Identification of Key Factors for Optimized Health Care Services: Protocol for a Multiphase Study of the Dubai Vaccination Campaign.

BACKGROUND: Mass vaccination of the global population against the novel COVID-19 outbreak posed multiple challenges, including effectively administering millions of doses in a short period of time while ensuring public safety and accessibility. The government of Dubai launched a mass campaign in December 2020 to vaccinate all its citizens and residents, targeting the population aged >18 years against COVID-19. The vaccination campaign involved a transformation of multiple commercial spaces into mass vaccination centers across the city of Dubai, the largest of which was the Dubai One Central (DOC) vaccination center. It was operational between January 17, 2021, and 27 January 27, 2022. OBJECTIVE: The multiphase research study aims to empirically explore the opinions of multiple health care stakeholders, elicit the key success factors that can influence the effective delivery of emergency health care services such as a COVID-19 mass vaccination center, and explore how these factors relate to one another. METHODS: To understand more about the operations of the DOC vaccination center, the study follows a multiphase design divided into 2 phases. The study is being conducted by the Institute for Excellence in Health Professions Education at Mohammed Bin Rashid University of Medicine and Health Sciences between December 2021 and January 2023. To elicit the key success factors that contributed to the vaccination campaign administered at DOC, the research team conducted 30 semistructured interviews (SSIs) with a sample of staff and volunteers who worked at the DOC vaccination center. Stratified random sampling was used to select the participants, and the interview cohort included representatives from the management team, team leaders, the administration and registration team, vaccinators, and volunteers. A total of 103 people were invited to take part in the research study, and 30 agreed to participate in the SSIs. To validate the participation of various stakeholders, phase 2 will analytically investigate one's subjectivity through Q-methodology and empirically investigate the opinions obtained from the research participants during phase 1. RESULTS: As of July 2022, 30 SSIs were conducted with the research participants. CONCLUSIONS: The study will provide a comprehensive 2-phase approach to obtaining the key success factors that can influence the delivery of high-quality health care services such as emergency services launched during a global pandemic. The study's findings will be translated into key factors that could support designing future health care services utilizing evidence-based practice. In line with future plans, a study will use data, collected through the DOC vaccination center, to develop a simulation model outlining the process of the customer journey and center workflow. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/42278.

The Patient Flow Effect of Pandemic Policies: A Hybrid Simulation Study in a Norwegian Emergency Department.

The COVID-19 pandemic required several interventions within emergency departments, complicating the patient flow. This study explores the effect of intervention policies on patient flow in emergency departments under pandemic conditions. The patient flow interventions under evaluation here are the addition of extra treatment rooms and the addition of a waiting zone. A predeveloped hybrid simulation model was used to conduct five scenarios: (1) pre-pandemic patient flow, (2) patient flow with a 20% contamination rate, (3) adding extra treatment rooms to patient flow, (4) adding a waiting zone to the patient flow, (5) adding extra treatment rooms and a waiting zone to the patient flow. Experiments were examined based on multiple patient flow metrics incorporated into the model. Running the scenarios showed that introducing the extra treatment rooms improved all the patient flow parameters. Adding the waiting zone further improved only the contaminated patient flow parameters. Still, the benefit of achieving this must be weighed against the disadvantage for ordinary patients. Introducing the waiting zone in addition to the extra treatment room has one positive effect, decreasing time that the treatment rooms are blocked for contaminated patients entering the treatment room.

COVID-19 transmission in U.S. transit buses: A scenario-based approach with agent-based simulation modeling (ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19 (SARS-CoV-2) virus. Modeling disease transmission in public buses remains a challenge, especially with uncertainties in passenger boarding, alighting, and onboard movements. Although there are initial findings on the effectiveness of some of the mitigation policies (such as face-covering and ventilation), evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways, boarding and alighting patterns, and seating capacity control. This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic in USA, in which it brings crucial insights on combating current and future epidemics. We use an agent-based simulation model (ABSM) based on standard design characteristics for urban buses in USA and two different service frequency settings (10-min and 20-min headways). We find that wearing face-coverings (surgical masks) significantly reduces onboard transmission rates, from no mitigation rates of 85% in higher-frequency buses and 75% in lower-frequency buses to 12.5%. The most effective prevention outcome is the combination of KN-95 masks, open window policies, and half-capacity seating control during higher-frequency bus services, with an outcome of nearly 0% onboard infection rate. Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design, which is crucial to ensuring passenger safety. The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies. © 2023

Evaluating Policies to Decrease the Risk of Introducing SARS-CoV-2 Infections to Nursing Home Facilities.

We used an individual-based microsimulation model of North Carolina to determine what facility-level policies would result in the greatest reduction in the number of individuals with SARS-CoV-2 entering the nursing home environment from 12/15/2021 to 1/3/2022 (e.g., Omicron variant surge). On average, there were 14,287 (Credible Interval [CI]: 13,477-15,147) daily visitors and 17,168 (CI: 16,571-17,768) HCW coming from the community into 426 nursing home facilities. Policies requiring a negative rapid test or vaccinated status for visitors resulted in the greatest reduction in the number of individuals with SARS-CoV-2 infection entering the nursing home environment with a 29.6% (26.9%-32.0%) and 24.0% (CI: 22.2%-25.5%) reduction, respectively. Policies halving visits (21.2% [20.0%-28.2%]), requiring all vaccinated HCW to receive a booster (7.8% [CI: 7.4%-8.7%]), and limiting visitation to a primary visitor (6.5% [CI: 3.5%-9.7%]) reduced infectious contacts to a lesser degree.

COVID-19 Spread Modeling Considering Vaccination and Re-Morbidity

To effectively counter the COVID-19 spread, using scientifically based decision-making methods in this area is required. The disease characteristics and the methods applied to stem it are constantly changing, so it is necessary to update existing methods for predicting the COVID-19 spread in light of new trends. The present paper deals with developing a new SVEIRS model from the SEIR class, taking into account the vaccination campaign and the possibility of recurrent morbidity cases. These improvements make it possible to increase the accuracy of the disease spread prediction due to a more direct correspondence to reality. The developed SVEIRS model was verified when predicting the COVID-19 spread in Moscow in July-September of 2022 and showed higher prediction accuracy compared to the SEVIS reference model. Based on the developed model, it is possible to predict the COVID-19 spread in various regions to form an optimal vaccination campaign strategy.

COVID-19 transmission in U.S. transit buses: A scenario-based approach with agent-based simulation modeling (ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19 (SARS-CoV-2) virus. Modeling disease transmission in public buses remains a challenge, especially with uncertainties in passenger boarding, alighting, and onboard movements. Although there are initial findings on the effectiveness of some of the mitigation policies (such as face-covering and ventilation), evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways, boarding and alighting patterns, and seating capacity control. This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic inUSA, in which it brings crucial insights on combating current and future epidemics. We use an agent-based simulation model (ABSM) based on standard design characteristics for urban buses in USA and two different service frequency settings (10-min and 20-min headways). We find that wearing face-coverings (surgical masks) significantly reduces onboard transmission rates, from no mitigation rates of 85% in higher-frequency buses and 75% in lower-frequency buses to 12.5%. The most effective prevention outcome is the combination of KN-95 masks, open window policies, and half-capacity seating control during higher-frequency bus services, with an outcome of nearly 0% onboard infection rate. Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design, which is crucial to ensuring passenger safety. The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies.

A model-based analysis of the health impacts of COVID-19 disruptions to primary cervical screening by time since last screen for current and future disruptions.

We evaluated how temporary disruptions to primary cervical cancer (CC) screening services may differentially impact women due to heterogeneity in their screening history and test modality. We used three CC models to project the short- and long-term health impacts assuming an underlying primary screening frequency (i.e., 1, 3, 5, or 10 yearly) under three alternative COVID-19-related screening disruption scenarios (i.e., 1-, 2-, or 5-year delay) versus no delay in the context of both cytology-based and human papillomavirus (HPV)-based screening. Models projected a relative increase in symptomatically detected cancer cases during a 1-year delay period that was 38% higher (Policy1-Cervix), 80% higher (Harvard), and 170% higher (MISCAN-Cervix) for underscreened women whose last cytology screen was 5 years prior to the disruption period compared with guidelines-compliant women (i.e., last screen 3 years prior to disruption). Over a woman's lifetime, temporary COVID-19-related delays had less impact on lifetime risk of developing CC than screening frequency and test modality; however, CC risks increased disproportionately the longer time had elapsed since a woman's last screen at the time of the disruption. Excess risks for a given delay period were generally lower for HPV-based screeners than for cytology-based screeners. Our independent models predicted that the main drivers of CC risk were screening frequency and screening modality, and the overall impact of disruptions from the pandemic on CC outcomes may be small. However, screening disruptions disproportionately affect underscreened women, underpinning the importance of reaching such women as a critical area of focus, regardless of temporary disruptions.

A Resilience Analysis of a Medical Mask Supply Chain during the COVID-19 Pandemic: A Simulation Modeling Approach.

The COVID-19 pandemic has caused severe consequences such as long-term disruptions and ripple effects on regional and global supply chains. In this paper, firstly, we design simulation models using AnyLogistix to investigate and predict the pandemic's short-term and long-term disruptions on a medical mask supply chain. Then, the Green Field Analysis experiments are used to locate the backup facilities and optimize their inventory levels. Finally, risk analysis experiments are carried out to verify the resilience of the redesigned mask supply chain. Our major research findings include the following. First, when the pandemic spreads to the downstream of the supply chain, the duration of the downstream facilities disruption plays a critical role in the supply chain operation and performance. Second, adding backup facilities and optimizing their inventory levels are effective in responding to the pandemic. Overall, this paper provides insights for predicting the impacts of the pandemic on the medical mask supply chain. The results of this study can be used to redesign a medical mask supply chain to be more resilient and flexible.

Modeling Patient Flow in an Emergency Department under COVID-19 Pandemic Conditions: A Hybrid Modeling Approach.

Emergency departments (EDs) had to considerably change their patient flow policies in the wake of the COVID-19 pandemic. Such changes affect patient crowding, waiting time, and other qualities related to patient care and experience. Field experiments, surveys, and simulation models can generally offer insights into patient flow under pandemic conditions. This paper provides a thorough and transparent account of the development of a multi-method simulation model that emulates actual patient flow in the emergency department under COVID-19 pandemic conditions. Additionally, a number of performance measures useful to practitioners are introduced. A conceptual model was extracted from the main stakeholders at the case hospital through incremental elaboration and turned into a computational model. Two agent types were mainly modeled: patient and rooms. The simulated behavior of patient flow was validated with real-world data (Smart Crowding) and was able to replicate actual behavior in terms of patient occupancy. In order to further the validity, the study recommends several phenomena to be studied and included in future simulation models such as more agents (medical doctors, nurses, beds), delays due to interactions with other departments in the hospital and treatment time changes at higher occupancies.

A COMPREHENSIVE WASTE MANAGEMENT SIMULATION MODEL FOR THE ASSESSMENT OF WASTE SEGREGATION IN THE HEALTH SECTOR

It has been determined that a high percentage of medical waste could be classed as domestic waste due to the lack of segregation at hospitals. Better segregation could thus substantially decrease the amount of medical waste that is required to be treated as hazardous waste. This study aims to assess different segregation levels of domestic waste mixed with medical waste. To do so, the Stella and Vensim simulation packages were used to evaluate medical waste flows in the Thrace Region of Turkey. The most important advantage of the simulation modeling used in this study is the flexibility for adjusting parameters based on circumstances, e.g. in the case of an unforeseen event (such as the COVID-19 pandemic), the system parameters can be modified according to the situation. In this study, it is anticipated for the medical waste generation to increase from almost 2000 tons/year to 3000 tons/year in 2045 in the region, which is more than the capacity of current medical waste treatment plants. Projected waste generation flows show that it is possible to avoid 300 tons of medical waste annually by reducing the domestic content of medical waste to 50%. Precisely, for the current regional treatment capacity to be sufficient up to 2045, it will be crucial to reduce the domestic content in medical waste to 10% in the chronic care departments at regional hospitals. The importance of this further arises, as lack of meeting this need will result in an urgent requirement for installation of new units for the treatment of all the medical waste generated in the region. © 2021 Gheorghe Asachi Technical University of Iasi, Romania. All rights reserved.

Epidemic Informatics and Control: A Review from System Informatics to Epidemic Response and Risk Management in Public Health

Epidemic outbreaks such as Coronavirus disease 2019 (COVID-19) impact the health of our society and bring significant disruptions to the US and the world. Each country has to dynamically adjust health policies, plan healthcare resources, control travels with little time latency to mitigate risks and safeguard the population. With rapid advances in health and information technology, more and more data are collected in the dynamically evolving process of epidemic outbreaks. The availability of data calls upon the development of analytical methods and tools to gain a better understanding of virus spreading dynamics, optimize the design of healthcare policies for epidemic control, and improve the resilience of health systems. This paper presents a holistic review of the system informatics approach, i.e., Define, Measure, Analyze, Improve, and Control (DMAIC), for epidemic response and management through the intensive use of data, statistics and optimization. Despite the sustained successes of system informatics in a variety of established industries such as manufacturing, logistics, services and beyond, there is a dearth of concentrated review and application of the data-driven DMAIC approach in the context of epidemic outbreaks. First, we define specific challenges posed by epidemic outbreaks to populational health, health systems, as well as economic challenges to different industries such as retailing, education and manufacturing. Second, we present a review of medical testing and statistical sampling methods for data collection, as well as existing efforts in data management and data visualization. Third, we discuss the importance to realizing the full potential of data for epidemic insights, and emphasize the need to leverage analytical methods and tools for decision support. Fourth, an epidemic brings imperative changes to health systems. We discuss the new trend of healthcare solutions to improve system resilience, including telehealth, artificial intelligence, resource allocation, and system re-design. In closing, prescriptive approaches are discussed to optimize the health policies and action strategies for controlling the spread of virus. We posit that this work will catalyze more in-depth investigations and multi-disciplinary research efforts to accelerate the application of system informatics methods and tools in epidemic response and risk management. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Intelligent Data Analysis for Infection Spread Prediction

Intelligent data analysis based on artificial intelligence and Big Data tools is widely used by the scientific community to overcome global challenges. One of these challenges is the worldwide coronavirus pandemic, which began in early 2020. Data science not only provides an opportunity to assess the impact caused by a pandemic, but also to predict the infection spread. In addition, the model expansion by economic, social, and infrastructural factors makes it possible to predict changes in all spheres of human activity in competitive epidemiological conditions. This article is devoted to the use of anonymized and personal data in predicting the coronavirus infection spread. The basic “Susceptible–Exposed–Infected–Recovered” model was extended by including a set of demographic, administrative, and social factors. The developed model is more predictive and applicable in assessing future pandemic impact. After a series of simulation experiment results, we concluded that personal data use in high-level modeling of the infection spread is excessive. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Multi-Agent modeling of the spread of diseases using the example of coronavirus disease COVID-19

Throughout the history of humanity, large-scale epidemics and pandemics have repeatedly erupted. Athenian ulcer, several plague and cholera pandemics, Spanish flu, Avian influenza, Swine influenza, HIV/AIDS-millions of people have died due to lack of medicines and medical knowledge. In the 21st century, it would seem that world medicine is ready and capable of preventing many diseases, but by the beginning of 2020, a new pandemic of the coronavirus disease COVID-19 caused by the SARS-CoV-2 virus broke out. The paper provided a brief systematic overview of modeling methods in epidemiology. A modified SEIRD simulation model of epidemic spread is presented. The proposed model was implemented in the AnyLogic system. © 2021 IEEE.

SIMULATION MODELING AND PRACTICE OF INPATIENT EMERGENCY DEPARTMENT IN COVID-19 PANDEMIC

The article presents the experience of using simulation modeling to optimize inpatient emergency department as an admission unit of a hospital — Center for treatment of patients with new coronavirus infection COVID-19. It was noted that the inpatient emergency department effectively performed the functions of the inpatient department of the Center for treatment of patients with new COVID-19 coronavirus infection for a total of more than 7 months. A correct calculation of staffing and a competent use of the department "zones" ensured efficient and rapid reception of patients during both "waves" of the pandemic. The model also proved positive role of such departments with a large number of patients in a multimillion metropolis needed to be hospitalized on a daily basis. © Burnasyan FMBC FMBA.

THE FORGOTTEN EFFECTS OF THE COVID 19 PANDEMIC ON THE AGING POPULATION

Humanity is experiencing intense and frequent changes caused by the COVID-19 pandemic, which increases uncertainty and complexity in decision-making. New challenges become urgent, mainly to issues related to the aging population, since the coronavirus affects the elderly more than adults. Finding alternatives to preserve the quality of life and work support of the elderly becomes a great challenge for society. In this context, science plays a vital role in proposing new solutions to solve the problems derived from this crisis, being this the primary motivation. The objectives of the article are to learn about the forgotten effects of the pandemic on economically active older adults and to indicate how fuzzy logic can help reduce risks by facilitating decision-making. The main contribution would be to correctly identify its causes and effects, such as the digital gap and job loss by this age group, and to point out corrective measures. The study's methodology is based on applied research, with a quantitative modeling and simulation approach through the Forgotten Effects Theory. The results allow us to predict and act more effectively on problems, seeking to increase the welfare, employability, and life expectancy of the elderly. The study highlights future lines of research on the subject.

Online Public Rumor Engagement Model and Intervention Strategy in Major Public Health Emergencies: From the Perspective of Social Psychological Stress.

During major public health emergencies, a series of coupling problems of rumors getting out of control and public psychological imbalance always emerge in social media, which bring great interference for crisis disposal. From the perspective of social psychological stress, it is important to depict the interactive infection law among distinct types of rumor engagers (i.e., advocates, supporters, and amplifiers) under different social psychological stress states, and explore the effectiveness of rumor intervention strategies (i.e., hindering and persuasion) from multiple dimensions, to scientifically predict the situation of public opinion field and guide the public to restore psychological stability. Therefore, this paper constructs an interactive infection model of multiple rumor engagers under different intervention situations based on a unique user-aggregated dataset collected from a Chinese leading online microblogging platform ("Sina Weibo") during the COVID-19 in 2020. The simulation result shows that (1) in the period of social psychological alarm reaction, the strong level of hindering intervention on the rumor engagers leads to more serious negative consequences; (2) in the period of social psychological resistance, the persuasion and hindering strategies can both produce good outcomes, which can effectively reduce the overall scale of rumor supporters and amplifiers and shorten their survival time in social media; (3) in the period of social psychological exhaustion, rumor intervention strategies are not able to have a significant impact; (4) the greater the intensity of intervention, the more obvious the outcome. Experimental findings provide a solid research basis for enhancing social psychological stress outcomes and offer decision-making references to formulate the rumor combating scheme.

Simulation model for Covid-19 pandemic

This paper outlines computer modeling algorithms designed to predict and forecast a COVID-19. In this paper, we consider a deterministic model. Theongoing COVID-19 epidemic quickly spread across the globe. Significant behavioural, social initiatives to limit city transport, case identification and touch tracking, quarantine, advice, and knowledge to the public, creation of detection kits, etc. and state measures were conducted to reduce the epidemic and eliminate coronavirus persistence in humans around the world from stopping the global coronavirus outbreak. In this paper, we propose a basic SIR epidemic model to show a simulation, the MATLAB algorithm using bouncing dots to depict safe and sick people to simulate infection spread. The graphical model shown here is implemented using MATLAB package version 3.0. In this paper, we discuss the importance of models because they help one explore what could happen. They demonstrate how different possible futures might be shaped by what we are doing now. We can examine the effects of specific interventions in different ways such as quarantine or a lockdown & explore how simulations may predict, how infectious diseases advanced to show the possible result of an outbreak, and better guide initiatives in public health regarding the pandemic response and pandemic past including an overview of the key characteristics of adverse pandemic consequences and epidemic outbreak.

Long-term impact of HPV vaccination and COVID-19 pandemic on oropharyngeal cancer incidence and burden among men in the USA: A modeling study.

BACKGROUND: Oropharyngeal cancer (OPC) incidence is rising rapidly among men in the United States of America (USA). We aimed to project the impact of maintaining the current HPV vaccination uptake and achieving 80% national (Healthy People) goal on OPC incidence and burden. METHODS: We developed an open-cohort micro-simulation model of OPC natural history among contemporary and future birth cohorts of men, accounting for sexual behaviors, population growth, aging, and herd immunity. We used data from nationally representative databases, cancer registries from all 50 states, large clinical trials, and literature. We evaluated the status quo scenario (the current HPV vaccination uptake remained stable) and alternative scenarios of improvements in uptake rates in adolescents (aged 9-17 years) and young adults (aged 18-26 years) by 2025 to achieve and maintain the 80% goal. The primary outcome was to project OPC incidence and burden from 2009 to 2100. We also assessed the impact of disruption in HPV vaccine uptake during the COVID-19 pandemic. FINDINGS: OPC incidence is projected to rise until the mid-2030s, reaching the age-standardized incidence rate of 9·8 (95% uncertainty interval [UI] 9·5-10·1) per 100 000 men, with the peak annual burden of 23 850 (UI, 23 200-24 500) cases. Under the status quo scenario, HPV vaccination could prevent 124 000 (UI, 117 000-131 000) by 2060, 400 000 (UI, 384 000-416 000) by 2080, and 792 000 (UI, 763 000-821 000) by 2100 OPC cases among men. Achievement and maintenance of 80% coverage among adolescent girls only, adolescent girls and boys, and adolescents plus young adults could prevent an additional number of 100 000 (UI, 95 000-105 000), 118 000 (UI, 113 000-123 000), and 142 000 (UI, 136 000-148 000) male OPC cases by 2100. Delayed recovery of the HPV vaccine uptake during the COVID-19 pandemic could lead to 600 (UI, 580-620) to 6200 (UI, 5940-6460) additional male OPC cases by 2100, conditional on the decline in the extent of the national HPV vaccination coverage and potential delay in rebounding. INTERPRETATION: Oropharyngeal cancer burden is projected to rise among men in the USA. Nationwide efforts to achieve the HPV vaccination goal of 80% coverage should be a public health priority. Rapid recovery of the declined HPV vaccination uptake during the COVID-19 pandemic is also crucial to prevent future excess OPC burden. FUNDING: National Cancer Institute and National Institute on Minority Health and Health Disparities of the USA.

Predictive Modeling of COVID and non-COVID Pneumonia Trajectories.

Today pneumonia is one of the main problems of all countries around the world. This disease can lead to early disability, serious complications, and severe cases of high probabilities of lethal outcomes. A big part of cases of pneumonia are complications of COVID-19 disease. This type of pneumonia differs from ordinary pneumonia in symptoms, clinical course, and severity of complications. For optimal treatment of disease, humans need to study specific features of providing 19 pneumonia in comparison with well-studied ordinary pneumonia. In this article, the authors propose a new approach to identifying these specific features. This method is based on creating dynamic disease models for COVID and non-COVID pneumonia based on Bayesian Network design and Hidden Markov Model architecture and their comparison. We build models using real hospital data. We created a model for automatically identifying the type of pneumonia (COVID-19 or ordinary pneumonia) without special COVID tests. And we created dynamic models for simulation future development of both types of pneumonia. All created models showed high quality. Therefore, they can be used as part of decision support systems for medical specialists who work with pneumonia patients.

A benefit-cost analysis of different response scenarios to COVID-19: A case study.

BACKGROUND: This paper compares the direct benefits to the State of Western Australia from employing a "suppression" policy response to the COVID-19 pandemic rather than a "herd immunity" approach. METHODS: An S-I-R (susceptible-infectious-resolved) model is used to estimate the likely benefits of a suppression COVID-19 response compared to a herd immunity alternative. Direct impacts of the virus are calculated on the basis of sick leave, hospitalizations, and fatalities, while indirect impacts related to response actions are excluded. RESULTS: Preliminary modeling indicates that approximately 1700 vulnerable person deaths are likely to have been prevented over 1 year from adopting a suppression response rather than a herd immunity response, and approximately 4500 hospitalizations. These benefits are valued at around AUD4.7 billion. If a do nothing policy had been adopted, the number of people in need of hospitalization is likely to have overwhelmed the hospital system within 50 days of the virus being introduced. Maximum hospital capacity is unlikely to be reached in either a suppression policy or a herd immunity policy. CONCLUSION: Using early international estimates to represent the negative impact each type of policy response is likely to have on gross state product, results suggest the benefit-cost ratio for the suppression policy is slightly higher than that of the herd immunity policy, but both benefit-cost ratios are less than one.