation in the event of possible remission variations. This descriptive measure, which is very easy to calculate and interpret, is revealed as informative and adequate, has the advantage of being distribution-free and can be estimated in real time, while the data is being collected. We use the 2014-2016 Western Africa Ebola virus epidemic to demonstrate this new approach. A couple of examples analyzing COVID-19 data are also included.

10.1002/sim.8807https://pubmed.ncbi.nlm.nih.gov/33174250