Are we there yet? Adaptive SIR model for continuous estimation of COVID-19 infection rate and reproduction number in the United States (Preprint)

BACKGROUND The dynamics of the COVID-19 epidemic vary due to local population density and policy measures. When making decisions, policy makers consider an estimate of the effective reproduction number R_t which is the expected number of secondary infections by a single infected individual. OBJECTIVE We propose a simple method for estimating the time-varying infection rate and reproduction number R_t . METHODS We use a sliding window approach applied to a Susceptible-Infectious-Removed model. The infection rate is estimated using the reported cases for a seven-day window to obtain continuous estimation of R_t. The proposed adaptive SIR (aSIR) model was applied to data at the state and county levels. RESULTS The aSIR model showed an excellent fit for the number of reported COVID-19 positive cases, a one-day forecast MAPE was less than 2.6% across all states. However, a seven-day forecast MAPE reached 16.2% and strongly overestimated the number of cases when the reproduction number was high and changing fast. The maximal R_t showed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We demonstrate that the aSIR model can quickly adapt to an increase in the number of tests and associated increase in the reported cases of infections. Our results also suggest that intensive testing may be one of the effective methods of reducing R_t. CONCLUSIONS The aSIR model provides a simple and accurate computational tool to obtain continuous estimation of the reproduction number and evaluate the impact of mitigation measures.

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Are we there yet? An adaptive SIR model for continuous estimation of COVID-19 infection rate and reproduction number in the United States

10.1101/2020.09.13.20193896 ◽

2020 ◽

Author(s):

Mark B Shapiro ◽

Fazle Karim ◽

Guido Muscioni ◽

Abel Saju Augustine

Keyword(s):

New York ◽

Infection Rate ◽

Reproduction Number ◽

Infected Individual ◽

Local Population ◽

The United States ◽

Mitigation Measures ◽

Simple Method ◽

Wide Range ◽

Continuous Estimation

AbstractBackgroundThe dynamics of the COVID-19 epidemic vary due to local population density and policy measures. When making decisions, policy makers consider an estimate of the effective reproduction number ℛt which is the expected number of secondary infections by a single infected individual.ObjectiveWe propose a simple method for estimating the time-varying infection rate and reproduction number ℛt.MethodsWe use a sliding window approach applied to a Susceptible-Infectious-Removed model. The infection rate is estimated using the reported cases for a seven-day window to obtain continuous estimation of ℛt. The proposed adaptive SIR (aSIR) model was applied to data at the state and county levels.ResultsThe aSIR model showed an excellent fit for the number of reported COVID-19 positive cases, a one-day forecast MAPE was less than 2.6% across all states. However, a seven-day forecast MAPE reached 16.2% and strongly overestimated the number of cases when the reproduction number was high and changing fast. The maximal ℛt showed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We demonstrate that the aSIR model can quickly adapt to an increase in the number of tests and associated increase in the reported cases of infections. Our results also suggest that intensive testing may be one of the effective methods of reducing ℛt.ConclusionThe aSIR model provides a simple and accurate computational tool to obtain continuous estimation of the reproduction number and evaluate the efficacy of mitigation measures.

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Time fused coefficient SIR model with application to COVID-19 epidemic in the United States

Journal of Applied Statistics ◽

10.1080/02664763.2021.1936467 ◽

2021 ◽

pp. 1-15

Author(s):

Hou-Cheng Yang ◽

Yishu Xue ◽

Yuqing Pan ◽

Qingyang Liu ◽

Guanyu Hu

Keyword(s):

United States ◽

The United States ◽

Sir Model

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Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England

Science ◽

10.1126/science.abg3055 ◽

2021 ◽

Vol 372 (6538) ◽

pp. eabg3055 ◽

Cited By ~ 82

Author(s):

Nicholas G. Davies ◽

Sam Abbott ◽

Rosanna C. Barnard ◽

Christopher I. Jarvis ◽

Adam J. Kucharski ◽

...

Keyword(s):

United States ◽

Severe Acute Respiratory Syndrome ◽

Reproduction Number ◽

The United States ◽

Control Measures ◽

Transmission Model ◽

Educational Institutions ◽

Stringent Control ◽

Dynamic Transmission Model ◽

Credible Intervals

A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States.

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Maximum entropy method for estimating the reproduction number: An investigation for COVID-19 in China and the United States

Physical Review E ◽

10.1103/physreve.102.032136 ◽

2020 ◽

Vol 102 (3) ◽

Author(s):

Yong Tao

Keyword(s):

United States ◽

Maximum Entropy ◽

Maximum Entropy Method ◽

Reproduction Number ◽

The United States ◽

Entropy Method

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Anomalous influenza seasonality in the United States and the emergence of novel influenza B viruses

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2012327118 ◽

2021 ◽

Vol 118 (5) ◽

pp. e2012327118

Author(s):

Rebecca K. Borchering ◽

Christian E. Gunning ◽

Deven V. Gokhale ◽

K. Bodie Weedop ◽

Arash Saeidpour ◽

...

Keyword(s):

United States ◽

Influenza A ◽

Reproduction Number ◽

Influenza Season ◽

The United States ◽

Influenza B ◽

Relative Dynamics ◽

2009 H1n1 ◽

Kinetics Of ◽

Early Activity

The 2019/2020 influenza season in the United States began earlier than any season since the 2009 H1N1 pandemic, with an increase in influenza-like illnesses observed as early as August. Also noteworthy was the numerical domination of influenza B cases early in this influenza season, in contrast to their typically later peak in the past. Here, we dissect the 2019/2020 influenza season not only with regard to its unusually early activity, but also with regard to the relative dynamics of type A and type B cases. We propose that the recent expansion of a novel influenza B/Victoria clade may be associated with this shift in the composition and kinetics of the influenza season in the United States. We use epidemiological transmission models to explore whether changes in the effective reproduction number or short-term cross-immunity between these viruses can explain the dynamics of influenza A and B seasonality. We find support for an increase in the effective reproduction number of influenza B, rather than support for cross-type immunity-driven dynamics. Our findings have clear implications for optimal vaccination strategies.

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Hasty Reduction of COVID-19 Lockdown Measures Leads to the Second Wave of Infection

10.1101/2020.05.23.20111526 ◽

2020 ◽

Cited By ~ 1

Author(s):

Yara Hazem ◽

Suchitra Natarajan ◽

Essam R. Berikaa

Keyword(s):

United States ◽

United Kingdom ◽

The United States ◽

Sir Model ◽

Global Impact ◽

The United Kingdom ◽

The Impact ◽

Second Wave

AbstractThe outbreak of COVID-19 has an undeniable global impact, both socially and economically. March 11th, 2020, COVID-19 was declared as a pandemic worldwide. Many governments, worldwide, have imposed strict lockdown measures to minimize the spread of COVID-19. However, these measures cannot last forever; therefore, many countries are already considering relaxing the lockdown measures. This study, quantitatively, investigated the impact of this relaxation in the United States, Germany, the United Kingdom, Italy, Spain, and Canada. A modified version of the SIR model is used to model the reduction in lockdown based on the already available data. The results showed an inevitable second wave of COVID-19 infection following loosening the current measures. The study tries to reveal the predicted number of infected cases for different reopening dates. Additionally, the predicted number of infected cases for different reopening dates is reported.

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Explaining COVID-19 Outbreaks with Reactive SEIRD Models

10.1101/2021.02.09.21251440 ◽

2021 ◽

Author(s):

Kunal Menda ◽

Lucas Laird ◽

Mykel J. Kochenderfer ◽

Rajmonda S. Caceres

Keyword(s):

Neural Network ◽

United States ◽

Infection Rate ◽

Expectation Maximization ◽

The United States ◽

County Level ◽

Training Set ◽

Single Model ◽

Partial Observability ◽

Level Data

AbstractCOVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. In this work, we seek to explain the diversity in epidemic progressions by considering an extension to the compartmental SEIRD model. The model we propose uses a neural network to predict the infection rate as a function of time and of the prevalence of the disease. We provide a methodology for fitting this model to available county-level data describing aggregate cases and deaths. Our method uses Expectation-Maximization in order to overcome the challenge of partial observability—that the system’s state is only partially reflected in available data. We fit a single model to data from multiple counties in the United States exhibiting different behavior. By simulating the model, we show that it is capable of exhibiting both single peak and multi-peak behavior, reproducing behavior observed in counties both in and out of the training set. We also numerically compare the error of simulations from our model with a standard SEIRD model, showing that the proposed extensions are necessary to be able to explain the spread of COVID-19.

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