Predicting an epidemic trajectory is difficult.
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ABSTRACT:
SUBMITTER: Wilke CO
PROVIDER: S-EPMC7682412 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A pipeline involving data acquisition, curation, carefully chosen graphs and mathematical models, allows analysis of COVID-19 outbreaks at 3,546 locations world-wide (all countries plus smaller administrative divisions with data available). Comparison of locations with over 50 deaths shows all outbreaks have a common feature: H(t) defined as loge(X(t)/X(t-1)) decreases linearly on a log scale, where X(t) is the total number of Cases or Deaths on day, t (we use ln for loge). The downward slopes vary by about a factor of three with time constants (1/slope) of between 1 and 3 weeks; this suggests it may be possible to predict when an outbreak will end. Is it possible to go beyond this and perform early prediction of the outcome in terms of the eventual plateau number of total confirmed cases or deaths? We test this hypothesis by showing that the trajectory of cases or deaths in any outbreak can be converted into a straight line. Specifically Y(t) ≡ -ln(ln(N / X(t)), is a straight line for the correct plateau value N, which is determined by a new method, Best-Line Fitting (BLF). BLF involves a straight-line facilitation extrapolation needed for prediction; it is blindingly fast and amenable to optimization. We find that in some locations that entire trajectory can be predicted early, whereas others take longer to follow this simple functional form. Fortunately, BLF distinguishes predictions that are likely to be correct in that they show a stable plateau of total cases or death (N value). We apply BLF to locations that seem close to a stable predicted N value and then forecast the outcome at some locations that are still growing wildly. Our accompanying web-site will be updated frequently and provide all graphs and data described here.
| S-EPMC7325180 | biostudies-literature
Project description:BackgroundAccurate prediction of the difficult airway (DA) could help to prevent catastrophic consequences in emergency resuscitation, intensive care, and general anesthesia. Until now, there is no nomogram prediction model for DA based on ultrasound assessment. In this study, we aimed to develop a predictive model for difficult tracheal intubation (DTI) and difficult laryngoscopy (DL) using nomogram based on ultrasound measurement. We hypothesized that nomogram could utilize multivariate data to predict DTI and DL.MethodsA prospective observational DA study was designed. This study included 2254 patients underwent tracheal intubation. Common and airway ultrasound indicators were used for the prediction, including thyromental distance (TMD), modified Mallampati test (MMT) score, upper lip bite test (ULBT) score temporomandibular joint (TMJ) mobility and tongue thickness (TT). Univariate and the Akaike information criterion (AIC) stepwise logistic regression were used to identify independent predictors of DTI and DL. Nomograms were constructed to predict DL and DTL based on the AIC stepwise analysis results. Receiver operating characteristic (ROC) curves were used to evaluate the accuracy of the nomograms.ResultsAmong the 2254 patients enrolled in this study, 142 (6.30%) patients had DL and 51 (2.26%) patients had DTI. After AIC stepwise analysis, ULBT, MMT, sex, TMJ, age, BMI, TMD, IID, and TT were integrated for DL nomogram; ULBT, TMJ, age, IID, TT were integrated for DTI nomogram. The areas under the ROC curves were 0.933 [95% confidence interval (CI), 0.912-0.954] and 0.974 (95% CI, 0.954-0.995) for DL and DTI, respectively.ConclusionNomograms based on airway ultrasonography could be a reliable tool in predicting DA.Trial registrationChinese Clinical Trial Registry (No. ChiCTR-RCS-14004539 ), registered on 13th April 2014.
| S-EPMC8756724 | biostudies-literature
Project description:When we get sick, we want to be resilient and recover our original health. To measure resilience, we need to quantify a host's position along its disease trajectory. Here we present Looper, a computational method to analyze longitudinally gathered datasets and identify gene pairs that form looping trajectories when plotted in the space described by these phases. These loops enable us to track where patients lie on a typical trajectory back to health. We analyzed two publicly available, longitudinal human microarray datasets that describe self-resolving immune responses. Looper identified looping gene pairs expressed by human donor monocytes stimulated by immune elicitors, and in YF17D-vaccinated individuals. Using loops derived from training data, we found that we could predict the time of perturbation in withheld test samples with accuracies of 94% in the human monocyte data, and 65-83% within the same cohort and in two independent cohorts of YF17D vaccinated individuals. We suggest that Looper will be useful in building maps of resilient immune processes across organisms.
| S-EPMC6175499 | biostudies-literature
Project description:ObjectivesTo investigate the characteristics and prognostic value of fecal lactoferrin trajectories in ulcerative colitis (UC).MethodsThis study used data from the UNIFI trial (ClinicalTrials.gov, NCT02407236) and included patients who received ustekinumab during induction for trajectory modeling (n = 637). Patients who received ustekinumab during maintenance therapy were used for 1-year outcome analyses (n = 403). The levels of fecal lactoferrin, fecal calprotectin, and serum C-reactive protein were measured at weeks 0, 2, 4, and 8. The trajectories of these biomarkers were developed using a latent class growth mixed model.ResultsThe trajectories of fecal lactoferrin, fecal calprotectin, and serum C-reactive protein were distinct, but all were associated with prior exposure to anti-tumor necrosis factor agents and vedolizumab. Furthermore, the fecal lactoferrin trajectory was the most valuable predictor of endoscopic, clinical, and histological remission. Compared to the high/moderate-rapid decrease trajectory group, the moderate-slow decrease, high-slow decrease, and high-stable groups had adjusted odds ratios (95% confidence interval) of 0.38 (0.18, 0.78; P = 0.010), 0.47 (0.23, 0.93; P = 0.032), and 0.33 (0.17, 0.63; P = 0.001), respectively, of 1-year endoscopic remission. Patients with high/moderate-rapid decrease trajectories also had the highest likelihood of achieving clinical and histological remission. Finally, we developed a patient-stratification scheme based on fecal lactoferrin trajectories and concentrations. Patients with good, moderate, and poor prognoses in the scheme had a distinct probability of achieving 1-year endoscopic remission (52.7%, 30.9%, and 12.8%, respectively).ConclusionsThe trajectory of fecal lactoferrin is a valuable prognostic factor for 1-year remission in UC.
| S-EPMC10680133 | biostudies-literature
Project description:Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system's functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system's pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node's loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node's epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies.
| S-EPMC4357450 | biostudies-literature
Project description:Chronic medical conditions show substantial heterogeneity in their clinical features and progression. We develop the novel data-driven, network-based Trajectory Profile Clustering (TPC) algorithm for 1) identification of disease subtypes and 2) early prediction of subtype/disease progression patterns. TPC is an easily generalizable method that identifies subtypes by clustering patients with similar disease trajectory profiles, based not only on Parkinson's Disease (PD) variable severity, but also on their complex patterns of evolution. TPC is derived from bipartite networks that connect patients to disease variables. Applying our TPC algorithm to a PD clinical dataset, we identify 3 distinct subtypes/patient clusters, each with a characteristic progression profile. We show that TPC predicts the patient's disease subtype 4 years in advance with 72% accuracy for a longitudinal test cohort. Furthermore, we demonstrate that other types of data such as genetic data can be integrated seamlessly in the TPC algorithm. In summary, using PD as an example, we present an effective method for subtype identification in multidimensional longitudinal datasets, and early prediction of subtypes in individual patients.
| S-EPMC7299311 | biostudies-literature
Project description:Rationale & objectiveEarly prediction of chronic kidney disease (CKD) progression to end-stage kidney disease (ESKD) currently use Cox models including baseline estimated glomerular filtration rate (eGFR) only. Alternative approaches include a Cox model that includes eGFR slope determined over a baseline period of time, a Cox model with time varying GFR, or a joint modeling approach. We studied if these more complex approaches may further improve ESKD prediction.Study designProspective cohort.Setting & participantsWe re-used data from two CKD cohorts including patients with baseline eGFR >30ml/min per 1.73m2. MASTERPLAN (N = 505; 55 ESKD events) was used as development dataset, and NephroTest (N = 1385; 72 events) for validation.PredictorsAll models included age, sex, eGFR, and albuminuria, known prognostic markers for ESKD.Analytical approachWe trained the models on the MASTERPLAN data and determined discrimination and calibration for each model at 2 years follow-up for a prediction horizon of 2 years in the NephroTest cohort. We benchmarked the predictive performance against the Kidney Failure Risk Equation (KFRE).ResultsThe C-statistics for the KFRE was 0.94 (95%CI 0.86 to 1.01). Performance was similar for the Cox model with time-varying eGFR (0.92 [0.84 to 0.97]), eGFR (0.95 [0.90 to 1.00]), and the joint model 0.91 [0.87 to 0.96]). The Cox model with eGFR slope showed the best calibration.ConclusionIn the present studies, where the outcome was rare and follow-up data was highly complete, the joint models did not offer improvement in predictive performance over more traditional approaches such as a survival model with time-varying eGFR, or a model with eGFR slope.
| S-EPMC6508737 | biostudies-literature
Project description:BackgroundMultiple scores have been created in order to predict difficult cholecystectomy, nonetheless there is not a consensuated standard on which to use. The importance of a predictive score to be able to establish a difficult cholecystectomy would be a relevant instrument in order to better inform the patient, properly call for help when needed, choose the correct staff, and schedule and plan the surgical procedure accordingly.MethodsA diagnostic trial study was performed. All different predictive scores for difficult cholecystectomy were calculated for each patient. The correlation between the preoperative score and cholecystectomies considered as "difficult" were measured estimating the preoperative score's predictive value using a receiver operating characteristics curve in order to predict findings for difficult cholecystectomy.ResultsA total of 635 patients between 2014 and 2021 were selected. Selected patients had a mean age of 55.0 (interquartile range: 28.00) and were mostly female (64.25%). Surgical outcomes of patients with difficult cholecystectomy had statistically significant higher rates of subtotal cholecystectomies, drain usage, complications and reinterventions, prolonged surgical times, and longer hospital stay. When analyzing the predictive value on each of the different scores applied, score 4 had the highest performance for predicting difficult cholecystectomy with an area under the curve=0.783 (CI 95% 0.745-0.822).ConclusionsDifficult cholecystectomies are associated with worse surgical outcomes. The standardization and use of predictive scores for difficult cholecystectomy must be implemented in order to improve surgical outcomes as a result of more meticulous planning when scheduling the procedure.
| S-EPMC10389651 | biostudies-literature
Project description:BACKGROUND:To develop a mathematical model to estimate daily evolution of disease severity using routinely available parameters in patients admitted to the intensive care unit (ICU). METHODS:Over a 3-year period, we prospectively enrolled consecutive adults with sepsis and categorized patients as (1) being at risk for developing (more severe) organ dysfunction, (2) having (potentially still reversible) limited organ failure, or (3) having multiple-organ failure. Daily probabilities for transitions between these disease states, and to death or discharge, during the first 2 weeks in ICU were calculated using a multi-state model that was updated every 2 days using both baseline and time-varying information. The model was validated in independent patients. RESULTS:We studied 1371 sepsis admissions in 1251 patients. Upon presentation, 53 (4%) were classed at risk, 1151 (84%) had limited organ failure, and 167 (12%) had multiple-organ failure. Among patients with limited organ failure, 197 (17%) evolved to multiple-organ failure or died and 809 (70%) improved or were discharged alive within 14?days. Among patients with multiple-organ failure, 67 (40%) died and 91 (54%) improved or were discharged. Treatment response could be predicted with reasonable accuracy (c-statistic ranging from 0.55 to 0.81 for individual disease states, and 0.67 overall). Model performance in the validation cohort was similar. CONCLUSIONS:This prediction model that estimates daily evolution of disease severity during sepsis may eventually support clinicians in making better informed treatment decisions and could be used to evaluate prognostic biomarkers or perform in silico modeling of novel sepsis therapies during trial design. CLINICAL TRIAL REGISTRATION:ClinicalTrials.gov NCT01905033.
| S-EPMC6909511 | biostudies-literature
Project description:ObjectiveTo screen the cell differentiation trajectory-related genes and build a cell differentiation trajectory-related signature for predicting the prognosis of lung adenocarcinoma (LUAD).MethodsLUAD single cell mRNA expression profile, TCGA-LUAD transcriptome data were obtained from GEO and TCGA databases. Single-cell RNA-seq data were used for cell clustering and pseudotime analysis after dimensionality reduction analysis, and the cell differentiation trajectory-related genes were acquired after differential expression analysis conducted between the main branches. Then, the consensus clustering analysis was carried out on TCGA-LUAD samples, and the GSEA analysis was performed, then the differences on the expression levels of immune checkpoint genes and immunotherapy response were compared among clusters. The prognostic model was constructed, and the GSE42127 dataset was used to validate. A nomogram evaluation model was used to predict prognosis.ResultsTwo subsets with distinct differentiation states were found after cell differentiation trajectory analysis. TCGA-LUAD samples were divided into two cell differentiation trajectory-related gene-based clusters, GSEA found that cluster 1 was significantly related to 20 pathways, cluster 2 was significantly enriched in three pathways, and it was also shown that clusters could better predict immune checkpoint gene expression and immunotherapy response. A six cell differentiation-related genes-based prognostic signature was constructed, and the patients in the high-risk group had poorer prognosis than those in the low-risk group. Moreover, a nomogram was constructed based on the prognostic signature and clinicopathological features, and this nomogram had strong predictive performance and high accuracy.ConclusionThe cell differentiation-related signature and the prognostic nomogram could accurately predict survival.
| S-EPMC9398881 | biostudies-literature