COVID-19: Short term prediction model using daily incidence data

• Published in: PloS one Vol. 16; no. 4; p. e0250110
• Main Authors: Zhao, Hongwei, Merchant, Naveed N, McNulty, Alyssa, Radcliff, Tiffany A, Cote, Murray J, Fischer, Rebecca S B, Sang, Huiyan, Ory, Marcia G
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.04.2021; Public Library of Science (PLoS)
• Subjects: Analysis; Autoregressive models; Basic Reproduction Number; Computer programs; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - transmission; Data smoothing; Disease transmission; Disease Transmission, Infectious - prevention & control; Disease Transmission, Infectious - statistics & numerical data; Drafting software; Earth Sciences; Editing; Epidemics; Forecasting; Government agencies; Humans; Incidence; Infections; Infectious diseases; Markov chains; Medical supplies; Medicine and Health Sciences; Models, Statistical; Pandemics; People and places; Physical Sciences; Population; Prediction models; Public Health; Research and Analysis Methods; SARS-CoV-2 - isolation & purification; Severe acute respiratory syndrome coronavirus 2; Software; Statistical analysis; Time series; United States - epidemiology; Viral diseases; Viruses; United States; United States > US; Texas
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0250110

Prediction of the dynamics of new SARS-CoV-2 infections during the current COVID-19 pandemic is critical for public health planning of efficient health care allocation and monitoring the effects of policy interventions. We describe a new approach that forecasts the number of incident cases in the near future given past occurrences using only a small number of assumptions. Our approach to forecasting future COVID-19 cases involves 1) modeling the observed incidence cases using a Poisson distribution for the daily incidence number, and a gamma distribution for the series interval; 2) estimating the effective reproduction number assuming its value stays constant during a short time interval; and 3) drawing future incidence cases from their posterior distributions, assuming that the current transmission rate will stay the same, or change by a certain degree. We apply our method to predicting the number of new COVID-19 cases in a single state in the U.S. and for a subset of counties within the state to demonstrate the utility of this method at varying scales of prediction. Our method produces reasonably accurate results when the effective reproduction number is distributed similarly in the future as in the past. Large deviations from the predicted results can imply that a change in policy or some other factors have occurred that have dramatically altered the disease transmission over time. We presented a modelling approach that we believe can be easily adopted by others, and immediately useful for local or state planning.