Comparing the dynamics of COVID-19 infection and mortality in the United States, India, and Brazil

James, Nick; Menzies, Max; Bondell, Howard D.

doi:10.1016/j.physd.2022.133158

Cited by 28 publications

(16 citation statements)

References 55 publications

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“…The analysis of several cases studies within a larger region provides spatial dynamics concerning virus spread. The chosen federative units for the study are known to be heterogeneous among each other 35 – 37 since Brazil is a large country where there were several different outbreaks dates, local government policies, and population behavior. Brazilian spatial epidemic progression studies 35 , 36 indicated multiple initial outbreaks spread progressively to neighboring territories.…”

Section: Mathematical Modelmentioning

confidence: 99%

Recursive state and parameter estimation of COVID-19 circulating variants dynamics

Silva

Secchi

2022

Sci Rep

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COVID-19 pandemic response with non-pharmaceutical interventions is an intrinsic control problem. Governments weigh social distancing policies to avoid overload in the health system without significant economic impact. The mutability of the SARS-CoV-2 virus, vaccination coverage, and mobility restriction measures change epidemic dynamics over time. A model-based control strategy requires reliable predictions to be efficient on a long-term basis. In this paper, a SEIR-based model is proposed considering dynamic feedback estimation. State and parameter estimations are performed on state estimators using augmented states. Three methods were implemented: constrained extended Kalman filter (CEKF), CEKF and smoother (CEKF & S), and moving horizon estimator (MHE). The parameters estimation was based on vaccine efficacy studies regarding transmissibility, severity of the disease, and lethality. Social distancing was assumed as a measured disturbance calculated using Google mobility data. Data from six federative units from Brazil were used to evaluate the proposed strategy. State and parameter estimations were performed from 1 October 2020 to 1 July 2021, during which Zeta and Gamma variants emerged. Simulation results showed that lethality increased between 11 and 30% for Zeta mutations and between 44 and 107% for Gamma mutations. In addition, transmissibility increased between 10 and 37% for the Zeta variant and between 43 and 119% for the Gamma variant. Furthermore, parameter estimation indicated temporal underreporting changes in hospitalized and deceased individuals. Overall, the estimation strategy showed to be suitable for dynamic feedback as simulation results presented an efficient detection and dynamic characterization of circulating variants.

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Section: Mathematical Modelmentioning

confidence: 99%

Recursive state and parameter estimation of COVID-19 circulating variants dynamics

Silva

Secchi

2022

Sci Rep

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“…Such methodologies have been applied to a wide variety of asset classes including equity markets [11], FX markets [19], cryptocurrencies [20,21,22,18,23,24,25,26,27,28,29,30,31,32,33] and debt-related instruments [34]. There is a litany of work where such techniques have been used in other domains including COVID-19 [35,36,37], extreme human behaviours [38] and epidemiology. Readers interested in recent work related to temporal dynamics with various societal impacts on the economy should see [39,40,41].…”

Section: Introductionmentioning

confidence: 99%

Economic state classification and portfolio optimisation with application to stagflationary environments

James,

Menzies,

Chin

2022

Preprint

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Motivated by the current fears of a potentially stagflationary global economic environment, this paper uses new and recently introduced mathematical techniques to study multivariate time series pertaining to country inflation (CPI), economic growth (GDP) and equity index behaviours. We start by assessing the temporal evolution among various economic phenomena, and complement this analysis with "economic driver analysis", where we decouple country economic trajectories and determine what is most important in their association. Next, we study the temporal self-similarity of global inflation, growth and equity index returns to identify the most anomalous historic periods, and windows in the past that are most similar to current market dynamics. We then introduce a new algorithm to construct economic state classifications and compute an economic state integral, where countries are determined to belong in one of four candidate states based on their inflation and growth behaviours. Finally, we implement a decade-by-decade portfolio optimization to determine which equity indices and portfolio assets have been most beneficial in maximizing portfolio risk-adjusted returns in various market conditions. This could be of great interest to those looking for asset allocation guidance in the current period of high economic uncertainty.

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“…Our analysis relies on a broad existing literature of time series analysis as used by applied mathematicians and statisticians. Such methods have been applied widely within the physical and social sciences, including epidemiology [27,28,29,30,31,32], finance [33,34,35,36,37,38], and other fields [39,40,41,42]. There are a variety of time series analysis frameworks that we draw upon, including parametric models [43,44], techniques built upon distance metrics and similarity, [45,46,47,48,49], network analysis [50,51,52], clustering [53], and others [54,55,56,57].…”

Section: Introductionmentioning

confidence: 99%

Global and regional changes in carbon dioxide emissions: 1970-2019

James¹,

Menzies²

2022

Preprint

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We introduce new frameworks to study spatio-temporal patterns in carbon dioxide emissions, demographic trends and economic patterns across 50 countries over the past 50 years. Our analysis is broken up into four sections. First, we introduce a new method to classify countries into one of three characteristic emissions classes based on a one, two or three-segment piecewise linear model. We reveal that most countries are best represented by a piecewise linear model with one change point. Next, we perform a decade-by-decade study of carbon dioxide trajectories. There, we demonstrate notable changes in cluster structures in each decade. We then study the spatial propagation of emissions over time, highlighting a peak in spatial dispersion in 2000, beyond which there has been a gradual decline in spatial emissions variance over space. Finally, we use carbon dioxide, GDP, and population data and apply dimensionality reduction and clustering to group countries based on similarity in their real and carbon economies.

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Comparing the dynamics of COVID-19 infection and mortality in the United States, India, and Brazil

Cited by 28 publications

References 55 publications

Recursive state and parameter estimation of COVID-19 circulating variants dynamics

Recursive state and parameter estimation of COVID-19 circulating variants dynamics

Economic state classification and portfolio optimisation with application to stagflationary environments

Global and regional changes in carbon dioxide emissions: 1970-2019

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