Global spatial clustering is the tendency of points, here cases of infectious disease, to occur closer together than expected by chance. The extent of global clustering can provide a window into the spatial scale of disease transmission, thereby providing insights into the mechanism of spread, and informing optimal surveillance and control. Here the authors present an interpretable measure of spatial clustering, τ, which can be understood as a measure of relative risk. When biological or temporal information can be used to identify sets of potentially linked and likely unlinked cases, this measure can be estimated without knowledge of the underlying population distribution. The greater our ability to distinguish closely related (i.e., separated by few generations of transmission) from more distantly related cases, the more closely τ will track the true scale of transmission. The authors illustrate this approach using examples from the analyses of HIV, dengue and measles, and provide an R package implementing the methods described. The statistic presented, and measures of global clustering in general, can be powerful tools for analysis of spatially resolved data on infectious diseases.

Context

These data contain counts and rates for Centers for Infectious Diseases-related disease cases among California residents by county, disease, sex, and year spanning 2001-2014 (As of September, 2015). Data were extracted on communicable disease cases with an estimated onset or diagnosis date from 2001 through 2014 from California Confidential Morbidity Reports and/or Laboratory Report that were submitted to CDPH by September 2015 and which met the surveillance case definition for that disease. A cleansing and exploration steps have been performed to generate the train and test datasets.

Content

The train dataset contains 75614 rows and the test data has 18904 rows ****Features:**** ****Disease****:Plain text: The name of the disease reported for the patient. ****County****: Plain text "The county in which the case resided when they were diagnosed and/or where they are currently receiving care; in most cases this will be the county that reported the case.
****Year ****:Number: Year is derived from the estimated illness onset date. We defined the estimated illness onset date for each case as the date closest to the time when symptoms first appeared. Because date of illness onset may not be recorded, the estimated date of illness onset can range from the first appearance of symptoms to the date the report was made to CDPH. For diseases with insidious illness onset (for instance, coccidioidomycosis), estimated illness onset was more frequently drawn from the diagnosis date Values include: years spanning 2001-2014, unless otherwise indicated below ****Sex ****:Plain text : Values include: Male, Female, **Count **:Number: The number of occurrences of each disease that meet the surveillance definition and/or inclusion criteria specific to that disease for that County, Year, Sex strata. National surveillance case definitions for these conditions can be found at http://wwwn.cdc.gov/nndss/case-definitions.html. ****Population ****:Number: The estimated population size (rounded to the nearest integer) for each County, Year, Sex strata. California Department of Finance (DOF) Population Projection data (P-3 data table) were used to determine the population proportion of a particular demographic subgroup relative to the total State/County population for a given year. These proportions were then applied to the DOF Estimate totals (E-2 data table) for the given State/County and year total, to obtain the estimates used. These data are available at http://www.dof.ca.gov/research/demographic/reports/view.php. Value: a number (a positive integer)" ****Rate ****:Number:The rate of disease per 100,000 population for the corresponding County, Year, Sex strata using the standard calculation (Count *100,000/Population) Value: a number (a positive real number xxx.xxx)" ****CI.lower****:Number: The lower bound of the 95% confidence interval for the calculated rate. The confidence interval was calculated with the R software package (R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.) using the ""Exact Pearson-Klopper"" method as implement in the ""binom"" package (Sundar Dorai-Raj (2014). binom: Binomial Confidence Intervals For Several Parameterizations. R package version 1.1-1. http://CRAN.R-project.org/package=binom) Value: a number (a positive real number xxx.xxx)" ****CI.uppe**r**:Number:The upper bound of the 95% confidence interval for the calculated rate, calculated as above. Value: a number (a positive real number xxx.xxx)"

Acknowledgements

Individual comparison of main outcome variables with patient factors.

This file contains data regarding a 7-day average of the estimated instantaneous reproduction number, R(t), of COVID-19 in Ottawa. The reproduction number, R, is the average number of secondary cases of disease caused by a single infected individual over his or her infectious period. R(t) values greater than 1 indicate the virus is spreading faster and each case infects more than one contact, and less than 1 indicates the spread is slowing and the epidemic is coming under control.

R(t) was calculated using the EpiEstim package, developed by Cori et al. (2013; DOI: 10.1093/aje/kwt133), in the R software environment for statistical computing and graphics. Accurate episode date was used as the time anchor and cases were assigned as having a local or travel-related source of infection.

Accuracy: Points of consideration for interpretation of the data: Data are entered into and extracted by Ottawa Public Health from la Solution de gestion des cas et des contacts pour la santé publique (Solution GCC). The CCM is a dynamic disease reporting system that allows for ongoing updates; data represent a snapshot at the time of extraction and may differ from previous or subsequent reports.As the cases are investigated and more information is available, the dates are updated.A person’s exposure may have occurred up to 14 days prior to onset of symptoms. Symptomatic cases occurring in approximately the last 14 days are likely under-reported due to the time for individuals to seek medical assessment, availability of testing, and receipt of test results.Confirmed cases are those with a confirmed COVID-19 laboratory result as per the Ministry of Health Public health management of cases and contacts of COVID-19 in Ontario. March 25, 2020 version 6.0.Counts will be subject to varying degrees of underreporting due to a variety of factors, such as disease awareness and medical care seeking behaviours, which may depend on severity of illness, clinical practice, changes in laboratory testing, and reporting behaviours.Surveillance testing for COVID-19 began in long term care facilities on April 25, 2020. Attributes: Data fields: Date – the earliest of symptom onset, test or reported date for cases (YYYY-MM-DD H:MM).Lower Bound - 95% Confidence Interval - lower bound of the 95% confidence interval for the 7-day average of the R(t) estimate. Upper Bound - 95% Confidence Interval - upper bound of the 95% confidence interval for the 7-day average of the R(t) estimate.Estimate of R(t) (7 Day Average) - 7-day average of the estimated instantaneous reproduction number, R(t), of COVID-19 in Ottawa. Nowcasting Adjusted Cases by Episode Date – number of Ottawa residents with confirmed COVID-19 by episode date. Counts for the most recent 14 days represent a nowcasting adjusted estimate developed by R. Imgrund in 2020. The model uses linear regression to estimate the number of future cases expected to have an accurate episode date within that 14-day window. Update Frequency: As of March 2022, the dataset is no longer updated. Historical data only. Contact: OPH Epidemiology Team

Graph and download economic data for Equity Market Volatility: Infectious Disease Tracker (INFECTDISEMVTRACKD) from 1985-01-01 to 2025-06-23 about infection, disease, pandemic, volatility, equity, and USA.

4 inputs, 1 output, 2000 observations. Data from a stochastic susceptible-infectious-recovered (SIR) model which simulates the spread of an infectious disease. Simulations are performed using the "EpiModel" R package (Jenness et al. 2018). We consider a closed population of $5000$ susceptible people and a single infectious individual on day zero. During each potentially transmissible interaction (PTI), an infectious individual will infect a susceptible individual with probability x_1 and all individuals in the population interact at random with an average of x_2 PTIs per person per day. Each day, infectious individuals will recover from the disease with probability x_3. After 14 days, an intervention with efficacy x_4 is implemented. The response variable is taken to be the cumulative number of infected individuals at the end of a 21 day period. The relevant simulation inputs, and their ranges, are described in Rumsey et al. (2024).

Summary of patient factors across the timeline of the study.

My PhD thesis with title "Investigating Changes in COVID-19 Epidemiological Parameters from Different Perspectives" focus on using line list data (anonymized), patient hospitalization data (anonymized) and viral load data (anonymized) to improve the estimatin of different key epidemiological parameters during the COVID-19 pandemic in Hong Kong.This dataset contains supporting data for reproducibility, it has 6 subfolders correspond to 6 chapters of the thesis (chapters 2, 4, 5, 6, 7, 8) where contain figures and data analyses, each sub folder contains data and R code for reproducing the figures and other analytical results, with README file accompanied with each sub folder.In chapter 2, I provided an overview of the COVID-19 pandemic in Hong Kong and worldwide, and thus used datasets contain case incidence data and a R code to generate incidence figure. I also conducted a systematic review of the latent period estimation, and I provided the endnote library with spreadsheet of the endnote output that contain my paper screening process, which are included in subfolder dataset chapter 2.In chapter 4, I did a detailed statistical analyses of the changing serial interval of COVID-19 in Hong Kong, and thus sub folder dataset chapter 4 contained anonymized transmission pair line list data for estimating the serial interval, I provided R codes and essential subset of the data output for reproducibility of my results. The related published work is on American Journal of Epidemiology, in README chapter4.txt I have put the DOI of this paper.In chapter 5, I developed an inferential framework to infer the generation interval on temporal time scale, sub folder dataset chapter 5 contained public available line list data from mainland China, and R codes and essential subset of the data output for reproducibility of my results. The related published work is on Nature Communications, and the data and code are also available on github, I have out the DOI and github link in README chapter5.txt.In chapter 6, I investigated the superspreading potential and setting-specific generation interval in Hong Kong, subfolder dataset chapter 6 contained simplified and anonymized transmission cluster size information, and related R code to reproduce the result, and also the R code for modelling buildig and estimation summary of the generation interval estimates.In chapter 7, I estimated the latent period of COVID-19 based on different settings in Hong Kong, sub folder dataset chapter 7 contained processed and anonymized viral load record and transmission pair information of COVID-19 cases in Hong Kong, and related R code to reproduce the result, together with two spreadsheets for estimation summary. The entire R programming process contain a lot of R scripts, which I put two sub folders (R and Stan) under sub folder dataset chapter 7, and also put the original Github link for R programming of the method in README chapter 7.txtIn chapter 8, I analyzed the length of stay in hospital of COVID-19 patients in Hong Kong and the potential association with vaccination status. In sub folder dataset chapter 8 I put a simplified and anonymized dataset of patient's hospitalization record regarding their vaccination status and length of stay in hospital for the analysis. I also put R code and essential subset of the data output to reproduce the result.

The conserved macrodomain encoded as non-structural protein 3 (Nsp3 Mac1) is employed by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to remove host-derived ribosylation, which is a post-translational modification involved in the production of antiviral cytokines. This TEP provides early tools to develop Nsp3 Mac1 inhibitors, including purification protocols of recombinant proteins, reproducible crystallisation condition suitable for X-ray crystallography fragment screening, biophysical (activity and binding) assays and over 200 fragment hits representing a wide range of chemotypes, that are a starting point for the development of more selective and potent compounds.

Shown is a summary, based on an evaluation on synthetic and real-world data, of the qualitative behaviour of i) different approaches for Rt estimation and truncation adjustment, and ii) different approaches for missing data imputation.

This repository contains all data and code necessary to reproduce the results and figures of the paper:

Remacha, C., Ramírez, A., Arriero, E. and Pérez-Tris, J. 2023. Haemosporidian infections influence risk-taking behaviours in young male blackcaps Sylvia atricapilla. Animal Behaviour, 196, 113-126. https://doi.org/10.1016/j.anbehav.2022.12.001

The repository contains a readme file (README_SYAT_MS_ANIBEH_Scripts.txt) with a description of the code and the data. The code is organised in eight R script files. Instructions to run the code are provided in the readme file. The data are organised in two separate files. One file (SYAT_MS_BH_ANIBEHdata.txt) contains data of exploratory and antipredatory behaviours of 43 young male blackcaps. The other one (SYAT_MS_BH_BIOL_ANIBEHdata.txt) contains biological and experimental attributes of the same individuals: status and intensity of parasite infection, experimental treatment, morphology and body mass.

SETDB1 is a H3K9 methyltransferase involved in transcriptional silencing with a catalytic SET domain and a triple Tudor domain containing a methyl-lysine binding site. SGC Toronto previously solved the apo structure of the Tudor domain (PDB code 3DLM). Amplification of SETDB1 in over 15% lung adenocarcinoma correlates with high mRNA and protein levels and its depletion in SETDB1-amplified cells reduces cancer growth in cell culture and nude mice models, whereas its overexpression increases tumour invasiveness (Rodriguez-Paredes et al. Oncogene 2014, Shah et al. Epigenetic Chromatin 2014). Several histone methyltransferases are known to have non-catalytic functions that might be alternative targeting strategies. For instance, recognition of H3K9 methylation by the ankyrin repeat of the methyltransferase GLP is required for efficient establishment of H3K9 methylation (Liu et al. Genes Dev. 2015). No catalytic domain inhibitor of SETDB1 has been reported to date. The goal of this TEP is to enable the discovery of potent, selective compounds targeting the Tudor domain of SETDB1.

Innate lymphoid cells (ILCs) are enriched at mucosal surfaces where they respond rapidly to environmental stimuli and contribute to both tissue inflammation and healing. To gain insight into the role of ILCs in the pathology and recovery from COVID-19 infection, we employed a multi-omic approach consisting of Abseq and targeted mRNA sequencing to respectively probe the surface marker expression, transcriptional profile and heterogeneity of ILCs in peripheral blood of patients with COVID-19 compared with healthy controls. We found that the frequency of ILC1 and ILC2 cells was significantly increased in COVID-19 patients. Moreover, all ILC subsets displayed a significantly higher frequency of CD69-expressing cells, indicating a heightened state of activation. ILC2s from COVID-19 patients had the highest number of significantly differentially expressed (DE) genes. The most notable genes DE in COVID-19 vs healthy participants included a) genes associated with responses to virus infections and b) genes that support ILC self-proliferation, activation and homeostasis. In addition, differential gene regulatory network analysis revealed ILC-specific regulons and their interactions driving the differential gene expression in each ILC. Overall, this study provides mechanistic insights into the characteristics of ILC subsets activated during COVID-19 infection. Methods Study participants, blood draws and processing Participants were recruited as described previously from adults who had a positive SARS-COV-2 RT-PCR test at Stanford Health Care (NCT04373148). Collection of Covid samples occurred between May to December 2020. The cohort used in this study consisted of asymptomatic (n=2), mild (n=17), and moderate (n=3) COVID-19 infections, some of whom developed long term COVID-19 (n=15). The clinical case severities at the time of diagnosis were defined as asymptomatic, moderate or mild according to the guidelines released by NIH. Long term (LT) COVID was defined as symptoms occurring 30 or more days after infection, consistent with CDC guidelines. Some participants in our study continued to have LT COVID symptoms 90 days after diagnosis (n=12). Exclusion criteria for COVID sample study were NIH severity diagnosis of severe or critical at the time of positive covid test. Samples selected for this study were obtained within 76 days of positive PCR COVID-19 test date. Healthy controls were selected who had sample collection before 2020. Informed consent was obtained from all participants. All protocols were approved by the Stanford Administrative Panel on Human Subjects in Medical Research. Peripheral blood was drawn by venipuncture and using validated and published procedures, peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll-based density gradient centrifugation, frozen in aliquots and stored in liquid nitrogen at -80°C , until thawing. A summary of participant demographics is presented in Supp. Table 1.
ILC Enrichment, single cell captures for Abseq and targeted mRNAseq Participant PBMCs were thawed, and each sample stained with Sample Tag (BD #633781) at room temperature for 20 minutes. Samples were combined in healthy control or COVID-19 tubes. Cells were surface stained with a panel of fluorochrome-conjugated antibodies (Supp. Table 2) in buffer (PBS with 0.25% BSA and 1mM EDTA) for 20 minutes at room temperature prior to immunomagnetic negative selection for ILCs. Following ILC enrichment using the EasySep human Pan-ILC enrichment kit (StemCell Technologies #17975), cells from healthy and COVID-19 recovered participants were counted and normalized before combining. ILCs were sorted using a BD FACS Aria at the Stanford FACS facility prior to incubation with AbSeq oligo-linked mAbs (Supp. Table 3). Sorted cells were processed by the Stanford Human Immune Monitoring Center (HIMC) using the BD Rhapsody platform. Library was prepared using the BD Immune Response Targeting Panel (BD Kit #633750) with addition of custom gene panel reagents (Supp. Table 4) and sequenced on Illumina NovaSeq 6000 at Stanford Genomics Sequencing Center (SGSC). ILCs were identified as Lineageneg (CD3neg, CD14neg, CD34neg, CD19neg), NKG2Aneg, CD45+ and ILCs further defined as CD127+CD161+ and as subsets: ILC1 (CD117negCRTH2neg), ILC2 (CRTH2+) and ILCp (CD117+CRTH2neg) (Supp. Fig. 1). Computational data analysis The above multi-modal setup allowed paired measurements of cellular transcriptome and cell surface protein abundance. The ILC1, ILC2 and ILCp cells were manually gated based on the abundance profile of CD127, CD117, CD161 and CRTH2 (Supp. Fig. 1). Before the integrative analysis, the complete multi-modal single cell dataset containing ILC subsets was converted into single Seurat object. All the subsequent protein-level and gene-level analyses were performed using multimodal data analysis pipeline of Seurat R package version 4.0. The normalized and scaled protein abundance profile was used for estimating the integrated harmony dimensions using runHarmony function in Seurat R package (reduction= ‘apca’ and group.by.vars = ‘batch’) . The batch corrected harmony embeddings were then used for computing the Uniform Manifold Approximation and Projection (UMAP) dimensions to visualize the clusters of ILC subsets. Differential marker analysis of surface proteins, between two groups of cells (COVID-19 and Healthy cohort), from abseq panels was computed with normalized and scaled expression values using FindMarkers function from Seurat R package (test.use=’wilcox’). Similarly, differential gene expression was performed on normalized and scaled gene expression values from between two groups of cells (COVID-19 and Healthy cohort) using the FindMarkers function from Seurat R package (test.use=’MAST’ and latent.vars=’batch’). Genes with log-fold change > 0.5 and adjusted p-value < 0.05 (method: Benjamini-Hochberg) (were considered as significant for further evaluation. The resulting adjusted p-values box-plots were plotted using ggplot2 R package (version 3.4.2) after computing the number of cells expressing a given protein or gene in each sample. Pathway enrichment analysis of DE genes was performed using web-server metascape (version 3.5). The AUCells score and gene regulatory network analysis was performed using pySCENIC pipeline (version 0.12.1). Gene regulatory network was reconstructed using GRNBoost2 algorithm and the list of TFs in humans (genome version: hg38) were obtained from cisTarget database. (https://resources.aertslab.org/cistarget). Cellular enrichment (aka AUCell) analysis that measures the activity of TF or gene signatures across all single cells was performed using aucell function in pySCENIC python library. The ggplot2 R package (version 3.4.2) was used for boxplot visualization. The differential gene co-expression analysis was performed using scSFMnet R package. Circular plots were generated using the R package circlize (version 0.4.15).

ZIKV_1.1.tar.gzR-package: ZIKV. R-package containing data and functions associated with this manuscript.ZIKV manualPDF manual for the R-package: ZIKVZIKV-manual.pdf

There are ten members of the TMEM16/Anoctamin family of proteins in mammals. Although the first members of this family to be discovered, TMEM16A and TMEM16B, have a calcium-regulated chloride channel function, subsequently other members of the family, such as TMEM16F, were found to have lipid scramblase activity combined with non-selective ion channel activity. TMEM16K was a relatively understudied member of the family despite the observation that mutations in TMEM16K have been linked to the genetic disease autosomal recessive spinocerebellar ataxia Type 10 (Also known as SCAR10 or ARCA3). SCAR10 is a late-onset neurodegenerative disorder which causes marked atrophy of the cerebellum with consequential deterioration in limb co-ordination, speech and eye movement. We have solved several structures of human TMEM16K through X-ray crystallography and cryo-EM capturing both active and inactive conformational states. Through collaborations, we have investigated TMEM16K’s function and location in cells. We were able to show that TMEM16K acts as a lipid scramblase with non-selective ion channel activity that is sensitive to both Ca²⁺ and lipid chain lengths. We also showed that TMEM16K mainly resides in the endoplasmic reticulum where it may be regulated by the ER’s unique lipid profile. Our highest resolution cryo-EM structure for TMEM16K allowed us to identify a bound lipid in the cavity behind the groove that transports the lipid headgroups and this lipid binding site may represent an allosteric modulator site, providing a direction for the design of binders which could modulate TMEM16K activity in cells.

The data was collected from experiments at the USDA-ARS in Beltsville, MD. The RTqPCR results were collected from processing samples on our BioRad machines. Count data was organized in Excel after being transferred from labnotebooks. Then data was analyzed in R.

Transcriptional response of mouse lungs to infection with Rhizopus delemar.

This talk discussed a data analytic toolbox developed by Song Lab at UM, which enables public health workers to analyze and evaluate time-course infection dynamics of the novel coronavirus disease (COVID-19) using the public available data from the China CDC. This toolbox provides forecast of some key turning points. The data analytics are built upon a state-space SIR model, a hierarchical modeling framework in which the two-dimensional observed time series of daily incidences of infected and removed cases are emitted from the underlying infection system governed by the classical SIR infectious disease mechanism. We extend the SIR model to incorporate different types of time-varying quarantine protocols, including government-level macro isolation policies and community-level micro inspection measures. Part of the output includes forecast of two key turning points: the time of daily infected proportions smaller than the previous ones and the time of daily infected proportions smaller than that of daily removed proportion. An R software package is made available for the public, and some examples on the use of this software package are illustrated. Some possible extensions of our toolbox are also discussed. This talk is in Chinese.

FAM83A-H are newly identified oncogenes characterised by a conserved DUF1669 domain. FAM83B can substitute for RAS to promote malignant transformation. Ablation of FAM83B or mutation of Lys230 inhibits malignant phenotypes, implicating FAM83B as potential therapeutic target. As part of this TEP, we solved the first crystal structures from the FAM83 family, including FAM83A and FAM83B. The structures of the DUF1669 domain reveal a phospholipase D-like fold lacking conservation of key catalytic residues. We deorphanise the FAM83 DUF1669 domain as a critical docking scaffold for binding of casein kinase 1 isoforms. Finally, using XChem fragment screening we report chemical fragments that bind to Lys230 in the central pocket of the DUF1669 and form starting points for potential drug development.

This project provides the tools and data to develop small molecule inhibitors for an inherited metabolic disorder (Primary hyperoxaluria type 1) due to the defective enzyme (AGXT), by targeting the enzyme (HAO1) upstream of the glyoxylate metabolic pathway to mitigate the defect (i.e. substrate reduction approach). This TEP package includes recombinant human HAO1 purification protocols, structures of the HAO1 in different states, in vitro assays to detect ligand/inhibitor binding (DSF, SPR) and enzyme activity (amplex red assay) of human HAO1, as well as initial chemical matters identified from crystallography-based fragment screening.