A Market Scheduling for New Normal Logistics in the Wake of Corona Virus Diseases-19 in Bandung City

Recent developments on the worldwide spread of Corona Virus Diseases-19 (COVID-19) show the vulnerability of human beings to pandemic risks in terms of biological, social, and economic factors. While human lives are the most important factor, a proposed solution dealing with pandemics should be sustainable which also includes other factors. Quarantines and physical distancing have been seen as effective ways to slow down the spread of COVID-19.  We therefore propose a market scheduling model with multi-objectives to support physical distancing minimizing the number of people in a certain area in a given time (crowds) and minimizing the virus spread rates. An analytical model is proposed and solved for Bandung City. The results show some promising ideas on how to slow down the virus spread without compromising both health and economic objectives. The future potential research of the model is also presented

A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels

<p>Abstract</p> <p>Background</p> <p>In recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several <it>concerns </it>about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these <it>concerns </it>and identify means of enhancing the current models for higher operational use.</p> <p>Methods</p> <p>We surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers.</p> <p>Results</p> <p>While examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values.</p> <p>Conclusions</p> <p>To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility.</p

People's perception and cost effectiveness of home confinement during an influenza pandemic evidence from the French case

Online firstIn France, home confinement is not a common preventive measure against an influenza pandemic, although it is used around the world. Based on a stated method approach, we analyze the attitude that the French would adopt if this measure were put in place. Next, we propose a cost–benefit analysis to discuss the cost-effectiveness of this measure. We find that over three-quarters of respondents report complying with home confinement. Their choice depends on their individual characteristics, the interaction they may have with an infected person and home confinement conditions, but not their experience with preventive measures. We find that behaviors such as sensitivity to certainty, selfishness and altruism emerge. As far as cost-effectiveness is concerned, our study shows that home confinement is a prevention path that should not be neglected and should even be prescribed.Bien que le confinement soit utilisé dans le monde entier comme mesure préventive contre une pandémie de grippe, en France, il n'est encore pas très employé. Nous analysons l'attitude que les Français adopteraient si cette mesure était mise en place. Ensuite, nous proposons une analyse pour discuter du rapport coût-efficacité de cette mesure. Nous obtenons que plus des trois quarts des répondants déclarent qu'ils se conformeraient au confinement. Leur choix dépend de leurs caractéristiques individuelles, de l'interaction qu'ils peuvent avoir avec une personne infectée et des conditions de confinement, mais pas de leur expérience des mesures préventives. Nous constatons que des comportements tels que la sensibilité à la certitude, l'égoïsme et l'altruisme émergent. En ce qui concerne le rapport coût-efficacité, notre étude montre que le confinement est une voie de prévention qui ne devrait pas être négligée et devrait même être prescrite

Literature Review - the vaccine supply chain

Vaccination is one of the most effective ways to prevent the outbreak of an infectious disease. This medical intervention also brings about many logistical quest

Dose-optimal vaccine allocation over multiple populations

For a large number of infectious diseases, vaccination is the most effective way to prevent an epidemic. However, the vaccine stockpile is hardly ever sufficient to treat the entire population, which brings about the challenge of vaccine allocation. To aid decision makers facing this challenge, we provide insights into the structure of this problem. We first investigate the dependence of health benefit on the fraction of people that receive vaccination, where we define health benefit as the total number of people that escape infection. We start with the seminal SIR compartmental model. Using implicit function analysis, we prove the existence of a unique vaccination fraction that maxi- mizes the health benefit per dose of vaccine, and that the health benefit per dose of vaccine decreases monotonically when moving away from this fraction in either direc- tion. Surprisingly, this fraction does not coincide with the so-called critical vaccination coverage that has been advocated in literature. We extend these insights to other compartmental models such as the SEIR model. These results allow us to provide new insights into vaccine allocation to multiple non-interacting or weakly interacting populations. We explain the counter-intuitive switching behavior of optimal allocation. We show that allocations that maximize health benefits are rarely equitable, while equitable allocations may be significantly non-optimal

Applications of stochastic modeling and data analytics techniques in healthcare decision making

We present approaches utilizing aspects of data analytics and stochastic modeling techniques and applied to various areas in healthcare. In general, the thesis has composed of three major components. Firtsly, we propose a comparison analysis between two of the very well-known infectious disease modeling techniques to derive effective vaccine allocation strategies. This study, has emerged from the fact that individuals are prioritized based on their risk profiles when allocating limited vaccine stocks during an influenza pandemic. Computationally expensive but realistic agent-based simulations and fast but stylized compartmental models are typically used to derive effective vaccine allocation strategies. A detailed comparison of these two approaches, however, is often omitted. We derive age-specific vaccine allocation strategies to mitigate a pandemic influenza outbreak in Seattle by applying derivative-free optimization to an agent-based simulation and also to a compartmental model. We compare the strategies derived by these two approaches under various infection aggressiveness and vaccine coverage scenarios. We observe that both approaches primarily vaccinate school children, however they may allocate the remaining vaccines in different ways. The vaccine allocation strategies derived by using the agent-based simulation are associated with up to 70% decrease in total cost and 34% reduction in the number of infections compared to the strategies derived by the compartmental model. Nevertheless, the latter approach may still be competitive for very low and/or very high infection aggressiveness. Our results provide insights about the possible differences between the vaccine allocation strategies derived by using agent-based simulations and those derived by using compartmental models. Secondly, we introduce a novel and holistic scheme to capture the gradual amyotrophic lateral sclerosis progression based on the critical events referred as tollgates. Amyotrophic lateral sclerosis is neuro-degenerative and terminal disease. Patients with amyotrophic lateral sclerosis lose control of voluntary movements over time due to continuous degeneration of motor neurons. Using a comprehensive longitudinal dataset from Mayo Clinic’s ALS Clinic in Rochester, MN, we characterize the progression through tollgates at the body segment (e.g., arm, leg, speech, swallowing, breathing) and patient levels over time. We describe how the progression based on the followed tollgate pathways varies among patients and ultimately, how this type of progression characterization may be utilized for further studies. Kaplan-Meier analysis are conducted to derive the probability of passing each tollgate over time. We observe that, in each body segment, the majority of the patients have their abilities affected or worse (Level1) at the first visit. Especially, the proportion of patients at higher tollgate levels is larger for arm and leg segments compared to others. For each segment, we derive the over-time progression pathways of patients in terms of the reached tollgates. Tollgates towards later visits show a great diversity among patients who were at the same tollgate level at the first clinic visit. The proposed tollgate mechanism well captures the variability among patients and the history plays a role on when patients reach tollgates. We suggest that further and comprehensive studies should be conducted to observe the whole effect of the history in the future progression. Thirdly, based on the fact that many available databases may not have detailed medical records to derive the necessary data, we propose a classification-based approach to estimate the tollgate data using ALSFRS-R scores which are available in most databases. We observed that tollgates are significantly associated with the ALSFRS-R scores. Multiclass classification techniques are commonly used in such problem; however, traditional classification techniques are not applicable to the problem of finding the tollgates due to the constraint of that a patients’ tollgates under a specific segment for multiple visit should be non-decreasing over time. Therefore, we propose two approaches to achieve a multi-class estimation in a non-decreasing manner given a classification method. While the first approach fixes the class estimates of observation in a sequential manner, the second approach utilizes a mixed integer programming model to estimate all the classes of a patients’ observations. We used five different multi-class classification techniques to be employed by both of the above implementations. Thus, we investigate the performance of classification model employed under both approaches for each body segment

Resource Allocation Models in Healthcare Decision Making

We present models for allocating limited healthcare resources efficiently among target populations in order to maximize society's welfare and/or minimize the expected costs. In general, this thesis is composed of two major parts. Firstly, we formulate a novel uncapacitated fixed-charge location problem which considers the preferences of customers and the reliability of facilities simultaneously. A central planner selects facility locations from a set of candidate sites to minimize the total cost of opening facilities and providing service. Each customer has a strict preference order over a subset of the candidate sites, and uses her most preferred available facility. If that facility fails due to a disruptive event, the customer attends her next preferred available facility. This model bridges the gap between the location models that consider the preferences of customers and the ones that consider the reliability of facilities. It applies to many healthcare settings, such as preventive care clinics, senior centers, and disaster response centers. In such situations, patient (or customer) preferences vary significantly. Therefore, there could be a large number of subgroups within the population depending on their preferences of potential facility sites. In practice, solving problems with large numbers of population subgroups is very important to increase granularity when considering diverse preferences of several different customer types. We develop a Lagrangian branch-and-bound algorithm and a branch-and-cut algorithm. We also propose valid inequalities to tighten the LP relaxation of the model. Our numerical experiments show that the proposed solution algorithms are efficient, and can be applied to problems with extremely large numbers of customers. Secondly, we study the allocation of colorectal cancer (CRC) screening resources among individuals in a population. CRC can be early-detected, and even prevented, by undergoing periodic cancer screenings via colonoscopy. Current guidelines are based on existing medical evidence, and do not explicitly consider (i) all possible alternative screening policies, and (ii) the effect of limited capacity of colonoscopy screening on the economic feasibility of the screening program. We consider the problem of allocating limited colonoscopy capacity for CRC screening and surveillance to a population composed of patients of different risk groups based on risk factors including age, CRC history, etc. We develop a mixed integer program that maximizes the quality-adjusted life years for a given patient population considering the population's demographics, CRC progression dynamics, and relevant constraints on the system capacity and the screening program effectiveness. We show that the current guidelines are not always optimal. In general, when screening capacity is high, the optimal screening programs recommend higher screening rates than the current guidelines, and the optimal screening policies change with age and gender. This shows the significance of incorporating screening capacity into the decisions of optimal screening policies