Dynamic optimization model for allocating medical resources in epidemic controlling

Purpose: The model proposed in this paper addresses a dynamic optimization model for allocating medical resources in epidemic controlling. Design/methodology/approach: In this work, a three-level and dynamic linear programming model for allocating medical resources based on epidemic diffusion model is proposed. The epidemic diffusion model is used to construct the forecasting mechanism for dynamic demand of medical resources. Heuristic algorithm coupled with MTLAB mathematical programming solver is adopted to solve the model. A numerical example is presented for testing the model’s practical applicability. Findings: The main contribution of the present study is that a discrete time-space network model to study the medical resources allocation problem when an epidemic outbreak is formulated. It takes consideration of the time evolution and dynamic nature of the demand, which is different from most existing researches on medical resources allocation. Practical implications: In our model, the medicine logistics operation problem has been decomposed into several mutually correlated sub-problems, and then be solved systematically in the same decision scheme. Thus, the result will be much more suitable for real operations. Originality/value: In our model, the rationale that the medical resources allocated in early periods will take effect in subduing the spread of the epidemic spread and thus impact the demand in later periods has been for the first time incorporated. A win-win emergency rescue effect is achieved by the integrated and dynamic optimization model. The total rescue cost is controlled effectively, and meanwhile, inventory level in each urban health departments is restored and raised gradually.Peer Reviewe

Optimal logistics scheduling with dynamic information in emergency response: case studies for humanitarian objectives

The mathematical model of infectious disease is a typical problem in mathematical modeling, and the common infectious disease models include the susceptible-infected (SI) model, the susceptible-infected-recovered model (SIR), the susceptible-infected-recovered-susceptible model (SIRS) and the susceptible-exposed-infected-recovered (SEIR) model. These models can be used to predict the impact of regional return to work after the epidemic. In this paper, we use the SEIR model to solve the dynamic medicine demand information in humanitarian relief phase. A multistage mixed integer programming model for the humanitarian logistics and transport resource is proposed. The objective functions of the model include delay cost and minimum running time in the time-space network. The model describes that how to distribute and deliver medicine resources from supply locations to demand locations with an efficient and lower-cost way through a transportation network. The linear programming problem is solved by the proposed Benders decomposition algorithm. Finally, we use two cases to calculate model and algorithm. The results of the case prove the validity of the model and algorithm

New Spatio-temporal Hawkes Process Models For Social Good

Indiana University-Purdue University Indianapolis (IUPUI)As more and more datasets with self-exciting properties become available, the demand for robust models that capture contagion across events is also getting stronger. Hawkes processes stand out given their ability to capture a wide range of contagion and self-excitation patterns, including the transmission of infectious disease, earthquake aftershock distributions, near-repeat crime patterns, and overdose clusters. The Hawkes process is flexible in modeling these various applications through parametric and non-parametric kernels that model event dependencies in space, time and on networks. In this thesis, we develop new frameworks that integrate Hawkes Process models with multi-armed bandit algorithms, high dimensional marks, and high-dimensional auxiliary data to solve problems in search and rescue, forecasting infectious disease, and early detection of overdose spikes. In Chapter 3, we develop a method applications to the crisis of increasing overdose mortality over the last decade. We first encode the molecular substructures found in a drug overdose toxicology report. We then cluster these overdose encodings into different overdose categories and model these categories with spatio-temporal multivariate Hawkes processes. Our results demonstrate that the proposed methodology can improve estimation of the magnitude of an overdose spike based on the substances found in an initial overdose. In Chapter 4, we build a framework for multi-armed bandit problems arising in event detection where the underlying process is self-exciting. We derive the expected number of events for Hawkes processes given a parametric model for the intensity and then analyze the regret bound of a Hawkes process UCB-normal algorithm. By introducing the Hawkes Processes modeling into the upper confidence bound construction, our models can detect more events of interest under the multi-armed bandit problem setting. We apply the Hawkes bandit model to spatio-temporal data on crime events and earthquake aftershocks. We show that the model can quickly learn to detect hotspot regions, when events are unobserved, while striking a balance between exploitation and exploration. In Chapter 5, we present a new spatio-temporal framework for integrating Hawkes processes with multi-armed bandit algorithms. Compared to the methods proposed in Chapter 4, the upper confidence bound is constructed through Bayesian estimation of a spatial Hawkes process to balance the trade-off between exploiting and exploring geographic regions. The model is validated through simulated datasets and real-world datasets such as flooding events and improvised explosive devices (IEDs) attack records. The experimental results show that our model outperforms baseline spatial MAB algorithms through rewards and ranking metrics. In Chapter 6, we demonstrate that the Hawkes process is a powerful tool to model the infectious disease transmission. We develop models using Hawkes processes with spatial-temporal covariates to forecast COVID-19 transmission at the county level. In the proposed framework, we show how to estimate the dynamic reproduction number of the virus within an EM algorithm through a regression on Google mobility indices. We also include demographic covariates as spatial information to enhance the accuracy. Such an approach is tested on both short-term and long-term forecasting tasks. The results show that the Hawkes process outperforms several benchmark models published in a public forecast repository. The model also provides insights on important covariates and mobility that impact COVID-19 transmission in the U.S. Finally, in chapter 7, we discuss implications of the research and future research directions

Depression, Anxiety and Stress among Students amidst COVID-19 Pandemic: A Cross-Sectional Study in Philippines

Background: COVID-19 considered as a global health crisis poses mental health problems among individual and specifics to students. Purpose: This study aimed to determine the depression, anxiety, and stress levels among students amidst COVID-19 pandemic in Philippines. Methods: A cross-sectional study was undertaken in Region 8, Eastern Visayas Philippines. A total of 311 tertiary respondents randomly selected both private and government owned higher education institutions. The data compilation was done using online questionnaires through Google Forms with validated version of the Stress, Anxiety and Depression Scales 21 (DASS21) is used to calculate students’ level of stress, anxiety and, depression with their socio-demographic features Results: We revealed that depression, anxiety and stress are instituted in 18.6 %, 35.1 % and 2.85% of students, respectively, amidst the COVID – 19 pandemics. The symptoms of disorders were moderate to extremely severe in 6.1%, 23.5%, and 0.6% of the study sample, respectively. Age, gender, marital status and family history of illness are significantly different with age group 20 and below, females, singles, and families with no history of illness displaying high level of anxiety. Results also established an association between anxiety and family’s monthly income and history of illness. The higher the monthly income and no presence of illness of families, the more anxious the person. Conclusion: Finally, the variables used, explained only 1.5% depression, 3.4% anxiety and 1.4% stress in this time of COVID-19 outbreak. It is therefore recommended to essentially develop community-based mental health program for preventive purposes

Stochastic programming and agent-based simulation approaches for epidemics control and logistics planning

This dissertation addresses the resource allocation challenges of fighting against infectious disease outbreaks. The goal of this dissertation is to formulate multi-stage stochastic programming and agent-based models to address the limitations of former literature in optimizing resource allocation for preventing and controlling epidemics and pandemics. In the first study, a multi-stage stochastic programming compartmental model is presented to integrate the uncertain disease progression and the logistics of resource allocation to control a highly contagious infectious disease. The proposed multi-stage stochastic program, which involves various disease growth scenarios, optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals due to an epidemic. Two new equity metrics are defined and formulated, namely infection and capacity equity, to explicitly consider equity for allocating treatment funds and facilities for fair resource allocation in epidemics control. The multi-stage value of the stochastic solution (VSS), demonstrating the superiority of the proposed stochastic programming model over its deterministic counterpart, is studied. The first model is applied to the Ebola Virus Disease (EVD) case in West Africa, including Guinea, Sierra Leone, and Liberia. In the following study, the previous model is extended to a mean-risk multi-stage vaccine allocation model to capture the influence of the outbreak scenarios with low probability but high impact. The Conditional Value at Risk (CVaR) measure used in the model enables a trade-off between the weighted expected loss due to the outbreak and expected risks associated with experiencing disastrous epidemic scenarios. A method is developed to estimate the migration rate between each infected region when limited migration data is available. The second study is applied to the case of EVD in the Democratic Republic of the Congo. In the third study, a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental stochastic programming model is developed to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through deriving a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. In the fourth study, a simulation-optimization approach is introduced to address the vaccination facility location and allocation challenges of the COVID-19 vaccines. A detailed agent-based simulation model of the COVID-19 is extended and integrated with a new vaccination center and vaccine-allocation optimization model. The proposed agent-based simulation-optimization framework simulates the disease transmission first and then minimizes the total number of infections over all the considered regions by choosing the optimal vaccine center locations and vaccine allocation to those centers. Specifically, the simulation provides the number of susceptible and infected individuals in each geographical region for the current time period as an input into the optimization model. The optimization model then minimizes the total number of estimated infections and provides the new vaccine center locations and vaccine allocation decisions for the following time period. Decisions are made on where to open vaccination centers and how many people should be vaccinated at each future stage in each region of the considered geographical location. Then these optimal decision values are imported back into the simulation model to simulate the number of susceptible and infected individuals for the subsequent periods. The agent-based simulation-optimization framework is applied to controlling COVID-19 in the states of New Jersey. The results provide insights into the optimal vaccine center location and vaccine allocation problem under varying budgets and vaccine types while foreseeing potential epidemic growth scenarios over time and spatial locations

Supply Chain Operations Management in Pandemics: A State-of-the-Art Review Inspired by COVID-19

Pandemics cause chaotic situations in supply chains (SC) around the globe, which can lead towards survivability challenges. The ongoing COVID-19 pandemic is an unprecedented humanitarian crisis that has severely affected global business dynamics. Similar vulnerabilities have been caused by other outbreaks in the past. In these terms, prevention strategies against propagating disruptions require vigilant goal conceptualization and roadmaps. In this respect, there is a need to explore supply chain operation management strategies to overcome the challenges that emerge due to COVID-19-like situations. Therefore, this review is aimed at exploring such challenges and developing strategies for sustainability, and viability perspectives for SCs, through a structured literature review (SLR) approach. Moreover, this study investigated the impacts of previous epidemic outbreaks on SCs, to identify the research objectives, methodological approaches, and implications for SCs. The study also explored the impacts of epidemic outbreaks on the business environment, in terms of effective resource allocation, supply and demand disruptions, and transportation network optimization, through operations management techniques. Furthermore, this article structured a framework that emphasizes the integration of Industry 4.0 technologies, resilience strategies, and sustainability to overcome SC challenges during pandemics. Finally, future research avenues were identified by including a research agenda for experts and practitioners to develop new pathways to get out of the crisis.</jats:p

Radiation medicine

ПОСОБИЯРАДИАЦИОННАЯ ГИГИЕНАTUTORIALRADIATION MEDICINEThe content of the tutorial "Radiation medicine" for students of high medical educational establishments corresponds with the basic educational plan and program, proved by Ministry of Health Care of the Republic of Belarus. The tutorial is prepared for students of medical, pharmaceutical, stomatological, medical-preventive, medical-diagnostic faculties of institutes of higher education. Соответствует основному учебному плану и программе, утвержденным Министерством здравоохранения Республики Беларусь. Для студентов медицинских, фармацевтических, стоматологических, лечебно-профилактических, лечебно-диагностических факультетов высших учебных заведений