hybridModels: An R Package for the Stochastic Simulation of Disease Spreading in Dynamic Networks

Imagem de Miniatura
Arquivos
art_MARQUES_hybridModels_An_R_Package_for_the_Stochastic_Simulation_2020.PDF (1006.57 KB)
Citações na Scopus
4
Tipo de produção
article
Data de publicação
2020
DOI
Scopus
Web of Science
Título da Revista
ISSN da Revista
Título do Volume
Editora
JOURNAL STATISTICAL SOFTWARE
Autores
MARQUES, Fernando S.
GRISI-FILHO, Jose H. H.
SILVA, Jean C. R.
ALMEIDA, Erivania C.
SILVA JUNIOR, Jose L.
Citação
JOURNAL OF STATISTICAL SOFTWARE, v.94, n.6, p.1-32, 2020
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Disease spreading simulations are traditionally performed using coupled differential equations. However, in the setting of metapopulations, most of the solutions provided by this method do not account for the dynamic topography of subpopulations. Conversely, the alternative approach of individual-based modeling (IBM) may add computational cost and complexity. Hybrid models allow for the study of disease spreading because they combine both aforementioned approaches by separating them across different scales: a local scale that addresses subpopulation dynamics using coupled differential equations and a global scale that addresses the contact between these subpopulations using IBM. We present a simple way of simulating the spread of disease in dynamic networks using the high-level statistical computational language R and the hybridModels package. We built four examples using disease spread models at the local scale in several different networks: an animal movement network; a three-node network, whose model solution using a stochastic simulation algorithm is compared with the ordinary differential equations approach; the commuting of individuals between patches, which we compare with the permanent migration of individuals; and the commuting of individuals within the metropolitan area of Sao Paulo.
Palavras-chave
modeling, dynamic networks, epidemic, stochastic simulation, R
URI
https://observatorio.fm.usp.br/handle/OPI/54055
Referências
  1. Aznar MN, 2011, PREV VET MED, V98, P119, DOI 10.1016/j.prevetmed.2010.11.004
  2. Bigras-Poulin M, 2006, PREV VET MED, V76, P11, DOI 10.1016/j.prevetmed.2006.04.004
  3. Bigras-Poulin M, 2007, PREV VET MED, V80, P143, DOI 10.1016/j.prevetmed.2007.02.004
  4. Buttner K, 2015, TRANSBOUND EMERG DIS, V62, P188, DOI 10.1111/tbed.12106
  5. Buhnerkempe MG, 2013, PREV VET MED, V112, P318, DOI 10.1016/j.prevetmed.2013.08.002
  6. Cao Y, 2006, J CHEM PHYS, V124, DOI 10.1063/1.2159468
  7. Chatterjee A, 2005, J CHEM PHYS, V122, DOI 10.1063/1.1833357
  8. Chowell G, 2008, EPIDEMIOL INFECT, V136, P852, DOI 10.1017/S0950268807009144
  9. Chowell G, 2010, P ROY SOC B-BIOL SCI, V277, P1857, DOI 10.1098/rspb.2009.1897
  10. Coelho FC, 2008, SOURCE CODE BIOL MED, V3, DOI 10.1186/1751-0473-3-3
  11. Csardi G., 2006, INTERJOURNAL COMPLEX, V1695
  12. Dube C, 2008, TRANSBOUND EMERG DIS, V55, P382, DOI 10.1111/j.1865-1682.2008.01053.x
  13. Frossling J, 2012, PREV VET MED, V105, P202, DOI 10.1016/j.prevetmed.2011.12.011
  14. Garner MG, 2005, AUST VET J, V83, P758, DOI 10.1111/j.1751-0813.2005.tb11589.x
  15. Gillespie DT, 2007, ANNU REV PHYS CHEM, V58, P35, DOI 10.1146/annurev.physchem.58.032806.104637
  16. GILLESPIE DT, 1976, J COMPUT PHYS, V22, P403, DOI 10.1016/0021-9991(76)90041-3
  17. Gillespie DT, 2001, J CHEM PHYS, V115, P1716, DOI 10.1063/1.1378322
  18. GILLESPIE DT, 1977, J PHYS CHEM-US, V81, P2340, DOI 10.1021/j100540a008
  19. Green DM, 2009, PREV VET MED, V91, P261, DOI 10.1016/j.prevetmed.2009.05.031
  20. Grenfell BT, 2001, NATURE, V414, P716, DOI 10.1038/414716a
  21. Grimm V, 2005, SCIENCE, V310, P987, DOI 10.1126/science.1116681
  22. Grimm V., 2005, INDIVIDUAL BASED MOD
  23. Grimm V, 2006, ECOL MODEL, V198, P115, DOI 10.1016/j.ecolmodel.2006.04.023
  24. Grimm V, 2010, ECOL MODEL, V221, P2760, DOI 10.1016/j.ecolmodel.2010.08.019
  25. Grisi JHH, 2013, PREV VET MED, V110, P304, DOI 10.1016/j.prevetmed.2012.12.013
  26. Harvey N, 2007, PREV VET MED, V82, P176, DOI 10.1016/j.prevetmed.2007.05.019
  27. Kane MJ, 2013, J STAT SOFTW, V55, P1
  28. Kao RR, 2007, J R SOC INTERFACE, V4, P907, DOI 10.1098/rsif.2007.1129
  29. Keeling M, 2005, THEOR POPUL BIOL, V67, P1, DOI 10.1016/j.tpb.2004.08.002
  30. Longini IM, 2004, AM J EPIDEMIOL, V159, P623, DOI 10.1093/aje/kwh092
  31. Mills CE, 2004, NATURE, V432, P904, DOI 10.1038/nature03063
  32. Monk N, 2012, CPIB SUMMER SCH 2012
  33. Negreiros RL, 2010, THESIS
  34. Noremark M, 2014, BMC VET RES, V10, DOI 10.1186/1746-6148-10-71
  35. Noremark M, 2011, PREV VET MED, V99, P78, DOI 10.1016/j.prevetmed.2010.12.009
  36. Noremark M, 2009, ACTA VET SCAND, V51, DOI 10.1186/1751-0147-51-37
  37. Osgood N, 2007, 2007 INT C SYST DYN
  38. Pineda-Krch M, 2008, J STAT SOFTW, V25
  39. R Development Core Team, 2008, R LANG ENV STAT COMP
  40. Rahmandad H, 2008, MANAGE SCI, V54, P998, DOI 10.1287/mnsc.1070.0787
  41. Rasamoelina-Andriamanivo H, 2014, ACTA TROP, V135, P10, DOI 10.1016/j.actatropica.2014.03.008
  42. Rautureau S, 2011, TRANSBOUND EMERG DIS, V58, P110, DOI 10.1111/j.1865-1682.2010.01187.x
  43. Shirley MDF, 2005, ECOL COMPLEX, V2, P287, DOI 10.1016/j.ecocom.2005.04.005
  44. Soetaert K, 2010, J STAT SOFTW, V33, P1, DOI 10.18637/jss.v033.i09
  45. Stevenson MA, 2013, PREV VET MED, V109, P10, DOI 10.1016/j.prevetmed.2012.08.015
  46. Thakur KK, 2016, TRANSBOUND EMERG DIS, V63, pE14, DOI 10.1111/tbed.12225
  47. Van Rossum G, 2011, PYTH PROGR LANG
  48. Vincenot CE, 2011, ECOL INFORM, V6, P177, DOI 10.1016/j.ecoinf.2011.04.002
  49. Vincenot CE, 2011, ECOL MODEL, V222, P210, DOI 10.1016/j.ecolmodel.2010.09.029
  50. Wickham H, 2009, USE R, P1, DOI 10.1007/978-0-387-98141-3
  51. Wickham H, 2007, J STAT SOFTW, V21, P1

Coleções
Artigos e Materiais de Revistas Científicas - FM/MPT
Artigos e Materiais de Revistas Científicas - LIM/01