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Estimation of expected number of accidents and workforce unavailability through Bayesian population variability analysis and Markov-based model

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  • das Chagas Moura, Márcio
  • Azevedo, Rafael Valença
  • Droguett, Enrique López
  • Chaves, Leandro Rego
  • Lins, Isis Didier
  • Vilela, Romulo Fernando
  • Filho, Romero Sales

Abstract

Occupational accidents pose several negative consequences to employees, employers, environment and people surrounding the locale where the accident takes place. Some types of accidents correspond to low frequency-high consequence (long sick leaves) events, and then classical statistical approaches are ineffective in these cases because the available dataset is generally sparse and contain censored recordings. In this context, we propose a Bayesian population variability method for the estimation of the distributions of the rates of accident and recovery. Given these distributions, a Markov-based model will be used to estimate the uncertainty over the expected number of accidents and the work time loss. Thus, the use of Bayesian analysis along with the Markov approach aims at investigating future trends regarding occupational accidents in a workplace as well as enabling a better management of the labor force and prevention efforts. One application example is presented in order to validate the proposed approach; this case uses available data gathered from a hydropower company in Brazil.

Suggested Citation

  • das Chagas Moura, Márcio & Azevedo, Rafael Valença & Droguett, Enrique López & Chaves, Leandro Rego & Lins, Isis Didier & Vilela, Romulo Fernando & Filho, Romero Sales, 2016. "Estimation of expected number of accidents and workforce unavailability through Bayesian population variability analysis and Markov-based model," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 136-146.
    • Handle: RePEc:eee:reensy:v:150:y:2016:i:c:p:136-146
    DOI: 10.1016/j.ress.2016.01.017
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    References listed on IDEAS

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    1. Ale, B.J.M. & Bellamy, L.J. & Baksteen, H. & Damen, M. & Goossens, L.H.J. & Hale, A.R. & Mud, M. & Oh, J. & Papazoglou, I.A. & Whiston, J.Y., 2008. "Accidents in the construction industry in the Netherlands: An analysis of accident reports using Storybuilder," Reliability Engineering and System Safety, Elsevier, vol. 93(10), pages 1523-1533.
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    3. Carmen Carnero, María & José Pedregal, Diego, 2010. "Modelling and forecasting occupational accidents of different severity levels in Spain," Reliability Engineering and System Safety, Elsevier, vol. 95(11), pages 1134-1141.
    4. Stephen C. Hora & Ronald L. Iman, 1990. "Bayesian Modeling of Initiating Event Frequencies at Nuclear Power Plants," Risk Analysis, John Wiley & Sons, vol. 10(1), pages 103-109, March.
    5. Yakovlev, Pavel & Sobel, Russell S., 2010. "Occupational safety and profit maximization: Friends or foes?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 39(3), pages 429-435, June.
    6. Enrique López Droguett & Ali Mosleh, 2013. "Integrated treatment of model and parameter uncertainties through a Bayesian approach," Journal of Risk and Reliability, , vol. 227(1), pages 41-54, February.
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    Cited by:

    1. Westreich, Sara & Perlman, Yael & Winkler, Michael, 2021. "Analysis and Implications of the Management of Near-Miss Events: A Game Theoretic Approach," Reliability Engineering and System Safety, Elsevier, vol. 212(C).

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