Blog sobre a (minha) vida científica – "E toda banda larga será inútil se a mente for estreita"
On 11 June the World Health Organization officially raised the phase of pandemic alert (with regard to the new H1N1 influenza strain) to level 6. We use a global structured metapopulation model integrating mobility and transportation data worldwide in order to estimate the transmission potential and the relevant model parameters we used the data on the chronology of the 2009 novel influenza A(H1N1). The method is based on the maximum likelihood analysis of the arrival time distribution generated by the model in 12 countries seeded by Mexico by using 1M computationally simulated epidemics. An extended chronology including 93 countries worldwide seeded before 18 June was used to ascertain the seasonality effects. We found the best estimate R0 = 1.75 (95% CI 1.64 to 1.88) for the basic reproductive number. Correlation analysis allows the selection of the most probable seasonal behavior based on the observed pattern, leading to the identification of plausible scenarios for the future unfolding of the pandemic and the estimate of pandemic activity peaks in the different hemispheres. We provide estimates for the number of hospitalizations and the attack rate for the next wave as well as an extensive sensitivity analysis on the disease parameter values. We also studied the effect of systematic therapeutic use of antiviral drugs on the epidemic timeline. The analysis shows the potential for an early epidemic peak occurring in October/November in the Northern hemisphere, likely before large-scale vaccination campaigns could be carried out. We suggest that the planning of additional mitigation policies such as systematic antiviral treatments might be the key to delay the activity peak inorder to restore the effectiveness of the vaccination programs.
|Comments:||Paper: 29 Pages, 3 Figures and 5 Tables. Supplementary Information: 29 Pages, 5 Figures and 7 Tables. Print version: this http URL|
|Subjects:||Populations and Evolution (q-bio.PE); Statistical Mechanics (cond-mat.stat-mech); Physics and Society (physics.soc-ph)|
|Journal reference:||BMC Medicine 2009, 7:45|
|Cite as:||arXiv:0909.2417v1 [q-bio.PE]|
Fechamento das escolas pode reduzir procura de serviços de saúde em até 50% no auge da pandemia
Portanto, enfatizando aqui o caráter didático-científico, aposto um kit de cervejas Colorado (R$ 37,00) que dentro de um mês o ministro vai ter que digerir suas palavras, e o Brasil terá a maior taxa por cem mil habitantes do mundo. Alguém topa?
Com os novos dados, o novo modelo fica:
número de mortos anunciados = 0,00009*(dias desde 28 de junho)^3,882
R^2 = 0,9925
(Ainda tem um ajuste melhor do que o modelo exponencial – mesmo um que despreze os primeiros 20 dias desde a primeira morte no país.)
Pelo novo modelo, passamos de 500 mortes amanhã. A barreira dos 1.000 é ultrapassada dia 02/set (não mais 01/set).
Para daqui a 7 dias (28/ago) são previstas 767 mortes; daqui a duas semanas (04/set): 1.170; daqui a 30 dias (20/set): 2.656. Para o dia 07/set: 1.383.
O erro médio está na casa dos 14% para mais ou para menos.
27/07/09 – 08h40 – Atualizado em 27/07/09 – 08h40
Abstract: The transmissibility of many infectious diseases varies significantly in time, but has been thought impossible to measure directly. We devise a mathematical algorithm to recover the time-dependent transmission rate from epidemiological data. We apply our algorithm to historic UK measles data and observe that for most cities the main spectral peak of the transmission rate has a two-year period. All previous models assumed that the transmission rate has one-year period. Our construction also illustrates the danger of overfitting an epidemic transmission model with a variable transmission rate function.
Abstract: We present a new analytical formalism to study the spreading of diseases in complex networks. Our proposal differs from current studies based on mean-field approximations and focuses on the infection probability of individual nodes. We particularize on the Susceptible-Infected-Susceptible model. Within the new formalism, we construct the whole phase diagram of the system and recover well-known findings concerning the epidemic threshold. We compare the approach with intensive Monte Carlo (MC) simulations. Moreover, a new scaling law characterizing the dependence of the epidemic threshold with the frequency of contacts between neighbors is revealed. We illustrate the approach studying the disease spreading in the world-wide air transportation network.
|Comments:||4 pages, 3 figures|
|Subjects:||Computational Physics (physics.comp-ph); Physics and Society (physics.soc-ph)|
|Cite as:||arXiv:0907.1313v1 [physics.comp-ph]|
Abstract: Stochastic effects may cause fade-out of an infectious disease in a population immediately after an epidemic outbreak. We develop WKB theory to determine the most probable path of the system toward epidemic fade-out, and to evaluate the fade-out probability. The most probable path is an instanton-like orbit in the phase space of the underlying Hamiltonian flow.
|Comments:||4 pages, 4 figures|
|Subjects:||Populations and Evolution (q-bio.PE); Statistical Mechanics (cond-mat.stat-mech)|
|Cite as:||arXiv:0906.5550v1 [q-bio.PE]|
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