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Spatially varying auto-regressive models for prediction of new human immunodeficiency virus diagnoses.

ABSTRACT: In demand of predicting new HIV diagnosis rates based on publicly available HIV data that is abundant in space but has few points in time, we propose a class of spatially varying autoregressive (SVAR) models compounded with conditional autoregressive (CAR) spatial correlation structures. We then propose to use the copula approach and a flexible CAR formulation to model the dependence between adjacent counties. These models allow for spatial and temporal correlation as well as space-time interactions and are naturally suitable for predicting HIV cases and other spatio-temporal disease data that feature a similar data structure. We apply the proposed models to HIV data over Florida, California and New England states and compare them to a range of linear mixed models that have been recently popular for modeling spatio-temporal disease data. The results show that for such data our proposed models outperform the others in terms of prediction.

SUBMITTER: Shand L

PROVIDER: S-EPMC6404983 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Spatially varying auto-regressive models for prediction of new human immunodeficiency virus diagnoses.

Shand Lyndsay L Li Bo B Park Trevor T Albarracín Dolores D

Journal of the Royal Statistical Society. Series B, Statistical methodology 20180312 4

In demand of predicting new HIV diagnosis rates based on publicly available HIV data that is abundant in space but has few points in time, we propose a class of spatially varying autoregressive (SVAR) models compounded with conditional autoregressive (CAR) spatial correlation structures. We then propose to use the copula approach and a flexible CAR formulation to model the dependence between adjacent counties. These models allow for spatial and temporal correlation as well as space-time interact ...[more]

PMID: 30853848

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Project description:Most countries are reopening or considering lifting the stringent prevention policies such as lockdowns, consequently, daily coronavirus disease (COVID-19) cases (confirmed, recovered and deaths) are increasing significantly. As of July 25th, there are 16.5 million global cumulative confirmed cases, 9.4 million cumulative recovered cases and 0.65 million deaths. There is a tremendous necessity of supervising and estimating future COVID-19 cases to control the spread and help countries prepare their healthcare systems. In this study, time-series models - Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) are used to forecast the epidemiological trends of the COVID-19 pandemic for top-16 countries where 70%-80% of global cumulative cases are located. Initial combinations of the model parameters were selected using the auto-ARIMA model followed by finding the optimized model parameters based on the best fit between the predictions and test data. Analytical tools Auto-Correlation function (ACF), Partial Auto-Correlation Function (PACF), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to assess the reliability of the models. Evaluation metrics Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) were used as criteria for selecting the best model. A case study was presented where the statistical methodology was discussed in detail for model selection and the procedure for forecasting the COVID-19 cases of the USA. Best model parameters of ARIMA and SARIMA for each country are selected manually and the optimized parameters are then used to forecast the COVID-19 cases. Forecasted trends for confirmed and recovered cases showed an exponential rise for countries such as the United States, Brazil, South Africa, Colombia, Bangladesh, India, Mexico and Pakistan. Similarly, trends for cumulative deaths showed an exponential rise for countries Brazil, South Africa, Chile, Colombia, Bangladesh, India, Mexico, Iran, Peru, and Russia. SARIMA model predictions are more realistic than that of the ARIMA model predictions confirming the existence of seasonality in COVID-19 data. The results of this study not only shed light on the future trends of the COVID-19 outbreak in top-16 countries but also guide these countries to prepare their health care policies for the ongoing pandemic. The data used in this work is obtained from publicly available John Hopkins University's COVID-19 database.

Project description:BackgroundAn increasing number of studies have identified spatial differences in colorectal cancer survival. However, little is known about the spatially varying effects of predictors in survival prediction modeling studies of colorectal cancer that have focused on estimating the absolute survival risk for patients from a wide range of populations. This study aimed to demonstrate the spatially varying effects of predictors of survival for nonmetastatic colorectal cancer patients.MethodsPatients diagnosed with nonmetastatic colorectal cancer from 2004 to 2013 who were followed up through the end of 2013 were extracted from the Surveillance Epidemiology End Results registry (Patients: 128061). The log-rank test and the restricted mean survival time were used to evaluate survival outcome differences among spatial clusters corresponding to a widely used clinical predictor: stage determined by AJCC 7th edition staging system. The heterogeneity test, which is used in meta-analyses, revealed the spatially varying effects of single predictors. Then, considering the above predictors in a standard survival prediction model based on spatially clustered data, the spatially varying coefficients of these models revealed that some covariate effects may not be constant across the geographic regions of the study. Then, two types of survival prediction models (a statistical model and a machine learning model) were built; these models considered the predictors and enabled survival prediction for patients from a wide range of geographic regions.ResultsBased on univariate and multivariate analysis, some prognostic factors, such as "TNM stage", "tumor size" and "age at diagnosis," have significant spatially varying effects among different regions. When considering these spatially varying effects, machine learning models have fewer assumption constraints (such as proportional hazard assumptions) and better predictive performance compared with statistical models. Upon comparing the concordance indexes of these two models, the machine learning model was found to be more accurate (0.898[0.895,0.902]) than the statistical model (0.732 [0.726, 0.738]).ConclusionsBased on this study, it's recommended that the spatially varying effect of predictors should be considered when building survival prediction models involving large-scale and multicenter research data. Machine learning models that are not limited by the requirement of a statistical hypothesis are promising alternative models.

Project description:ObjectivesGiven an aging human immunodeficiency virus (HIV) population, we aimed to determine the prevalence of HIV for long-stay residents in US nursing homes (NHs) between 2001 and 2010 and to compare characteristics and diagnoses of HIV-positive (HIV+) and negative (HIV-) residents. Also, for residents with dementia diagnoses, we compared antipsychotic (APS) medication receipt by HIV status.DesignA cross-sectional comparative study.SettingNHs in the 14 states accounting for 75% of persons living with HIV.ParticipantsA total of 9 245 009 long-stay NH residents.MeasurementsUsing Medicaid fee-for-service claims data in the years 2001 to 2010, together with Medicare resident assessment and Chronic Condition Warehouse data, we identified long-stay (more than 89 days) NH residents by HIV status and dementia presence. We examined dementia presence by age groups and APS medication receipt by younger (aged younger than 65 years) vs older (aged 65 years or older) residents, using logistic regression.ResultsBetween 2001 and 2010, the prevalence of long-stay residents with HIV in NHs increased from 0.7% to 1.2%, a 71% increase. Long-stay residents with HIV were younger and less often female or white. For younger NH residents, rates of dementia were 20% and 16% for HIV+ and HIV- residents, respectively; they were 53% and 57%, respectively, for older residents. In adjusted analyses, younger HIV+ residents with dementia had greater odds of APS medication receipt than did HIV- residents (AOR = 1.3; 95% confidence interval [CI] = 1.2-1.4), but older HIV residents had lower odds (AOR = 0.9; 95% CI = 0.8-0.9).ConclusionThe prevalence of long-stay HIV+ NH residents has increased over time, and given the rapid aging of the HIV population, this increase is likely to have continued. This study raises concern about potential differential quality of care for (younger) residents with HIV in NHs, but not for those aged 65 years and older. These findings contribute to the evidence base needed to ensure high-quality care for younger and older HIV+ residents in NHs.

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Spatially varying auto-regressive models for prediction of new human immunodeficiency virus diagnoses.

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Spatially varying auto-regressive models for prediction of new human immunodeficiency virus diagnoses.

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