Space-Time Covid-19 Bayesian SIR modeling in South Carolina

The Covid-19 pandemic has spread across the world since the beginning of 2020. Many regions have experienced its effects. The state of South Carolina in the USA has seen cases since early March 2020 and a primary peak in early April 2020. A lockdown was imposed on April 6th but lifting of restrictions started on April 24th. The daily case and death data as reported by NCHS (deaths) via the New York Times GitHUB repository have been analyzed and approaches to modeling of the data are presented. Prediction is also considered and the role of asymptomatic transmission is assessed as a latent unobserved effect. Two different time periods are examined and one step prediction is provided.Comment: 18 pages, 14 figure

Evaluation of Bayesian Spatial-Temporal Latent Models in Small Area Health Data

Health outcomes are linked to air pollution, demographic, or socioeconomic factors which vary across space and time. Thus, it is often found that relative risks in spatial health data have locally different patterns. In such cases, latent modeling is useful in the disaggregation of risk proï¬les. In particular, spatial-temporal mixture models can help to isolate spatial clusters each of which has a homogeneous temporal pattern in relative risks. Mixture models are assumed as they have various weight structures and considered in two situations: the number of underlying components is known or unknown. In this paper, we compare spatial-temporal mixture models with different weight structures in both situations. For comparison, we propose a set of spatial cluster detection diagnostics which are based on the posterior distribution of weights. We also develop new accuracy measures to assess the recovery of true relative risk. Based on the simulation study, we examine the performance of various spatial-temporal mixture models in terms of proposed methods and goodness-of-ï¬t measures. We examine two real data sets: low birth weight data and chronic obstructive pulmonary disease data

Are clusters of mental retardation correlated with clusters of developmental delay?

Mental retardation (MR) is a subset of developmental delay (DD), a broader classification of childhood disability. The purpose of this study was to determine if clusters of these two conditions were statistically significantly correlated. The residential addresses of 81,935 Medicaid insured pregnant women during each month of pregnancy were used to identify clusters of MR and DD in their children. Correlations between MR and DD were computed based on the sets of P-value surface from selected centroid points, where the P-value for cumulative relative risk of MR and DD was known. The correlations are quite small for all the 10 gestational months for which maternal addresses were available, but they are all statistically significant. This indicates MR and DD are correlated, but they are not linear. When MR was used as the centroid point to identify a cluster the only correlations that were statistically significant were for gestational month 5 and 6 with correlation 0.14 (P = 0.007) for both months. When the centroid points were selected based on the significance of risk of DD, the correlations between MR and DD are not statistically significant for any month. Correlation between MR and DD based on the sets of P-value surfaces from 4 MR clusters are significant in gestational month 5, 6 and 7 with correlation 0.17 (P = 0.047), 0.16 (P = 0.060) and 0.17 (P = 0.044), respectively. Our finding suggests that locations of high risk for the more severe condition, MR, also identify a spatial area where less severe cases of DD might be present, however the reverse is not the case

Current practices in cancer spatial data analysis: a call for guidance

There has long been a recognition that place matters in health, from recognition of clusters of yellow fever and cholera in the 1800s to modern day analyses of regional and neighborhood effects on cancer patterns. Here we provide a summary of discussions about current practices in the spatial analysis of georeferenced cancer data by a panel of experts recently convened at the National Cancer Institute

Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance:a case study of national dengue surveillance in Thailand

BACKGROUND: Dengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs.METHODS: To evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord [Formula: see text], Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility.RESULTS: In the simulation study, Getis Ord [Formula: see text] and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord [Formula: see text] and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies.CONCLUSIONS: Bayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.</p

A Novel Bayesian Spatio-Temporal Surveillance Metric to Predict Emerging Infectious Disease Areas of High Disease Risk

ABSTRACT Identification of areas of high disease risk has been one of the top goals for infectious disease public health surveillance. Accurate prediction of these regions leads to effective resource allocation and faster intervention. This paper proposes a novel prediction surveillance metric based on a Bayesian spatio-temporal model for infectious disease outbreaks. Exceedance probability, which has been commonly used for cluster detection in statistical epidemiology, was extended to predict areas of high risk. The proposed metric consists of three components: the area's risk profile, temporal risk trend, and spatial neighborhood influence. We also introduce a weighting scheme to balance these three components, which accommodates the characteristics of the infectious disease outbreak, spatial properties, and disease trends. Thorough simulation studies were conducted to identify the optimal weighting scheme and evaluate the performance of the proposed prediction surveillance metric. Results indicate that the area's own risk and the neighborhood influence play an important role in making a highly sensitive metric, and the risk trend term is important for the specificity and accuracy of prediction. The proposed prediction metric was applied to the COVID-19 case data of South Carolina from March 12, 2020, and the subsequent 30 weeks of data

Bayesian 2-Stage Space-Time Mixture Modeling with Spatial Misalignment of the Exposure in Small Area Health Data

We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999-2008

Statistical methods for multivariate meta-analysis of diagnostic tests: An overview and tutorial

In this article, we present an overview and tutorial of statistical methods for meta-analysis of diagnostic tests under two scenarios: 1) when the reference test can be considered a gold standard; and 2) when the reference test cannot be considered a gold standard. In the first scenario, we first review the conventional summary receiver operating characteristics (ROC) approach and a bivariate approach using linear mixed models (BLMM). Both approaches require direct calculations of study-specific sensitivities and specificities. We next discuss the hierarchical summary ROC curve approach for jointly modeling positivity criteria and accuracy parameters, and the bivariate generalized linear mixed models (GLMM) for jointly modeling sensitivities and specificities. We further discuss the trivariate GLMM for jointly modeling prevalence, sensitivities and specificities, which allows us to assess the correlations among the three parameters. These approaches are based on the exact binomial distribution and thus do not require an ad hoc continuity correction. Last, we discuss a latent class random effects model for meta-analysis of diagnostic tests when the reference test itself is imperfect for the second scenario. A number of case studies with detailed annotated SAS code in procedures MIXED and NLMIXED are presented to facilitate the implementation of these approaches