epischisto & mosimbio .upc

1. Modelling a Real Data Cellular Automaton to Analyze theSchistosomiasis Expansion Process along the coastline of Brazil www.epischisto.org & mosimbio.upc.edu These slides: http://200.17.137.109:8081/xiscanoe/publicacoes/invited-speakers/

2. researchers mosimbio.upc.edu www.pmt.es www.upc.edu www.cesar.org.br www.ufrpe.br epischisto.org www.fiocruz.br

3. schistosomiasis... Diseasecycle: http://www.wellcome.ac.uk/en/labnotes5/animation_popups/schisto.html (animation 5´24´´) Dead: Mar, 27th. 2009 Brazilianpolitician!! http://wwwnc.cdc.gov/travel/images/map3-14-distribution-schistosomiasis.jpg

4. a neglecteddisease in Brazil... No data No case reports No statistical series No reliable data Onlypoorcomunities Fiocruz (SchistosomiasisLaboratory) works to discover, to controland to report Fiocruz starts a newstudy in 2006... http://200.17.137.109:8081/xiscanoe/infra-estrutura

5. 2006 starts a newmonitoring Praia Carne de Vaca Praia Enseada dos Golfinhos BRAZIL Praia do Forte Praia Pau Amarelo Praia do Janga Lagoa do Náutico Praia Porto de Galinhas

6. why Carne de Vaca? Tourisminterest Isolatedpopulation Identified cases Notanalysedyet FIOCRUZ starts a newstudy Nearfrom UFRPE Local support: politicians, population The village comprises around 1600 people in 1041 households distributed in 70 blocks and covering approximately 4 km2.

7. 2006 – 2007, data collect in-loco

8. 2006 – 2007, data collectin-loco http://200.17.137.109:8081/xiscanoe/infra-estrutura/expedicoes

9. 2008 – 2009, data analysis and reports...Parasitological exams on 1100 residents Spatial pattern, water use and risk levels associated with the transmission of schistosomiasis on the north coast of Pernambuco, Brazil. Cad. Saúde Pública vol.26 no.5 Rio de Janeiro May 2010. http://dx.doi.org/10.1590/S0102-311X2010000500023

10. 2008 and 2009 data analysis and reports...Summary data for molluscscollected... Ecological aspects and malacological survey to identification of transmission risk' sites for schistosomiasis in Pernambuco North Coast, Brazil. Iheringia, Sér. Zool. 2010, vol.100, n.1, pp. 19-24. http://dx.doi.org/10.1590/S0073-47212010000100003

11. 2009-2010, modellingwith 15 real parameters (?) Fromoneyear (population 1 snapshot, molluscs 12 snapshots) withoutprevioushistorical...

12. a cellularautomaton 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

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

14. 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).

15. Cellsandinfection 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)

16. thealgorithm Main Eventsprocess

17. sumulations Mathematica 7.0 (Mathematica, 2011) with a processor Intel i5 3GHz, 4MB Cache, 8GB RAM. Computational costs of a complete simulation when assuming a fixed world size (10x10 cells) and extent (365 time steps) and an increasing number of parameters being swept for rejection sampling (from 1 to 15)

18. 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”

19. simulations 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

20. observations The risk of infection varies with time reproducing the patterns of infection spreading observed in reality; By the end of a year, the model predicts that around 25% of the blocks (18 of 70) are at risk of being infected (I ≥ 20% ) and that for at least one block, the infection is ensured; The blocks near the mollusk collection points III, VII and VIII, suggesting that they are the most active infection foci. These spots turned out to be the most environmentally affected areas, suggesting a correlation between contamination and risk.

21. observations Simplemodelscan compute endemicscenarios A bottom-upstrategywillbeimplementedusingABMswiththe data from 2006,2007 and 2010, 2011 Otherprojects are in progress...

22. Otherprojects in progress

23. www.ankos.com.br http://ankos.sourceforge.net/

24. AnAutomaticProposal For DiagnosisOfSchistosomiasis... http://200.17.137.109:8081/xiscanoe/projeto/graduate-projects

25. ABM project... 2011 – 2012: Postdoctoral research with MOSINBIO Group... nexttalk...  joa@deinfo.ufrpe.br Jones.albuquerque@gmail.com Thanks for theoportunity!