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Challenges in Modeling of an Outbreak’s Prediction, Forecasting and Decision Making for Policy Makers

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Analysis of Infectious Disease Problems (Covid-19) and Their Global Impact

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Abstract

In this work an attempt has been made to review the current state of arts in epidemiological modeling, assessment of predictive models as well as forecasting of new pathogen. The primary concern is the containment of the outbreak from wide spread of the disease among the whole population. This article also focuses for the development of management tools and techniques in decision making for policy makers that are based on scientific evidence. Moreover, the identification, detection and reporting for outbreak of an infectious disease particularly a new pathogen in timely manner is quite challenging and tedious. Apparently understanding and reporting of such events are commonly rely on statistical and mathematical tools and both these approaches commonly depend upon a priory estimates as well as some reliable data. For example: statistical models requires a sizable number of events to develop predictive models, which is impossible at the outset of an outbreak of the disease to collate enough number of samples. Whereas, the mathematical models are reliable as well as have better predictive behavior, but they also require better initial guess apart from some rigid constraints to fully satisfy the model’s assumptions. Apart from these issues, the other important features to study in epidemiology of the disease is how fast and quickly the scientific community promptly can pinpoint and able to address any causal factor which may suffice to account for the magnitude and severity of the epidemics of new pathogen that may have been taken place to any geographic locations. Hence in this work, first of all the SIR model (susceptible: S, infected: I, and recovered: R) will be outlined, as it is the most commonly used model in epidemiology of infectious diseases. Moreover, the applicability and utilization of \(R_0\) in public health domain especially adaptive policy with management tools will be developed for the healthcare workers as well as the higher management of healthcare facility.

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Notes

  1. 1.

    Agent based, network based, harmonic decomposition analysis, and digital microbes notes were adopted from: Computational Modeling and Simulation of Epidemic Infectious Diseases Donald S. Burke (M.D.) Bloomberg School of Public Health, Johns Hopkins University (USA), and appendix from: Microbial Threats to Health: Emergence, Detection, and Response (2003), National Academic Press.

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Author information

Authors and Affiliations

  1. Department of Biostatistics and Bioinformatics, King Abdullah International Medical Research Center, National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia

    Altaf H. Khan

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  1. Altaf H. Khan
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Editors and Affiliations

  1. Anand International College of Engineering, Jaipur, Rajasthan, India

    Praveen Agarwal

  2. Institute of Mathematics, University of Santiago de Compostela, Santiago, Spain

    Juan J. Nieto

  3. Mathematical Sciences, Queen Mary University of London, London, UK

    Michael Ruzhansky

  4. Department of Mathematics, University of Aveiro, Aveiro, Portugal

    Delfim F. M. Torres

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Khan, A.H. (2021). Challenges in Modeling of an Outbreak’s Prediction, Forecasting and Decision Making for Policy Makers. In: Agarwal, P., Nieto, J.J., Ruzhansky, M., Torres, D.F.M. (eds) Analysis of Infectious Disease Problems (Covid-19) and Their Global Impact. Infosys Science Foundation Series(). Springer, Singapore. https://doi.org/10.1007/978-981-16-2450-6_17

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