Statistical Inference for Partially Observed Markov Processes via the R Package pomp

Partially observed Markov process (POMP) models, also known as hidden Markov models or state space models, are ubiquitous tools for time series analysis. The R package pomp provides a very flexible framework for Monte Carlo statistical investigations using nonlinear, non-Gaussian POMP models. A range of modern statistical methods for POMP models have been implemented in this framework including sequential Monte Carlo, iterated filtering, particle Markov chain Monte Carlo, approximate Bayesian computation, maximum synthetic likelihood estimation, nonlinear forecasting, and trajectory matching. In this paper, we demonstrate the application of these methodologies using some simple toy problems. We also illustrate the specification of more complex POMP models, using a nonlinear epidemiological model with a discrete population, seasonality, and extra-demographic stochasticity. We discuss the specification of user-defined models and the development of additional methods within the programming environment provided by pomp.Comment: In press at the Journal of Statistical Software. A version of this paper is provided at the pomp package website: http://kingaa.github.io/pom

Long-run determinants of atmospheric CO2: Granger-causality and cointegration analysis

Atmospheric concentrations of CO2 grew annually 1.12 +- 0.48 parts per million (ppm) in 1958-1984, and 1.72 +- 0.54 ppm (mean +- standard deviation) in 1985-2009, so that the rate growth is growing itself. Natural phenomena that influence short-run changes in CO2 atmospheric levels (through their influence on CO2 emissions and sinks) are stationary processes that cannot explain the growth of CO2 levels at an increasing rate. Cointegration tests show at a high level of statistical significance that the annual increase of CO2 concentrations is roughly proportional to “human activities” as measured by the money value of the world economy and the size of the world population. We find that population and world GDP help to predict CO2 concentrations, but CO2 concentrations do not help to predict the othervariables; that is, there is Ganger causality from population and world economic output to CO2. Though the smallness of the time series involved and the theoretical and practical issues posed by cointegration allow only for a limited confidence in these results, they have obvious major implications. For business-as-usual conditions and a world economy growing annually 3.5%—the mean annual growth of the world economy since 1960—the required world population to maintain or reduce CO2 levels would be 1.3 billion or less. For a world population of 7 billion as the present one, CO2 atmospheric levels would decrease if the global economy contracted annually 24.5% or more.http://deepblue.lib.umich.edu/bitstream/2027.42/88162/1/Long-run determinants of CO2 - A3- Dec 2011+refs.pd

Macroeconomic effects on mortality revealed by panel analysis with nonlinear trends

Many investigations have used panel methods to study the relationships between fluctuations in economic activity and mortality. A broad consensus has emerged on the overall procyclical nature of mortality: perhaps counter-intuitively, mortality typically rises above its trend during expansions. This consensus has been tarnished by inconsistent reports on the specific age groups and mortality causes involved. We show that these inconsistencies result, in part, from the trend specifications used in previous panel models. Standard econometric panel analysis involves fitting regression models using ordinary least squares, employing standard errors which are robust to temporal autocorrelation. The model specifications include a fixed effect, and possibly a linear trend, for each time series in the panel. We propose alternative methodology based on nonlinear detrending. Applying our methodology on data for the 50 US states from 1980 to 2006, we obtain more precise and consistent results than previous studies. We find procyclical mortality in all age groups. We find clear procyclical mortality due to respiratory disease and traffic injuries. Predominantly procyclical cardiovascular disease mortality and countercyclical suicide are subject to substantial state-to-state variation. Neither cancer nor homicide have significant macroeconomic association.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS624 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Pathogenic variants in the CYP21A2 gene cause isolated autosomal dominant congenital posterior polar cataracts

Background: Congenital cataracts are the most common cause of visual impairment worldwide. Inherited cataract is a clinically and genetically heterogeneous disease. Here we report disease-causing variants in a novel gene, CYP21A2, causing autosomal dominant posterior polar cataract. Variants in this gene are known to cause autosomal recessive congenital adrenal hyperplasia (CAH). / Methods: Using whole-exome sequencing (WES), we have identified disease-causing sequence variants in two families of British and Irish origin, and in two isolated cases of Asian-Indian and British origin. Bioinformatics analysis confirmed these variants as rare with damaging pathogenicity scores. Segregation was tested within the families using direct Sanger sequencing. / Results: A nonsense variant NM_000500.9 c.955 C > T; p.Q319* was identified in CYP21A2 in two families with posterior polar cataract and in an isolated case with unspecified congenital cataract phenotype. This is the same variant previously linked to CAH and identified as Q318* in the literature. We have also identified a rare missense variant NM_000500.9 c.770 T > C; p.M257T in an isolated case with unspecified congenital cataract phenotype. / Conclusion: This is the first report of separate sequence variants in CYP21A2 associated with congenital cataract. Our findings extend the genetic basis for congenital cataract and add to the phenotypic spectrum of CYP21A2 variants and particularly the CAH associated Q318* variant. CYP21A2 has a significant role in mineralo- and gluco-corticoid biosynthesis. These findings suggest that CYP21A2 may be important for extra-adrenal biosynthesis of aldosterone and cortisol in the eye lens

Multimorbidity due to novel pathogenic variants in the WFS1/RP1/NOD2 genes: autosomal dominant congenital lamellar cataract, retinitis pigmentosa and Crohn’s disease in a British family

Background: A five generation family has been analysed by whole exome sequencing (WES) for genetic associations with the multimorbidities of congenital cataract (CC), retinitis pigmentosa (RP) and Crohn’s disease (CD). // Methods: WES was performed for unaffected and affected individuals within the family pedigree followed by bioinformatic analyses of these data to identify disease-causing variants with damaging pathogenicity scores. // Results: A novel pathogenic missense variant in WFS1: c.1897G>C; p.V633L, a novel pathogenic nonsense variant in RP1: c.6344T>G; p.L2115* and a predicted pathogenic missense variant in NOD2: c.2104C>T; p.R702W are reported. The three variants cosegregated with the phenotypic combinations of autosomal dominant CC, RP and CD within individual family members. // Conclusions: Here, we report multimorbidity in a family pedigree listed on a CC register, which broadens the spectrum of potential cataract associated genes to include both RP1 and NOD2

Statistical Inference for Spatiotemporal Partially Observed Markov Processes via the R Package spatPomp

We consider inference for a class of nonlinear stochastic processes with latent dynamic variables and spatial structure. The spatial structure takes the form of a finite collection of spatial units that are dynamically coupled. We assume that the latent processes have a Markovian structure and that unit-specific noisy measurements are made. A model of this form is called a spatiotemporal partially observed Markov process (SpatPOMP). The R package spatPomp provides an environment for implementing SpatPOMP models, analyzing data, and developing new inference approaches. We describe the spatPomp implementations of some methods with scaling properties suited to SpatPOMP models. We demonstrate the package on a simple Gaussian system and on a nontrivial epidemiological model for measles transmission within and between cities. We show how to construct user-specified SpatPOMP models within spatPomp

Mortality and macroeconomic fluctuations in contemporary Sweden

Recent research has provided strong evidence that, in the United States in particular and in high- or middle-income economies in general, mortality tends to evolve better in recessions than in expansions. It has been suggested that Sweden may be an exception to this pattern. The present investigation shows, however, that in the period 1968–2003 mortality oscillated procyclically in Sweden, deviating from its trend upward during expansions and downward during recessions. This pattern is evidenced by the oscillations of life expectancy, total mortality, and age- and sex-specific mortality rates at the national level, and also by regional mortality rates for the major demographic groups during recent decades. Results are robust for different economic indicators, methods of detrending, and models. In lag regression models macroeconomic effects on annual mortality tend to appear lagged one year. As in other countries, traffic mortality rises in expansions and declines in recessions, and the same is found for total cardiovascular mortality. However, macroeconomic effects on ischemic heart disease mortality appearing at lag two are hard to interpret. Reasons for the procyclical oscillations of mortality, for in-consistent results found in previous studies, as well as for the differences observed between Sweden and the United States are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78558/1/MORT_&_MACRO_FLUCT_IN_SWEDEN_Nov_2010_expanded.pdf-