A Fuzzy-Based Approach for Cholera Risk Assessment and Vaccine Allocation

Ahmed Gailan Qasem, Sarah S. Lam, Faisal Aqlan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Common interventions to control the spread of cholera include improving sanitation, hygiene, and access to safe drinking water and providing epidemic regions with sufficient treatment kits and oral vaccines. Due to resources limitation, these interventions should be guided by a risk assessment of cholera-affected regions, thereby targeting regions based on their risk level. Cholera risk assessment is very challenging because of the lack of precise and reliable data. This study proposes an approach for cholera risk assessment and vaccine allocation, which consists of two phases: (i) cholera risk assessment, where a fuzzy inference system (FIS) is proposed to evaluate the risk level of cholera-affected regions based on five cholera risk indicators: (1) attack rate, (2) case fatality rate, (3) the number of internally displaced persons, (4) accessibility of water, sanitation and hygiene, and (5) accessibility of cholera treatment; (ii) cholera vaccine allocation, where a mixed-integer programming model is used to optimize the allocation of limited vaccine doses among multiple regions over multiple periods while considering the risk level, population of regions, and vaccine efficacy. The model answers the questions of where, what amounts, and when to send vaccines during a 2-year horizon. Implementation of the proposed approach is illustrated using a case study from Yemen, which is currently experiencing the world’s worst cholera outbreak according to the World Health Organization. The results reveal the usefulness of the proposed approach in mapping the cholera risk, which in turn is used as effective guidance for the allocation of cholera vaccine.

Original languageEnglish (US)
Pages (from-to)3366-3383
Number of pages18
JournalInternational Journal of Fuzzy Systems
Issue number8
StatePublished - Nov 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Theoretical Computer Science
  • Information Systems
  • Control and Systems Engineering
  • Computational Theory and Mathematics


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