Monitoring Italian COVID-19 spread by a forced SEIRD model

Piccolomini, Elena Loli; Zama, Fabiana

doi:10.1371/journal.pone.0237417

Cited by 96 publications

(88 citation statements)

References 18 publications

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“…Epidemiological methods try to model disease states taking into account the biological and disease processes such as disease transmission processes and individual and population variables ( Hyder et al, 2013 , Shaman and Karspeck, 2012 , Shaman et al, 2013 ), thus making them inevitably more complex and more computationally challenging. The most common model in epidemiological forecasting of infectious diseases is the SEIR (Susceptible - Exposed - Infected - Recovered) model and its variation the SEIRD model ( Piccolomini & Zama, 2020 ), which has a deaths compartment added as well. The population is divided into the appropriate compartment, and they move between compartments during different stages of the disease.…”

Section: Literature Reviewmentioning

confidence: 99%

COVID-19: Forecasting confirmed cases and deaths with a simple time series model

Petropoulos

Makridakis

Stylianou

2022

International Journal of Forecasting

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Forecasting the outcome of outbreaks as early and as accurately as possible is crucial for decision making and policy implementations. A significant challenge faced by forecasters is that not all outbreaks and epidemics turn into pandemics making the prediction of their severity difficult. At the same time, the decisions made to enforce lockdowns and other mitigating interventions versus their socioeconomic consequences are not only hard to make, but also highly uncertain. The majority of modeling approaches to outbreaks, epidemics, and pandemics take an epidemiological approach that considers biological and disease processes. In this paper, we accept the limitations of forecasting to predict the long-term trajectory of an outbreak, and instead, we propose a statistical, time-series approach to modelling and predicting the short-term behaviour of COVID-19. Our model assumes a multiplicative trend, aiming to capture the continuation of the two variables we predict (global confirmed cases and deaths) as well as their uncertainty. We present the timeline of producing and evaluating 10-day-ahead forecasts over a period of four months. Our simple model offers competitive forecast accuracy and estimates of uncertainty that are useful and practically relevant.

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Section: Literature Reviewmentioning

confidence: 99%

COVID-19: Forecasting confirmed cases and deaths with a simple time series model

Petropoulos

Makridakis

Stylianou

2022

International Journal of Forecasting

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“…However, the same values for (q up , q down ) fit satisfactorily both the numbers of active cases and of deaths per day for a given country, as shown in Figures 3 and 4. At this point, it would be worth noting that although the present model, unlike, for example, the SEIRD model [48,49], does not distinguish deaths from healings, the deceased cases will still be roughly proportional to infected people. It is this proportionality, we believe, which makes us obtain reasonable fits using the variable I of the model, again incorporating the proportionality into N eff The high value at q up 1 reflects the divergence expected in the limit q up q down 1.…”

Section: Application Of Q-seir Model To Covid-19 Pandemicmentioning

confidence: 92%

Epidemiological Model With Anomalous Kinetics: Early Stages of the COVID-19 Pandemic

Tirnakli

Tsallis

2020

Front. Phys.

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We generalize the phenomenological, law of mass action-like, SIR and SEIR epidemiological models to situations with anomalous kinetics. Specifically, the contagion and removal terms, normally linear in the fraction I of infected people, are taken to depend on I qup and I qdown, respectively. These dependencies can be understood as highly reduced effective descriptions of contagion via anomalous diffusion of susceptible and infected people in fractal geometries and removal (i.e., recovery or death) via complex mechanisms leading to slowly decaying removal-time distributions. We obtain rather convincing fits to time series for both active cases and mortality with the same values of (qup,qdown) for a given country, suggesting that such aspects may in fact be present in the early evolution of the COVID-19 pandemic. We also obtain approximate values for the effective population Neff, which turns out to be a small percentage of the entire population N for each country.

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“…3 and 4. At this point it would be worth noting that although the present model, unlike for example the SEIRD model [48, 49], does not distinguish deaths from healings, the deceased cases will still be roughly proportional to infected people. It is this proportionality, we believe, which makes us to obtain reasonable fits using the variable I of the model, again incorporating the proportionality into N eff , hence into ρ .…”

Section: Application Of Q-seir Model To Covid-19 Pandemicsmentioning

confidence: 99%

Epidemiological model with anomalous kinetics - The Covid-19 pandemics

Tirnakli

Tsallis

2020

Preprint

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We generalize the phenomenological, law of mass action-like, SIR and SEIR epidemiological models to situations with anomalous kinetics. Specifically, the contagion and removal terms, normally linear in the fraction $I$ of infecteds, are taken to depend on $I^{\,q_{up}}$ and $I^{\,q_{down}}$, respectively. These dependencies can be understood as highly reduced effective descriptions of contagion via anomalous diffusion of susceptibles and infecteds in fractal geometries, and removal (i.e., recovery or death) via complex mechanisms leading to slowly decaying removal-time distributions. We obtain rather convincing fits to time series for both active cases and mortality with the same values of $(q_{up},q_{down})$ for a given country, suggesting that such aspects may in fact be present in the evolution of the Covid-19 pandemic. We also obtain approximate values for the effective population $N_{eff}$, which turns out to be a small percentage of the entire population $N$ for each country.

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Monitoring Italian COVID-19 spread by a forced SEIRD model

Cited by 96 publications

References 18 publications

COVID-19: Forecasting confirmed cases and deaths with a simple time series model

COVID-19: Forecasting confirmed cases and deaths with a simple time series model

Epidemiological Model With Anomalous Kinetics: Early Stages of the COVID-19 Pandemic

Epidemiological model with anomalous kinetics - The Covid-19 pandemics

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