A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics

Agosto, Arianna; Giudici, Paolo

doi:10.2139/ssrn.3551626

Cited by 39 publications

(37 citation statements)

References 6 publications

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“…Agosto and Giudici [2] drew a similar conclusion from their analysis of the Chinese data, which covers the period from January 20 to March 15, 2020. Their estimated β and γ parameters (final column of Table 1) revealed that the contagion cycle was in a downward trend (γ < β).…”

Section: Resultsmentioning

confidence: 67%

“…Poisson autoregressive as a function of a short-term dependence only 2. Poisson autoregressive as a function of both a short-term dependence and a long-term dependence Following Agosto and Giudici [2], the number of new cases yt reported at time (day) t is assumed to follow a Poisson distribution i.e. yt ~ Poisson (λt), with a log-linear autoregressive intensity specification, as follows:…”

Section: Data Sourcementioning

confidence: 99%

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Modeling and predicting the spread of COVID-19 in Lebanon: A Bayesian perspective

Kharroubi

2020

Preprint

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In this article, we investigate the problem of modelling the trend of the current Coronavirus disease 2019 pandemic in Lebanon along time. Two different models were developed using Bayesian Markov chain Monte Carlo simulation methods. The models fitted included Poisson autoregressive as a function of a short-term dependence only and Poisson autoregressive as a function of both a short-term dependence and a long-term dependence. The two models are compared in terms of their predictive ability using root mean squared error and deviance information criterion. The Poisson autoregressive model that allows to capture both short and long term memory effects performs best under all criterions. The use of such a model can greatly improve the estimation of number of new infections, and can indicate whether disease has an upward/downward trend, andwhere about every country is on that trend, so that containment measures can be applied and/or relaxed.

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Section: Resultsmentioning

confidence: 67%

Section: Data Sourcementioning

confidence: 99%

Modeling and predicting the spread of COVID-19 in Lebanon: A Bayesian perspective

Kharroubi

2020

Preprint

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“…For notational convenience, we will omit the subscript i for the remainder of this section. We sample the joint posterior distribution for θ = [α 0 , α, β, γ, δ, η, n, κ], p(θ | D) ∝ p(D | θ)p(θ), (1) where p(D | θ) is the likelihood function and p(θ) is the prior distribution. The likelihood function is intractable, due to the unobserved populations S t , I t , and R u t .…”

Section: Bayesian Analysismentioning

confidence: 99%

Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic

Warne

Ebert

Drovandi

et al. 2020

Preprint

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Since the initial outbreak in Wuhan (Hubei, China) in December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), has rapidly spread to cause one of the most pressing challenges facing our world today: the COVID-19 pandemic. Within four months of the first reported cases, more than two and a half million cases were confirmed with over two hundred thousand deaths globally, and many countries had taken extreme measures to stop the spread. In this work, we analyse the response to the COVID-19 outbreak for 103 countries over the period 22 January to 13 April 2020. We utilise a novel stochastic epidemiological model that includes a regulatory mechanism that captures the level of tolerance to rising confirmed cases within the response behaviour. Using approximate Bayesian computation, we identify that the top ten outbreaks as of 31 March are characterised by a high tolerance to rising cases tallies, whereas countries that avoided severe outbreak have a low tolerance. Countries that recovered rapidly also have a higher identification rate. As of 13 April, almost all countries show declines in transmission rates and basic reproductive numbers. Furthermore, countries approaching recovery also increased their identification rate between 31 March and 13 April. We also demonstrate that uncertainty in undocumented infections dramatically impacts uncertainty in predictions. Overall, we recommend that broader testing is required to understand the magnitude of undocumented infections.

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“…A variety of modelling strategies have been applied. Techniques include: empirical approaches such as phenomenological growth curves [29]; data-driven, statistical approaches using non-linear autoregressive models [31]; and mechanistic models based on epidemiological theory [32] with various extensions [33,34].…”

Section: Introductionmentioning

confidence: 99%

Hindsight is 2020 Vision: A Characterisation of the Global Response to the COVID-19 Pandemic

Warne

Ebert

Drovandi

et al. 2020

Preprint

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Background The global impact of COVID-19 and the country-specific responses to the pandemic provide an unparalleled opportunity to learn about different patterns of the outbreak and interventions. We model the global pattern of trajectories of reported COVID-19 cases during the primary response period, with the aim of learning from the past to prepare for the future. Methods Using Bayesian methods, we analyse the response to the COVID-19 outbreak for 158 countries for the period 22 January to 9 June 2020. This encompasses the period in which many countries imposed a variety of response measures and initial relaxation strategies. Instead of modelling specific intervention types and timings for each country explicitly, we adopt a stochastic epidemiological model including a feedback mechanism on virus transmission to capture complex nonlinear dynamics arising from continuous changes in community behaviour in response to rising case numbers. We analyse the overall effect of interventions and community responses across diverse regions. This approach mitigates explicit consideration of issues such as period of infectivity and public adherence to government restrictions. Results Countries with the largest cumulative case tallies are characterised by a delayed response, whereas countries that avoid substantial community transmission during the period of study responded quickly. Countries that recovered rapidly also have a higher case identification rate and small numbers of undocumented community transmission at the early stages of the outbreak. We also demonstrate that uncertainty in numbers of undocumented infections dramatically impacts the risk of second waves. Our approach is also effective at pre-empting potential second waves and flare-ups. Conclusions We demonstrate the utility of modelling to interpret community behaviour in the early epidemic stages. Two lessons learnt that are important for the future are: i) countries that imposed strict containment measures early in the epidemic fared better with respect to numbers of reported cases; and ii) broader testing is required early in the epidemic to understand the magnitude of undocumented infections and recover rapidly. We conclude that clear patterns of containment are essential prior to relaxation of restrictions and show that modelling can provide insights to this end.

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A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics

Cited by 39 publications

References 6 publications

Modeling and predicting the spread of COVID-19 in Lebanon: A Bayesian perspective

Modeling and predicting the spread of COVID-19 in Lebanon: A Bayesian perspective

Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic

Hindsight is 2020 Vision: A Characterisation of the Global Response to the COVID-19 Pandemic

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