A predictive model for Covid-19 spread – with application to eight US states and how to end the pandemic

Khan, Zeina S.; Bussel, Frank Van; Hussain, Fazle

doi:10.1017/s0950268820002423

Cited by 33 publications

(29 citation statements)

References 39 publications

(58 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second advantage of our approach is that it directly measures and informs policy-relevant variables. For example, the White House issued guidance on reopening the US economy that depends on a decrease in the documented number of cases and in the proportion of positive test results over a 14-day period, among other criteria and considerations [23,[78][79][80][81][82][83]. As noted above, the number and proportion of positive test results are the outcomes of a data generating process that includes not only the underlying contagion process but a multitude of mediating factors as well as idiosyncrasies of the data collection and a delayed reporting process.…”

Section: Model Developmentmentioning

confidence: 99%

Dynamic Panel Estimate–Based Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation (Preprint)

Oehmke¹,

Oehmke²,

Singh³

et al. 2020

Preprint

View full text Add to dashboard Cite

BACKGROUND The Great Covid Shutdown worked when it was well implemented. Some countries completely eliminated SARS-CoV-2 while others flattened the curve. The US is more complicated due to varying social isolation policies which begs the question, when can we reopen? Reopening should be predicated on a sustained decline in Covid. Existing models of Covid-19 contagion rely on parameters such as R0 and use intensive data collection efforts, however these models use static statistical methods that do not capture all of the relevant dynamics such as varying specificity and sensitivity of diagnostic testing or asymptotic individuals unwittingly carrying the novel corona virus who are never tested. Existing Covid models use data that are subject to significant measurement error and other contaminants. Moreover, timely information is needed to improve statistical methods by extracting the information that is available from various datasets posted on websites. OBJECTIVE This study applies state-of-the-art statistical modeling applied to existing data on the internet to extract the best available estimates of the state-level dynamics of Covid-19 infection in the U.S. METHODS Dynamic panel data (DPD) models are estimated with the Arellano-Bond estimator utilizing the Generalized Method of Methods. This statistical technique allows for control of a variety of deficiencies in the existing dataset. Tests of the validity of the model and statistical technique are applied. RESULTS The results indicate 1) that the statistical approach is valid, including for determining recent changes in the pattern of infection, and 2) during the May 16-25 period the evolution of the pandemic changed with less inter-temporal persistence of the infection rate. This change represents a decline in the contagion model R value for that period, and is consistent with a ‘flattening of the curve’ but not with an imminent end to the pandemic. CONCLUSIONS Opening America comes with two certainties: 1)we will be Covid-free only when there is an effective vaccine; and 2)the “social” end is going to occur before the “medical” end of the pandemic, therefore, we need improved surveillance techniques to inform our leaders how to open sections of America more safely. DPD models can support opening America in combination with the extraction of Covid data from existing websites. CLINICALTRIAL NA

show abstract

Section: Model Developmentmentioning

confidence: 99%

Dynamic Panel Estimate–Based Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation (Preprint)

Oehmke¹,

Oehmke²,

Singh³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This study aims to take advantage of available worldwide data on COVID-19 (Roser et al, 2021;Dong et al, 2020) to benchmark and assign error bars to minimal models, like the susceptibleinfected-recovered (SIR) with different sophistication levels (Kermack and McKendrick, 1927;Weiss, 2013;He et al, 2020a;Yang and Wang, 2020;Khan et al, 2020;Annas et al, 2020), a straightforward least-squares best-fit (LS) Statistical Heuristic Regression based on a lognormal distribution (Lam, 1988), or basic Monte-Carlo simulation (Girona, 2020;Gang, 2020). It is wellknown that finding a global minimum of non-linear least-squares problems for p free parameters requires, at worst, a brute force search in p-dimensional parameter space.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Forecasting COVID-19: Heuristic Regression, Susceptible-Infected-Removed Model and, Spatial Stochastic

Andres

Andres-Bragado

Hoessly

2021

Front. Appl. Math. Stat.

View full text Add to dashboard Cite

The COVID-19 pandemic has had worldwide devastating effects on human lives, highlighting the need for tools to predict its development. The dynamics of such public-health threats can often be efficiently analyzed through simple models that help to make quantitative timely policy decisions. We benchmark a minimal version of a Susceptible-Infected-Removed model for infectious diseases (SIR) coupled with a simple least-squares Statistical Heuristic Regression (SHR) based on a lognormal distribution. We derive the three free parameters for both models in several cases and test them against the amount of data needed to bring accuracy in predictions. The SHR model is ≈±2% accurate about 20 days past the second inflexion point in the daily curve of cases, while the SIR model reaches a similar accuracy a fortnight before. All the analyzed cases assert the utility of SHR and SIR approximants as a valuable tool to forecast the disease’s evolution. Finally, we have studied simulated stochastic individual-based SIR dynamics, which yields a detailed spatial and temporal view of the disease that cannot be given by SIR or SHR methods.

show abstract

“…In general, the models consider the classification of a given population in some parts: Susceptible (S), Infective (I), Recovered (R), among others, and are then dubbed in terms of which of them they consider for the dynamics of the disease: SIR, SEIR, etcetera. There is historical evidence that such models are in good agreement with the dynamics of past epidemics [1], and such past successes have triggered its use in the present crisis, see for instance, [3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Time-dependent SI model for epidemiology and applications to Covid-19

Ureña–López

2021

Rev. Mex. Fís.

View full text Add to dashboard Cite

A generalisation of the Susceptible-Infectious model is made to include a time-dependent transmission rate, which leads to a close analytical expression in terms of a logistic function. The solution can be applied to any continuous function chosen to describe the evolution of the transmission rate with time. Taking inspiration from real data of the Covid-19, for the case of cumulative confirmed positives and deaths, we propose an exponentially decaying transmission rate with two free parameters, one for its initial amplitude and another one for its decaying rate. The resultant time-dependent SI model, which under extra conditions recovers the standard Gompertz functional form, is then compared with data from selected countries and its parameters fit using Bayesian inference. We make predictions about the asymptotic number of confirmed positives and deaths, and discuss the possible evolution of the disease in each country in terms of our parametrisation of the transmission rate.

show abstract

A predictive model for Covid-19 spread – with application to eight US states and how to end the pandemic

Cited by 33 publications

References 39 publications

Dynamic Panel Estimate–Based Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation (Preprint)

Dynamic Panel Estimate–Based Health Surveillance of SARS-CoV-2 Infection Rates to Inform Public Health Policy: Model Development and Validation (Preprint)

Monitoring and Forecasting COVID-19: Heuristic Regression, Susceptible-Infected-Removed Model and, Spatial Stochastic

Time-dependent SI model for epidemiology and applications to Covid-19

Contact Info

Product

Resources

About