Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19

Biggerstaff, Matthew; Cowling, Benjamin J.; Cucunubá, Zulma M.; Dinh, Linh; Ferguson, Neil; Gao, Huizhi; Hill, Verity; Imai, Natsuko; Johansson, Michael A.; Kada, Sarah; Morgan, Oliver; Piontti, Ana Pastore y; Polonsky, Jonathan; Prasad, Pragati; Quandelacy, Talía M.; Rambaut, Andrew; Tappero, Jordan W.; Vandemaele, Katelijn; Vespignani, Alessandro; Warmbrod, Kelsey Lane; Wong, Jessica Y.

doi:10.3201/eid2611.201074

Cited by 53 publications

(44 citation statements)

References 58 publications

(17 reference statements)

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“…The inferred parameters were physically reasonable and matched up to the widely used parameter values derived from the national datasets. 28 We validate the predictive power of our model by using a subset of the available data and compare the model predictions for the next 10, 20, and 30 days. The model exhibits a high accuracy in the prediction, even when using only as few as 20 data points for the fitting.…”

Section: Resultsmentioning

confidence: 99%

Predicting and forecasting the impact of local resurgence and outbreaks of COVID-19: Use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity

Campillo-Funollet

Yperen

Allman³

et al. 2020

Preprint

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Rapid evidence-based decision-making and public policy based on quantitative modelling and forecasting by local and regional National Health Service (NHS-UK) managers and planners in response to the deadly severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), a virus causing COVID-19, has largely been missing. In this pilot study, we present a data-driven epidemiological modelling framework that allows to integrate quantitative modelling, validation and forecasting based on current available local and regional datasets to investigate and mitigate the impact of COVID-19 on local NHS hospitals in terms of healthcare demand and capacity as well as allowing for a systematic evaluation of the predictive accuracy of the modelling framework for long-term forecasting. We present an epidemiological model tailored and designed to meet the needs of the local health authorities, formulated to be fitted naturally to datasets which incorporate regional and local demographics. The model yields quantitative information on the healthcare demand and capacity required to manage and mitigate the COVID pandemic at the regional level. Furthermore, the model is rigorously validated using partial historical datasets, which is then used to demonstrate the forecasting power of the model and also to quantify the risk associated with the decision taken by healthcare managers and planners. Model parameters are fully justified, these are derived purely based on the time series data available at the regional level, with minimal assumptions. Using these inferred parameters, the model is able to make predictions under which secondary waves and re-infection scenarios could occur. Hence, our modelling approach addresses one of the major criticisms associated with the lack of transparency and precision of current COVID-19 models. Our approach offers a robust quantitative modelling framework where the probability of the model giving a wrong or correct prediction can be quantified.

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Section: Resultsmentioning

confidence: 99%

Predicting and forecasting the impact of local resurgence and outbreaks of COVID-19: Use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity

Campillo-Funollet

Yperen

Allman³

et al. 2020

Preprint

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“…The parameters are estimated using Markov Chain Monte Carlo methods with Gibbs sampling and non-informative flat prior. The boundaries of uniformly distributed priors are set upon from the literature (Biggerstaff et al, 2020) and the data collected from Shaanxi province ( Figures S3 and S4). We give the details of the MCM sampling method below.…”

Section: Inference Methodsmentioning

confidence: 99%

“…The knowledge of the well-known coronaviruses such as SARS and MERS have been borrowed for understanding the early transmission dynamics of COVID-19 (e.g., Wu J et al, 2020). Nevertheless, epidemiological characteristics of COVID-19 appear quite different from those of both SARS and MERS (Biggerstaff et al, 2020). The basic knowledge of COVID-19 epidemiological features should be obtained from the epidemic data during outbreaks.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Estimating the Transmissibility of COVID-19: Transmission Dynamics in Shaanxi Province of China as a Case Study

Zhang¹,

Liu²,

Xiong³

et al. 2020

Preprint

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To control and contain the outbreaks of emerging infectious diseases such as COVID-19, it is important to know how easy and fast they transmit among people. To explore the essential information of the novel infectious agents, people always confront an inverse problem: using (partially) observed numbers of infected people by time and region to dig up the underlying characteristics of unknown infectious agents. Epidemics armed with advanced statistical inference and mathematical theory has been developed to help reconstruct transmission dynamics processes and to estimate key features of infectious diseases. In this study we use COVID-19 outbreak in Shaanxi province as an example to illustrate how the infectious disease dynamics method can be used to help build the transmission process and to estimate the transmissibility of COVID-19. Three transmission dynamics models were proposed for this. By separating continuous importation from local transmission and treating imported cases as the source rather than results of local transmission, the basic reproduction number of COVID-19 in Shaanxi province was estimated in the range from 0.46 to 0.61, well below the critical value of 1.0. This indicates that COVID-19 cannot self-sustain in Shaanxi province and reflects the timely and strong control measures taken in Shaanxi province.

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“…While the estimated reproduction number (R 0 ) in the initial outbreaks in Wuhan, China, and other countries was relatively high, ranging from 2.5 to 5.1, in outbreaks that started by disease introduction through the importation of cases, R 0 was lower, from 2.1 to 3.2 [ 5 ]. The lowered R 0 might have resulted from better awareness and knowledge about COVID-19 transmission, better preparedness, or corresponding interventions [ 6 ].…”

Section: R 0 and Case Fatality Rate: Choosing Betwmentioning

confidence: 99%

Proactive and blended approach for COVID-19 control in Taiwan

Cheng

Huang²

2021

Biochemical and Biophysical Research Communications

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Coronavirus disease 2019 (COVID-19) has become the greatest threat to human society in a century. To better devise control strategies, policymakers should adjust policies based on scientific evidence in hand. Several countries have limited the epidemics of COVID-19 by prioritizing containment strategies to mitigate the impacts on public health and healthcare systems. However, asymptomatic/pre-symptomatic transmission of COVID-19 complicated traditional symptom-based approaches for disease control. In addition, drastic population-based interventions usually have significant societal and economic impacts. Therefore, in Taiwan, the containment strategies consisted of the more extended case-based interventions (e.g., case detection with enhanced surveillance and contact tracing with active monitoring and quarantine of close contacts) and more targeted population-based interventions (e.g., face mask use in recommended settings and risk-oriented border control with corresponding quarantine requirement). The success of the blended approach emphasizes not only the importance of evidence-supported policymaking but also the coordinated efforts between the government and the people.

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Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19

Cited by 53 publications

References 58 publications

Predicting and forecasting the impact of local resurgence and outbreaks of COVID-19: Use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity

Predicting and forecasting the impact of local resurgence and outbreaks of COVID-19: Use of SEIR-D quantitative epidemiological modelling for healthcare demand and capacity

Modelling and Estimating the Transmissibility of COVID-19: Transmission Dynamics in Shaanxi Province of China as a Case Study

Proactive and blended approach for COVID-19 control in Taiwan

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