Simulation-based what-if analysis for controlling the spread of Covid-19 in universities

Forecasting and what-if analysis of new positive COVID-19 cases during the first three waves in Italy

The joint exploitation of data related to epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms can support the development of predictive models that can be used to forecast new positive cases and study the impact of more or less severe restrictions. In this work, we integrate heterogeneous data from several sources and solve a multivariate time series forecasting task, specifically targeting the Italian case at both national and regional levels, during the first three waves of the pandemic. The goal is to build a robust predictive model to predict the number of new cases over a given time horizon so that any restrictive actions can be better planned. In addition, we perform a what-if analysis based on the best-identified predictive models to evaluate the impact of specific restrictions on the trend of positive cases. Our focus on the first three waves is motivated by the fact that it represents a typical emergency scenario (when no stable cure or vaccine is available) that may occur when a new pandemic spreads. Our experimental results prove that exploiting the considered heterogeneous data leads to accurate predictive models, reaching a WAPE of 5.75% at the national level. Furthermore, in the subsequent what-if analysis, we observed that strong all-in-one initiatives, such as total lockdowns, may not be adequate, while more specific and targeted solutions should be adopted. The developed models can help policy and decision-makers better plan intervention strategies and retrospectively analyze the effects of the decisions made at different scales. Joint exploitation of data on epidemiological, mobility, and restriction aspects of COVID-19 with machine learning algorithms to learn predictive models to forecast new positive cases.

Monitoring and responding to emerging infectious diseases in a university setting: A case study using COVID-19

Emerging infection diseases (EIDs) are an increasing threat to global public health, especially when the disease is newly emerging. Institutions of higher education (IHEs) are particularly vulnerable to EIDs because student populations frequently share high-density residences and strongly mix with local and distant populations. In fall 2020, IHEs responded to a novel EID, COVID-19. Here, we describe Quinnipiac University's response to SARS-CoV-2 and evaluate its effectiveness through empirical data and model results. Using an agent-based model to approximate disease dynamics in the student body, the University established a policy of dedensification, universal masking, surveillance testing via a targeted sampling design, and app-based symptom monitoring. After an extended period of low incidence, the infection rate grew through October, likely due to growing incidence rates in the surrounding community. A super-spreader event at the end of October caused a spike in cases in November. Student violations of the University's policies contributed to this event, but lax adherence to state health laws in the community may have also contributed. The model results further suggest that the infection rate was sensitive to the rate of imported infections and was disproportionately impacted by non-residential students, a result supported by the observed data. Collectively, this suggests that campus-community interactions play a major role in campus disease dynamics. Further model results suggest that app-based symptom monitoring may have been an important regulator of the University's incidence, likely because it quarantined infectious students without necessitating test results. Targeted sampling had no substantial advantages over simple random sampling when the model incorporated contact tracing and app-based symptom monitoring but reduced the upper boundary on 90% prediction intervals for cumulative infections when either was removed. Thus, targeted sampling designs for surveillance testing may mitigate worst-case outcomes when other interventions are less effective. The results' implications for future EIDs are discussed.

Perceived COVID-19 threat, perceived healthcare system inequities, personal experiences of healthcare discrimination and their associations with COVID-19 preventive behavioral intentions among college students in the U.S

College students are often reluctant to follow U.S. preventive guidelines to lower their risk of COVID-19 infection, despite an increased risk of transmission in college settings. Prior research suggested that college students who perceived greater COVID-19 severity and susceptibility (i.e., COVID-19 threat) were more likely to engage in COVID-19 preventive behaviors, yet there is limited research examining whether perceived COVID-19 threat, perceived U.S. healthcare system inequities, and personal experiences of healthcare discrimination collectively influence college students' COVID-19 preventive behaviors. This study identified latent classes of perceived COVID-19 threat, perceived U.S. healthcare system inequities, and personal experiences of healthcare discrimination, examined whether latent classes were associated with COVID-19 preventive behavioral intentions, and assessed whether latent class membership varied across racial/ethnic groups.Students from the University of Maryland, College Park (N = 432) completed the Weighing Factors in COVID-19 Health Decisions survey (December 2020-December 2021). Latent class analysis identified latent classes based on perceived COVID-19 threat, perceived U.S. healthcare system inequities, and personal experiences of healthcare discrimination. Regression analyses examined associations between the latent classes and COVID-19 preventive behavioral intentions (i.e., social distancing, mask-wearing, COVID-19 vaccination) and whether latent class membership varied across racial/ethnic groups.Students in Latent Class 1 (27.3% of the sample) had high perceived COVID-19 threat and U.S. healthcare system inequities and medium probability of experiencing personal healthcare discrimination. Students in Latent Class 1 had higher social distancing, mask-wearing, and vaccination intentions compared to other latent classes. Compared to Latent Class 4 (reference group), students in Latent Class 1 had higher odds of identifying as Hispanic or Latino, Non-Hispanic Asian, Non-Hispanic Black or African American, and Non-Hispanic Multiracial versus Non-Hispanic White.Latent classes of higher perceived COVID-19 threat, perceived U.S. healthcare system inequities, and personal experiences of healthcare discrimination were associated with higher COVID-19 preventive behavioral intentions and latent class membership varied across racial/ethnic groups. Interventions should emphasize the importance of COVID-19 preventive behaviors among students who perceive lower COVID-19 threat.

COVID-19 dynamics in Madrid (Spain): A new convolutional model to find out the missing information during the first three waves

This article presents a novel mathematical model to describe the spread of an infectious disease in the presence of social and health events: it uses 15 compartments, 7 convolution integrals and 4 types of infected individuals, asymptomatic, mild, moderate and severe. A unique feature of this work is that the convolutions and the compartments have been selected to maximize the number of independent input parameters, leading to a 56-parameter model where only one had to evolve over time. The results show that 1) the proposed mathematical model is flexible and robust enough to describe the complex dynamic of the pandemic during the first three waves of the COVID-19 spread in the region of Madrid (Spain) and 2) the proposed model allows us to calculate the number of asymptomatic individuals and the number of persons who presented antibodies during the first waves. The study shows that the following results are compatible with the reported data: close to 28% of the infected individuals were asymptomatic during the three waves, close to 29% of asymptomatic individuals were detected during the subsequent waves and close to 26% of the Madrid population had antibodies at the end of the third wave. This calculated number of persons with antibodies is in great agreement with four direct measurements obtained from an independent sero-epidemiological research. In addition, six calculated curves (total number of confirmed cases, asymptomatic who are confirmed as positive, hospital admissions and discharges and intensive care units admissions) show good agreement with data from an epidemiological surveillance database.

Assessment of the Effectiveness of Omicron Transmission Mitigation Strategies for European Universities Using an Agent-Based Network Model

BACKGROUND

Returning universities to full on-campus operations while the coronavirus disease 2019 pandemic is ongoing has been a controversial discussion in many countries. The risk of large outbreaks in dense course settings is contrasted by the benefits of in-person teaching. Transmission risk depends on a range of parameters, such as vaccination coverage and efficacy, number of contacts, and adoption of nonpharmaceutical intervention measures. Owing to the generalized academic freedom in Europe, many universities are asked to autonomously decide on and implement intervention measures and regulate on-campus operations. In the context of rapidly changing vaccination coverage and parameters of the virus, universities often lack sufficient scientific insight on which to base these decisions.

METHODS

To address this problem, we analyzed a calibrated, data-driven agent-based simulation of transmission dynamics among 13 284 students and 1482 faculty members in a medium-sized European university. Wed use a colocation network reconstructed from student enrollment data and calibrate transmission risk based on outbreak size distributions in education institutions. We focused on actionable interventions that are part of the already existing decision process of universities to provide guidance for concrete policy decisions.

RESULTS

Here we show that, with the Omicron variant of the severe acute respiratory syndrome coronavirus 2, even a reduction to 25% occupancy and universal mask mandates are not enough to prevent large outbreaks, given the vaccination coverage of about 85% reported for students in Austria.

CONCLUSIONS

Our results show that controlling the spread of the virus with available vaccines in combination with nonpharmaceutical intervention measures is not feasible in the university setting if presence of students and faculty on campus is required.

Intelligent Decision-Support System for Epidemiological Diagnostics. II. Information Technologies Development*, *

The article projects the components of the intelligent decision support system for epidemiological diagnostics and investigates their interaction with the user. The system includes a bank of models and machine learning methods, a bank of population dynamics models, visualization and reporting tools, and management decision-making unit. The concept of information technology to ensure biosafety of the population is provided. A model of specified information technology use cases is developed and a sequence diagram is constructed. A model of information technology components and ways of their deployment on a server are proposed.

Effect of mandating vaccination on COVID-19 cases in colleges and universities

BACKGROUND

With the introduction of COVID-19 vaccines, many colleges and universities decided to mandate vaccination for all students and employees. The objective of this paper is to empirically investigate the effect of the mandate policy on Fall 2021 COVID-19 cases in institutions of higher education.

METHOD

We construct a unique dataset of a sample of 94 colleges and universities in the east and southeast regions of the United States, 41 of which required vaccination prior to Fall 2021. A difference-in-differences analysis is conducted, considering vaccine requirement as a policy implemented only in a sub-group of these institutions. We control for several factors, including state-level case per capita and student population.

RESULTS

Our analysis shows that mandatory vaccination substantially decreased cases in institutions of higher education by 1,473 cases per 100,000 student population (95 CI: 132, 2813).

CONCLUSIONS

The results suggest that a COVID-19 vaccine requirement is an effective policy in decreasing cases in such institutions, leading to a safer educational experience.

School Virus Infection Simulator for customizing school schedules during COVID-19

Even as the COVID-19 pandemic raged worldwide, schools strived to provide consistent education to their students. In such situations, schools require customized schedules that can address the health concerns and safety of the students to safely reopen and remain open. School schedules can be customized in many ways, and different approaches' impact on education and effectiveness in reducing infectious risks are different. To address this issue, we developed the School Virus Infection Simulation-Model (SVISM) for teachers and education policymakers. By taking into account the students' lesson schedules, classroom volume, air circulation rates in the classrooms, and infectability of the students, SVISM simulates the spread of infection at a school. We demonstrate the impact of several school schedules in self-contained and departmentalized classrooms and evaluate them in terms of the maximum number of students infected simultaneously, and the percentage of face-to-face lessons. The results show that the impact of increasing the classroom ventilation rate is not as stable as that of customizing school schedules. In addition, school schedules can differently impact the maximum number of students infected simultaneously, depending on whether classrooms are self-contained or departmentalized. We found that the maximum number of students infected simultaneously under a certain schedule with 50 percentage of face-to-face lessons in self-contained classrooms is higher than the maximum number of students infected simultaneously having schedules with a higher percentage of face-to-face lessons; this phenomenon was not found in departmentalized classrooms. These results show that the SVISM can help teachers and education policymakers plan school schedules appropriately to reduce the maximum number of students infected simultaneously, while also maintaining a certain rate of face-to-face lessons.

A Study on CKD Progression and Health Disparities Using System Dynamics Modeling

Chronic kidney disease (CKD) is one of the most prevalent national health problems in the United States. According to the Center for Disease Control and Prevention (CDC), as of 2019, 37 million of the US’s adult population have been estimated to have CKD. In this respect, health disparities are major national concerns regarding the treatments for patients with CKD nationwide. The disparities observed in the healthcare interventions for patients with this disease usually indicate some significant healthcare gaps in the national public health system. However, there is a need for immediate intervention to improve the present healthcare conditions of minorities experiencing CKD nationwide. In this research, the application of system dynamics modeling is proposed to model the CKD progression and health disparities. This process is based on the health interventions administered to minorities experiencing CKD. The graphical results from the model show that there are relationships among the dynamic factors influencing the incidence and prevalence of CKD. Hence, healthcare disparities are inherent challenges in the treatment and management of this disease.

Eye-SCOR: A Supply Chain Operations Reference-Based Framework for Smart Eye Status Monitoring Using System Dynamics Modeling

This work is a unique integration of three different areas, including smart eye status monitoring, supply chain operations reference (SCOR), and system dynamics, to explore the dynamics of the supply chain network of smart eye/vision monitoring systems. Chronic eye diseases such as glaucoma affect millions of individuals worldwide and, if left untreated, can lead to irreversible vision loss. Nearly half of the affected population is unaware of the condition and can be informed with frequent, accessible eye/vision tests. Tonometry is the conventional clinical method used in healthcare settings to determine the intraocular pressure (IOP) level for evaluating the risk of glaucoma. There are currently very few (under development) non-contact and non-invasive methods using smartphones to determine the risk of IOP and/or the existence of other eye-related diseases conveniently at home. With the overall goal of improving health, well-being, and sustainability, this paper proposes Eye-SCOR: a supply chain operations reference (SCOR)-based framework to evaluate the effectiveness of smartphone-based eye status monitoring apps. The proposed framework is designed using system dynamics modeling as a subset of a new causal model. The model includes interaction/activities between the main players and enablers in the supply chain network, namely suppliers/service providers, smartphone app/device factors, customers, and healthcare professionals, as well as cash and information flow. The model has been tested under various scenarios and settings. Simulation results reveal the dynamics of the model and show that improving the eye status monitoring device/app factors directly increases the efficiency/Eye-SCOR level. The proposed framework serves as an important step towards understanding and improving the overall performance of the supply chain network of smart eye/vision monitoring systems.

Effectiveness of alternative semester break schedules on reducing COVID-19 incidence on college campuses

Despite COVID-19 vaccination programs, the threat of new SARS-CoV-2 strains and continuing pockets of transmission persists. While many U.S. universities replaced their traditional nine-day spring 2021 break with multiple breaks of shorter duration, the effects these schedules have on reducing COVID-19 incidence remains unclear. The main objective of this study is to quantify the impact of alternative break schedules on cumulative COVID-19 incidence on university campuses. Using student mobility data and Monte Carlo simulations of returning infectious student size, we developed a compartmental susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) model to simulate transmission dynamics among university students. As a case study, four alternative spring break schedules were derived from a sample of universities and evaluated. Across alternative multi-break schedules, the median percent reduction of total semester COVID-19 incidence, relative to a traditional nine-day break, ranged from 2 to 4% (for 2% travel destination prevalence) and 8-16% (for 10% travel destination prevalence). The maximum percent reduction from an alternate break schedule was estimated to be 37.6%. Simulation results show that adjusting academic calendars to limit student travel can reduce disease burden. Insights gleaned from our simulations could inform policies regarding appropriate planning of schedules for upcoming semesters upon returning to in-person teaching modalities.

Association mining based approach to analyze COVID-19 response and case growth in the United States

Containing the COVID-19 pandemic while balancing the economy has proven to be quite a challenge for the world. We still have limited understanding of which combination of policies have been most effective in flattening the curve; given the challenges of the dynamic and evolving nature of the pandemic, lack of quality data etc. This paper introduces a novel data mining-based approach to understand the effects of different non-pharmaceutical interventions in containing the COVID-19 infection rate. We used the association rule mining approach to perform descriptive data mining on publicly available data for 50 states in the United States to understand the similarity and differences among various policies and underlying conditions that led to transitions between different infection growth curve phases. We used a multi-peak logistic growth model to label the different phases of infection growth curve. The common trends in the data were analyzed with respect to lockdowns, face mask mandates, mobility, and infection growth. We observed that face mask mandates combined with mobility reduction through moderate stay-at-home orders were most effective in reducing the number of COVID-19 cases across various states.

Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

Providing sufficient testing capacities and accurate results in a time-efficient way are essential to prevent the spread and lower the curve of a health crisis, such as the COVID-19 pandemic. In line with recent research investigating how simulation-based models and tools could contribute to mitigating the impact of COVID-19, a discrete event simulation model is developed to design optimal saliva-based COVID-19 testing stations performing sensitive, non-invasive, and rapid-result RT-qPCR tests processing. This model aims to determine the adequate number of machines and operators required, as well as their allocation at different workstations, according to the resources available and the rate of samples to be tested per day. The model has been built and experienced using actual data and processes implemented on-campus at the University of Illinois at Urbana-Champaign, where an average of around 10,000 samples needed to be processed on a daily basis, representing at the end of August 2020 more than 2% of all the COVID-19 tests performed per day in the USA. It helped identify specific bottlenecks and associated areas of improvement in the process to save human resources and time. Practically, the overall approach, including the proposed modular discrete event simulation model, can easily be reused or modified to fit other contexts where local COVID-19 testing stations have to be implemented or optimized. It could notably support on-site managers and decision-makers in dimensioning testing stations by allocating the appropriate type and quantity of resources.