COVID-19 severity determinants inferred through ecological and epidemiological modeling

Marković, S.; Rodic, Andjela; Salom, Igor; Milićević, Ognjen; Djordjevic, Magdalena; Djordjevic, M.

doi:10.1016/j.onehlt.2021.100355

Cited by 10 publications

(26 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, the estimate for Case Fatality Rate (CFR), which is defined as the final number of fatalities per detected cases (CFR = F f in /D f in ), acquires a simple form [67], involving a ratio of mortality (m) and healing (h) rates:…”

Section: Analytical Resultsmentioning

confidence: 99%

An Analytical Framework for Understanding Infection Progression Under Social Mitigation Measures

Ilic

Salom

Djordjevic

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

While there has been much computational work on the effect of intervention measures, such as vaccination or quarantine, the influence of social distancing on the epidemics' outbursts is not well understood. We present a realistic, analytically solvable, framework for COVID-19 dynamics in the presence of social distancing measures. The model is a generalization of the compartmental SEIR model that accounts for the effects of these measures. We derive a closed-form mathematical expressions for the time dependence of epidemiological observables, in particular, the detected cases and fatalities. These analytical solutions indicate simple quantitative relations between the model variables and epidemiological observables, which give insights into cause-effect connections that underlie the outburst dynamics but are obscured in more standard (numerical) approaches. While the obtained results and conclusions are based on the study of the COVID-19 pandemic, the presented analysis has general applicability to infection outbursts. Our findings are particularly important in the emergence of new pandemics when effective pharmaceutical treatments are unavailable, and one must rely on well-timed and appropriately chosen social mitigation measures.

show abstract

Section: Analytical Resultsmentioning

confidence: 99%

An Analytical Framework for Understanding Infection Progression Under Social Mitigation Measures

Ilic

Salom

Djordjevic

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Briefly, Global Health Security Indices data were taken from 1 , total and excess deaths data during the first peak of the pandemic were obtained from 32 , while COVID-19 counts and demographic data were collected similarly to those previously used in 27 . A measure of disease severity – mortality over recovery rate (m/r) – was inferred for each country, following the methodology introduced in 29 . Relative excess deaths and relative unexplained deaths (excess and unexplained deaths in further text and figures) were calculated as follows: …”

Section: Methodsmentioning

confidence: 99%

“…Data processing and subsequent univariate and multivariate statistical analysis, including regularization-based linear regressions (Lasso and Elastic Net) 30 , and non-linear machine learning techniques (Random Forest and Gradient Boost) 30 , employed similar methodology previously used in 27,29 -see a detailed description in Supplementary Material. The contribution of the relevant variables in Random Forest and Gradient Boost was estimated through Partial Dependence (PD) plots 30 , which estimate the effect of a single predictor by taking into account the average effects of all other predictors.…”

Section: Methodsmentioning

confidence: 99%

GHSI COVID-19 puzzle: did highly developed countries indeed fare worse?

Marković

Salom

Rodic

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Global Health Security Index (GHSI) categories are formulated to assess the capacity of world countries to deal with infectious disease risks. Thus, higher values of these indices were expected to translate to lower COVID-19 severity. However, it turned out to be the opposite, surprisingly suggesting that higher estimated country preparedness to epidemics may lead to higher disease mortality. To address this puzzle, we: i) use a model-derived measure of COVID-19 severity; ii) employ a range of statistical learning approaches, including non-parametric machine learning methods; iii) consider the overall excess mortality, in addition to official COVID-19 fatality counts. Our results suggest that the puzzle is, to a large extent, an artifact of oversimplified data analysis and a consequence of misclassified COVID-19 deaths, combined with the higher median age of the population and earlier epidemics onset in countries with high GHSI scores.

show abstract

“…To date, COVID‐19 pandemic involved more than 450 million cases worldwide ( https://www.ecdc.europa.eu/en/covid-19 ), including asymptomatic as well as paucisymptomatic, and severely symptomatic individuals. The symptoms and the severity of COVID‐19 disease differ from individual to individual (Markovic et al, 2021 ; Vrotsou et al, 2021 ), and the underlying causes have not yet been fully clarified (Bohn et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Untangling the link between the human gut microbiota composition and the severity of the symptoms of the COVID‐19 infection

Mancabelli

Milani

Fontana

et al. 2022

Environmental Microbiology

View full text Add to dashboard Cite

Recent pandemic infection caused by SARS‐CoV‐2 (COVID‐19) led the scientific community to investigate the possible causes contributing to the physiopathology of this disease. In this context, analyses of the intestinal microbiota highlighted possible correlation between host‐associated bacterial communities and development of the COVID‐19. Nevertheless, a detailed investigation of the role of the human microbiota in the severity of the symptoms of this disease is still lacking. This study performed a comprehensive meta‐analysis of 323 faecal samples from public and novel Italian data sets based on the shotgun metagenomic approach. In detail, the comparative analyses revealed possible differences in the microbial biodiversity related to the individual health status, highlighting a species richness decrease in COVID‐19 patients with a severe prognosis. Moreover, healthy subjects resulted characterized by a higher abundance of protective and health‐supporting bacterial species, while patients affected by COVID‐19 disease displayed a significant increase of opportunistic pathogen bacteria involved in developing putrefactive dysbiosis. Furthermore, prediction of the microbiome functional capabilities suggested that individuals affected by COVID‐19 subsist in an unbalanced metabolism characterized by an overrepresentation of enzymes involved in the protein metabolism at the expense of carbohydrates oriented pathways, which can impact on disease severity and in excessive systemic inflammation.

show abstract

COVID-19 severity determinants inferred through ecological and epidemiological modeling

Cited by 10 publications

References 45 publications

An Analytical Framework for Understanding Infection Progression Under Social Mitigation Measures

An Analytical Framework for Understanding Infection Progression Under Social Mitigation Measures

GHSI COVID-19 puzzle: did highly developed countries indeed fare worse?

Untangling the link between the human gut microbiota composition and the severity of the symptoms of the COVID‐19 infection

Contact Info

Product

Resources

About