6533b836fe1ef96bd12a0b32
RESEARCH PRODUCT
Nowcasting COVID‐19 incidence indicators during the Italian first outbreak
Pierfrancesco Alaimo Di LoroAlessio FarcomeniGiovanna Jona LasinioAntonello MaruottiAntonello MaruottiFabio DivinoMarco MingioneMarco MingioneGianfranco Lovisonsubject
FOS: Computer and information sciencesStatistics and ProbabilityNowcastingEpidemiologyComputer scienceCOVID-19 growth curves Richards’ equation SARS-CoV-2COVID-19; growth curves; Richards' equation; SARS-CoV-2; Disease Outbreaks; Humans; Incidence; Italy; SARS-CoV-2; COVID-19growth curvesStatistics - Applications01 natural sciencesSARS‐CoV‐2Disease Outbreaks010104 statistics & probability03 medical and health sciences0302 clinical medicineCOVID‐19StatisticsHumansApplications (stat.AP)030212 general & internal medicine0101 mathematicsResearch ArticlesParametric statisticsrichards' equationExternal variableDisease OutbreakSARS-CoV-2Estimation theorycovid-19; richards' equation; sars-cov-2; growth curvesIncidenceIncidence (epidemiology)COVID-19OutbreakRegression analysisReplicatesars-cov-2Richards' equationItalycovid-19Settore SECS-S/01Settore SECS-S/01 - StatisticaResearch Articlegrowth curveHumandescription
A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided. publishedVersion
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-10-23 |