Within-host dynamics of SARS-CoV-2 infection: A systematic review and meta-analysis

Within-host model specified by viral dynamic parameters is a mainstream tool to understand SARS-CoV-2 replication cycle in infected patients. The parameter uncertainty further affects the output of the model, such as the efficacy of potential antiviral drugs. However, gathering empirical data on these parameters is challenging. Here, we aim to conduct a systematic review of viral dynamic parameters used in within-host models by calibrating the model to the viral load data measured from upper respiratory specimens. We searched the PubMed, Embase and Web of Science databases (between 1 December 2019 and 10 February 2022) for within-host modelling studies. We identified seven independent within-host models from the above nine studies, including Type I interferon, innate response, humoral immune response or cell-mediated immune response. From these models, we extracted and analyse seven widely used viral dynamic parameters including the viral load at the point of infection or symptom onset, the rate of viral particles infecting susceptible cells, the rate of infected cells releasing virus, the rate of virus particles cleared, the rate of infected cells cleared and the rate of cells in the eclipse phase can become productively infected. We identified seven independent within-host models from nine eligible studies. The viral load at symptom onset is 4.78 (95% CI:2.93, 6.62) log(copies/ml), and the viral load at the point of infection is −1.00 (95% CI:−1.94, −0.05) log(copies/ml). The rate of viral particles infecting susceptible cells and the rate of infected cells cleared have the pooled estimates as −6.96 (95% CI:−7.66, −6.25) log([copies/ml]–1 day–1) and 0.92 (95% CI:−0.09, 1.93) day–1, respectively. We found that the rate of infected cells cleared was associated with the reported model in the meta-analysis by including the model type as a categorical variable (p <.01). Joint viral dynamic parameters estimates when parameterizing within-host models have been published for SARS-CoV-2. The reviewed viral dynamic parameters can be used in the same within-host model to understand SARS-CoV-2 replication cycle in infected patients and assess the impact of pharmaceutical interventions

Shorter serial intervals and incubation periods in SARS-CoV-2 variants than the SARS-CoV-2 ancestral strain

The Delta and Omicron variants have the pooled estimates of serial interval as 3.4 days (95% CI: 3.0, 3.7) and 3.1 days (95% CI: 2.9, 3.2), respectively; incubation periods as 4.8 days (95% CI: 3.9, 5.6) and 3.6 days (95% CI: 2.3, 4.9), respectively

Local Surveillance of the COVID-19 Outbreak

Given the worldwide pandemic of the novel coronavirus disease 2019 (COVID-19) and its continuing threat brought by the emergence of virus variants, there are great demands for accurate surveillance and monitoring of outbreaks. A valuable metric for assessing the current risk posed by an outbreak is the time-varying reproduction number ((Formula presented.)). Several methods have been proposed to estimate (Formula presented.) using different types of data. We developed a new tool that integrated two commonly used approaches into a unified and user-friendly platform for the estimation of time-varying reproduction numbers. This tool allows users to perform simulations and yield real-time tracking of local epidemic of COVID-19 with an R package

Reproduction number of monkeypox in the early stage of the 2022 multi-country outbreak

Monkeypox, a fast-spreading viral zoonosis outside of Africa in May 2022, has put scientists on alert. We estimated the reproduction number to be 1.39 (95% CrI: 1.37, 1.42) by aggregating all cases in 70 countries as of 22 July 2022

Optimizing Global Influenza Surveillance for Locations with Deficient Data (Student Abstract)

For better monitoring and controlling influenza, WHO has launched FluNet (recently integrated to FluMART) to provide a unified platform for participating countries to routinely collect influenza-related syndromic, epidemiological and virological data. However, the reported data were incomplete. We propose a novel surveillance system based on data from multiple sources to accurately assess the epidemic status of different countries, especially for those with missing surveillance data in some periods. The proposed method can automatically select a small set of reliable and informative indicators for assessing the underlying epidemic status and proper supporting data to train the predictive model. Our proactive selection method outperforms three other out-of-box methods (linear regression, multilayer perceptron, and long-short term memory) to make accurate predictions

An exosome mRNA-related gene risk model to evaluate the tumor microenvironment and predict prognosis in hepatocellular carcinoma

Background: The interplay between exosomes and the tumor microenvironment (TME) remains unclear. We investigated the influence of exosomes on the TME in hepatocellular carcinoma (HCC), focusing on their mRNA expression profile. Methods: mRNA expression profiles of exosomes were obtained from exoRBase. RNA sequencing data from HCC patients’ tumors were acquired from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). An exosome mRNA-related risk score model of prognostic value was established. The patients in the two databases were divided into high- and low-risk groups based on the median risk score value, and used to validate one another. Functional enrichment analysis was performed based on a differential gene prognosis model (DGPM). CIBERSORT was used to assess the abundance of immune cells in the TME. The correlation between the expression levels of immune checkpoint-related genes and DGPM was analyzed alongside the prediction value to drug sensitivity. Results: A prognostic exosome mRNA-related 4-gene signature (DYNC1H1, PRKDC, CCDC88A, and ADAMTS5) was constructed and validated. A prognostic nomogram had prognostic ability for HCC. The genes for this model are involved in extracellular matrix, extracellular matrix (ECM)-receptor interaction, and the PI3K-Akt signaling pathway. Expression of genes here had a positive correlation with immune cell infiltration in the TME. Conclusions: Our study results demonstrate that an exosome mRNA-related risk model can be established in HCC, highlighting the functional significance of the molecules in prognosis and risk stratification.</p

RiskEstim: A Software Package to Quantify COVID-19 Importation Risk

We present an R package developed to quantify coronavirus disease 2019 (COVID-19) importation risk. Quantifying and visualizing the importation risk of COVID-19 from inbound travelers is urgent and imperative to trigger public health responses, especially in the early stages of the COVID-19 pandemic and emergence of new SARS-CoV-2 variants. We provide a general modeling framework to estimate COVID-19 importation risk using estimated pre-symptomatic prevalence of infection and air traffic data from the multi-origin places. We use Hong Kong as a case study to illustrate how our modeling framework can estimate the COVID-19 importation risk into Hong Kong from cities in Mainland China in real time. This R package can be used as a complementary component of the pandemic surveillance system to monitor spread in the next pandemic

Designing laser powder bed fusion low-alloyed titanium with superior strength-ductility trade-off via machine learning

Designing laser powder bed fusion low-alloyed titanium with superior strength-ductility trade-off via machine learnin

International risk of SARS-CoV-2 Omicron variant importations originating in South Africa

Omicron, a fast-spreading SARS-CoV-2 variant of concern reported to the World Health Organization on 24 November 2021, has raised international alarm. We estimated there is at least 50% chance that Omicron had been introduced by travellers from South Africa into 11 of the 14 countries studied by 28 November 2021

Cost effectiveness of fractional doses of COVID-19 vaccine boosters in India

Background: Coronavirus disease 2019 (COVID-19) continues to be a major global public health crisis that exacts significant human and economic costs. Booster vaccination of individuals can improve waning immunity and reduce the impact of community epidemics. Methods: Using an epidemiological model that incorporates population-level severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and waning of vaccine-derived immunity, we identify the hypothetical potential of mass vaccination with fractionated vaccine doses specific to ChAdOx1 nCoV-19 (AZD1222 [Covishield]; AstraZeneca) as an optimal and cost-effective strategy in India's Omicron outbreak. Findings: We find that the optimal strategy is 1/8 fractional dosing under mild (Re ∼ 1.2) and rapid (Re ∼ 5) transmission scenarios, leading to an estimated $6 (95$2 (95% CI: −26, 30) billion in health-related net monetary benefit over 200 days, respectively. Rapid and broad use of fractional dosing for boosters, together with delivery costs divided by fractionation, could substantially gain more net monetary benefit by $11 (95$2 (95% CI: −23, 28) billion, respectively, under the mild and rapid transmission scenarios. Conclusions: Mass vaccination with fractional doses of COVID-19 vaccines to boost immunity in a vaccinated population could be a cost-effective strategy for mitigating the public health costs of resurgences caused by vaccine-evasive variants, and fractional dosing deserves further clinical and regulatory evaluation. Funding: Financial support was provided by the AIR@InnoHK Program from Innovation and Technology Commission of the Government of the Hong Kong Special Administrative Region