flepiMoP: The evolution of a flexible infectious disease modeling pipeline during the COVID-19 pandemic

Lemaitre, Joseph C.; Loo, Sara L.; Kaminsky, Joshua; Lee, Elizabeth C.; McKee, Clifton; Smith, Claire; Jung, Sung-mok; Sato, Koji; Carcelen, Erica; Hill, Alison; Lessler, Justin; Truelove, Shaun

doi:10.1016/j.epidem.2024.100753

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…These can be very generic, e.g. to estimate reproduction numbers [178], but also capture the dynamics of pathogens with a variety of life cycles [179]. Other epidemiology packages are designed to analyse specic types of data, such as serologies [180].…”

Section: A Retrozoonotic Pandemicmentioning

confidence: 99%

SARS-CoV-2 epidemiology, kinetics, and evolution: a narrative review

Alizon,

Sofonea

2024

Preprint

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Since winter 2019, SARS-CoV-2 emerged, spread, and evolved all around the globe. We explore four years of evolutionary epidemiology of this virus, ranging from the applied public health challenges to the more conceptual evolutionary biology perspectives. Through this review, we revisit key episodes of the pandemic, highlighting important epidemiology and evolution notions they may raise, to discuss how the pandemic has transformed (or should transform) the surveillance and prevention of viral respiratory infections.

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Section: A Retrozoonotic Pandemicmentioning

confidence: 99%

SARS-CoV-2 epidemiology, kinetics, and evolution: a narrative review

Alizon,

Sofonea

2024

Preprint

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Preface: COVID-19 Scenario Modeling Hubs

Loo,

Chinazzi,

Srivastava

et al. 2024

Epidemics

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A New Bimodal Growth Speed Model With Application in Multiwave Epidemic Simulations

Sabzpoushan,

Nozadmakvandi

2024

BioMed Research International

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An epidemic can cause enormous damage through financial and economic losses in addition to impaired health and loss of life. Good epidemic models can provide insights into ongoing waves that may facilitate public health responses and other readiness. Epidemic systems can undergo or consist of more than two driving forces; actually, they involve several. This paper deals with the modeling epidemics with two wave dynamics. We present a mathematical model with three driving forces, which is more compatible with the biology of the epidemic than conventional bilogistic and bi‐Richards ones, which have two driving forces. Simulation results show that our proposed model can predict turning points (waves) and cumulative case numbers during the two waves of epidemics satisfactorily. As a sample examination of our proposed model against real data, we show that the second phase of a typical epidemic can be detected by using less case data past the beginning of the epidemic than the bi‐Richards model.

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flepiMoP: The evolution of a flexible infectious disease modeling pipeline during the COVID-19 pandemic

Cited by 3 publications

References 27 publications

SARS-CoV-2 epidemiology, kinetics, and evolution: a narrative review

SARS-CoV-2 epidemiology, kinetics, and evolution: a narrative review

Preface: COVID-19 Scenario Modeling Hubs

A New Bimodal Growth Speed Model With Application in Multiwave Epidemic Simulations

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