Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using QuantDiffForecast: A MATLAB toolbox and tutorial.

Mathematical models based on systems of ordinary differential equations (ODEs) are frequently applied in various scientific fields to assess hypotheses, estimate key model parameters, and generate predictions about the system's state. To support their application, we present a comprehensive, easy-to-use, and flexible MATLAB toolbox, QuantDiffForecast, and associated tutorial to estimate parameters and generate short-term forecasts with quantified uncertainty from dynamical models based on systems of ODEs. We provide software ( https://github.com/gchowell/paramEstimation_forecasting_ODEmodels/) and detailed guidance on estimating parameters and forecasting time-series trajectories that are characterized using ODEs with quantified uncertainty through a parametric bootstrapping approach. It includes functions that allow the user to infer model parameters and assess forecasting performance for different ODE models specified by the user, using different estimation methods and error structures in the data. The tutorial is intended for a diverse audience, including students training in dynamic systems, and will be broadly applicable to estimate parameters and generate forecasts from models based on ODEs. The functions included in the toolbox are illustrated using epidemic models with varying levels of complexity applied to data from the 1918 influenza pandemic in San Francisco. A tutorial video that demonstrates the functionality of the toolbox is included.

SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble spatial wave sub-epidemic modeling framework.

BACKGROUND: Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox, SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US. RESULTS: This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and students can use the toolbox to conduct real-time short-term forecasts. The five-parameter epidemic wave model in the toolbox aggregates linked overlapping sub-epidemics and captures a rich spectrum of epidemic wave dynamics, including oscillatory wave behavior and plateaus. An ensemble strategy aims to improve forecasting performance by combining the resulting top-ranked models. The toolbox provides a tutorial for forecasting time-series trajectories, including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. CONCLUSIONS: We have developed the first comprehensive toolbox to characterize and forecast time-series data using an ensemble spatial wave sub-epidemic wave model. As an epidemic situation or contagion occurs, the tools presented in this tutorial can facilitate policymakers to guide the implementation of containment strategies and assess the impact of control interventions. We demonstrate the functionality of the toolbox with examples, including a tutorial video, and is illustrated using daily data on the COVID-19 pandemic in the USA.

Real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, July-October 2022.

BACKGROUND: Beginning May 7, 2022, multiple nations reported an unprecedented surge in monkeypox cases. Unlike past outbreaks, differences in affected populations, transmission mode, and clinical characteristics have been noted. With the existing uncertainties of the outbreak, real-time short-term forecasting can guide and evaluate the effectiveness of public health measures. METHODS: We obtained publicly available data on confirmed weekly cases of monkeypox at the global level and for seven countries (with the highest burden of disease at the time this study was initiated) from the Our World in Data (OWID) GitHub repository and CDC website. We generated short-term forecasts of new cases of monkeypox across the study areas using an ensemble n-sub-epidemic modeling framework based on weekly cases using 10-week calibration periods. We report and assess the weekly forecasts with quantified uncertainty from the top-ranked, second-ranked, and ensemble sub-epidemic models. Overall, we conducted 324 weekly sequential 4-week ahead forecasts across the models from the week of July 28th, 2022, to the week of October 13th, 2022. RESULTS: The last 10 of 12 forecasting periods (starting the week of August 11th, 2022) show either a plateauing or declining trend of monkeypox cases for all models and areas of study. According to our latest 4-week ahead forecast from the top-ranked model, a total of 6232 (95% PI 487.8, 12,468.0) cases could be added globally from the week of 10/20/2022 to the week of 11/10/2022. At the country level, the top-ranked model predicts that the USA will report the highest cumulative number of new cases for the 4-week forecasts (median based on OWID data: 1806 (95% PI 0.0, 5544.5)). The top-ranked and weighted ensemble models outperformed all other models in short-term forecasts. CONCLUSIONS: Our top-ranked model consistently predicted a decreasing trend in monkeypox cases on the global and country-specific scale during the last ten sequential forecasting periods. Our findings reflect the potential impact of increased immunity, and behavioral modification among high-risk populations.

An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA.

We analyze an ensemble of n-sub-epidemic modeling for forecasting the trajectory of epidemics and pandemics. These ensemble modeling approaches, and models that integrate sub-epidemics to capture complex temporal dynamics, have demonstrated powerful forecasting capability. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. We systematically assess their calibration and short-term forecasting performance in short-term forecasts for the COVID-19 pandemic in the USA from late April 2020 to late February 2022. We compare their performance with two commonly used statistical ARIMA models. The best fit sub-epidemic model and three ensemble models constructed using the top-ranking sub-epidemic models consistently outperformed the ARIMA models in terms of the weighted interval score (WIS) and the coverage of the 95% prediction interval across the 10-, 20-, and 30-day short-term forecasts. In our 30-day forecasts, the average WIS ranged from 377.6 to 421.3 for the sub-epidemic models, whereas it ranged from 439.29 to 767.05 for the ARIMA models. Across 98 short-term forecasts, the ensemble model incorporating the top four ranking sub-epidemic models (Ensemble(4)) outperformed the (log) ARIMA model 66.3% of the time, and the ARIMA model, 69.4% of the time in 30-day ahead forecasts in terms of the WIS. Ensemble(4) consistently yielded the best performance in terms of the metrics that account for the uncertainty of the predictions. This framework can be readily applied to investigate the spread of epidemics and pandemics beyond COVID-19, as well as other dynamic growth processes found in nature and society that would benefit from short-term predictions.

Nonlinear function-on-scalar regression via functional universal approximation.

We consider general nonlinear function-on-scalar (FOS) regression models, where the functional response depends on multiple scalar predictors in a general unknown nonlinear form. Existing methods either assume specific model forms (e.g., additive models) or directly estimate the nonlinear function in a space with dimension equal to the number of scalar predictors, which can only be applied to models with a few scalar predictors. To overcome these shortcomings, motivated by the classic universal approximation theorem used in neural networks, we develop a functional universal approximation theorem which can be used to approximate general nonlinear FOS maps and can be easily adopted into the framework of functional data analysis. With this theorem and utilizing smoothness regularity, we develop a novel method to fit the general nonlinear FOS regression model and make predictions. Our new method does not make any specific assumption on the model forms, and it avoids the direct estimation of nonlinear functions in a space with dimension equal to the number of scalar predictors. By estimating a sequence of bivariate functions, our method can be applied to models with a relatively large number of scalar predictors. The good performance of the proposed method is demonstrated by empirical studies on various simulated and real datasets.

Nonpharmaceutical Interventions in Georgia: Public Health Implications.

OBJECTIVES: As coronavirus disease 2019 (COVID-19) spread, many states implemented nonpharmaceutical interventions in the absence of effective therapies with varying degrees of success. Our aim was to evaluate restrictions comparing two regions of Georgia and their impact on outcomes as measured by confirmed illness and deaths. METHODS: Using The New York Times COVID-19 incidence data and mandate information from various web sites, we examined trends in cases and deaths using joinpoint analysis at the region and county level before and after the implementation of a mandate. RESULTS: We found that rates of cases and deaths showed the greatest decrease in acceleration after the simultaneous implementation of a statewide shelter-in-place for vulnerable populations combined with social distancing for businesses and limiting gatherings to <10 people. County-level shelters-in-place, business closures, limits on gatherings to <10, and mask mandates showed significant case rate decreases after a county implemented them. School closures had no consistent effect on either outcome. CONCLUSIONS: Our findings indicate that protecting vulnerable populations, implementing social distancing, and mandating masks may be effective countermeasures to containment while mitigating the economic and psychosocial effects of strict shelters-in-place and business closures. In addition, states should consider allowing local municipalities the flexibility to enact nonpharmaceutical interventions that are more or less restrictive than the state-level mandates under some conditions in which the data indicate it is necessary to protect communities from disease or undue economic burden.

Restricted function-on-function linear regression model.

The usual function-on-function linear regression model depicts the association between functional variables in the whole rectangular region and the value of response curve at any point is influenced by the entire trajectory of the predictor curve. But in addition to this, there are cases where the value of the response curve at a point is only influenced by the value of the predictor curve in a subregion, such as the historical relationship and the short-term association. We will consider the restricted function-on-function regression model, where the value of response curve at any point is influenced by a subtrajectory of the predictor. We have two major purposes. First, we propose a novel estimation procedure that is more accurate and computational efficient for the restricted function-on-function model with a given subregion. Second, as the subregion is seldom specified in practice, we propose a subregion selection procedure that can lead to models with better interpretation and predictive performance. Algorithms are developed for both model estimation and subregion selection.

Ensemble bootstrap methodology for forecasting dynamic growth processes using differential equations: application to epidemic outbreaks.

BACKGROUND: Ensemble modeling aims to boost the forecasting performance by systematically integrating the predictive accuracy across individual models. Here we introduce a simple-yet-powerful ensemble methodology for forecasting the trajectory of dynamic growth processes that are defined by a system of non-linear differential equations with applications to infectious disease spread. METHODS: We propose and assess the performance of two ensemble modeling schemes with different parametric bootstrapping procedures for trajectory forecasting and uncertainty quantification. Specifically, we conduct sequential probabilistic forecasts to evaluate their forecasting performance using simple dynamical growth models with good track records including the Richards model, the generalized-logistic growth model, and the Gompertz model. We first test and verify the functionality of the method using simulated data from phenomenological models and a mechanistic transmission model. Next, the performance of the method is demonstrated using a diversity of epidemic datasets including scenario outbreak data of the Ebola Forecasting Challenge and real-world epidemic data outbreaks of including influenza, plague, Zika, and COVID-19. RESULTS: We found that the ensemble method that randomly selects a model from the set of individual models for each time point of the trajectory of the epidemic frequently outcompeted the individual models as well as an alternative ensemble method based on the weighted combination of the individual models and yields broader and more realistic uncertainty bounds for the trajectory envelope, achieving not only better coverage rate of the 95% prediction interval but also improved mean interval scores across a diversity of epidemic datasets. CONCLUSION: Our new methodology for ensemble forecasting outcompete component models and an alternative ensemble model that differ in how the variance is evaluated for the generation of the prediction intervals of the forecasts.

Evaluating latent tuberculosis infection diagnostics using latent class analysis.

BACKGROUND: Lack of a gold standard for latent TB infection has precluded direct measurement of test characteristics of the tuberculin skin test and interferon-γ release assays (QuantiFERON Gold In-Tube and T-SPOT.TB). OBJECTIVE: We estimated test sensitivity/specificity and latent TB infection prevalence in a prospective, US-based cohort of 10 740 participants at high risk for latent infection. METHODS: Bayesian latent class analysis was used to estimate test sensitivity/specificity and latent TB infection prevalence among subgroups based on age, foreign birth outside the USA and HIV infection. RESULTS: Latent TB infection prevalence varied from 4.0% among foreign-born, HIV-seronegative persons aged <5 years to 34.0% among foreign-born, HIV-seronegative persons aged ≥5 years. Test sensitivity ranged from 45.8% for the T-SPOT.TB among foreign-born, HIV-seropositive persons aged ≥5 years to 80.7% for the tuberculin skin test among foreign-born, HIV-seronegative persons aged ≥5 years. The skin test was less specific than either interferon-γ release assay, particularly among foreign-born populations (eg, the skin test had 70.0% specificity among foreign-born, HIV-seronegative persons aged ≥5 years vs 98.5% and 99.3% specificity for the QuantiFERON and T-SPOT.TB, respectively). The tuberculin skin test's positive predictive value ranged from 10.0% among foreign-born children aged <5 years to 69.2% among foreign-born, HIV-seropositive persons aged ≥5 years; the positive predictive values of the QuantiFERON (41.4%) and T-SPOT.TB (77.5%) were also low among US-born, HIV-seropositive persons aged ≥5 years. CONCLUSIONS: These data reinforce guidelines preferring interferon-γ release assays for foreign-born populations and recommending against screening populations at low risk for latent TB infection. TRIAL REGISTRATION NUMBER: NCT01622140.

Improvement of Geographic Disparities: Amelioration or Displacement?

Progression of geographic disparities in social determinants of health is a global concern. Using an Urban Health Index (UHI) approach, we proposed a framework of examining the change of geographic disparities in social determinants in small areas. Using the City of Atlanta in Georgia (USA) as a case study, we standardized six census-based social determinant indicators in 2000 and in 2010, respectively, and calculated their geometric mean to assign each census tract a UHI value for 2000 and for 2010. We then evaluated the temporal change of the UHIs in relation to the demographic changes using spatial and statistical methods. We found that Atlanta experienced an improvement in social determinant status and a reduction of disparities in the 10 years. The areas that experienced improvement, however, underwent demographic changes as well. This analysis provides support for displacement, rather than improvement, as the underlying factor for apparent change in geographic disparities. Findings suggest the importance of local evaluation for future policies to reduce disparities in cities.

Changing Places and Partners: Associations of Neighborhood Conditions With Sexual Network Turnover Among African American Adults Relocated From Public Housing.

Neighborhood conditions and sexual network turnover have been associated with the acquisition of HIV and other sexually transmitted infections (STIs). However, few studies investigate the influence of neighborhood conditions on sexual network turnover. This longitudinal study used data collected across 7 visits from a predominantly substance-misusing cohort of 172 African American adults relocated from public housing in Atlanta, Georgia, to determine whether post-relocation changes in exposure to neighborhood conditions influence sexual network stability, the number of new partners joining sexual networks, and the number of partners leaving sexual networks over time. At each visit, participant and sexual network characteristics were captured via survey, and administrative data were analyzed to describe the census tracts where participants lived. Multilevel models were used to longitudinally assess the relationships of tract-level characteristics to sexual network dynamics over time. On average, participants relocated to neighborhoods that were less economically deprived and violent, and had lower alcohol outlet densities. Post-relocation reductions in exposure to alcohol outlet density were associated with fewer new partners joining sexual networks. Reduced perceived community violence was associated with more sexual partners leaving sexual networks. These associations were marginally significant. No post-relocation changes in place characteristics were significantly associated with overall sexual network stability. Neighborhood social context may influence sexual network turnover. To increase understanding of the social-ecological determinants of HIV/STIs, a new line of research should investigate the combined influence of neighborhood conditions and sexual network dynamics on HIV/STI transmission over time.

Public Housing Relocations and Partnership Dynamics in Areas With High Prevalences of Sexually Transmitted Infections.

BACKGROUND: We investigated the implications of one structural intervention--public housing relocations--for partnership dynamics among individuals living areas with high sexually transmitted infection (STI) prevalence. High-prevalence areas fuel STI endemicity and are perpetuated by spatially assortative partnerships. METHODS: We analyzed 7 waves of data from a cohort of black adults (n = 172) relocating from 7 public housing complexes in Atlanta, Georgia. At each wave, data on whether participants' sexual partners lived in the neighborhood were gathered via survey. Participant addresses were geocoded to census tracts, and measures of tract-level STI prevalence, socioeconomic conditions, and other attributes were created for each wave. "High-prevalence tracts" were tracts in the highest quartile of STI prevalence in Georgia. Descriptive statistics and hierarchical generalized linear models examined trajectories of spatially assortative partnerships and identified predictors of assortativity among participants in high-prevalence tracts. RESULTS: All 7 tracts containing public housing complexes at baseline were high-prevalence tracts; most participants relocated to high-prevalence tracts. Spatially assortative partnerships had a U-shaped distribution: the mean percent of partners living in participants' neighborhoods at baseline was 54%; this mean declined to 28% at wave 2 and was 45% at wave 7. Participants who experienced greater postrelocation improvements in tract-level socioeconomic conditions had a lower odds of having spatially assortative partnerships (adjusted odds ratio, 1.55; 95% confidence interval [95% CI], 1.06-2.26). CONCLUSIONS: Public housing relocation initiatives may disrupt high-prevalence areas if residents experience significant postrelocation gains in tract-level socioeconomic conditions.

Mortality rates and the causes of death related to diabetes mellitus in Shanghai Songjiang District: an 11-year retrospective analysis of death certificates.

BACKGROUND: China is one of the countries with the highest prevalence of diabetes in the world. We analysed all the death certificates mentioning diabetes from 2002 to 2012 in Songjiang District of Shanghai to estimate morality rates and examine cause of death patterns. METHODS: Mortality data of 2654 diabetics were collected from the database of local CDC. The data set comprises all causes of death, contributing causes and the underlying cause, thereby the mortality rates of diabetes and its specified complications were analysed. RESULTS: The leading underlying causes of death were various cardiovascular diseases (CVD), which collectively accounted for about 30% of the collected death certificates. Diabetes was determined as the underlying cause of death on 28.7%. The trends in mortality showed that the diabetes related death rate increased about 1.78 fold in the total population during the 11-year period, and the death rate of diabetes and CVD comorbidity increased 2.66 fold. In all the diabetes related deaths, the proportion of people dying of ischaemic heart disease or cerebrovascular disease increased from 18.0% in 2002 to 30.5% in 2012. But the proportions attributed directly to diabetes showed a downtrend, from 46.7-22.0%. CONCLUSIONS: The increasing diabetes related mortality could be chiefly due to the expanding prevalence of CVD, but has nothing to do with diabetes as the underlying cause. Policy makers should pay more attention to primary prevention of diabetes and on the prevention of cardiovascular complications to reduce the burden of diabetes on survival.

Campus climate impacts on sexual violence: a Bayesian comparison of undergraduate and community colleges.

OBJECTIVE: Sexual violence is endemic on college campuses. Four-year campuses present high-risk environments for sexual violence and heavy episodic drinking is a robust risk factor for victimization. However, limited literature exists on sexual violence at two-year institutions, with most research focused on four-year campuses. We examined whether campus climates affect sexual violence prevalence rates. PARTICIPANTS: Sexual misconduct campus climate data from two-year and four-year campus students. METHODS: We used Bayesian logistic regressions to compare sexual victimization odds between two- and four-year campuses. RESULTS: Four-year students were twice as likely to have experienced sexual victimization and 2.5 times more likely to engage in heavy episodic drinking compared to two-year students. The risk of sexual victimization associated with heavy episodic drinking was reliably similar across campus types. CONCLUSIONS: Campus climates reliably impact student's risk of sexual victimization. Based on these findings, two- and four-year campuses may need to implement distinct prevention services.

GrowthPredict: A toolbox and tutorial-based primer for fitting and forecasting growth trajectories using phenomenological growth models.

Simple dynamic modeling tools can help generate real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. This tutorial-based primer introduces and illustrates GrowthPredict, a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to a broad audience, including students training in mathematical biology, applied statistics, and infectious disease modeling, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 1-parameter exponential growth model and the 2-parameter generalized-growth model, which have proven useful in characterizing and forecasting the ascending phase of epidemic outbreaks. It also includes the 2-parameter Gompertz model, the 3-parameter generalized logistic-growth model, and the 3-parameter Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks. We provide detailed guidance on forecasting time-series trajectories and available software ( https://github.com/gchowell/forecasting_growthmodels ), including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. This tutorial and toolbox can be broadly applied to characterizing and forecasting time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can help create forecasts to guide policy regarding implementing control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and the examples use publicly available data on the monkeypox (mpox) epidemic in the USA.

SubEpiPredict: A tutorial-based primer and toolbox for fitting and forecasting growth trajectories using the ensemble n-sub-epidemic modeling framework.

An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds.

Retrospective evaluation of short-term forecast performance of ensemble sub-epidemic frameworks and other time-series models: The 2022-2023 mpox outbreak across multiple geographical scales, July 14th, 2022, through February 26th, 2023.

In May 2022, public health officials noted an unprecedented surge in mpox cases in non-endemic countries worldwide. As the epidemic accelerated, multi-model forecasts of the epidemic's trajectory were critical in guiding the implementation of public health interventions and determining policy. As the case levels have significantly decreased as of early September 2022, evaluating model performance is essential to advance the growing field of epidemic forecasting. Using laboratory-confirmed mpox case data from the Centers for Disease Control and Prevention (CDC) and Our World in Data (OWID) teams through the week of January 26th, 2023, we generated retrospective sequential weekly forecasts (e.g., 1-4-weeks) for Brazil, Canada, France, Germany, Spain, the United Kingdom, the USA, and at the global scale using models that require minimal input data including the auto-regressive integrated moving average (ARIMA), general additive model (GAM), simple linear regression (SLR), Facebook's Prophet model, as well as the sub-epidemic wave (spatial-wave) and n-sub-epidemic modeling frameworks. We assess forecast performance using average mean squared error (MSE), mean absolute error (MAE), weighted interval score (WIS), 95% prediction interval coverage (95% PI coverage), and skill scores. Average Winkler scores were used to calculate skill scores for 95% PI coverage. Overall, the n-sub-epidemic modeling framework outcompeted other models across most locations and forecasting horizons, with the unweighted ensemble model performing best across all forecasting horizons for most locations regarding average MSE, MAE, WIS, and 95% PI coverage. However, many locations had multiple models performing equally well for the average 95% PI coverage. The n-sub-epidemic and spatial-wave frameworks improved considerably in average MSE, MAE, and WIS, and Winkler scores (95% PI coverage) relative to the ARIMA model. Findings lend further support to sub-epidemic frameworks for short-term forecasting epidemics of emerging and re-emerging infectious diseases.

A MATLAB toolbox to fit and forecast growth trajectories using phenomenological growth models: Application to epidemic outbreaks.

Background: Simple dynamic modeling tools can be useful for generating real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking. Results: In this tutorial-based primer, we introduce and illustrate a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to various audiences, including students training in time-series forecasting, dynamic growth modeling, parameter estimation, parameter uncertainty and identifiability, model comparison, performance metrics, and forecast evaluation, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 2-parameter generalized-growth model, which has proved useful to characterize and forecast the ascending phase of epidemic outbreaks, and the Gompertz model as well as the 3-parameter generalized logistic-growth model and the Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks.The toolbox provides a tutorial for forecasting time-series trajectories that include the full uncertainty distribution, derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. Conclusions: We have developed the first comprehensive toolbox to characterize and forecast time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can facilitate policymaking to guide the implementation of control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and is illustrated using weekly data on the monkeypox epidemic in the USA.

Quantitative proteomics of caveolin-1-regulated proteins: characterization of polymerase i and transcript release factor/CAVIN-1 IN endothelial cells.

Caveolae are organelles abundant in the plasma membrane of many specialized cells including endothelial cells (ECs), epithelial cells, and adipocytes, and in these cells, caveolin-1 (Cav-1) is the major coat protein essential for the formation of caveolae. To identify proteins that require Cav-1 for stable incorporation into membrane raft domains, a quantitative proteomics analysis using isobaric tagging for relative and absolute quantification was performed on rafts isolated from wild-type and Cav-1-deficient mice. In three independent experiments, 117 proteins were consistently identified in membrane rafts with the largest differences in the levels of Cav-2 and in the caveola regulatory proteins Cavin-1 and Cavin-2. Because the lung is highly enriched in ECs, we validated and characterized the role of the newly described protein Cavin-1 in several cardiovascular tissues and in ECs. Cavin-1 was highly expressed in ECs lining blood vessels and in cultured ECs. Knockdown of Cavin-1 reduced the levels of Cav-1 and -2 and weakly influenced the formation of high molecular weight oligomers containing Cav-1 and -2. Cavin-1 silencing enhanced basal nitric oxide release from ECs but blocked proangiogenic phenotypes such as EC proliferation, migration, and morphogenesis in vitro. Thus, these data support an important role of Cavin-1 as a regulator of caveola function in ECs.

Investigating oral health among individuals with depression: NHANES 2015-2016.

Objectives: Depression is highly prevalent across populations, yet studies on its contribution to oral health are lacking. Therefore, our goal is to examine the association of depression and oral health problems (preventative care, access to dental care, and oral condition in relation to quality of life) controlling for sociodemographic and chronic disease indicators (CDI). Methods: 5,992 respondents' data 18+ years old were analyzed from the 2015-2016 National Health and Nutrition Examination Survey (NHANES). The independent variable of interest was depression symptoms status. Oral health outcomes were the dependent variables. We used the Patient Health Questionnaire-9 (PHQ-9) for depression and the Oral Health Questionnaire (OHQ) to measure oral health outcomes. Covariates included sociodemographics (age, education, sex, race/ethnicity, and income) and CDI included current smoking, diabetes, and body mass index. All data were weighted using 2 years sample weight. Results: The mean age of respondents was 47.22 years (45.97-48.46) and 46% were males. Participants with depression present 6.93%, and females 63.85% were higher than males 36.15%.Participants with depression have significantly low income 43.10% than others p value < 0.0001. After adjusting for sociodemographics and CDI, participants who have depression were more prone to report fair/poor oral condition [aOR = 1.91 (1.29-2.84)], oral pain [aOR = 2.66 (1.91-3.71)], and difficulty accessing needed dental care [aOR = 2.52 (1.96-3.24)] than others. Having depression was associated with poor oral health perceptions [aOR = 2.10 (1.41-3.13)], and having difficulty at job/school because of their oral health [aOR = 2.85 (1.90-4.26)]. Conclusion: Based on the empirical evidence provided by our study, there is an association between depression and oral health outcomes and oral health-related quality of life.