990 resultados para BASIC REPRODUCTION RATE
Resumo:
A dimensional analysis of the classical equations related to the dynamics of vector-borne infections is presented. It is provided a formal notation to complete the expressions for the Ross' Threshold Theorem, the Macdonald's basic reproduction "rate" and sporozoite "rate", Garret-Jones' vectorial capacity and Dietz-Molineaux-Thomas' force of infection. The analysis was intended to provide a formal notation that complete the classical equations proposed by these authors.
Resumo:
A dimensional analysis of the classical equations related to the dynamics of vector-borne infections is presented. It is provided a formal notation to complete the expressions for the Ross' threshold theorem, the Macdonald's basic reproduction "rate" and sporozoite "rate", Garret-Jones' vectorial capacity and Dietz-Molineaux-Thomas' force of infection. The analysis was intended to provide a formal notation that complete the classical equations proposed by these authors.
Resumo:
The basic reproduction number is a key parameter in mathematical modelling of transmissible diseases. From the stability analysis of the disease free equilibrium, by applying Routh-Hurwitz criteria, a threshold is obtained, which is called the basic reproduction number. However, the application of spectral radius theory on the next generation matrix provides a different expression for the basic reproduction number, that is, the square root of the previously found formula. If the spectral radius of the next generation matrix is defined as the geometric mean of partial reproduction numbers, however the product of these partial numbers is the basic reproduction number, then both methods provide the same expression. In order to show this statement, dengue transmission modelling incorporating or not the transovarian transmission is considered as a case study. Also tuberculosis transmission and sexually transmitted infection modellings are taken as further examples.
Resumo:
We study the spreading of contagious diseases in a population of constant size using susceptible-infective-recovered (SIR) models described in terms of ordinary differential equations (ODEs) and probabilistic cellular automata (PCA). In the PCA model, each individual (represented by a cell in the lattice) is mainly locally connected to others. We investigate how the topological properties of the random network representing contacts among individuals influence the transient behavior and the permanent regime of the epidemiological system described by ODE and PCA. Our main conclusions are: (1) the basic reproduction number (commonly called R(0)) related to a disease propagation in a population cannot be uniquely determined from some features of transient behavior of the infective group; (2) R(0) cannot be associated to a unique combination of clustering coefficient and average shortest path length characterizing the contact network. We discuss how these results can embarrass the specification of control strategies for combating disease propagations. (C) 2009 Elsevier B.V. All rights reserved.
Resumo:
In epidemiology, the basic reproduction number R-0 is usually defined as the average number of new infections caused by a single infective individual introduced into a completely susceptible population. According to this definition. R-0 is related to the initial stage of the spreading of a contagious disease. However, from epidemiological models based on ordinary differential equations (ODE), R-0 is commonly derived from a linear stability analysis and interpreted as a bifurcation parameter: typically, when R-0 >1, the contagious disease tends to persist in the population because the endemic stationary solution is asymptotically stable: when R-0 <1, the corresponding pathogen tends to naturally disappear because the disease-free stationary solution is asymptotically stable. Here we intend to answer the following question: Do these two different approaches for calculating R-0 give the same numerical values? In other words, is the number of secondary infections caused by a unique sick individual equal to the threshold obtained from stability analysis of steady states of ODE? For finding the answer, we use a susceptibleinfective-recovered (SIR) model described in terms of ODE and also in terms of a probabilistic cellular automaton (PCA), where each individual (corresponding to a cell of the PCA lattice) is connected to others by a random network favoring local contacts. The values of R-0 obtained from both approaches are compared, showing good agreement. (C) 2012 Elsevier B.V. All rights reserved.
Resumo:
Epidemiological processes leave a fingerprint in the pattern of genetic structure of virus populations. Here, we provide a new method to infer epidemiological parameters directly from viral sequence data. The method is based on phylogenetic analysis using a birth-death model (BDM) rather than the commonly used coalescent as the model for the epidemiological transmission of the pathogen. Using the BDM has the advantage that transmission and death rates are estimated independently and therefore enables for the first time the estimation of the basic reproductive number of the pathogen using only sequence data, without further assumptions like the average duration of infection. We apply the method to genetic data of the HIV-1 epidemic in Switzerland.
Resumo:
Differences amongst wheat cultivars in the rate of reproductive development are largely dependent on differences in their sensitivity to photoperiod and vernalization. However, when these responses are accounted for, by growing vernalized seedlings under long photoperiods, cultivars can still differ markedly in time to ear emergence. Control of rate of development by this ‘third factor’ has been poorly understood and is variously referred to as intrinsic earliness, earliness in the narrow sense, basic vegetative period, earliness per se, and basic development rate. Certain assumptions are made in the concept of intrinsic earliness. They are that differences in intrinsic earliness (i) are independent of the responses of the cultivars to photoperiod and vernalization, (ii) apply only to the length of the vegetative period up to floral initiation (as suggested by several authors), (iii) are maintained under different temperatures, measured either in days or degree days. As a consequence of this, the ranking of cultivars (from intrinsically early to intrinsically late) must be maintained at different temperatures. This paper, by the re-analysis of published data, examines the extent to which these assumptions can be supported. Although it is shown that intrinsic earliness operates independently of photoperiod and vernalization responses, the other assumptions were not supported. The differences amongst genotypes in time to ear emergence, grown under above-optimum vernalization and photoperiod (that is when the response to these factors is saturated), were not exclusively due to parallel differences in the length of the vegetative phase, and the length of the reproductive phase was independent of that of the vegetative phase. Thus, it would be possible to change the relative allocation of time to vegetative and reproductive periods with no change in the full period to ear emergence. The differences in intrinsic earliness between cultivars were modified by the temperature regime under which they were grown, i.e. the difference between cultivars (both considering the full phase to ear emergence or some sub-phases) was not a constant amount of time or thermal time at different temperatures. In addition, in some instances genotypes changed their ranking for ‘intrinsic earliness’ depending on the temperature regime. This was interpreted to mean that while all genotypes are sensitive to temperature they differ amongst themselves in the extent of that sensitivity. Therefore, ‘intrinsic earliness’ should not be considered as a static genotypic characteristic, but the result of the interaction between the genotype and temperature. Intrinsic earliness is therefore likely to be related to temperature sensitivity. Some implications of these conclusions for plant breeding and crop simulation modelling are discussed.
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Even though resistance is the most promising tactic for root-knot nematode management on soybean (Glycine max), virulent biotypes may occur and be selected on specific resistant plant genotypes. In the present study, reproduction rate of Meloidogyne arenaria race 1 increased after four sequences of continuous culture of the parasite on resistant soybean genotypes.
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A mathematical model for Banana Xanthomonas Wilt (BXW) spread by insect is presented. The model incorporates inflorescence infection and vertical transmission from the mother corm to attached suckers, but not tool-based transmission by humans. Expressions for the basic reproduction number R0 are obtained and it is verified that disease persists, at a unique endemic level, when R0 > 1. From sensitivity analysis, inflorescence infection rate and roguing rate were the parameters with most influence on disease persistence and equilibrium level. Vertical transmission parameters had less effect on persistence threshold values. Parameters were approximately estimated from field data. The model indicates that single stem removal is a feasible approach to eradication if spread is mainly via inflorescence infection. This requires continuous surveillance and debudding such that a 50% reduction in inflorescence infection and 2–3 weeks interval of surveillance would eventually lead to full recovery of banana plantations and hence improved production.
Resumo:
Strongylosis in equids, despite being very common, have never been studied from a strictly ecological point of view. Mathematical models are important ecological tools used to study the temporal dynamics of parasite populations, and are useful to study the effect of different biological parameters, as well as to analyse the outcome produced by perturbations such as anthelmintic treatments. This work describes the study of the temporal dynamics of strongyles infection in an organic donkey population, performed using coprological quantitative analysis and donkeys’ age as a proxy of the time of infection. Force of infection was then estimated for Strongylus vulgaris and small strongyles and the results used as the basis for the development of mathematical models. In particular, the comparison of models output and field data made it possible to estimate the transmission coefficient and to consequently calculate the basic reproduction number R0 and the threshold host density. Small strongyles model includes hypobiosis and, more interestingly as never found in literature, a density-dependent development rate of hypobiotic larvae in adult parasites in order to simulate a negative feedback between larvae emergence from hypobiosis and adult parasite abundance. Simulations of pharmacological and environmental treatments showed that parasite eradication was possible for S. vulgaris only, while small strongyles, due to hypobiosis and density-dependent development rate of their hypobiotic larvae, are very difficult to control and impossible to eradicate. In addition, density-dependence in larval development has been demonstrated to act as a key factor in improving parasite population survival and abundance even in absence of human intervention.
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The 2014 Ebola virus (EBOV) outbreak in West Africa is the largest outbreak of the genus Ebolavirus to date. To better understand the spread of infection in the affected countries, it is crucial to know the number of secondary cases generated by an infected index case in the absence and presence of control measures, i.e., the basic and effective reproduction number. In this study, I describe the EBOV epidemic using an SEIR (susceptible-exposed-infectious-recovered) model and fit the model to the most recent reported data of infected cases and deaths in Guinea, Sierra Leone and Liberia. The maximum likelihood estimates of the basic reproduction number are 1.51 (95% confidence interval [CI]: 1.50-1.52) for Guinea, 2.53 (95% CI: 2.41-2.67) for Sierra Leone and 1.59 (95% CI: 1.57-1.60) for Liberia. The model indicates that in Guinea and Sierra Leone the effective reproduction number might have dropped to around unity by the end of May and July 2014, respectively. In Liberia, however, the model estimates no decline in the effective reproduction number by end-August 2014. This suggests that control efforts in Liberia need to be improved substantially in order to stop the current outbreak.
Rapid drop in the reproduction number during the Ebola outbreak in the Democratic Republic of Congo.
Resumo:
The Democratic Republic of Congo (DRC) experienced a confined rural outbreak of Ebola virus disease (EVD) with 69 reported cases from July to October 2014. Understanding the transmission dynamics during the outbreak can provide important information for anticipating and controlling future EVD epidemics. I fitted an EVD transmission model to previously published data of this outbreak and estimated the basic reproduction number R 0 = 5.2 (95% CI [4.0-6.7]). The model suggests that the net reproduction number Rt fell below unity 28 days (95% CI [25-34] days) after the onset of symptoms in the index case. This study adds to previous epidemiological descriptions of the 2014 EVD outbreak in DRC, and is consistent with the notion that a rapid implementation of control interventions helped reduce further spread.
Resumo:
The dengue virus has a single-stranded positive-sense RNA genome of similar to 10.700 nucleotides with a single open reading frame that encodes three structural (C, prM, and E) and seven nonstructural (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5) proteins. It possesses four antigenically distinct serotypes (DENV 1-4). Many phylogenetic studies address particularities of the different serotypes using convenience samples that are not conducive to a spatio-temporal analysis in a single urban setting. We describe the pattern of spread of distinct lineages of DENV-3 circulating in Sao Jose do Rio Preto, Brazil, during 2006. Blood samples from patients presenting dengue-like symptoms were collected for DENV testing. We performed M-N-PCR using primers based on NS5 for virus detection and identification. The fragments were purified from PCR mixtures and sequenced. The positive dengue cases were geo-coded. To type the sequenced samples, 52 reference sequences were aligned. The dataset generated was used for iterative phylogenetic reconstruction with the maximum likelihood criterion. The best demographic model, the rate of growth, rate of evolutionary change, and Time to Most Recent Common Ancestor (TMRCA) were estimated. The basic reproductive rate during the epidemics was estimated. We obtained sequences from 82 patients among 174 blood samples. We were able to geo-code 46 sequences. The alignment generated a 399-nucleotide-long dataset with 134 taxa. The phylogenetic analysis indicated that all samples were of DENV-3 and related to strains circulating on the isle of Martinique in 2000-2001. Sixty DENV-3 from Sao Jose do Rio Preto formed a monophyletic group (lineage 1), closely related to the remaining 22 isolates (lineage 2). We assumed that these lineages appeared before 2006 in different occasions. By transforming the inferred exponential growth rates into the basic reproductive rate, we obtained values for lineage 1 of R(0) = 1.53 and values for lineage 2 of R(0) = 1.13. Under the exponential model, TMRCA of lineage 1 dated 1 year and lineage 2 dated 3.4 years before the last sampling. The possibility of inferring the spatio-temporal dynamics from genetic data has been generally little explored, and it may shed light on DENV circulation. The use of both geographic and temporally structured phylogenetic data provided a detailed view on the spread of at least two dengue viral strains in a populated urban area.
Resumo:
Tetranychus evansi Baker and Pritchard and Tetranychus urticae Koch (Acari: Tetranychidae) are important pests of Solanaceae in many countries. Several studies have demonstrated that T. urticae is an acceptable prey to many predatory mites, although the suitability of this prey depends on the host plant. T. evansi, has been shown to be an unfavorable prey to most predatory mites that have been tested against it. The predator Phytoseiulus fragariae Denmark and Schicha (Acari: Phytoseiidae) has been found in association with the two species in Brazil. The objective of this work was to compare biological parameters of P. fragariae on T. evansi and on T. urticae as prey. The study was conducted under laboratory conditions at 10, 15, 20, 25 and 30 degrees C. At all temperatures, survivorship was lower on T. evansi than on T. urticae. No predator reached adulthood at 10 degrees C on the former species; even on the latter species, only about 36% of the predators reached adulthood at 10 degrees C. For both prey, in general, duration of each life stage was shorter, total fecundity was lower and intrinsic rate of population increase (r(m) ) was higher with increasing temperatures. The slower rate of development of P. fragariae on T. evansi resulted in a slightly higher thermal requirement (103.9 degree-days) on that prey than on T. urticae (97.1 degree-days). The values of net reproduction rate (R-0), intrinsic rate of increase (r (m) ) and finite rate of increase (lambda) were significantly higher on T. urticae, indicating faster population increase of the predator on this prey species. The highest value of r (m) of the predator was 0.154 and 0.337 female per female per day on T. evansi and on T. urticae, respectively. The results suggested that P. fragariae cannot be considered a good predator of T. evansi.
Resumo:
This paper analyzes the factors that influence the issuing price of debentures in Brazil in the period from year 2000 to 2004, applying a factor model, in which exogenous variables explain return and price behavior. The variables in this study include: rating, choice of index, maturity, country risk, basic interest rate, long-term and short-term rate spread, the stock market index, and the foreign exchange rate. Results indicate that the index variable, probability of default and bond`s maturity influence pricing and points out associations of long-term bonds with better rating issues. (C) 2008 Elsevier Inc. All rights reserved.