Bayesian inference in an extended SEIR model with nonparametric disease transmission rate: an application to the Ebola epidemic in Sierra Leone

Frasso, Gianluca; Lambert, Philippe

doi:10.1093/biostatistics/kxw027

Cited by 19 publications

(24 citation statements)

References 20 publications

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“…Our focus is on model comparison rather than measuring uncertainty of parameters in a specific model, as is currently being done for hospital demand forecasts in Los Angeles ( 16 ). For interval forecasts with uncertainty quantification, one may consider a negative binomial alternative to Poisson regression that captures overdispersion in case and death counts ( 44 , 45 ).…”

Section: Resultsmentioning

confidence: 99%

The challenges of modeling and forecasting the spread of COVID-19

Bertozzi

Franco

Mohler

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

512

475

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The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional-scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.

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Section: Resultsmentioning

confidence: 99%

The challenges of modeling and forecasting the spread of COVID-19

Bertozzi

Franco

Mohler

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

512

475

View full text Add to dashboard Cite

show abstract

“…In this article, we used the Bayesian inference in the transmission dynamic model which was also used in the study of Ebola epidemic before. On 2016, GIANLUCA FRASSO et al [9] estimated unknown parameters of SEIR model in a Bayesian framework based on the combination of the reported data and prior distribution, and they mentioned that the flexible modelling made the estimation of reproductive number robust, despite the fact of misspecification of the initial epidemic states and underreporting of the infectious cases.…”

Section: Related Workmentioning

confidence: 99%

Transmission in Latent Period Causes A Large Number of Infected People in the United States

Liu

Zhu

Liu

et al. 2020

Preprint

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The cumulative number of confirmed cases in the United States exceeded one million on 29 April 2020, becoming the country of the most serious pandemic in the world. We proposed a model to analyze the real situation and follow-up trend of the epidemic in the US.The proposed model divides the epidemic period into two phases, and includes three different categories of transmitters: the latent population, the documented infectious population, and the undocumented infectious population. We use metapopulation network to simulate the spread of the COVID-19 in the US, and apply the Bayesian inference to estimate the key parameters of the model. We also perform component analysis and sensitivity analysis, researching the compositions of the people with COVID-19.The results show that the basic reproduction number in the early period of propagation is 4.06. As of April 13, 2020, only 45% (95% CI: 35% -73%) of symptom onset cases in the United States were documented. The incubation period of COVID-19 is 10.69 days (95% CI: 10.02 -11.74). If the current level of interventions is continued, the cumulative number of confirmed cases is expected to reach more than 1.7 million in July and continue to grow.

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“…При анализе риска здоровью, обусловлен-ного ООИ, для оценки вероятности заболевания и смерти может быть использовано математи-ческое моделирование эпидемиологического процесса, позволяющее имитировать его разви-тие без противоэпидемических мер с количест-венной характеристикой числа случаев заболе-ваемости и смертности [4,10,14,17,20]. По-добная информация вместе с фактическими данными о непредотвращенных даже в услови-ях проведения мероприятий по купированию эпидемии потерях может быть использована в качестве базиса для оценки экономического эффекта противоэпидемических мер и, соот-ветственно, для совершенствования планиро-вания будущих мероприятий по противодей-ствию ООИ и их трансграничному распро-странению.…”

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