Application of Multi-Criteria Decision Analysis Techniques and Decision Support Framework for Informing Arbovirus Risk Assessments for Planning, Preparedness and Response

Segaran P Pillai^1*Elizabeth Fox¹Ann M Powers²Stephen Allen Morse³

• ¹US Food and Drug Administration, Silver Spring, United States
• ²Centers for Disease Control and Prevention, Atlanta, United States
• ³IHRC inc., Atlanta, GA, Atlanta, United States

Globally, more than 17% of human infections are caused by vector-borne viruses, which result in more than 700,000 deaths annually as per the World Health Organization. Mosquitoes and ticks are the primary arthropod vectors, along with sandflies and midges. More than 500 arthropod-borne viruses (arboviruses) have been described, with more than 150 causing human disease. To understand the public health risk associated with arboviruses, we used multi-criteria decision analysis (MCDA) techniques and a Decision Support Framework (DSF) employing a logic tree format to identify high-risk arboviruses, applying these approaches to only those arboviruses transmitted by flying insects (i.e., mosquitos, sandflies, and midges) due to their potential for efficient transmission and habitat expansion. A literature review of 54 arboviruses against 14 criteria was conducted for assessing risk and documenting the findings that support this assessment. The most prominent data gaps were those for the annual global incidence, the severity of disease, and long-term impact. Technical review of published data and associated scoring recommendations by subject matter experts (SMEs) were found to be critical, particularly for pathogens with very few known cases. The MCDA analysis supported the intuitive sense that agents with high mortality and morbidity rates should rank higher on the relative risk scale when considering disease persistence and severity. However, comparing scores to suggest thresholds for designating high risk versus (vs) moderate risk vs low risk, was challenging and will require additional real time data during an outbreak. The DSF utilized a logic tree approach to identify arboviruses that were of sufficiently low enough concern that they could be ruled out from further consideration. In contrast to the MCDA approach, the DSF ruled out an arbovirus if it failed to meet even one criteria threshold. Nevertheless, both the MCDA and DSF approaches arrived at similar conclusions, suggesting that using these analytical approaches added robustness for decision making.