Empirical Model of Spring 2020 Decrease in Daily Confirmed COVID-19 Cases in King County, Washington
Roach, J. C.
Show abstract
Projections of the near future of daily case incidence of COVID-19 are valuable for informing public policy. Near-future estimates are also useful for outbreaks of other diseases. Short-term predictions are unlikely to be affected by changes in herd immunity. In the absence of major net changes in factors that affect reproduction number (R), the two-parameter exponential model should be a standard model - indeed, it has been standard for epidemiological analysis of pandemics for a century but in recent decades has lost popularity to more complex compartmental models. Exponential models should be routinely included in reports describing epidemiological models as a reference, or null hypothesis. Exponential models should be fitted separately for each epidemiologically distinct jurisdiction. They should also be fitted separately to time intervals that differ by any major changes in factors that affect R. Using an exponential model, incidence-count half-life (t1/2) is a better statistic than R. Here an example of the exponential model is applied to King County, Washington during Spring 2020. During the pandemic, the parameters and predictions of this model have remained stable for intervals of one to four months, and the accuracy of model predictions has outperformed models with more parameters. The COVID pandemic can be modeled as a series of exponential curves, each spanning an interval ranging from one to four months. The length of these intervals is hard to predict, other than to extrapolate that future intervals will last about as long as past intervals.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Bayesian Space-time SIR modeling of Covid-19 in two US states during the 2020-2021 pandemic 95%
- Space-Time Covid-19 Bayesian SIR modeling in South Carolina 95%
- A Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered Model to Predict Demand for COVID-19 Inpatient Care in a Large Healthcare System 95%
Similar papers in this journal
- Using an Agent-Based Model to Assess K-12 School Reopenings Under Different COVID-19 Spread Scenarios – United States, School Year 2020/21 94%
- Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States 94%
- Scenario Design for Infectious Disease Projections: Integrating Concepts from Decision Analysis and Experimental Design 94%
Similar papers in this journal
- Incorporating the mutational landscape of SARS-COV-2 variants and case-dependent vaccination rates into epidemic models 95%
- Analysis of Intervention Effectiveness Using Early Outbreak Transmission Dynamics to Guide Future Pandemic Management and Decision-Making in Kuwait 95%
- The impact of policy timing on the spread of COVID-19 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.