Dataset Information

The Heterogeneous Severity of COVID-19 in African Countries: A Modeling Approach.

ABSTRACT: The COVID-19 pandemic has had a considerable impact on global health and economics. The impact in African countries has not been investigated thoroughly via fitting epidemic models to the reported COVID-19 deaths. We downloaded the data for the 12 most-affected countries with the highest cumulative COVID-19 deaths to estimate the time-varying basic reproductive number ([Formula: see text]) and infection attack rate. We develop a simple epidemic model and fitted it to reported COVID-19 deaths in 12 African countries using iterated filtering and allowing a flexible transmission rate. We observe high heterogeneity in the case-fatality rate across the countries, which may be due to different reporting or testing efforts. South Africa, Tunisia, and Libya were most affected, exhibiting a relatively higher [Formula: see text] and infection attack rate. Thus, to effectively control the spread of COVID-19 epidemics in Africa, there is a need to consider other mitigation strategies (such as improvements in socioeconomic well-being, healthcare systems, the water supply, and awareness campaigns).

SUBMITTER: Musa SS

PROVIDER: S-EPMC8784278 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Understanding variations in the severity of infectious diseases is essential for planning proper mitigation strategies. Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Instead, we use epidemiological modeling to propose a disease severity measure that accounts for the underlying disease dynamics. The measure corresponds to the ratio of population-averaged mortality and recovery rates (m/r), is independent of the disease transmission dynamics (i.e., the basic reproduction number), and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological, and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate, and multivariate analyses based on machine learning techniques, is used for selecting important predictors. The usefulness of the introduced severity measure and the validity of the approach are confirmed by the fact that, without using prior knowledge from clinical studies, we recover the main significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) significant predictors. The proposed measure is applicable for inferring severity determinants not only of COVID-19 but also of other infectious diseases, and the obtained results may aid a better understanding of the present and future epidemics. Our holistic, systematic investigation of disease severity at the human-environment intersection by epidemiological dynamical modeling and machine learning ecological regressions is aligned with the One Health approach. The obtained results emphasize a syndemic nature of COVID-19 risks.

Project description:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.

Dataset Information

The Heterogeneous Severity of COVID-19 in African Countries: A Modeling Approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure