Estimation of SARS-CoV-2 Infection Prevalence in Santa Clara County
Yadlowsky, S.; Shah, N.; Steinhardt, J.
Show abstract
To reliably estimate the demand on regional health systems and perform public health planning, it is necessary to have a good estimate of the prevalence of infection with SARS-CoV-2 (the virus that causes COVID-19) in the population. In the absence of wide-spread testing, we provide one approach to infer prevalence based on the assumption that the fraction of true infections needing hospitalization is fixed and that all hospitalized cases of COVID-19 in Santa Clara are identified. Our goal is to estimate the prevalence of SARS-CoV-2 infections, i.e. the true number of people currently infected with the virus, divided by the total population size. Our analysis suggests that as of March 17, 2020, there are 6,500 infections (0.34% of the population) of SARS-CoV-2 in Santa Clara County. Based on adjusting the parameters of our model to be optimistic (respectively pessimistic), the number of infections would be 1,400 (resp. 26,000), corresponding to a prevalence of 0.08% (resp. 1.36%). If the shelter-in-place led to R0 < 1, we would expect the number of infections to remain about constant for the next few weeks. However, even if this were true, we expect to continue to see an increase in hospitalized cases of COVID-19 in the short term due to the fact that infection of SARS-CoV-2 on March 17th can lead to hospitalizations up to 14 days later.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- A Bayesian Susceptible-Infectious-Hospitalized-Ventilated-Recovered Model to Predict Demand for COVID-19 Inpatient Care in a Large Healthcare System 95%
- Estimating the impact of interventions against COVID-19: from lockdown to vaccination 94%
- Bayesian Space-time SIR modeling of Covid-19 in two US states during the 2020-2021 pandemic 94%
Similar papers in this journal
- A Comprehensive County Level Framework to Identify Factors Affecting Hospital Capacity and Predict Future Hospital Demand 94%
- Quantifying the effect of isolation and negative certification on COVID-19 transmission 94%
- Mathematical modelling of SARS-CoV-2 variant outbreaks reveals their probability of extinction 94%
Similar papers in this journal
- Evaluation of Contact-Tracing Policies Against the Spread of SARS-CoV-2 in Austria– An Agent-Based Simulation 95%
- Effects of Mitigation and Control Policies in Realistic Epidemic Models Accounting for Household Transmission Dynamics 92%
- Multilevel and Quasi Monte Carlo methods for the calculation of the Expected Value of Partial Perfect Information 91%
Similar papers in this journal
- A Predictive Modelling Framework for COVID-19 Transmission to Inform the Management of Mass Events 93%
- Isolation Considered Epidemiological Model for the Prediction of COVID-19 Trend in Tokyo, Japan 92%
- A Multivariate Forecasting Model for the COVID-19 Hospital Census Based on Local Infection Incidence 92%
"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.