Dataset Information

Forecasting of COVID-19 onset cases: a data-driven analysis in the early stage of delay.

ABSTRACT: The outbreak of COVID-19 has become a global public health event. Many researchers have proposed many epidemiological models to predict the outbreak trend of COVID-19, but all use confirmed cases to predict "onset cases." In this article, a total of 5434 cases were collected from National Health Commission and other provincial Health Commission in China, spanning from 1 December 2019 to 23 February 2020. We studied the delayed distribution of patients from onset to be confirmed. The delay is divided into two stages, which takes about 15 days or even longer. Therefore, considering the right truncation of the data, we proposed a "predict-in-advance" method, used the number of "visiting hospital cases" to predict the number of "onset cases." The results not only show that our prediction shortens the delay of the second stage, but also the predicted value of onset cases is quite close to the real value of onset cases, which can effectively predict the epidemic trend of sudden infectious diseases, and provide an important reference for the government to formulate control measures in advance.

SUBMITTER: Wang X

PROVIDER: S-EPMC7786867 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Forecasting of COVID-19 onset cases: a data-driven analysis in the early stage of delay.

Wang Xueli X Li Ying Y Jia Jinzhu J

Environmental science and pollution research international 20210106 16

The outbreak of COVID-19 has become a global public health event. Many researchers have proposed many epidemiological models to predict the outbreak trend of COVID-19, but all use confirmed cases to predict "onset cases." In this article, a total of 5434 cases were collected from National Health Commission and other provincial Health Commission in China, spanning from 1 December 2019 to 23 February 2020. We studied the delayed distribution of patients from onset to be confirmed. The delay is div ...[more]

PMID: 33405171

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Project description:BackgroundThe COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation.MethodsWe developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021.ResultsThe model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible.ConclusionsWe successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.

Dataset Information

Forecasting of COVID-19 onset cases: a data-driven analysis in the early stage of delay.

Publications

Forecasting of COVID-19 onset cases: a data-driven analysis in the early stage of delay.

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