A generalized Poisson model to predict host-seeking female Aedes aegypti marked by dusted Metarhizium anisopliae-exposed males

We developed a biological control method directed toward Aedes aegypti using the release of Metarhizium anisopliae-contaminated males to spread the fungus to wild females. A generalized Poisson model was used to relate Ae. aegypti marked females (MKF) to M. anisopliae-exposed males (FEM). In a mark-recapture parallel arm trial, FEM release was a better predictor than unexposed male (UM) releases to forecast MKF by FEM. Total females (TF), marked males (MKM), and wild males (WM) as predictors were counted in human-landings in 15 households treated with 40 FEM each, vs 40 UM released/household/week in 15 households for eight weeks. Fit of MKF to standard, generalized Poisson (GP), and negative binomial models/arm built by TF, MKM, WM, and interactions as predictors were computed. In both arms, MKF was better modeled by GP, which in treated, all but one of the eight observed data fell within the confidence intervals predicted by the model. However, the control GP had two outliers and MKM as a single predictor. Likewise, the pseudo-R² measures of 95% and 46% for treated and control groups also showed that the GP with FEM was more suitable to predict MKF. It should thus be possible to use the GP model to indirectly estimate that an increase of one TF or one fungus-exposed male would increase the number of marked-females by 8% or 9%, respectively, while wild males were an irrelevant predictor to the model.

On high-dimensional Poisson models with measurement error: Hypothesis testing for nonlinear nonconvex optimization

We study estimation and testing in the Poisson regression model with noisy high dimensional covariates, which has wide applications in analyzing noisy big data. Correcting for the estimation bias due to the covariate noise leads to a non-convex target function to minimize. Treating the high dimensional issue further leads us to augment an amenable penalty term to the target function. We propose to estimate the regression parameter through minimizing the penalized target function. We derive the L1 and L2 convergence rates of the estimator and prove the variable selection consistency. We further establish the asymptotic normality of any subset of the parameters, where the subset can have infinitely many components as long as its cardinality grows sufficiently slow. We develop Wald and score tests based on the asymptotic normality of the estimator, which permits testing of linear functions of the members if the subset. We examine the finite sample performance of the proposed tests by extensive simulation. Finally, the proposed method is successfully applied to the Alzheimer's Disease Neuroimaging Initiative study, which motivated this work initially.

Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data

Tracking the progress of an infectious disease is critical during a pandemic. However, the incubation period, diagnosis, and treatment most often cause uncertainties in the reporting of both cases and deaths, leading in turn to unreliable death rates. Moreover, even if the reported counts were accurate, the "crude" estimates of death rates which simply divide country-wise reported deaths by case numbers may still be poor or even non-computable in the presence of small (or zero) counts. We present a novel methodological contribution which describes the problem of analyzing COVID-19 data by two nested Poisson models: (i) an "upper model" for the cases infected by COVID-19 with an offset of population size, and (ii) a "lower" model for deaths of COVID-19 with the cases infected by COVID-19 as an offset, each equipped with their own random effect. This approach generates robustness in both the numerator as well as the denominator of the estimated death rates to the presence of small or zero counts, by "borrowing" information from other countries in the overall dataset, and guarantees positivity of both the numerator and denominator. The estimation will be carried out through non-parametric maximum likelihood which approximates the random effect distribution through a discrete mixture. An added advantage of this approach is that it allows for the detection of latent subpopulations or subgroups of countries sharing similar behavior in terms of their death rates.

Modeling zero inflation is not necessary for spatial transcriptomics

BACKGROUND

Spatial transcriptomics are a set of new technologies that profile gene expression on tissues with spatial localization information. With technological advances, recent spatial transcriptomics data are often in the form of sparse counts with an excessive amount of zero values.

RESULTS

We perform a comprehensive analysis on 20 spatial transcriptomics datasets collected from 11 distinct technologies to characterize the distributional properties of the expression count data and understand the statistical nature of the zero values. Across datasets, we show that a substantial fraction of genes displays overdispersion and/or zero inflation that cannot be accounted for by a Poisson model, with genes displaying overdispersion substantially overlapped with genes displaying zero inflation. In addition, we find that either the Poisson or the negative binomial model is sufficient for modeling the majority of genes across most spatial transcriptomics technologies. We further show major sources of overdispersion and zero inflation in spatial transcriptomics including gene expression heterogeneity across tissue locations and spatial distribution of cell types. In particular, when we focus on a relatively homogeneous set of tissue locations or control for cell type compositions, the number of detected overdispersed and/or zero-inflated genes is substantially reduced, and a simple Poisson model is often sufficient to fit the gene expression data there.

CONCLUSIONS

Our study provides the first comprehensive evidence that excessive zeros in spatial transcriptomics are not due to zero inflation, supporting the use of count models without a zero inflation component for modeling spatial transcriptomics.

Predicting coronavirus disease (COVID-19) outcomes in the United States early in the epidemic

•

Our study uses 50 US states’ public health surveillance datasets to measure COVID-19 outcomes relationships with populational, social, air travel related and environmental factors.

•

Found associations are used to predict expected numbers of cases, hospitalizations and deaths due to COVID-19 early in the epidemic.

•

The COVID-19 associated factors enplanements, population density, race, humidity and sun exposure predicted COVID-19 outcomes with reasonable accuracy in approximately 50% of states.

•

This study models can help public health identify communities at higher risk for rapid growth of cases, hospitalizations and deaths in a future respiratory-disease epidemic like COVID-19.

By 21 October 2020, the coronavirus disease (COVID-19) epidemic in the United States (US) had infected 8.3 million people, resulting in 61,364 laboratory-confirmed hospitalizations and 222,157 deaths. Currently, policymakers are trying to better understand this epidemic, especially the human-to-human transmissibility of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in relation to social, populational, air travel related and environmental exposure factors. Our study used 50 US states’ public health surveillance datasets (January 1-April 1, 2020) to measure associations of confirmed COVID-19 cases, hospitalizations and deaths with these variables. Using the resulting associations and multivariate regression (Negative Binomial and Poisson), predicted cases, hospitalizations and deaths were generated for each US state early in the epidemic. Factors associated with a significantly increased risk of COVID-19 disease, hospitalization and death included: population density, enplanement, Black race and increased sun exposure; in addition, COVID-19 disease and hospitalization were also associated with morning humidity. Although predictions of the number of cases, hospitalizations and deaths due to COVID-19 were not accurate for every state, those states with a combination of large number of enplanements, high population density, high proportion of Black residents, high humidity or low sun exposure may expect more rapid than expected growth in the number of COVID-19 events early in the epidemic.

Neighborhood-level COVID-19 hospitalizations and mortality relationships with built environment, active and sedentary travel

Most of the existing literature concerning the links between built environment and COVID-19 outcomes is based on aggregate spatial data averaged across entire cities or counties. We present neighborhood level results linking census tract-level built environment and active/sedentary travel measures with COVID-19 hospitalization and mortality rates in King County Washington. Substantial variations in COVID-19 outcomes and built environment features existed across neighborhoods. Using rigorous simulation-assisted discrete outcome random parameter models, the results shed new lights on the direct and indirect connections between built environment, travel behavior, positivity, hospitalization, and mortality rates. More mixed land use and greater pedestrian-oriented street connectivity is correlated with lower COVID-19 hospitalization/fatality rates. Greater participation in sedentary travel correlates with higher COVID-19 hospitalization and mortality whereas the reverse is true for greater participation in active travel. COVID-19 hospitalizations strongly mediate the relationships between built environment, active travel, and COVID-19 survival. Ignoring unobserved heterogeneity even when higher resolution smaller area spatial data are harnessed leads to inaccurate conclusions.

Overview of statistical methods for estimating the relative risk of delayed respiratory effect related to ambient air pollution exposure

INTRODUCTION

The occurrence of smog episodes and their significant impact on human health have forced research focused on risk assessment. Over the years, methods of exposure measuring have been improved, as well as statistical models necessary to the biological response estimation including the risk of incidence or death.

AIM

The aim of presented study is to review and evaluate possibilities of statistical methods of delayed respiratory health effects risk assessment related to ambient air pollution exposure.

MATERIAL AND METHODS

The review of published data was carried using the PubMed platform from 1994 to the 2020 year. Over 80 references were include in the analysis identifying general characteristics, construction of models estimating the relative risk of respiratory incidents with delayed health effect, and modelling tools available in statistical packages R, SAS, and Statistica.

RESULTS

Among various methods of health risk assessment, the Almon model, the Poisson model, and the Distributed Lag Non-Linear Models (DLNM) were most common used. Initially, the Poisson model was used, close to 60% of the cited works apply this method. The interest in the nonlinear modelling implementation has increased (34% of cited papers) in recent years. Mostly researchers used R or SAS statistical software. Usually, was calculated the relative risk of health effect related to short-term exposure (up to a week). About 75% of available papers concern measurements of relative risk in response to the concentration of pollution increase by unit=10 μg/m3. Other describe the risk associated with the exposure increasing by the interquartile range (IQR).

CONCLUSIONS

Distributed Lag Non-linear Model DLNM is classified as the statistical tool recommended by researchers due to its flexibility in defining, simplicity in interpretation, and increasingly frequent applications to environmental epidemiology.

On continuous-time capture-recapture in closed populations

Schofield et al. (2018, Biometrics 74, 626-635) presented simple and efficient algorithms for fitting continuous-time capture-recapture models based on Poisson processes. They also demonstrated by real examples that the standard method of discretizing continuous-time capture-recapture data and then fitting traditional discrete-time models may lead to information loss in population size estimation. In this article, we aim to clarify that key to the approach of Schofield et al. (2018) is the Poisson model assumed for the number of captures of each individual throughout the study, rather than the fact of data being collected in continuous time. We further show that the method of data discretization works equally well as the method of Schofield et al. (2018), provided that a Poisson model is applied instead of the traditional Bernoulli model to the number of captures for each individual on each sampling occasion.

[Estimates of influenza-associated excess mortality by three regression models in Shanxi Province during 2013-2017]

Objective: Using three models too estimate excess mortality associated with influenza of Shanxi Province during 2013-2017. Methods: Mortality data and influenza surveillance data of 11 cities of Shanxi Province from the 2013-2014 through 2016-2017 were used to estimate influenza-associated all cause deaths, circulatory and respiratory deaths and respiratory deaths. Three models were used: (i) Serfling regression, (ii)Poisson regression, (iii)General line model. Results: The total reported death cases of all cause were 157 733, annual death cases of all cause were 39 433, among these cases, male cases 93 831 (59.50%), cases above 65 years old 123 931 (78.57%). Annual influenza-associated excess mortality, for all causes, circulatory and respiratory deaths, respiratory deaths were 8.62 deaths per 100 000, 6.33 deaths per 100 000 and 0.68 deaths per 100 000 estimated by Serfling model, respectively; and 21.30 deaths per 100 000, 16.89 deaths per 100 000 and 2.14 deaths per 100 000 estimated by General line model, respectively; and 21.76 deaths per 100 000, 17.03 deaths per 100 000 and 2.05 deaths per 100 000, estimated by Poisson model, respectively. Influenza-related excess mortality was higher in people over 75 years old; influenza-associated excess mortalityfor all causes, circulatory and respiratory deaths, respiratory deaths were 259.67 deaths per 100 000, 229.90 deaths per 100 000 and 32.63 deaths per 100 000, estimated by GLM model, respectively; and 269.49 deaths per 100 000, 233.69 deaths per 100 000 and 31.27 deaths per 100 000, estimated by Poisson model,respectively. Conclusion: Excess mortality associated with influenza mainly caused by A (H3N2), Influenza caused the most associated death amongold people.

Space-Time Relationship between Short-Term Exposure to Fine and Coarse Particles and Mortality in a Nationwide Analysis of Korea: A Bayesian Hierarchical Spatio-Temporal Model

Previous studies have shown an association between mortality and ambient air pollution in South Korea. However, these studies may have been subject to bias, as they lacked adjustment for spatio-temporal structures. This paper addresses this research gap by examining the association between air pollution and cause-specific mortality in South Korea between 2012 and 2015 using a two-stage Bayesian spatio-temporal model. We used 2012–2014 mortality and air pollution data for parameter estimation (i.e., model fitting) and 2015 data for model validation. Our results suggest that the relative risks of total, cardiovascular, and respiratory mortality were 1.028, 1.047, and 1.045, respectively, with every 10-µg/m³ increase in monthly PM_2.5 (fine particulate matter) exposure. These findings warrant protection of populations who experience elevated ambient air pollution exposure to mitigate mortality burden in South Korea.

Non-monotonic accumulation of spike time variance during membrane potential oscillations

A spike-phase neural code has been proposed as a mechanism to encode stimuli based on the precise timing of spikes relative to the phase of membrane potential oscillations. This form of coding has been reported in both in vivo and in vitro experiments across several regions of the brain, yet there are concerns that such precise timing may be compromised by an effect referred to as variance accumulation, wherein spike timing variance increases over the phase of an oscillation. Here, we provide a straightforward explanation of this effect based on the theoretical spike time variance. The proposed theory is consistent with recordings of mitral neurons. It shows that spike time variance can increase in a nonlinear fashion with spike number, in a way that is dependent upon the frequency and amplitude of the oscillation. Further, non-monotonic accumulation of variance can arise from different combinations of oscillation parameters. Nonlinear accumulation sometimes leads to lower variance than that of a mean rate-matched homogeneous Poisson process, particularly for spikes that occur in later phases of oscillation. However, such an advantage is limited to a narrow range of oscillation amplitudes and frequencies. These results suggest fundamental constraints on spike-phase coding, and reveal how certain spikes in a sequence may exhibit increased firing time precision relative to their neighbors.

Modelling serial clustering and inter-annual variability of European winter windstorms based on large-scale drivers

Winter windstorms are known to be among the most dangerous and loss intensive natural hazards in Europe. In order to gain a better understanding of their variability and driving mechanisms, this study analyses the temporal variability which is often referred to as serial or seasonal clustering. This is realized by developing a statistical model relating the winter storm counts to known teleconnection patterns affecting European weather and climate conditions (e.g., North Atlantic Oscillation [NAO], Scandinavian pattern [SCA], etc.). The statistical model is developed via a stepwise Poisson regression approach that is applied to windstorm counts and large-scale indices retrieved from the ERA-20C reanalysis. Significant large-scale drivers accountable for the inter-annual variability of storms for several European regions are identified and compared. In addition to the SCA and the NAO which are found to be the essential drivers for most areas within the European domain, other teleconnections (e.g., East Atlantic pattern) are found to be more significant for the inter-annual variability in certain regions. Furthermore, the statistical model allows an estimation of the expected number of storms per winter season and also whether a season has the characteristic of being what we define an active or inactive season. The statistical model reveals high skill particularly over British Isles and central Europe; however, even for regions with less frequent storm events (e.g., southern and eastern Europe) the model shows adequate positive skill. This feature could be of specific interest for the actuarial sector.

Mediation analysis for count and zero-inflated count data without sequential ignorability and its application in dental studies

Mediation analysis seeks to understand the mechanism by which a treatment affects an outcome. Count or zero-inflated count outcomes are common in many studies in which mediation analysis is of interest. For example, in dental studies, outcomes such as the number of decayed, missing and filled teeth are typically zero inflated. Existing mediation analysis approaches for count data often assume sequential ignorability of the mediator. This is often not plausible because the mediator is not randomized so unmeasured confounders are associated with the mediator and the outcome. We develop causal methods based on instrumental variable approaches for mediation analysis for count data possibly with many 0s that do not require the assumption of sequential ignorability. We first define the direct and indirect effect ratios for those data, and then we propose estimating equations and use empirical likelihood to estimate the direct and indirect effects consistently. A sensitivity analysis is proposed for violations of the instrumental variables exclusion restriction assumption. Simulation studies demonstrate that our method works well for different types of outcome under various settings. Our method is applied to a randomized dental caries prevention trial and a study of the effect of a massive flood in Bangladesh on children's diarrhoea.

Odds Ratio or Prevalence Ratio? An Overview of Reported Statistical Methods and Appropriateness of Interpretations in Cross-sectional Studies with Dichotomous Outcomes in Veterinary Medicine

One of the most commonly observational study designs employed in veterinary is the cross-sectional study with binary outcomes. To measure an association with exposure, the use of prevalence ratios (PR) or odds ratios (OR) are possible. In human epidemiology, much has been discussed about the use of the OR exclusively for case–control studies and some authors reported that there is no good justification for fitting logistic regression when the prevalence of the disease is high, in which OR overestimate the PR. Nonetheless, interpretation of OR is difficult since confusing between risk and odds can lead to incorrect quantitative interpretation of data such as “the risk is X times greater,” commonly reported in studies that use OR. The aims of this study were (1) to review articles with cross-sectional designs to assess the statistical method used and the appropriateness of the interpretation of the estimated measure of association and (2) to illustrate the use of alternative statistical methods that estimate PR directly. An overview of statistical methods and its interpretation using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted and included a diverse set of peer-reviewed journals among the veterinary science field using PubMed as the search engine. From each article, the statistical method used and the appropriateness of the interpretation of the estimated measure of association were registered. Additionally, four alternative models for logistic regression that estimate directly PR were tested using our own dataset from a cross-sectional study on bovine viral diarrhea virus. The initial search strategy found 62 articles, in which 6 articles were excluded and therefore 56 studies were used for the overall analysis. The review showed that independent of the level of prevalence reported, 96% of articles employed logistic regression, thus estimating the OR. Results of the multivariate models indicated that logistic regression was the method that most overestimated the PR. The findings of this study indicate that although there are methods that directly estimate PR, many studies in veterinary science do not use these methods and misinterpret the OR estimated by the logistic regression.

Cancer risk among insulin users: comparing analogues with human insulin in the CARING five-country cohort study

AIMS/HYPOTHESIS

The aim of this work was to investigate the relationship between use of certain insulins and risk for cancer, when addressing the limitations and biases involved in previous studies.

METHODS

National Health Registries from Denmark (1996-2010), Finland (1996-2011), Norway (2005-2010) and Sweden (2007-2012) and the UK Clinical Practice Research Datalink database (1987-2013) were used to conduct a cohort study on new insulin users (N = 327,112). By using a common data model and semi-aggregate approach, we pooled individual-level records from five cohorts and applied Poisson regression models. For each of ten cancer sites studied, we estimated the rate ratios (RRs) by duration (≤0.5, 0.5-1, 1-2, 2-3, 3-4, 4-5, 5-6 and >6 years) of cumulative exposure to insulin glargine or insulin detemir relative to that of human insulin.

RESULTS

A total of 21,390 cancer cases occurred during a mean follow-up of 4.6 years. No trend with cumulative treatment time for insulin glargine relative to human insulin was observed in risk for any of the ten studied cancer types. Of the 136 associations tested in the main analysis, only a few increased and decreased risks were found: among women, a higher risk was observed for colorectal (RR 1.54, 95% CI 1.06, 2.25) and endometrial cancer (RR 1.78, 95% CI 1.07, 2.94) for ≤0.5 years of treatment and for malignant melanoma for 2-3 years (RR 1.92, 95% CI 1.02, 3.61) and 4-5 years (RR 3.55, 95% CI 1.68, 7.47]); among men, a lower risk was observed for pancreatic cancer for 2-3 years (RR 0.34, 95% CI 0.17, 0.66) and for liver cancer for 3-4 years (RR 0.36, 95% CI 0.14, 0.94) and >6 years (RR 0.22, 95% CI 0.05, 0.92). Comparisons of insulin detemir with human insulin also showed no consistent differences.

CONCLUSIONS/INTERPRETATION

The present multi-country study found no evidence of consistent differences in risk for ten cancers for insulin glargine or insulin detemir use compared with human insulin, at follow-up exceeding 5 years.

Epidemic characteristics, high-risk townships and space-time clusters of human brucellosis in Shanxi Province of China, 2005-2014

Background

Brucellosis, one of the world’s most important zoonosis, has been re-emerging in China. Shanxi Province, located in northern China, where husbandry development has been accelerated in recent years, has a rather high incidence of human brucellosis but drew little attention from the researchers. This study aimed to describe the changing epidemiology of human brucellosis in Shanxi Province from 2005 to 2014 and explore high-risk towns and space-time clusters for elucidating the necessity of decentralizing disease control resource to township level in epidemic regions, particularly in hotspot areas.

Methods

We extracted data from the Chinese National Notifiable Infectious Disease Reporting System to describe the incidence and spatiotemporal distribution of human brucellosis in Shanxi Province. Geographic information system was used to identify townships at high risk for the disease. Space-Time Scan Statistic was applied to detect the space-time clusters of human brucellosis during the past decade.

Results

From 2005 to 2014, a total of 50,002 cases of human brucellosis were recorded in Shanxi, with a male-to-female ratio of 3.9:1. The reported incidence rate increased dramatically from 7.0/100,000 in 2005 to 23.5/100,000 in 2014, with an average annual increase of 14.5%. There were still 33.8% cases delaying diagnosis in 2014. The proportion of the affected towns increased from 31.5% in 2005 to 82.5% in 2014. High-risk towns spread from the north to the center and then south of Shanxi Province, which were basins and adjacent highlands suitable for livestock cultivation. During the past decade, there were 55 space-time clusters of human brucellosis detected in high risk towns; the clusters could happen in any season. Some clusters’ location maintained stable over time.

Conclusions

During the last decade, Shanxi province’s human brucellosis epidemic had been aggravated and high-risk areas concentrated in some towns located in basins and adjacent highlands. Space-time clusters existed and some located steadily over time. Quite a few cases still missed timely diagnosis. Greater resources should be allocated and decentralized to mitigate the momentum of rise and improve the accessibility of prompt diagnosis treatment in the high-risk townships.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-2086-x) contains supplementary material, which is available to authorized users.

Spatial modeling of data with excessive zeros applied to reindeer pellet-group counts

We analyze a real data set pertaining to reindeer fecal pellet‐group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi‐Poisson hierarchical generalized linear model (HGLM), zero‐inflated Poisson (ZIP), and hurdle models. The quasi‐Poisson HGLM allows for both under‐ and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi‐Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi‐Poisson HGLM with spatial random effects.

Speed-profile-based road segmentation for accident occurrence modelling for hilly terrains

Modelling the accident occurrences per unit length of the road requires segmentation of road stretch. In the case of hilly terrains, homogeneity-based approach results in extremely small sections thus creating a biased accident data. This result is due to the high variations in geometry along the length of the road stretch. Constant length sections do not consider the effect of road's characteristics, hence may lead to undesirable results. It is hypothesized that the speed profile of a vehicle moving in free-flow conditions is governed by the road geometry as well as the road side environment. In this paper, a new methodology based on speed profile of a test vehicle (passenger car) has been proposed for segmenting a road stretch passing through hilly terrain. Comparative analysis of the results obtained from the constant length segmentation approach and the proposed approach shows that the later approach is producing statistically better results. Relatively more parameters were found to be statistically significant in the model based on speed-profile-based segmentation. Grade length, which could be one of the major parameters in hilly terrains, was found to be statistically significant only in the model based on speed-profile-based segmentation.

Influence diagnostics for count data under AB-BA crossover trials

This paper aims to develop diagnostic measures to assess the influence of data perturbations on estimates in AB-BA crossover studies with a Poisson distributed response. Generalised mixed linear models with normally distributed random effects are utilised. We show that in this special case, the model can be decomposed into two independent sub-models which allow to derive closed-form expressions to evaluate the changes in the maximum likelihood estimates under several perturbation schemes. The performance of the new influence measures is illustrated by simulation studies and the analysis of a real dataset.

Statistical estimations for Plasmodium vivax malaria in South Korea

OBJECTIVE

To calculate the numbers of weekly infections and prevalence of malaria, and to predict future trend of malaria incidences in South Korea.

METHODS

Weekly incidences of malaria for 13 years from the period 2001-2013 in South Korea were analyzed. The back-calculation equations were used with incubation period distributions. The maximum likelihood estimation for Poisson model was also used. The confidence intervals of the estimates were obtained by a bootstrap method. A regression model for time series of malaria incidences over 13 years was fitted by the non-linear least squares method, and used to predict futuretrend.

RESULTS

The estimated infection curve is narrower and more concentrated in the summer than in the incidence distribution. Infection started around the 19th week and was over around the 41st week. The maximum weekly infection 110 was obtained at the 29th week. The prevalence at the first week was around 496 persons, the minimum number was 366 at 22nd week, and the maximum prevalence was 648 at 34th week. Prevalence drops in late spring with people that falling ill and had had long incubation periods and rose in the summer with new infections. Our future forecast based on the regression model was that an increase at year 2014 compared to 2013 may reach a peak (at maximum about 70 weekly cases) at year 2015, with a decreasing trend after then.

CONCLUSIONS

This work shows that back-calculation methods could work well in estimating the infection rates and the prevalence of malaria. The obtained results can be useful in establishing an efficient preventive program for malaria infection. The method presented here can be used in other countries where incidence data and incubation period are available.

Multiple time scales in multi-state models

In multi-state models, it has been the tradition to model all transition intensities on one time scale, usually the time since entry into the study ('clock-forward' approach). The effect of time since an intermediate event has been accommodated either by changing the time scale to time since entry to the new state ('clock-back' approach) or by including the time at entry to the new state as a covariate. In this paper, we argue that the choice of time scale for the various transitions in a multi-state model should be dealt with as an empirical question, as also the question of whether a single time scale is sufficient. We illustrate that these questions are best addressed by using parametric models for the transition rates, as opposed to the traditional Cox-model-based approaches. Specific advantages are that dependence of failure rates on multiple time scales can be made explicit and described in informative graphical displays. Using a single common time scale for all transitions greatly facilitates computations of probabilities of being in a particular state at a given time, because the machinery from the theory of Markov chains can be applied. However, a realistic model for transition rates is preferable, especially when the focus is not on prediction of final outcomes from start but on the analysis of instantaneous risk or on dynamic prediction. We illustrate the various approaches using a data set from stem cell transplant in leukemia and provide supplementary online material in R.

A computational workflow to identify allele-specific expression and epigenetic modification in maize

Allele-specific expression refers to the preferential expression of one of the two alleles in a diploid genome, which has been thought largely attributable to the associated cis-element variation and allele-specific epigenetic modification patterns. Allele-specific expression may contribute to the heterosis (or hybrid vigor) effect in hybrid plants that are produced from crosses of closely-related species, subspecies and/or inbred lines. In this study, using Illumina high-throughput sequencing of maize transcriptomics, chromatic H3K27me3 histone modification and DNA methylation data, we developed a new computational framework to identify allele-specifically expressed genes by simultaneously tracking allele-specific gene expression patterns and the epigenetic modification landscape in the seedling tissues of hybrid maize. This approach relies on detecting nucleotide polymorphisms and any genomic structural variation between two parental genomes in order to distinguish paternally or maternally derived sequencing reads. This computational pipeline also incorporates a modified Chi-square test to statistically identify allele-specific gene expression and epigenetic modification based on the Poisson distribution.

Extracting functionally feedforward networks from a population of spiking neurons

Neuronal avalanches are a ubiquitous form of activity characterized by spontaneous bursts whose size distribution follows a power-law. Recent theoretical models have replicated power-law avalanches by assuming the presence of functionally feedforward connections (FFCs) in the underlying dynamics of the system. Accordingly, avalanches are generated by a feedforward chain of activation that persists despite being embedded in a larger, massively recurrent circuit. However, it is unclear to what extent networks of living neurons that exhibit power-law avalanches rely on FFCs. Here, we employed a computational approach to reconstruct the functional connectivity of cultured cortical neurons plated on multielectrode arrays (MEAs) and investigated whether pharmacologically induced alterations in avalanche dynamics are accompanied by changes in FFCs. This approach begins by extracting a functional network of directed links between pairs of neurons, and then evaluates the strength of FFCs using Schur decomposition. In a first step, we examined the ability of this approach to extract FFCs from simulated spiking neurons. The strength of FFCs obtained in strictly feedforward networks diminished monotonically as links were gradually rewired at random. Next, we estimated the FFCs of spontaneously active cortical neuron cultures in the presence of either a control medium, a GABA(A) receptor antagonist (PTX), or an AMPA receptor antagonist combined with an NMDA receptor antagonist (APV/DNQX). The distribution of avalanche sizes in these cultures was modulated by this pharmacology, with a shallower power-law under PTX (due to the prominence of larger avalanches) and a steeper power-law under APV/DNQX (due to avalanches recruiting fewer neurons) relative to control cultures. The strength of FFCs increased in networks after application of PTX, consistent with an amplification of feedforward activity during avalanches. Conversely, FFCs decreased after application of APV/DNQX, consistent with fading feedforward activation. The observed alterations in FFCs provide experimental support for recent theoretical work linking power-law avalanches to the feedforward organization of functional connections in local neuronal circuits.