Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies

Wang, Qinxia; Xie, Shanghong; Wang, Yuanjia; Zeng, Donglin

doi:10.1101/2020.04.16.20067306

Cited by 16 publications

(12 citation statements)

References 28 publications

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“…In early stages of the pandemic there was quite some confusion, particularly in western countries, about the protective effects of facial masks and physical distancing until the established evidence surfaced 29,30 . To this point, several restrictions levied have proven effective in slowing down the spread of COVID‐19, but the greatest effect is obtained by applying a combination of measures 27,31 . Interestingly, during the 1918‐19 Spanish Flu pandemic similar travel restrictions and quarantine measures were implemented in many countries.…”

Section: Clinical Featuresmentioning

confidence: 99%

COVID‐19: Current knowledge in clinical features, immunological responses, and vaccine development

et al. 2021

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In December 2019, a cluster of patients with unexplained viral pneumonia was identified in Wuhan, China. 1 To identify the causative agent of this disease, a large number of tests were conducted, which ruled out several etiological agents that may cause similar symptoms, including the severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV), and other common respiratory pathogens. Finally, researchers identified the cause being a novel coronavirus termed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). 1 With a rapid increase in the number of infected people, on March 11th the World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) as a pandemic 2 (Figure 1). SARS-CoV-2 has infected over 100 million individuals and has

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Section: Clinical Featuresmentioning

confidence: 99%

COVID‐19: Current knowledge in clinical features, immunological responses, and vaccine development

et al. 2021

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“…( Canabarro et al., 2020 ) and Liu, etc ( Liu, Magal, & Webb, 2020 ) to make the predictions. These models may involve a large number of parameters and assumptions and are therefore vulnerable to the perturbation parameters and previous assumptions, do not evaluate the goodness of the fit of the observed data and may lead to a wider prediction interval ( Wang, Xie, Wang, & Zeng, 2020 ).…”

Section: Review Of Epidemiological Modelmentioning

confidence: 99%

Predict new cases of the coronavirus 19; in Michigan, U.S.A. or other countries using Crow-AMSAA method

Wang¹

2020

Infectious Disease Modelling

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Statistical predictions are useful to predict events based on statistical models. The data is useful to determine outcomes based on inputs and calculations. The Crow-AMSAA method will be explored to predict new cases of Coronavirus 19 (COVID19). This method is currently used within engineering reliability design to predict failures and evaluate the reliability growth. The author intents to use this model to predict the COVID19 cases by using daily reported data from Michigan, New York City, U.S.A and other countries. The piece wise Crow-AMSAA (CA) model fits the data very well for the infected cases and deaths at different phases during the start of the COVID19 outbreak. The slope β of the Crow-AMSAA line indicates the speed of the transmission or death rate. The traditional epidemiological model is based on the exponential distribution, but the Crow-AMSAA is the Non Homogeneous Poisson Process (NHPP) which can be used to modeling the complex problem like COVID19, especially when the various mitigation strategies such as social distance, isolation and locking down were implemented by the government at different places.

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“…Fitted SEIR D No C CDDEP 5 Bayesian SEIR C No D CMU 6 Autoreg. time-series D No C Columbia 7 Metapop SEIR C,D No ⊆ C Columbia-UNC 57 Stat. survival-conv.…”

Section: Models Evaluated For Comparisonmentioning

confidence: 99%

Globally Local: Hyper-local Modeling for Accurate Forecast of COVID-19

Gopalakrishnan

Pethe

Kefayati

et al. 2020

Preprint

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Multiple efforts to model the epidemiology of SARS-CoV-2 have recently been launched in support of public health response at the national, state, and county levels. While the pandemic is global, the dynamics of this infectious disease varies with geography, local policies, and local variations in demographics. An underlying assumption of most infectious disease compartment modeling is that of a well mixed population at the resolution of the areas being modeled. The implicit need to model at fine spatial resolution is impeded by the quality of ground truth data for fine scale administrative subdivisions. To understand the trade-offs and benefits of such modeling as a function of scale, we compare the predictive performance of a SARS-CoV-2 modeling at the county, county cluster, and state level for the entire United States. Our results demonstrate that accurate prediction at the county level requires hyper-local modeling with county resolution. State level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. As an important use case, leveraging high resolution modeling with public health data and admissions data from Hillsborough County Florida, we performed weekly forecasts of both hospital admission and ICU bed demand for the county. The repeated forecasts between March and August 2020 were used to develop accurate resource allocation plans for Tampa General Hospital.2010 MSC92-D30, 91-C20

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Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies

Cited by 16 publications

References 28 publications

COVID‐19: Current knowledge in clinical features, immunological responses, and vaccine development

COVID‐19: Current knowledge in clinical features, immunological responses, and vaccine development

Predict new cases of the coronavirus 19; in Michigan, U.S.A. or other countries using Crow-AMSAA method

Globally Local: Hyper-local Modeling for Accurate Forecast of COVID-19

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