Short-term Covid-19 forecast for latecomers

Medeiros, Marcelo C.; Street, Alexandre; Valladão, Davi; Vasconcelos, Gabriel; Zilberman, Eduardo

doi:10.1016/j.ijforecast.2021.09.013

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…Other researchers used statistical approaches to COVID-19 prediction 13 ; Medeiros et al. 20 , Petropoulos et al. 23 .…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Medeiros et al. 20 proposed a simple statistical method for short-term real-time forecasting of the number of COVID-19 cases and fatalities in latecomer countries using a penalised LASSO regression model. Their model included an error correction mechanism to construct a model of latecomer countries in terms of other countries with a similar pandemic.…”

Section: Introductionmentioning

confidence: 99%

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Short-term forecasting of COVID-19 using support vector regression: An application using Zimbabwean data

Shoko¹,

Sigauke²

2023

American Journal of Infection Control

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“…Other researchers used statistical approaches to COVID-19 prediction 13 ; Medeiros et al. 20 , Petropoulos et al. 23 .…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-term forecasting of COVID-19 using support vector regression: An application using Zimbabwean data

Shoko¹,

Sigauke²

2023

American Journal of Infection Control

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“…Medeiros et al. ( 2021 ) propose a method based on regression models to forecast the evolution of the pandemic in countries using past data from other countries that have already passed through a similar stage. The work by Ribeiro et al.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Agent-Based Simulation Model for the Spreading of Diseases Through Social Interactions During Pandemics

Salem

Moreno

2022

J Control Autom Electr Syst

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Epidemiological models have a vital and consolidated role in aiding decision-making during crises such as the Coronavirus Disease 2019 (COVID-19) pandemic. However, the influence of social interactions in the spreading of communicable diseases is left aside from the main models in the literature. The main contribution of this work is the introduction of a probabilistic simulation model based on a multi-agent approach that is capable of predicting the spreading of diseases. Our proposal has a simple model for the main source of infections in pandemics of respiratory viruses: social interactions. This simplicity is key for incorporating complex networks topology into the model, which is a more accurate representation for real-world interactions. This flexibility in network structure allows the evaluation of specific phenomena, such as the presence of super-spreaders. We provide the modeling for the dynamical network topology in two different simulation scenarios. Another contribution is the generic microscopic model for infection evolution that enables the evaluation of impact from more specific behaviors and interventions on the overall spreading of the disease. It also enables a more intuitive process for going from data to model parameters. This ease of changing the infection evolution model is key for performing more complete analyses than would be possible in other models from the literature. Further, we give specific parameters for a controlled scenario with quick testing and tracing. We present computational results that illustrate the model utilization for predicting the spreading of COVID-19 in a city. Also, we show the results of applying the model for assessing the risk of resuming on-site activities at a collective use facility.

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“…Model-free approaches like ours or Medeiros et al [2020] should complement policy discussions or forecasting exercises based on those models, especially from a quantitative point of view. There are related papers using state or county level US data.…”

Section: Introductionmentioning

confidence: 99%

Lockdown effects in US states: an artificial counterfactual approach

Carneiro,

Ferreira,

Medeiros

et al. 2020

Preprint

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We adopt an artificial counterfactual approach to assess the impact of lockdowns on the short-run evolution of the number of cases and deaths in some US states. To do so, we explore the different timing in which US states adopted lockdown policies, and divide them among treated and control groups. For each treated state, we construct an artificial counterfactual. On average, and in the very short-run, the counterfactual accumulated number of cases would be two times larger if lockdown policies were not implemented.

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Short-term Covid-19 forecast for latecomers

Cited by 10 publications

References 18 publications

Short-term forecasting of COVID-19 using support vector regression: An application using Zimbabwean data

Short-term forecasting of COVID-19 using support vector regression: An application using Zimbabwean data

A Multi-Agent-Based Simulation Model for the Spreading of Diseases Through Social Interactions During Pandemics

Lockdown effects in US states: an artificial counterfactual approach

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