Dataset Information

Using Proper Mean Generation Intervals in Modeling of COVID-19.

ABSTRACT: In susceptible-exposed-infectious-recovered (SEIR) epidemic models, with the exponentially distributed duration of exposed/infectious statuses, the mean generation interval (GI, time lag between infections of a primary case and its secondary case) equals the mean latent period (LP) plus the mean infectious period (IP). It was widely reported that the GI for COVID-19 is as short as 5 days. However, many works in top journals used longer LP or IP with the sum (i.e., GI), e.g., >7 days. This discrepancy will lead to overestimated basic reproductive number and exaggerated expectation of infection attack rate (AR) and control efficacy. We argue that it is important to use suitable epidemiological parameter values for proper estimation/prediction. Furthermore, we propose an epidemic model to assess the transmission dynamics of COVID-19 for Belgium, Israel, and the United Arab Emirates (UAE). We estimated a time-varying reproductive number [R ₀(t)] based on the COVID-19 deaths data and we found that Belgium has the highest AR followed by Israel and the UAE.

SUBMITTER: Tang X

PROVIDER: S-EPMC8287506 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Using Proper Mean Generation Intervals in Modeling of COVID-19.

Tang Xiujuan X Musa Salihu S SS Zhao Shi S Mei Shujiang S He Daihai D

Frontiers in public health 20210705

In susceptible-exposed-infectious-recovered (SEIR) epidemic models, with the exponentially distributed duration of exposed/infectious statuses, the mean generation interval (GI, time lag between infections of a primary case and its secondary case) equals the mean latent period (LP) plus the mean infectious period (IP). It was widely reported that the GI for COVID-19 is as short as 5 days. However, many works in top journals used longer LP or IP with the sum (i.e., GI), e.g., >7 days. This discre ...[more]

PMID: 34291032

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Project description:The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.

Project description:Purpose: The goals of this study are to compare NGS-derived transcriptome profiling (RNA-seq) of COVID-19 kidney to normal controls Methods/Results: Kidney mRNA profile of human COVID-19 tissue was generated by deep sequencing using Illumina Novaseq6000 Paired-end 150. After filtering reads mapped to contamination database, the reads that were uniquely aligned to the exon and splicing-junction segments with a maximal 2 mismatches for each transcript were then counted as expression level for corresponding transcript. Next, RNA-seq reads count data were downloaded from public resource GTEx project (https://www.gtexportal.org/home/datasets) and 12 normal kidney tissue samples were extracted as controls. The differential analysis by fold change difference was carried out to identify dysregulated genes at 1.5 fold change. The differential expressed genes were then subjected to Gene Ontology function and Pathway (KEGG, Ingenuity IPA, BIOCARTA, NABA, Panther, PID, REACTOME, Wiki-pathway) enrichment analysis by Fisher-exact test. Conclusions: RNA sequencing data revealed that biological processes from upregulated genes were enriched for cell cycle, chromosome segregation, response to wounding, humoral immune response, and blood coagulation, suggesting that cell injury/regeneration, inflammatory response, and endothelial injury were the major disease processes involved. The biological processes from downregulated genes were enriched for ion transport, metabolic processes, and oxidation, likely secondary to severe tubular cell injury. Pathway analysis from both up- and downregulated genes showed enrichment of transmembrane transport, oxidation, and blood coagulation consistent with the GO terms analysis. Upregulated genes were enriched only for the FOXM1 pathway, which was recently reported to promote tubular cell proliferation during injury repair. Additionally, genes related to the renin-angiotensin system were downregulated, but ACE2 expression did not differ from normal controls.

Dataset Information

Using Proper Mean Generation Intervals in Modeling of COVID-19.

Publications

Using Proper Mean Generation Intervals in Modeling of COVID-19.

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