Modeling and Optimizing the Public-Health Infrastructure for Emergency Response

Abstract

Public-health emergencies, such as bioterrorist attacks or pandemics, demand fast, efficient, large-scale dispensing of critical medical countermeasures. By combining mathematical modeling, large-scale simulation, and powerful optimization engines, and coupling them with automatic graph-drawing tools and a user-friendly interface, we designed and implemented RealOpt©, a fast and practical emergency-response decision-support tool. RealOpt allows public-health emergency coordinators to (1) determine locations for point-of-dispensing (POD) facility setup; (2) design customized and efficient floor plans for PODs via an automatic graph-drawing tool; (3) determine required labor resources and provide efficient placement of staff at individual stations within a POD; (4) perform disease-propagation analysis, understand and monitor the intra-POD disease dilemma, and help to derive dynamic response strategies to mitigate casualties; (5) assess resources and determine minimum needs to prepare for treating their regional populations in emergency situations; (6) carry out large-scale virtual drills and performance analyses, and investigate alternative strategies; and (7) design a variety of dispensing scenarios that include emergency-event exercises to train personnel. These advanced and powerful computational strategies allow emergency coordinators to quickly analyze design decisions, generate feasible regional dispensing plans based on best estimates and analyses available, and reconfigure PODs as an event unfolds. The ability to analyze planning strategies, compare the various options, and determine the most cost-effective combination of dispensing strategies is critical to the ultimate success of any mass dispensing effort.

Relief distribution networks: a systematic review

“…Despite their efforts, it seems that most of these contributions did not focus adequately in the specific characteristics of humanitarian logistics like the knowledge of demand, the considered objectives, the periodicity and the decision-making structure (Holguín-Veras et al, 2012). Hopefully, our knowledge and comprehension level of humanitarian challenges increases and recent articles present more sophisticated models, which better suit the specific context and needs, especially in the case of transportation problems (Berkoune et al 2012;Gu 2011;Huang et al 2012;Lee et al 2009a;Lin et al 2012;Lin et al 2011;Murali et al 2012;). Nonetheless, we think that the sudden and dramatic nature of humanitarian problems should be emphasized in future research works.…”

Examining the feasibility of using a modelling tool to assess resilience across a health-care system and assist with decisions concerning service reconfiguration

“…Much of this work focuses on applications in security (Larson, 2004;Wein et al, 2009;Wright et al, 2006), the health care component of which is mainly concerned with the response to bioterrorist threats such as deliberate introduction of anthrax or smallpox (Hupert et al, 2002;Lee et al, 2009;Wein et al, 2003;Zaric et al, 2008). Other published examples of OR applied specifically to health care within a disaster emergency context, of which there are relatively few, include planning for emergency mass dispensing and vaccination clinics -in a pandemic influenza or otherwise (Aaby 4 et al, 2006), the routing of ambulances to hospitals in a disaster (Jotshi et al, 2009) and the design of interventions in a pandemic influenza (Eichner et al, 2007).…”

“…Rolland et al, 2010), probability and statistics, and simulation (Green and Kolesar, 2004;Simpson and Hancock, 2009). More specifically in health care, discrete event simulation has been applied to the problem of designing antibiotic and vaccination distribution centres in the case of bioterrorist attacks or pandemic influenza (Aaby et al, 2006;Hupert et al, 2002;Lee et al, 2009), whilst compartmental modelling and queuing theory have been used to compare various emergency responses in the event of an anthrax attack (Wein et al, 2003;Zaric et al, 2008). Tufekci and Wallace (1998) point out a lack of information and decision support systems in the field, whilst Simpson and Hancock (2009) note that OR studies within disaster emergency preparedness have had relatively little influence on policy or practice, highlighting the need for positive engagement with responders and policy makers in future research.…”

Optimal staffing strategies for points of dispensing

“…Immediate and aggressive distribution and dispensing of medical countermeasures -in this case, oral antibiotics -and the full use of all available resources to do so are essential elements of a successful response (Lee, 2009). Fast, efficient and large-scale dispensing of such critical medical countermeasures is complex and requires careful pre-incident planning and coordination from multiple federal, state and local agencies as well as the potential involvement of the private sector (Lee, Chen, Pietz, & Benecke, 2009).…”