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Among these, a larger proportion was men. About Us Contact Us. Search for:. Watch your portion sizes Men tend to think that bigger portions are the norm. How to reduce portion sizes? Our results did show that temperature played a role in delimiting the transmission season, that mosquito density influenced the timing and extent of transmission within a season, and that importation regulated the potential for local transmission in a given season.

Our modeling approach was unique in allowing us to isolate each of those effects by building on prior knowledge about them in such a way that we captured their differential influence at different lags and captured the extent to which imported dengue cases translated into locally acquired cases. Had we fitted our model solely to data from to , as others have 21 , 23 , 24 , 25 , we likely would have misestimated the contributions of these factors to local transmission and would not have been able to detect the anomalous local conditions in that appear to have driven the large epidemic that year.

At the same time, there are important limitations of our approach. First, even though it is well known that many DENV infections are inapparent 27 , we worked under the assumption that cases detected through passive surveillance were representative of the true incidence of infection. Combining data augmentation methods 41 with hypotheses about ways in which reporting rates might vary through time could offer one way to relax this assumption.

The limited data available pertaining to this question suggest that DENV immunity is indeed low 2. These effects could be stronger at finer spatial scales, however Third, although the sensitivity analyses that we performed did not indicate a compelling need to incorporate data on local variables other than temperature and mosquito density, there are biological reasons why additional variables, such as precipitation 11 and humidity 44 , could be important. Future work in this setting or elsewhere could potentially explain more inter-annual variation in dengue incidence if better ways to leverage additional, biologically appropriate covariates could be devised.

Our finding that epidemic size in any given year depends on a complex interaction between importation and local conditions suggests that public health authorities should not focus on only one of these factors at the exclusion of others. As some studies have done 21 , 22 , 23 , it is tempting to attribute the increase in local dengue incidence in Guangzhou to the concurrent increase in imported dengue. Our results suggest that doing so belies the important role that local conditions play in limiting or enhancing transmission in any given year.

What an overly simplistic view risks is allowing for another epidemic like the one in , which our results suggest was driven by favorable local conditions despite relatively low importation. Moreover, understanding and reducing the favorability of local conditions for transmission may also mean the difference between years like and , with importation high in both years but local transmission much lower in Given the global expansion of DENV and other viruses transmitted by Aedes mosquitoes, improved understanding of the interactions among multiple drivers in settings with potential for seasonal DENV transmission—including portions of Australia, the United States, and the Mediterranean—will be essential for reducing the risk of large epidemics such as the one observed in Guangzhou in We considered data from Guangzhou, the capital and most populous city of the Guangdong province, located in southern China with a humid subtropical climate Supplementary Fig.

As a statutorily notifiable infectious disease in China since , dengue is diagnosed according to national surveillance protocol with standardized case definitions described in detail elsewhere An imported case was defined as one for which the patient had traveled abroad to a dengue-endemic country within 15 days of the onset of illness. Among all dengue cases imported from other countries, the suspected country of origin was recorded for In the absence of meeting the criteria for an imported case, a dengue case was considered locally acquired.

This determination was made by local public health institutes. All the data used in this study were anonymized; the identity of any individual case cannot be uncovered. Information about DENV serotype was known for some cases, but not at sufficient resolution to be taken into account in our analysis. The proportion of locally acquired cases for which DENV serotype was identified ranged from As summarized elsewhere 28 , 29 , DENV-1 appeared to be the dominant serotype in —, with reports of the other three serotypes during — Within a given serotype, multiple genotypes were observed across all years and in in particular 37 , Adult Ae.

The MOI was defined as the number of positive ovitraps for adult and larval Ae. Breteau index BI , which measures the density of Ae. A general framework that can be used to model the relationship between cases from one generation to the next is the TSIR model This effective number of cases in the previous generation is defined as. This formulation takes into account lags associated with DENV incubation in humans intrinsic incubation period , DENV incubation in mosquitoes extrinsic incubation period , and mosquito longevity, resulting in a probabilistic summary of the time that elapses between one human case and another.

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The flexibility afforded by Eq. Due to the low incidence of dengue in Guangzhou on a per population basis 40, cases detected by surveillance during — in a city of 14 million , the number of susceptible people at any given time changes very little and remains very close to the overall population size.

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Also because of such low incidence, including many days with zero incidence, accounting for the role of stochasticity in transmission was essential. Although the role of these factors in driving transmission is commonly assumed by models 51 and consistent with the highly seasonal nature of DENV transmission in Guangzhou 17 , it is also clear that these factors may influence transmission considerably in advance of a case occurring. For example, high mosquito densities would be expected to affect transmission 2—3 weeks in advance, rather than instantaneously, to allow mosquitoes sufficient time to become infected, incubate the virus, and transmit it Whereas other models represent those factors explicitly based on mechanistic assumptions, we model their combined influence in a more statistical fashion.

We also represented the latent mosquito density variable m t using a univariate cubic B-spline function with three knots per year for the year time period. This variable allowed us to reconcile differences between the MOI and BI mosquito indices and to obtain daily values for mosquito abundance based on monthly indices.

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We used a two-step process to estimate the posterior probability distribution of model parameters. First, we fitted the entomological model i. Under this model, the probabilities of these data are. Together, Eqs. To safeguard against obtaining an estimate that represented a local rather than global optimum, we repeated this optimization procedure times under different initial conditions.


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The initial conditions for each of these runs came from separate draws from a posterior distribution obtained through SMC estimation using the BayesianTools R library Although the transmission model Eq. Consequently, we used a simulation-based approach to approximate the probability of each daily value of I t under a given value of the 51 model parameters. To do so, we performed simulations of the entire time series of local incidence across —, with each simulation driven by data on imported cases feeding into Eq. As new local cases were generated in these simulations of local transmission, those new local cases fed back into generating subsequent local cases, again following Eq.

Using these simulations, we approximated a probability of the local incidence data by treating the number of local cases on a given day as a beta-binomial random variable. This assumes that all residents of Guangzhou are subject to a probability of being infected and detected by surveillance as a locally acquired dengue case on each day.

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Uncertainty in that probability was assumed to follow a beta distribution with parameters informed by the ensemble of simulated incidence. This effectively treats simulated incidence values as prior observations that inform a posterior estimate of the daily probability of infection with DENV as a beta random variable. Given that numerous studies have investigated relationships between temperature, mosquito density, and DENV transmission, we sought to leverage that information by specifying prior distributions for epidemiological model parameters. Doing so still permits the data to influence parameter estimates in the posterior via the likelihood, but it does so in such a way that parameter values in the posterior are penalized somewhat if they deviate strongly from prior understanding of which parameter values are plausible based on previous work.

Given that the scales of m and that of Siraj et al. That is, at the temperature optimum of We obtained an estimate of the posterior distribution of epidemiological parameters using an SMC algorithm implemented in the BayesianTools R library To assess convergence, we performed three independent runs of the SMC algorithm set to ten iterations of 10, samples each Supplementary Figs.

To verify that the behavior of the transmission model was consistent with the data to which it was fitted, we simulated an ensemble of realizations of daily local incidence using parameter values drawn from the estimated posterior distribution. These simulations were performed for all of — in the same manner in which the likelihood was approximated; i. We compared simulated and empirical local incidence patterns in two ways. Second, we compared simulated and empirical patterns on an annual basis in terms of four features of local incidence patterns: annual incidence, peak weekly incidence, total number of weeks with non-zero local incidence, and number of weeks between the first and last local case.

To partition inter-annual variation in local incidence into portions attributable to inter-annual variation in local conditions or importation patterns, we performed a simulation experiment with a two-way factorial design. In this experiment, we grouped temperature, mosquito density, and residual variation in local conditions together as one set of predictor variables and importation patterns as the other.

Each year from to was considered as a factor for each set of predictors. An ensemble of simulations was generated for each of the combinations of 11 years of each of the two sets of predictors. We summed annual local incidence for each of these , simulations and performed a two-way analysis of variance, resulting in estimates of the variation defined in terms of sum of squared error, SSQ in annual incidence attributable to local conditions, to importation, and to a portion unexplained by either predictor set due to the stochastic nature of the simulations. Because the number of replicates was at our discretion in this simulation experiment, the p-value from this analysis of variance was not meaningful To quantify the overall portion of variation attributable to each predictor variable, we performed an additional simulation experiment with a four-way factorial design.

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An ensemble of simulations was generated for each of the 14, combinations of 11 years of all four predictors. Similar to the two-way factorial experiment, we summed annual local incidence for each of these simulations and performed a four-way analysis of variance. Patz, J. Impact of regional climate change on human health. Nature , — Lafferty, K. The ecology of climate change and infectious diseases. Ecology 90 , — Johansson, M.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico. Morin, C. Health Perspect. Koelle, K. Disentangling extrinsic from intrinsic factors in disease dynamics: a nonlinear time series approach with an application to cholera. Refractory periods and climate forcing in cholera dynamics. Wearing, H.

Ecological and immunological determinants of dengue epidemics. Natl Acad.

john-und.sandra-gaertner.de/noche-de-terror-atrapados-en.php USA , — Cummings, D. The impact of the demographic transition on dengue in Thailand: Insights from a statistical analysis and mathematical modeling. PLoS Med. Grubaugh, N. Genomic epidemiology reveals multiple introductions of Zika virus into the United States.


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Brady, O. Global temperature constraints on Aedes aegypti and Ae. Vectors 7 , Higa, Y. Seasonal changes in oviposition activity, hatching and embryonation rates of eggs of Aedes albopictus Diptera: Culicidae on three islands of the Ryukyu Archipelago, Japan. Spread of Aedes albopictus and Decline of Ae.

Smith, D. Statics and dynamics of malaria infection in Anopheles mosquitoes. Reiter, P.

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Climate change and mosquito-borne disease.