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Epidemiology Mathematics, statistics, computer science, … CODES and LANGUAGES

Epidemiology Mathematics, statistics, computer science, … CODES and LANGUAGES. fishy.com.br. www.epischisto.org. Statistical epidemiology - (spatial and temporal frequency) Mathematical epidemiology - (temporal dynamics) Computational epidemiology - (spatial dynamics)

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Epidemiology Mathematics, statistics, computer science, … CODES and LANGUAGES

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  1. Epidemiology Mathematics, statistics, computer science, … CODES and LANGUAGES fishy.com.br www.epischisto.org

  2. Statistical epidemiology - (spatial and temporal frequency) Mathematical epidemiology - (temporal dynamics) Computational epidemiology - (spatial dynamics) Genetic epidemiology - (genetic factors) Codes Languages Machines

  3. Epidemiologistas devem ter conhecimentos de [Naomar, 2006, http://www.livrariaodontosites.com.br/produtos_descricao.asp?lang=pt_br&codigo_produto=66]: • SAÚDE PÚBLICA – devido a ênfase na prevenção de enfermidades. • MEDICINA CLÍNICA – devido a ênfase na classificação das doenças e seus diagnósticos. • FISIOPATOLOGIA – devido a necessidade de entender mecanismos biológicos básicos da doença. • ESTATÍSTICA – devido a necessidade de quantificar a freqüência das doenças e sua relação com os antecedentes. • CIÊNCIAS SOCIAIS – devido a necessidade de entender o contexto social no qual a doença ocorre e se apresenta.

  4. What is statistics for epidemiologists? • a code! so “statistical epidemiology” is a redundancy... • a bit of philosophy of languages and codes... • Discrete versus continuous codes • Alphabet • Numbers • Languages

  5. e 2012?a bit of history, before… • http://library.thinkquest.org/22584/emh1000.htm

  6. resume… • 1650 BC – Papiro by Ahmes – Fractions • 600 – 300 BC – Thales and Euclides – Geometry • 1200 – Fibonacci – Series • 16th - Father of arithmetic by Widmann – Symbols and x^3 + mx = n • 17th – 1654 – Probability and Newton - Leibniz Calculus and symbols • 18th - Bernoulli´s differential equations, needles of Buffon http://mste.illinois.edu/reese/buffon/bufjava.html • 19th – Gauss, Cauchy, Poincaré, Cantor, Boole… • 20th – … [An Introduction to the History of Mathematics by Howard Eves.]

  7. Epidemiologistas devem ter conhecimentos de que códigos e linguagens [Epischisto, 2012]: • ??????????????? • tudo de 2006 + ...? • http://en.wikipedia.org/wiki/Epidemic_model • ??? Livro Naomar ´onicio´... Capítulo modelos... • Mathematical epidemiology! • and what else?...

  8. another code… Moving Things Around

  9. WHAT are these systems?

  10. Self-Reproducing Automata History... Cellular Spaces • John von Neumann, 40´s, but... [Ulam, Stanislaw 1952] [von Neumann, John, 1968] [Zuse, Konrad, 1970] [Burks, Arthur (ed.) Essays on Cellular Automata, Univ. Ill, 1970] [Holland, John, 1966] Calculating Spaces

  11. A famous and simple one: Game of Life • Take a look at this applet • http://www.bitstorm.org/gameoflife/ • MATHEMATICAL GAMESThe fantastic combinations of John Conway's new solitaire game "life" • Scientific American, 223 (October 1970): 120-123.

  12. some codes by machines... • A cell should be black whenever one or two, but not both, of its neighbors were black on the step before.

  13. Rule 30 - 1000 iterações

  14. Rule 110, 150 steps

  15. Flows in Rule 110!!

  16. Are these systems artificial ones?ANew Kind of Science! or ?

  17. natural biotic types Patterns of some seashells, like the ones in Conus and Cymbiola genus, are generated by natural CA. http://www.answers.com/topic/cellular-automaton

  18. arts

  19. What can we do with these “systems”?

  20. MUSIC is a code by machine... Let´s take a bit of time with this site • http://tones.wolfram.com/

  21. CA music generator

  22. What else?

  23. The Crucial Experiment – Stephen Wolfram, 1986 22.000 BC Arts Biology Psicology Physics Computing Mathematics Arqueology ... and Epidemiology?

  24. Statistical epidemiology - (spatial and temporal frequency) Mathematical epidemiology - (temporal dynamics) Computational epidemiology - (spatial dynamics) Genetic epidemiology - (genetic factors) Codes Languages Machines

  25. We have tried with ANKOS…

  26. Some codes...

  27. Disease Parameters Vaccination Population Demographics Interaction factors Visualization Data Sets cellular automata and epidemiology Distances

  28. a cellular automaton • Cellular automaton A is a set of four objectsA = <G, Z, N, f>, where • G– set of cells • Z– set of possible cells states • N – set, which describes cells neighborhood • f– transition function, rules of the automaton: • Z|N|+1Z (for automaton, which has cells “with memory”) • Z|N|Z (for automaton, which has “memoryless” cells) Moore Neighbourhood (in grey) of the cell marked with a dot in a 2D square grid

  29. oneproposal: a top-down approach using a cellularautomaton simulation space, a 10x10 square grid

  30. the dynamics Mollusk population dynamics (3a) a growth model for the number of individuals (N) that considers the intrinsic growth rate (r) and the maximum sustainable yield or carrying capacity (C) defined at each site (Verhulst, 1838): the model calculates the local increase of population using equation 1 and calculating N(t+1) out from N(t). The values for r and C are set at each site and each time step, using monthly meteorological inputs and considering the ecological quality of the habitat (3b) (1) Human infection dynamics (SIR - SI) This model splits the human population into three compartments: S (for susceptible), I (for infectious) and R (for recovered and not susceptible to infection) and the snail population into two compartments: MS (for susceptible mollusk) and MI (for infectious mollusk). Socioeconomic and environmental factors environmental quality of the nine collection sites in Carne de Vaca, according to the criteria of Callisto et al (Souza et al, 2010).

  31. Cells and infection forces • states • black: rate of human infection = 100%; • red: 80% ≤ rate of human infection < 100%; • light red: 60% ≤ rate of human infection < 80%; • yellow: 40% ≤ rate of human infection < 60%; • light yellow: 20% ≤ rate of human infection < 40%; • cyan: 0% ≤ rate of human infection < 20%. • infection forces • Human • S -> I (infected molluscs contact, pH) • I -> R (if treated (1-α), χ) • Molluscs • S -> I (infected human contact, pM)

  32. thealgorithm – likethe GAME OF LIFE! Main Events process

  33. simulations “according to the risk indicator, in the scattering diagram of Moran represented in the Box Map (Figure 2), indicated 18 areas of highest risk for the schistosomiasis, all located in the central sector of the village. Areas with lower risk and areas of intermediate risk for occurrence of the disease were located in the north and central portions with some irregularity in the distribution”

  34. Simulations – previsibility... Predictive scenarios generated with the parameter calibration of the year 2007 that show endemic schistosomiasis. I stands for the average percentage of infected humans per spatial cell predicted by the model

  35. Statistical epidemiology - (spatial and temporal frequency) Mathematical epidemiology - (temporal dynamics) Computational epidemiology - (spatial dynamics) Genetic epidemiology in Schistosomiasis???? - (genetic factors) Codes? Languages? Machines?

  36. Thanks a lot! jones.albuquerque

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