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Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

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Uncertainty quantification in Covid-19 spread: Lockdown effects.

Ana Carpio¹, Emile Pierret²

¹Departamento de Matemática Aplicada, Universidad Complutense, 28040 Madrid, Spain.

Results in Physics

|March 14, 2022

Summary

This summary is machine-generated.

This study quantifies uncertainties in COVID-19 epidemiological models using Bayesian inference. It reveals low diagnosis rates and highlights the need for sustained confinement or effective distancing to control the spread of infectious diseases.

Keywords:

Bayesian inference Covid-19 Numerical simulation SEIJR models Uncertainty quantification

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Area of Science:

Epidemiology
Mathematical Biology
Infectious Disease Modeling

Background:

Quantifying uncertainties in epidemiological models is crucial for predicting disease spread.
COVID-19 pandemic highlighted the need for robust models to inform public health interventions.
Understanding transmission dynamics, including asymptomatic cases, is key to epidemic control.

Purpose of the Study:

To develop a Bayesian inference framework for quantifying uncertainties in epidemiological models.
To infer rate constants and their variations in response to lockdown measures using SEIJR and SIJR models.
To track the time evolution of the affected population fraction, including asymptomatic individuals, with quantified uncertainty.

Main Methods:

Utilized Bayesian inference framework with SEIJR and SIJR epidemiological models.
Distinguished between confined and unconfined susceptible populations to model lockdown effects.
Inferred rate constants (transmission, recovery, diagnosis) from COVID-19 data, exemplified with data from Spain.

Main Results:

Demonstrated that transmission and recovery rates vary significantly between confined and unconfined populations.
Identified a low diagnosis rate, leading to a substantial number of undiagnosed infectious individuals.
Showed that late, drastic lockdowns delay but do not stop epidemic spread without sustained confinement or effective distancing measures.

Conclusions:

Bayesian inference provides a robust method for uncertainty quantification in epidemiological models.
Effective control of infectious disease spread requires addressing undiagnosed cases and implementing sustained public health interventions.
Confinement and strong distancing measures like mask-wearing can significantly impact epidemic trajectories.