Govind Hariharan

Location-allocation planning of stockpiles for effective disaster mitigation

In the existing framework for receiving and allocating Strategic National Stockpile (SNS) assistance, there are three noticeable delays: the delay by the state in requesting federal assets, the delay in the federal process which releases assets only upon the declaration of a disaster and lastly the time it takes to reach supplies rapidly from the SNS stockpile to where it is needed. The most efficient disaster preparedness plan is one that addresses all three delays taking into account the unique nature of each disaster. In this paper, we propose appropriate changes to the existing framework to address the first two delays and a generic model to address the third which determines the locations and capacities of stockpile sites that are optimal for a specific disaster. Specifically, our model takes into account the impact of disaster specific casualty characteristics, such as the severity and type of medical condition and the unique nature of each type of disaster, particularly with regard to advance warning and factors affecting damage. For disasters involving uncertainty (magnitude/severity) with regard to future occurrences, such as an earthquake, development of appropriate solution strategies involves an additional step using scenario planning and robust optimization. We illustrate the application of our model via case studies for hurricanes and earthquakes and are able to outline an appropriate response framework for each.

Matching intermediaries for information goods in the presence of direct search: an examination of switching costs and obsolescence of information

This paper investigates patterns of revenues earned by an intermediary that matches buyers and sellers in the presence of direct search markets. We develop a theoretical structure and a computer simulation model of such a marketplace where vendors are horizontally differentiated, and an intermediary matches clients to the optimal vendor for a fee. The model is applicable to information services such as application service providers (ASPs). The contribution of this paper is the identification of scenarios under which intermediaries that match clients and vendors are likely to be profitable considering switching costs and obsolescence of information.

Hospital capacity planning for efficient disaster mitigation during a bioterrorist attack

Effective hospital capacity planning can not only significantly enhance the capability and effectiveness of the treatment provided to patients during a bioterrorist attack but can also provide critical information. While a lot of work has been done to model hospital capacity estimates for natural disasters the same cannot be said for manmade biological disasters like anthrax or smallpox. In this paper, we develop a generic simulation model of hospital capacity planning during a bioterrorist attack. We model both cases in which the occurrence of the attack and the type of agent used are known as well as when they are not known. The model is also unique in developing a feedback loop to alert emergency management officials about the occurrence and type of an attack. Our results are able to pinpoint the characteristics of the hospitals that are most relevant at various stages of exposure and provide policy recommendations.

Competition in Mediation Services: Modeling the Role of Expertise, Satisfaction and Switching Costs

This article analyzes competition among mediation service providers that match clients and vendors in a horizontally differentiated market. This is an issue that is important for decision support of mediators in determining pricing and service strategies. We present a simulation model to simultaneously represent search as well as the behaviors of clients, vendors, and multiple competing mediators. Among our findings: intermediaries find it optimal to offer registration fee incentives and derive revenues from transaction fees from successful matches; as switching costs increase, incumbent utilities increase and entrant utilities decrease; expertise, modeled as the ability of mediators to assess vendor attributes accurately, is a powerful competitive weapon for entrants to erode the incumbent intermediarys first mover advantage. On the other hand, client satisfaction is an instrument for an incumbent intermediary to deter entrance by competitors.

Using Dynamic Graph Matching and Gravity Models for Early Detection of Bioterrorist Attacks

Timely detection of a bioterrorist attack is of profound significance for efficient emergency public health management. Various systems currently exist which are capable of detecting the biologic agents prior to (e.g. biosensors) and after exposure (syndromic surveillance) but suffer from limitations like high cost and false positives (Stoto et al., Williams). In this paper, we use novel dynamic graph matching and gravity models to formulate a more precise and efficient methodology for detection. The problem is complicated by the similarity of anthrax and small pox symptoms to common diseases like influenza, chickenpox, airborne characteristics of these agents (that increases the risk of infection spreading to proximal regions), and non uniform distribution of terrorism risk among areas belonging to the same region. Our methodology will analyze patient symptom data available at hospitals using dynamic graph matching algorithms. We propose a heuristic that dynamically updates the template graphs based on patient data before applying matching algorithms, a unique feature of this study. Successful matches will be used to update counters that generate alerts once the counters surpass the threshold values. We develop a heuristic that uses a gravity model to group hospitals in a region into clusters based on the population they serve. Hospitals grouped together as a cluster affect counters that are local to the population they serve and generate alarms to the Public Health Department when they surpass the set threshold values. In addition, we use the fact that some symptoms are unique to these agents to make our algorithms more robust. These models could be used to develop practical applications for agencies such as DHS due to its ability to increase not just the likelihood of detection of a bioterrorism attack but also to identify with greater precision the location(s) of the attack. With minor modification they could also be used to plan for other disasters/epidemics such as SARS, and bird flu.

Investments in Health and the Retirement Decision

This paper develops the first formal analysis of this length of retired life by incorporating retirement and Social Security in a simple model of health investment. We identify the determinants of length of retired life and find important interdependencies between length of life and retirement age (some of it identified in recent literature (Waldron 2001)). Using data from the Longitudinal Retirement History Survey, we find that greater benefits increase age at death by about half as much as those greater benefits increase the age at retirement. Thus, increases in Social Security benefits reduce the typical period spent as a beneficiary.