Accounting for imported cases in estimating the time-varying reproductive number of COVID-19 in Hong Kong.
Ontology highlight
ABSTRACT:
SUBMITTER: Tsang TK
PROVIDER: S-EPMC8244742 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:circulating microRNA analysis in Subarachnoid hemorrhages (SAH) patients and Heathy controls circulating microRNA profiling has been shown for biomarker discovery for early disease detection in serum, which is stable and accessible. we describe our progress and propose a workflow to investigate and directly compare the circulating microRNA profiles of SAH patients and healthy individuals, in order to characterize potential biomarkers for SAH. Then we verified the top microRNA by quantitative PCR. Informatics analysis of KEGG provided important mechanistic insight to their potential involved pathways. Finally we demonstrated a possible workflow and initial results which indicated that circulating miRNAs can be potential biomarkers of SAH.
2016-08-31 | GSE72340 | GEO
Project description:In this paper, we study time-varying graphical models based on data measured over a temporal grid. Such models are motivated by the needs to describe and understand evolving interacting relationships among a set of random variables in many real applications, for instance the study of how stock prices interact with each other and how such interactions change over time. We propose a new model, LOcal Group Graphical Lasso Estimation (loggle), under the assumption that the graph topology changes gradually over time. Specifically, loggle uses a novel local group-lasso type penalty to efficiently incorporate information from neighboring time points and to impose structural smoothness of the graphs. We implement an ADMM based algorithm to fit the loggle model. This algorithm utilizes blockwise fast computation and pseudo-likelihood approximation to improve computational efficiency. An R package loggle has also been developed and is available on https://cran.r-project.org/. We evaluate the performance of loggle by simulation experiments. We also apply loggle to S&P 500 stock price data and demonstrate that loggle is able to reveal the interacting relationships among stock prices and among industrial sectors in a time period that covers the recent global financial crisis. The supplemental materials for this paper are also available online.
| S-EPMC8023339 | biostudies-literature
Project description:After slowing down the spread of the novel coronavirus COVID-19, many countries have started to relax their confinement measures in the face of critical damage to socioeconomic structures. At this stage, it is desirable to monitor the degree to which political measures or social affairs have exerted influence on the spread of disease. Though it is difficult to trace back individual transmission of infections whose incubation periods are long and highly variable, estimating the average spreading rate is possible if a proper mathematical model can be devised to analyze daily event-occurrences. To render an accurate assessment, we have devised a state-space method for fitting a discrete-time variant of the Hawkes process to a given dataset of daily confirmed cases. The proposed method detects changes occurring in each country and assesses the impact of social events in terms of the temporally varying reproduction number, which corresponds to the average number of cases directly caused by a single infected case. Moreover, the proposed method can be used to predict the possible consequences of alternative political measures. This information can serve as a reference for behavioral guidelines that should be adopted according to the varying risk of infection.
| S-EPMC7875393 | biostudies-literature
Project description:Early in the COVID-19 pandemic, predictions of international outbreaks were largely based on imported cases from Wuhan, China, potentially missing imports from other cities. We provide a method, combining daily COVID-19 prevalence and flight passenger volume, to estimate importations from 18 Chinese cities to 43 international destinations, including 26 in Africa. Global case importations from China in early January came primarily from Wuhan, but the inferred source shifted to other cities in mid-February, especially for importations to African destinations. We estimate that 10.4 (6.2 - 27.1) COVID-19 cases were imported to these African destinations, which exhibited marked variation in their magnitude and main sources of importation. We estimate that 90% of imported cases arrived between 17 January and 7 February, prior to the first case detections. Our results highlight the dynamic role of source locations, which can help focus surveillance and response efforts.
| S-EPMC7804934 | biostudies-literature
Project description:Early in the COVID-19 pandemic, when cases were predominantly reported in the city of Wuhan, China, local outbreaks in Europe, North America, and Asia were largely predicted from imported cases on flights from Wuhan, potentially missing imports from other key source cities. Here, we account for importations from Wuhan and from other cities in China, combining COVID-19 prevalence estimates in 18 Chinese cities with estimates of flight passenger volume to predict for each day between early December 2019 to late February 2020 the number of cases exported from China. We predict that the main source of global case importation in early January was Wuhan, but due to the Wuhan lockdown and the rapid spread of the virus, the main source of case importation from mid February became Chinese cities outside of Wuhan. For destinations in Africa in particular, non-Wuhan cities were an important source of case imports (1 case from those cities for each case from Wuhan, range of model scenarios: 0.1-9.8). Our model predicts that 18.4 (8.5 - 100) COVID-19 cases were imported to 26 destination countries in Africa, with most of them (90%) predicted to have arrived between 7th January (±10 days) and 5th February (±3 days), and all of them predicted prior to the first case detections. We finally observed marked heterogeneities in expected imported cases across those locations. Our estimates shed light on shifting sources and local risks of case importation which can help focus surveillance efforts and guide public health policy during the final stages of the pandemic. We further provide a time window for the seeding of local epidemics in African locations, a key parameter for estimating expected outbreak size and burden on local health care systems and societies, that has yet to be defined in these locations.
| S-EPMC7276040 | biostudies-literature
Project description:Across diverse biological systems-ranging from neural networks to intracellular signaling and genetic regulatory networks-the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes. A pressing data-analysis challenge has thus been to efficiently estimate the amount of information that these dynamics convey from experimental data. Here we develop and evaluate decoding-based estimation methods to lower bound the mutual information about a finite set of inputs, encoded in single-cell high-dimensional time series data. For biological reaction networks governed by the chemical Master equation, we derive model-based information approximations and analytical upper bounds, against which we benchmark our proposed model-free decoding estimators. In contrast to the frequently-used k-nearest-neighbor estimator, decoding-based estimators robustly extract a large fraction of the available information from high-dimensional trajectories with a realistic number of data samples. We apply these estimators to previously published data on Erk and Ca2+ signaling in mammalian cells and to yeast stress-response, and find that substantial amount of information about environmental state can be encoded by non-trivial response statistics even in stationary signals. We argue that these single-cell, decoding-based information estimates, rather than the commonly-used tests for significant differences between selected population response statistics, provide a proper and unbiased measure for the performance of biological signaling networks.
| S-EPMC6743786 | biostudies-literature
Project description:RationaleChronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death in the United States. Studies have primarily assessed the relationship between smoking on COPD risk focusing on summary measures, like smoking status.ObjectiveDevelop a COPD risk prediction model incorporating individual time-varying smoking exposures.MethodsThe Nurses' Health Study (N = 86,711) and the Health Professionals Follow-up Study (N = 39,817) data was used to develop a COPD risk prediction model. Data was randomly split in 50-50 samples for model building and validation. Cox regression with time-varying covariates was used to assess the association between smoking duration, intensity and year-since-quit and self-reported COPD diagnosis incidence. We evaluated the model calibration as well as discriminatory accuracy via the Area Under the receiver operating characteristic Curve (AUC). We computed 6-year risk of COPD incidence given various individual smoking scenarios.ResultsSmoking duration, year-since-quit (if former smokers), sex, and interaction of sex and smoking duration are significantly associated with the incidence of diagnosed COPD. The model that incorporated time-varying smoking variables yielded higher AUCs compared to models using only pack-years. The AUCs for the model were 0.80 (95% CI: 0.74-0.86) and 0.73 (95% CI: 0.70-0.77) for males and females, respectively.ConclusionsUtilizing detailed smoking pattern information, the model predicts COPD risk with better accuracy than models based on only smoking summary measures. It might serve as a tool for early detection programs by identifying individuals at high-risk for COPD.
| S-EPMC7946316 | biostudies-literature
Project description:Human sparganosis is a foodborne zoonosis endemic in Asia. We report a series of 9 histologically confirmed human sparganosis cases in Hong Kong, China. All parasites were retrospectively identified as Spirometra erinaceieuropaei. Skin and soft tissue swelling was the most common symptom, followed by central nervous system lesions.
| S-EPMC5367436 | biostudies-literature
Project description:Raw sequence reads of rRNA of Chaetoceros, Pseudo-nitzschia, and Thalassiosira in Hong Kong coastal waters
| PRJNA1138948 | ENA
Project description:Dark fermentation batch experimental 16S rRNA sequencing data Genome sequencing and assembly
| PRJNA980953 | ENA