Estimating the True Burden of Legionnaires’ Disease

2019 ◽  
Vol 188 (9) ◽  
pp. 1686-1694 ◽  
Author(s):  
Kelsie Cassell ◽  
Paul Gacek ◽  
Therese Rabatsky-Ehr ◽  
Susan Petit ◽  
Matthew Cartter ◽  
...  
Keyword(s):  
Time Series Data ◽  
Community Acquired Pneumonia ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Series Data ◽  
Clinical Settings ◽  
Age Group ◽  
The Past ◽  
Legionnaires Disease ◽  
Syncytial Virus

Abstract Over the past decade, the reported incidence of Legionnaires’ disease (LD) in the northeastern United States has increased, reaching 1–3 cases per 100,000 population. There is reason to suspect that this is an underestimate of the true burden, since LD cases may be underdiagnosed. In this analysis of pneumonia and influenza (P&I) hospitalizations, we estimated the percentages of cases due to Legionella, influenza, and respiratory syncytial virus (RSV) by age group. We fitted mixed-effects models to estimate attributable percents using weekly time series data on P&I hospitalizations in Connecticut from 2000 to 2014. Model-fitted values were used to calculate estimates of numbers of P&I hospitalizations attributable to Legionella (and influenza and RSV) by age group, season, and year. Our models estimated that 1.9%, 8.8%, and 5.1% of total (all-ages) inpatient P&I hospitalizations could be attributed to Legionella, influenza, and RSV, respectively. Only 10.6% of total predicted LD cases had been clinically diagnosed as LD during the study period. The observed incidence rate of 1.2 cases per 100,000 population was substantially lower than our estimated rate of 11.6 cases per 100,000 population. Our estimates of numbers of P&I hospitalizations attributable to Legionella are comparable to those provided by etiological studies of community-acquired pneumonia and emphasize the potential for underdiagnosis of LD in clinical settings.

scholarly journals Population Pressure on Land Resources in Nigeria: The Past and Projected Outcome

2020 ◽  
pp. 20-34
Author(s):  
H. I. Eririogu ◽  
R. N. Echebiri ◽  
E. S. Ebukiba
Keyword(s):  
Carrying Capacity ◽  
Time Series Data ◽  
Arable Land ◽  
Population Pressure ◽  
Series Data ◽  
Land Resources ◽  
Exploration And Exploitation ◽  
The Past ◽  
Land Consumption ◽  
Per Capita

Aims: This paper assesses the population pressure on land resources in Nigeria: The past and projected outcome. Study Design: 1967 to 2068 time series data were used. The data sets were resorted to due to lack of complete national data. Place and Duration of Study: Past (1967-2017) and projected (2018-2068) five decades in Nigeria. Methodology: The time series data were obtained from the United Nations Population Division, Department of Economic and Social Affairs, National Population Commission, International Energy Statistics and Food and Agriculture Organization (FAO) on population levels, renewable and non renewable resources in Nigeria. Others such as transformity were adapted from Odum (1996) and Odum (2000) for specific objectives. Data collected were analyzed using modified ecological footprint/carrying capacity approach, descriptive statistics and Z-statistics. Results: Results showed that the mean annual pressure on land resources in the past five decades (1967-2017) was 9.323 hectares per capita, while the projected pressure in the next five decades (2018-2068) was 213.178 hectares per capita. Results also showed that about 73.08 percent of the pressure per capita in the past five decades emanated from arable land consumption (6.813ha), while 75.91percent of the pressure is expected to emanate from fossil land in the next projected five decades due to crude oil and mineral resource exploration and exploitation. The carrying capacity of land resources in the past five decades was 6.4091 hectares per capita, while that of the projected five decades was 1.667 hectares per capita, an indication of ecological overshoot in both periods. Conclusion: Population pressures on land resources per capita in the past and projected five decades are higher than the carrying capacity of these resources in the country. Citizens lived and are expected to live unsustainably by depleting and degrading available land resources. Arable land consumption is the major contributor to the total pressure on land resources in the past five decades, while the consumption of fossil land due to exploration and exploitation of crude oil and mineral resources is expected to contribute majorly to the total pressure on land resources in the next five decades. Limiting affluence (per capita consumption of resources) and improving technology will not only ensure sustainable use of arable and fossil lands but place consumption within the limits of these resources for a sustainable future.

scholarly journals Reconsidering the importance of the past in predator–prey models: both numerical and functional responses depend on delayed prey densities

2013 ◽  
Vol 280 (1768) ◽  
pp. 20131389 ◽  
Author(s):  
Jiqiu Li ◽  
Andy Fenton ◽  
Lee Kettley ◽  
Phillip Roberts ◽  
David J. S. Montagnes
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prey Abundance ◽  
Series Data ◽  
Functional Responses ◽  
New Approach ◽  
Predator Prey ◽  
The Past ◽  
Predator Prey Models ◽  
Underlying Mechanisms

We propose that delayed predator–prey models may provide superficially acceptable predictions for spurious reasons. Through experimentation and modelling, we offer a new approach: using a model experimental predator–prey system (the ciliates Didinium and Paramecium ), we determine the influence of past-prey abundance at a fixed delay (approx. one generation) on both functional and numerical responses (i.e. the influence of present : past-prey abundance on ingestion and growth, respectively). We reveal a nonlinear influence of past-prey abundance on both responses, with the two responding differently. Including these responses in a model indicated that delay in the numerical response drives population oscillations, supporting the accepted (but untested) notion that reproduction, not feeding, is highly dependent on the past. We next indicate how delays impact short- and long-term population dynamics. Critically, we show that although superficially the standard (parsimonious) approach to modelling can reasonably fit independently obtained time-series data, it does so by relying on biologically unrealistic parameter values. By contrast, including our fully parametrized delayed density dependence provides a better fit, offering insights into underlying mechanisms. We therefore present a new approach to explore time-series data and a revised framework for further theoretical studies.

scholarly journals Forecasting the Spread of COVID-19 Pandemic with Prophet

2021 ◽  
Vol 35 (2) ◽  
pp. 115-122
Author(s):  
Mohan Mahanty ◽  
K. Swathi ◽  
K. Sasi Teja ◽  
P. Hemanth Kumar ◽  
A. Sravani
Keyword(s):  
Data Science ◽  
Statistical Data ◽  
Time Series Data ◽  
Daily Basis ◽  
Series Data ◽  
Use Case ◽  
The Past ◽  
Open Source Framework ◽  
Forecast Models ◽  
Using Data

COVID-19 pandemic shook the whole world with its brutality, and the spread has been still rising on a daily basis, causing many nations to suffer seriously. This paper presents a medical stance on research studies of COVID-19, wherein we estimated a time-series data-based statistical model using prophet to comprehend the trend of the current pandemic in the coming future after July 29, 2020 by using data at a global level. Prophet is an open-source framework discovered by the Data Science team at Facebook for carrying out forecasting based operations. It aids to automate the procedure of developing accurate forecasts and can be customized according to the use case we are solving. The Prophet model is easy to work because the official repository of prophet is live on GitHub and is open for contributions and can be fitted effortlessly. The statistical data presented on the paper refers to the number of daily confirmed cases officially for the period January 22, 2020, to July 29, 2020. The estimated data produced by the forecast models can then be used by Governments and medical care departments of various countries to manage the existing situation, thus trying to flatten the curve in various nations as we believe that there is minimal time to do this. The inferences made using the model can be clearly comprehended without much effort. Furthermore, it tries to give an understanding of the past, present, and future trends by showing graphical forecasts and statistics. Compared to other models, prophet specifically holds its own importance and innovativeness as the model is fully automated and generates quick and precise forecasts that can be tunable additionally.

scholarly journals Time-Series Causality with Missing Data

2021 ◽  
Vol 6 (1) ◽  
pp. 1-4
Author(s):  
Bo Yuan Chang ◽  
Mohamed A. Naiel ◽  
Steven Wardell ◽  
Stan Kleinikkink ◽  
John S. Zelek
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Missing Values ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Gaussian Process Regression ◽  
Series Data ◽  
Causal Relationships ◽  
Sampled Data ◽  
The Past

Over the past years, researchers have proposed various methods to discover causal relationships among time-series data as well as algorithms to fill in missing entries in time-series data. Little to no work has been done in combining the two strategies for the purpose of learning causal relationships using unevenly sampled multivariate time-series data. In this paper, we examine how the causal parameters learnt from unevenly sampled data (with missing entries) deviates from the parameters learnt using the evenly sampled data (without missing entries). However, to obtain the causal relationship from a given time-series requires evenly sampled data, which suggests filling the missing data values before obtaining the causal parameters. Therefore, the proposed method is based on applying a Gaussian Process Regression (GPR) model for missing data recovery, followed by several pairwise Granger causality equations in Vector Autoregssive form to fit the recovered data and obtain the causal parameters. Experimental results show that the causal parameters generated by using GPR data filling offers much lower RMSE than the dummy model (fill with last seen entry) under all missing values percentage, suggesting that GPR data filling can better preserve the causal relationships when compared with dummy data filling, thus should be considered when dealing with unevenly sampled time-series causality learning.

scholarly journals BLC mini-series: New statistical approaches and research practices for bilingualism research

2021 ◽  
pp. 1-2
Author(s):  
João Veríssimo
Keyword(s):  
Mixed Effects ◽  
Mixed Effects Models ◽  
Statistical Techniques ◽  
Research Practices ◽  
Experimental Conditions ◽  
Bayesian Analyses ◽  
The Past ◽  
Experimental Linguistics ◽  
Sources Of Variation ◽  
Statistical Approaches

The past decade has witnessed some dramatic methodological changes in the wider disciplines of psycholinguistics, psychology, and experimental linguistics. One such set of changes comprises the development of open and transparent research practices, which have increasingly been adopted in response to concerns that empirical results often fail to replicate and may not generalise across samples and experimental conditions (Gibson & Fedorenko, 2013; Maxwell, Lau, & Howard, 2015; McElreath & Smaldino, 2015; Yarkoni, 2020). Another important set of changes concerns the use of sophisticated statistical techniques, such as mixed-effects models (Baayen, Davidson, & Bates, 2008) and Bayesian analyses (Vasishth, Nicenboim, Beckman, Li & Kong, 2018), which can provide much more information about magnitudes of effects and sources of variation than the more traditional statistical approaches.

scholarly journals Estimating time-varying selection coefficients from time series data of allele frequencies

2020 ◽  
Author(s):  
Iain Mathieson
Keyword(s):  
Time Series ◽  
East Asia ◽  
Ancient Dna ◽  
Experimental Evolution ◽  
Time Series Data ◽  
Natural Populations ◽  
Allele Frequencies ◽  
Series Data ◽  
Alcohol Metabolism ◽  
The Past

AbstractTime series data of allele frequencies are a powerful resource for detecting and classifying natural and artificial selection. Ancient DNA now allows us to observe these trajectories in natural populations of long-lived species such as humans. Here, we develop a hidden Markov model to infer selection coefficients that vary over time. We show through simulations that our approach can accurately estimate both selection coefficients and the timing of changes in selection. Finally, we analyze some of the strongest signals of selection in the human genome using ancient DNA. We show that the European lactase persistence mutation was selected over the past 5,000 years with a selection coefficient of 2-2.5% in Britain, Central Europe and Iberia, but not Italy. In northern East Asia, selection at the ADH1B locus associated with alcohol metabolism intensified around 4,000 years ago, approximately coinciding with the introduction of rice-based agriculture. Finally, a derived allele at the FADS locus was selected in parallel in both Europe and East Asia, as previously hypothesized. Our approach is broadly applicable to both natural and experimental evolution data and shows how time series data can be used to resolve fine-scale details of selection.

scholarly journals Comparative analysis of hydrologic signatures in two agricultural watersheds in east-central Illinois: legacies of the past to inform the future

2013 ◽  
Vol 17 (11) ◽  
pp. 4607-4623 ◽  
Author(s):  
M. A. Yaeger ◽  
M. Sivapalan ◽  
G. F. McIsaac ◽  
X. Cai
Keyword(s):  
Land Use ◽  
Comparative Analysis ◽  
Time Series Data ◽  
Past History ◽  
Series Data ◽  
Agricultural System ◽  
Current Response ◽  
Geologic History ◽  
The Past ◽  
Central Illinois

Abstract. Historically, the central Midwestern US has undergone drastic anthropogenic land use change, having been transformed, in part through government policy, from a natural grassland system to an artificially drained agricultural system devoted to row cropping corn and soybeans. Current federal policies are again influencing land use in this region with increased corn acreage and new biomass crops proposed as part of an energy initiative emphasizing biofuels. To better address these present and future challenges it is helpful to understand whether and how the legacies of past changes have shaped the current response of the system. To this end, a comparative analysis of the hydrologic signatures in both spatial and time series data from two central Illinois watersheds was undertaken. The past history of these catchments is reflected in their current hydrologic responses, which are highly heterogeneous due to differences in geologic history, artificial drainage patterns, and reservoir operation, and manifest temporally, from annual to daily timescales, and spatially, both within and between the watersheds. These differences are also apparent from analysis of the summer low flows, where the more tile-drained watershed shows greater variability overall than does the more naturally drained one. In addition, precipitation in this region is also spatially heterogeneous even at small scales, and this, interacting with and filtering through the historical modifications to the system, increases the complexity of the problem of predicting the catchment response to future changes.

scholarly journals Relative Contribution of Greenhouse Gases and Ozone-Depleting Substances to Temperature Trends in the Stratosphere: A Chemistry–Climate Model Study

2010 ◽  
Vol 23 (1) ◽  
pp. 28-42 ◽  
Author(s):  
Richard S. Stolarski ◽  
Anne R. Douglass ◽  
Paul A. Newman ◽  
Steven Pawson ◽  
Mark R. Schoeberl
Keyword(s):  
Time Series ◽  
Greenhouse Gases ◽  
Climate Model ◽  
Time Series Data ◽  
Temperature Time Series ◽  
Series Data ◽  
Future Evolution ◽  
The Past ◽  
Ozone Depleting Substances ◽  
Temperature Time

Abstract The temperature of the stratosphere has decreased over the past several decades. Two causes contribute to that decrease: well-mixed greenhouse gases (GHGs) and ozone-depleting substances (ODSs). This paper addresses the attribution of temperature decreases to these two causes and the implications of that attribution for the future evolution of stratospheric temperature. Time series analysis is applied to simulations of the Goddard Earth Observing System Chemistry–Climate Model (GEOS CCM) to separate the contributions of GHGs from those of ODSs based on their different time-dependent signatures. The analysis indicates that about 60%–70% of the temperature decrease of the past two decades in the upper stratosphere near 1 hPa and in the lower midlatitude stratosphere near 50 hPa resulted from changes attributable to ODSs, primarily through their impact on ozone. As ozone recovers over the next several decades, the temperature should continue to decrease in the middle and upper stratosphere because of GHG increases. The time series of observed temperature in the upper stratosphere is approaching the length needed to separate the effects of ozone-depleting substances from those of greenhouse gases using temperature time series data.

Schematic Assessments of Presidential Candidates

1986 ◽  
Vol 80 (2) ◽  
pp. 521-540 ◽  
Author(s):  
Arthur H. Miller ◽  
Martin P. Wattenberg ◽  
Oksana Malanchuk
Keyword(s):  
Time Series Data ◽  
Past Research ◽  
The United States ◽  
Series Data ◽  
Cognitive Representation ◽  
Cognitive Representations ◽  
Presidential Candidates ◽  
The Past ◽  
Candidate Personality ◽  
Election Studies

This article applies theories of social cognition in an investigation of the dimensions of the assessments of candidates employed by voters in the United States. An empirical description of the public's cognitive representations of presidential candidates, derived from responses to open-ended questions in the American National Election Studies from 1952 to 1984, reveals that perceptions of candidates are generally focused on “personality” characteristics rather than on issue concerns or partisan group connections. Contrary to the implications of past research, higher education is found to be correlated with a greater likelihood of using personality categories rather than with making issue statements. While previous models have interpreted voting on the basis of candidate personality as indicative of superficial and idiosyncratic assessments, the data examined here indicate that they predominately reflect performance-relevant criteria such as competence, integrity, and reliability. In addition, both panel and aggregate time series data suggest that the categories that voters have used in the past influence how they will perceive future candidates, implying the application of schematic judgments. The reinterpretation presented here argues that these judgments reflect a rich cognitive representation of the candidates from which instrumental inferences are made.

scholarly journals Likelihood Inference for Generalized Integer Autoregressive Time Series Models

Econometrics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 43 ◽  
Author(s):  
Harry Joe
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Probability Generating Function ◽  
Likelihood Estimation ◽  
Model Fit ◽  
Likelihood Inference ◽  
Series Data ◽  
Time Series Models ◽  
Autoregressive Time Series ◽  
The Past

For modeling count time series data, one class of models is generalized integer autoregressive of order p based on thinning operators. It is shown how numerical maximum likelihood estimation is possible by inverting the probability generating function of the conditional distribution of an observation given the past p observations. Two data examples are included and show that thinning operators based on compounding can substantially improve the model fit compared with the commonly used binomial thinning operator.

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