Challenges for modelling interventions for future pandemics

Kretzschmar, Mirjam; Ashby, Ben; Fearon, Elizabeth; Overton, Christopher E.; Panovska‐Griffiths, Jasmina; Pellis, Lorenzo; Quaife, Matthew; Rozhnova, Ganna; Scarabel, Francesca; Stage, Helena B.; Swallow, Ben; Thompson, Robin N; Tildesley, Michael J.; Villela, Daniel A. M.

doi:10.1016/j.epidem.2022.100546

Cited by 56 publications

(45 citation statements)

References 149 publications

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“…If it is not possible to collect these data within the necessary timeframe, the challenge arises of how to deal with biases in predictions that may depend on these missing data. Although data collection from contact tracing and contact patterns is continuously improving, challenges remain in how to estimate the level of risk associated with different types of contact ( Kretzschmar et al, 2021 ).…”

Section: Data Challenges For Estimation During a Pandemicmentioning

confidence: 99%

“…If these variations are not properly accounted for, inference about infections/prevalence may be biased. If a model does not accurately capture the impact of an intervention, inference regarding the transmission process may be inaccurate ( Kretzschmar et al, 2021 ). However, there may be insufficient data to quantify vaccine impacts on transmission/disease prevention accurately, creating a substantial modelling challenge.…”

Section: Data Challenges For Estimation During a Pandemicmentioning

confidence: 99%

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Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

et al. 2022

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Section: Data Challenges For Estimation During a Pandemicmentioning

confidence: 99%

Section: Data Challenges For Estimation During a Pandemicmentioning

confidence: 99%

Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

et al. 2022

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“…There are two simple interventions which are timescales to describe two waves or large spikes in the growth of COVID-19 cases in Indonesia, which divides the entire modeling time into three parts. Intervention on the model has also been carried out by Sennott et al (2016) and speci cally in modeling the future of the pandemic (Kretzschmar et al, 2022) . The intervention referred to here is shown in Figure 10 and the vaccination movement and its rate agree with the dynamic of the wave of infection cases.…”

Section: Solutions Of Sir Model and Svir Modelmentioning

confidence: 99%

Susceptible Vaccine Infected Removed (SVIR) Model for COVID-19 Cases in Indonesia

Parhusip

Trihandaru

Wicaksono

et al. 2022

Sci. Technol. Indones

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Analysis of data on COVID-19 cases in Indonesia is shown by using the Susceptible Vaccine Infected Removed (SVIR) in this article. In the previous research, cases in the period March-May 2021 were studied, and the reproduction number was computed based on the Susceptible Infected Removed (SIR) model. The prediction did not agree with the real data. Therefore the objective of this article is to improve the model by adding the vaccine variable leading to the new model called the SVIR model as the novelty of this article. The used data are collected from COVID-19 cases of the Indonesian population published by the Indonesian government from March 2020-April 2022. However, the vaccinated persons with COVID-19 cases have been recorded since January 2022. Therefore the models rely on the period January 2021-March 2022, where the parameters in the SIR and SVIR models are determined in this period. The method used is discretizing the models into linear systems, and these systems are solved by Ordinary Least Square (OLS) for time-dependent parameters. It is assumed that the birth rate and death rate in the considered period are constant. Additionally, individuals who have recovered from COVID-19 will not be infected again, and vaccination is not necessarily twice. Furthermore, individuals who have been vaccinated will not be infected with the COVID-19 virus. The SVIR model has captured 3 waves of COVID-19 cases that are appropriate to the real situation in Indonesia from January 2021-March 2022. Additionally, the reproduction numbers as functions of time have been generated. The fluctuations of reproduction numbers agree with the real data. For further research, different regions such as districts in Java and other islands will also be analyzed as the implication of this research.

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“…There is also the challenge of having specialists with high experience and knowledge in the application of mathematical models of data of different nature, including economic, political and health [5]; that contribute to the analysis of the past, present and future of pandemics.…”

Section: Editorialmentioning

confidence: 99%