A Business Model to Detect Disease Outbreaks

Authors

1 Faculty of Industrial, Tarbiat Modares University, Tehran, Iran

2 IT Department, Shariati Hospital, Tehran, Iran

Abstract

Introduction: Every year several disease outbreaks, such as influenza-like illnesses (ILI) and other contagious illnesses, impose various costs to public and non-government agencies. Most of these expenses are due to not being ready to handle such disease outbreaks. An appropriate preparation will reduce the expenses. A system that is able to recognize these outbreaks can earn income in two ways: first, selling the predictions to government agencies to equip and make preparations in order to reduce the imposed costs and second, selling predictions to pharmaceutical companies to guide them in producing the required drugs when a disease spreads. This production can specify probable markets to these companies.

Methods: Both earning methods would be considered in this modeling and costs and incomes will be discussed according to basic business models (especially in the health field). To execute this model, the internet is used as a recipient of information from the doctors and the service providers for prediction.
To ensure collaboration of doctors in the data collection process, the amount of money that is paid is proportional to the rate of sending the patients’ information. On the other hand, customers can access outbreak prediction information about a specific illness after payment or subscription of system for monthly periods. All the money transfered in this system would be via online credit systems.

Results: This business model has three main values: recognizing disease outbreaks at the right time, identifying factors and estimating the spreading rate of the disease and, the categorization of customers in this model is based on the value provided including pharmaceutical companies and importers of drugs, the government, insurance companies, universities and research centers. By considering various markets, this model has the ROI of 0.5 which means the investment in it reverses in 6 months.

Conclusion: According to the results, the business model developed in this study, has fair value and is feasible and suitable for the web. This model develops medical information network and proper marketing, earns good profits and the most critical resource of it is the algorithm that detects the disease outbreak which must be properly constructed and used.

Keywords


Cooper GF, Dash DH, Levander JD, Wong W-K, Hogan WR, Wagner M. Bayesian biosurveillance of disease outbreaks. In Proceedings of the 20th conference on Uncertainty in artificial intelligence. AUAI Press. 2004, p.94-103.
Widdowson MA, Bosman A, van Straten E, Tinga M, Chaves S, van Eerden L, et al. Automated, laboratory-based system using the Internet for disease outbreak detection, the Netherlands. Emerg Infect Dis. 2003 Sep;9(9):1046-52.
Taha M, Achtman M, Alonso J, Greenwood B, Ramsay M, Fox A, et al. Serogroup W135 meningococcal disease in Hajj pilgrims. Lancet. 2000 Dec;356(9248):2159.
Dentinger C, Bower A, Nainan O, Cotter S, Myers G, Dubusky L, et al. An outbreak of hepatitis A associated with green onions. J Infect Dis. 2001 Apr;183(8):1273-6.
Polanco C, Castañón-González JA, Macías AE, Samaniego JL, Buhse T, Villanueva-Martínez S. Detection of severe respiratory disease epidemic outbreaks by CUSUM-based overcrowd-severe-respiratory-disease-index model. Comput Math Methods Med. 2013;2013:213206.
Afuah A, Tucci CL. Internet business models and strategies. Text and cases. McGraw-Hill Higher Education; 2000.
Osterwalder A, Pigneur Y. Business model generation. Hoboken: John Wiley & Sons; 2010.
Osterwalder A. The business model ontology, a proposition in a design science approach, 2004.
Enki DG, Noufaily A, Garthwaite PH, Andrews NJ, Charlett A, Lane C, et al. Automated biosurveillance data from England and Wales, 1991-2011. Emerg Infect Dis. 2013 Jan;19(1):35-42.
Kammerer JS, Shang N, Althomsons SP, Haddad MB, Grant J, Navin TR. Using statistical methods and genotyping to detect tuberculosis outbreaks. Int J Health Geogr. 2013 Mar 16;12:15.
Mochalova A, Nanopoulos A. A targeted approach to viral marketing. Electronic Commerce Research and Applications. 2014;13(4):283-94.
Tang S. The analysis about the network expansion of enterprise viral marketing. J Manag Strategy. 2014;5(2):49-52.
Miyagawa S, Yamasaki S, Uchiyama E, Amoroso DL. Framework for information sharing with privacy and priority control in long-term care in Japan. Int