Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic.

Influenza and COVID-19 are infectious diseases with significant burdens. Information and awareness on preventative techniques can be spread through the use of social media, which has become an increasingly utilized tool in recent years. This study developed a dynamic transmission model to investigate the impact of social media, particularly tweets via the social networking platform, Twitter on the number of influenza and COVID-19 cases of infection and deaths. We modified the traditional Susceptible-Exposed-Infectious-Recovered (SEIR-V) model with an additional social media component, in order to increase the accuracy of transmission dynamics and gain insight on whether social media is a beneficial behavioral intervention for these infectious diseases. The analysis found that social media has a positive effect in mitigating the spread of contagious disease in terms of peak time, peak magnitude, total infected, and total death; and the results also showed that social media's effect has a non-linear relationship with the reproduction number R 0 and it will be amplified when a vaccine is available. The findings indicate that social media is an integral part in the humanitarian logistics of pandemic and emergency preparedness, and contributes to the literature by informing best practices in the response to similar disasters.

Scenario-robust pre-disaster planning for multiple relief items.

The increasing vulnerability of the population from frequent disasters requires quick and effective responses to provide the required relief through effective humanitarian supply chain distribution networks. We develop scenario-robust optimization models for stocking multiple disaster relief items at strategic facility locations for disaster response. Our models improve the robustness of solutions by easing the difficult, and usually impossible, task of providing exact probability distributions for uncertain parameters in a stochastic programming model. Our models allow decision makers to specify uncertainty parameters (i.e., point and probability estimates) based on their degrees of knowledge, using distribution-free uncertainty sets in the form of ranges. The applicability of our generalized approach is illustrated via a case study of hurricane preparedness in the Southeastern United States. In addition, we conduct simulation studies to show the effectiveness of our approach when conditions deviate from the model assumptions.

Method to allocate voting resources with unequal ballots and/or education.

Apportionment in election systems refers to determination of the number of voting resources (poll books, poll workers, or voting machines) needed to ensure that all voters can expect to wait no longer than an appropriate amount, even the voter who waits the longest. Apportionment is a common problem for election officials and legislatures. A related problem is "allocation," which relates to the deployment of an existing number of resources so that the longest expected wait is held to an appropritate amount. Provisioning and allocation are difficult because the numbers of expected voters, the ballot lengths, and the education levels of voters may all differ significantly from precinct-to-precinct in a county. Consider that predicting the waiting time of the voter who waits the longest generally requires discrete event simulation.•The methods here rigorously guarantee that all voters expect to wait a prescribed time with a bounded probability, e.g., everyone expects to wait less than thirty minutes with probability greater than 95%.•The methods here can handle both a single type of resource (e.g., voting machines or scan machines) and multiple resource types (e.g., voting machines and poll books).•The methods are provided in a freely available, easy-to-use Excel software program.

Optimizing Tele-ICU Operational Efficiency Through Workflow Process Modeling and Restructuring.

Little is known on how to best prioritize various tele-ICU specific tasks and workflows to maximize operational efficiency. We set out to: 1) develop an operational model that accurately reflects tele-ICU workflows at baseline, 2) identify workflow changes that optimize operational efficiency through discrete-event simulation and multi-class priority queuing modeling, and 3) implement the predicted favorable workflow changes and validate the simulation model through prospective correlation of actual-to-predicted change in performance measures linked to patient outcomes. SETTING: Tele-ICU of a large healthcare system in New York State covering nine ICUs across the spectrum of adult critical care. PATIENTS: Seven-thousand three-hundred eighty-seven adult critically ill patients admitted to a system ICU (1,155 patients pre-intervention in 2016Q1 and 6,232 patients post-intervention 2016Q3 to 2017Q2). INTERVENTIONS: Change in tele-ICU workflow process structure and hierarchical process priority based on discrete-event simulation. MEASUREMENTS AND MAIN RESULTS: Our discrete-event simulation model accurately reflected the actual baseline average time to first video assessment by both the tele-ICU intensivist (simulated 132.8 ± 6.7 min vs 132 ± 12.2 min actual) and the tele-ICU nurse (simulated 128.4 ± 7.6 min vs 123 ± 9.8 min actual). For a simultaneous priority and process change, the model simulated a reduction in average TVFA to 51.3 ± 1.6 min (tele-ICU intensivist) and 50.7 ± 2.1 min (tele-ICU nurse), less than the added simulated reductions for each change alone, suggesting correlation of the changes to some degree. Subsequently implementing both changes simultaneously resulted in actual reductions in average time to first video assessment to values within the 95% CIs of the simulations (50 ± 5.5 min for tele-intensivists and 49 ± 3.9 min for tele-nurses). CONCLUSIONS: Discrete-event simulation can accurately predict the effects of contemplated multidisciplinary tele-ICU workflow changes. The value of workflow process and task priority modeling is likely to increase with increasing operational complexities and interdependencies.

A model to create an efficient and equitable admission policy for patients arriving to the cardiothoracic ICU.

OBJECTIVE: To develop queuing and simulation-based models to understand the relationship between ICU bed availability and operating room schedule to maximize the use of critical care resources and minimize case cancellation while providing equity to patients and surgeons. DESIGN: Retrospective analysis of 6-month unit admission data from a cohort of cardiothoracic surgical patients, to create queuing and simulation-based models of ICU bed flow. Three different admission policies (current admission policy, shortest-processing-time policy, and a dynamic policy) were then analyzed using simulation models, representing 10 yr worth of potential admissions. Important output data consisted of the "average waiting time," a proxy for unit efficiency, and the "maximum waiting time," a surrogate for patient equity. SETTING: A cardiothoracic surgical ICU in a tertiary center in New York, NY. PATIENTS: Six hundred thirty consecutive cardiothoracic surgical patients admitted to the cardiothoracic surgical ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Although the shortest-processing-time admission policy performs best in terms of unit efficiency (0.4612 days), it did so at expense of patient equity prolonging surgical waiting time by as much as 21 days. The current policy gives the greatest equity but causes inefficiency in unit bed-flow (0.5033 days). The dynamic policy performs at a level (0.4997 days) 8.3% below that of the shortest-processing-time in average waiting time; however, it balances this with greater patient equity (maximum waiting time could be shortened by 4 days compared to the current policy). CONCLUSIONS: Queuing theory and computer simulation can be used to model case flow through a cardiothoracic operating room and ICU. A dynamic admission policy that looks at current waiting time and expected ICU length of stay allows for increased equity between patients with only minimum losses of efficiency. This dynamic admission policy would seem to be a superior in maximizing case-flow. These results may be generalized to other surgical ICUs.