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Spatiotemporal diffusion of influenza A (H1N1): Starting point and risk factors

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  • Ana Carolina Carioca da Costa
  • Cláudia Torres Codeço
  • Elias Teixeira Krainski
  • Marcelo Ferreira da Costa Gomes
  • Aline Araújo Nobre

Abstract

Influenza constitutes a major challenge to world health authorities due to high transmissibility and the capacity to generate large epidemics. This study aimed to characterize the diffusion process of influenza A (H1N1) by identifying the starting point of the epidemic as well as climatic and sociodemographic factors associated with the occurrence and intensity of transmission of the disease. The study was carried out in the Brazilian state of Paraná, where H1N1 caused the largest impact. The units of spatial and temporal analysis were the municipality of residence of the cases and the epidemiological weeks of the year 2009, respectively. Under the Bayesian paradigm, parametric inference was performed through a two-part spatiotemporal model and the integrated nested Laplace approximation (INLA) algorithm. We identified the most likely starting points through the effective distance measure based on mobility networks. The proposed estimation methodology allowed for rapid and efficient implementation of the spatiotemporal model, and provided evidence of different patterns for chance of occurrence and risk of influenza throughout the epidemiological weeks. The results indicate the capital city of Curitiba as the probable starting point, and showed that the interventions that focus on municipalities with greater migration and density of people, especially those with higher Human Development Indexes (HDIs) and the presence of municipal air and road transport, could play an important role in mitigation of effects of future influenza pandemics on public health. These results provide important information on the process of introduction and spread of influenza, and could contribute to the identification of priority areas for surveillance as well as establishment of strategic measures for disease prevention and control. The proposed model also allows identification of epidemiological weeks with high chance of influenza occurrence, which can be used as a reference criterion for creating an immunization campaign schedule.

Suggested Citation

  • Ana Carolina Carioca da Costa & Cláudia Torres Codeço & Elias Teixeira Krainski & Marcelo Ferreira da Costa Gomes & Aline Araújo Nobre, 2018. "Spatiotemporal diffusion of influenza A (H1N1): Starting point and risk factors," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-20, September.
    • Handle: RePEc:plo:pone00:0202832
    DOI: 10.1371/journal.pone.0202832
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    1. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    2. Lawrence M. Wein & Michael P. Atkinson, 2009. "Assessing Infection Control Measures for Pandemic Influenza," Risk Analysis, John Wiley & Sons, vol. 29(7), pages 949-962, July.
    3. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    4. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    5. Pastore Y Piontti, Ana & Gomes, Marcelo Ferreira Da Costa & Samay, Nicole & Perra, Nicola & Vespignani, Alessandro, 2014. "The infection tree of global epidemics," Network Science, Cambridge University Press, vol. 2(1), pages 132-137, April.
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    1. Alireza Dehghani & Mehdi Alidadi & Ayyoob Sharifi, 2022. "Compact Development Policy and Urban Resilience: A Critical Review," Sustainability, MDPI, vol. 14(19), pages 1-19, September.

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