A Contact Network-Based Approach for Online Planning of Containment Measures for COVID-19

  • Guilherme Thomaz Universidade de São Paulo
  • Denis Mauá Universidade de São Paulo
  • Leliane Barros Universidade de São Paulo

Resumo


We use data from the 2017 Origin-Destination survey to build a representative contact network for the city of São Paulo, where individuals are connected by different social relations (school, work, neighborhood, household). The network is used to devise a discrete time and state compartmental model for the spread of the COVID-19. We employed the model to compare different mitigation strategies. The results show that even simple Monte Carlo planners greatly improve the performance over reactive strategies in terms of balancing the economical and the health impacts of non-pharmaceutical interventions.

Palavras-chave: COVID-19, stochastic planning, complex networks

Referências

Bai, W., Zhou, T., and Wang, B. (2007). Immunization of susceptible–infected model on scale-free networks. Physica A: Statistical Mech. and Its Applications, 384:656–662.

Dureau, J., Kalogeropoulos, K., and Baguelin, M. (2013). Capturing the time-varying drivers of an epidemic using stochastic dynamical systems. Biostatistics, 14:541–55.

Ferguson, N. M., Laydon, D., and et al. (2020). Report 9: Impact of non-pharmaceutical interventions to reduce COVID-19 mortality and healthcare demand. Technical report, Imperial College London.

Halloran, M., Longini, I., Nizam, A., and Yang, Y. (2002). Containing bioterrorist smallpox. Science, 298:1428–1432.

Keeling, M. and Eames, K. (2005). Networks and epidemic models. Journal of the Royal Sociecity Interface, 2:295–307.

Kermack, W. O. and McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society A, 115(772):700–721.

Kinathil, S., Soh, H., and Sanner, S. (2017). Nonlinear optimization and symbolic dynamic programming for parameterized hybrid markov decision processes. In AAAI-17 Workshop on Symbolic Inference and Optimization.

Kucharski, A. J., Russell, T. W., and et al. (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet Infectious Diseases, 20(5):553–558.

Li, W., Zhang, B., Lu, J., Liu, S., Chang, Z., Cao, P., Liu, X., Zhang, P., Ling, Y., Tao, K., and Chen, J. (2020). The characteristics of household transmission of COVID-19. Clinical infectious diseases.

Linton, N. M. and et al. (2020). Incubation period and other epidemiological characteristics of 2019 novel coronavirus infections with right truncation: A statistical analysis of publicly available case data. MDPI, Multidisciplinary Digital Publishing Institute.

Mellan, T. A., Hoeltgebaum, H. H., Mishra, S., and et al. (2020). Estimating COVID-19 cases and reproduction number in Brazil. Technical report, Imperial College London.

Newman, M. (2002). Spread of epidemic disease on networks. Physical Review E, 66.

Silva, P. J. S. S., Pereira, T., and Nonato, L. G. (2020). Robot dance: a city-wise automatic control of COVID-19 mitigation levels. Technical report, Unicamp. medRxiv 2020.05.11.20098541.

Wang, Z., Ma, W., Zheng, X., Wu, G., and Zhang, R. (2020). Household transmission of SARS-CoV-2. The Journal of infection, 81(1):179–182.

Xue, S. (2020). Scheduling and Online Planning in Stochastic Diffusion Networks. PhD thesis, Oregon State University.

Zhang, J., Litvinova, M., Liang, Y., Wang, Y., Wang, W., Zhao, S., Wu, Q., Merler, S., Viboud, C., Vespignani, A., Ajelli, M., and Yu, H. (2020). Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. 368(6498):1481–1486.

Bai, W., Zhou, T., and Wang, B. (2007). Immunization of susceptible–infected model on scale-free networks. Physica A: Statistical Mech. and Its Applications, 384:656–662.

Dureau, J., Kalogeropoulos, K., and Baguelin, M. (2013). Capturing the time-varying drivers of an epidemic using stochastic dynamical systems. Biostatistics, 14:541–55.

Ferguson, N. M., Laydon, D., and et al. (2020). Report 9: Impact of non-pharmaceutical interventions to reduce COVID-19 mortality and healthcare demand. Technical report, Imperial College London.

Halloran, M., Longini, I., Nizam, A., and Yang, Y. (2002). Containing bioterrorist smallpox. Science, 298:1428–1432.

Keeling, M. and Eames, K. (2005). Networks and epidemic models. Journal of the Royal Sociecity Interface, 2:295–307.

Kermack, W. O. and McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society A, 115(772):700–721.

Kinathil, S., Soh, H., and Sanner, S. (2017). Nonlinear optimization and symbolic dynamic programming for parameterized hybrid markov decision processes. In AAAI-17 Workshop on Symbolic Inference and Optimization.

Kucharski, A. J., Russell, T. W., and et al. (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The Lancet Infectious Diseases, 20(5):553–558.

Li, W., Zhang, B., Lu, J., Liu, S., Chang, Z., Cao, P., Liu, X., Zhang, P., Ling, Y., Tao, K., and Chen, J. (2020). The characteristics of household transmission of COVID-19. Clinical infectious diseases.

Linton, N. M. and et al. (2020). Incubation period and other epidemiological characteristics of 2019 novel coronavirus infections with right truncation: A statistical analysis of publicly available case data. MDPI, Multidisciplinary Digital Publishing Institute.

Mellan, T. A., Hoeltgebaum, H. H., Mishra, S., and et al. (2020). Estimating COVID-19 cases and reproduction number in Brazil. Technical report, Imperial College London.

Newman, M. (2002). Spread of epidemic disease on networks. Physical Review E, 66.

Silva, P. J. S. S., Pereira, T., and Nonato, L. G. (2020). Robot dance: a city-wise automatic control of COVID-19 mitigation levels. Technical report, Unicamp. medRxiv 2020.05.11.20098541.

Wang, Z., Ma, W., Zheng, X., Wu, G., and Zhang, R. (2020). Household transmission of SARS-CoV-2. The Journal of infection, 81(1):179–182.

Xue, S. (2020). Scheduling and Online Planning in Stochastic Diffusion Networks. PhD thesis, Oregon State University.

Zhang, J., Litvinova, M., Liang, Y., Wang, Y., Wang, W., Zhao, S., Wu, Q., Merler, S., Viboud, C., Vespignani, A., Ajelli, M., and Yu, H. (2020). Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. 368(6498):1481–1486.
Publicado
20/10/2020
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THOMAZ, Guilherme; MAUÁ, Denis; BARROS, Leliane. A Contact Network-Based Approach for Online Planning of Containment Measures for COVID-19. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 234-245. DOI: https://doi.org/10.5753/eniac.2020.12132.