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After the identification of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at Wuhan, China, a pandemic was widely spread worldwide. In Italy, about 240,000 people were infected because of this virus including 34,721 deaths until the end of June 2020. To control this new pandemic, epidemiologists recommend the enforcement of serious mitigation measures like country lockdown, contact tracing or testing, social distancing and self-isolation.

This paper presents the most popular epidemic model of susceptible (S), exposed (E), infected (I) and recovered (R) collectively called SEIR to understand the virus spreading among the Italian population.

Developed SEIR model explains the infection growth across Italy and presents epidemic rates after and before country lockdown. The results demonstrated that follow-up of strict measures such that country lockdown along with high testing is making Italy practically a pandemic-free country.

These models largely help to estimate and understand how an infectious agent spreads in a particular country and how individual factors can affect the dynamics. Further studies like classical SEIR modeling can improve the quality of data and implementation of this modeling could represent a novelty of epidemic models.

This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

Understanding the computer-virus propagation is quite essential for the construction and development of anti-virus policy. While researches about the anti-virus policy have been extensively investigated, the viewpoint from sociological perspective is relatively ignored. Therefore, this paper aims to explore the dynamics of computer-virus propagation and evaluate the effectiveness of anti-virus policies through the sociological perspective.

This research constructs a virus-propagation model based on the susceptible-exposed-infective-recovered epidemic concept to simulate and explore the dynamic behavior of multipartite computer viruses through the tool of system dynamics. The effectiveness of various anti-virus policies is then evaluated via this model.

The frequency of media contact has a significant effect on the virus infection rate. The effectiveness of user self-prevention relies on the usefulness of the virus signatures. The reporting/alarm process can enhance the capability of anti-virus software company and the detected intensity of new threat. The quarantine policy can effectively reduce the spread of computer virus.

Individuals should strengthen the self-awareness of information security to reduce the negative impact. Managers should construct and implement the information security norm to regulate the behavior of staff. Anti-virus software companies should strengthen the capability of their automatic reporting/alarm mechanism to early detect the exceptional conditions and control new threats in time.

Information security management research is still in the growth phase, but it is critically important to establish the groundwork for understanding of computer viruses and the effectiveness of anti-virus policy from assorted perspectives. The major contribution of research is to explore the propagation of multipartite computer viruses and study how to prevent their destruction from the sociological and technical perspectives.

The COVID-19 pandemic is a new crisis in the world that caused many restrictions, from personal life to social and business. In this situation, the most vulnerable groups such as refugees who are living in the camps are faced with more serious problems. Therefore, a system dynamic approach has been developed to evaluate the effect of applying different scenarios to find out the best response to COVID-19 to improve refugees’ health and education.

The interaction of several health and education factors during an epidemic crisis among refugees leads to behavioral responses that consequently make the crisis control a complex problem. This research has developed an SD model based on the SIER model that responds to the public health and education system of Syrian refugees in Turkey affected by the COVID-19 virus and considered three policies of isolation, social distance/hygiene behavior and financial aid using the available data from various references.

The findings from the SD simulation results of applying three different policies identify that public health and education systems can increase much more by implementing the policy of social distance/hygiene behavior, and it has a significant impact on the control of the epidemic in comparison with the other two responses.

This paper contributes to humanitarian organizations, governments and refugees by discussing useful insights. Implementing the policy of social distance and hygiene behavior policies would help in a sharp reduction of death in refugees group. and public financial support has improved distance education during this pandemic.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

This paper aims to assess the economic impact of uniform COVID-controlling policies that were implemented by the US government in 2020 and compare it with hypothetical targeted policies that consider the heterogenous effect of COVID-19 on different age groups.

The author began by showing that the adjusted SEQIHR model is a good fit to the US COVID-induced daily death data in that it can capture the nonlinearities of the data very well. Then, he used this model with extra parameters to evaluate the economic effects of COVID-19 through its impact on the job market.

The results show that targeted COVID-controlling policies could reduce the US death rate and GDP loss to 0.03% and 2%, respectively. By comparing these results with uniform COVID-controlling policies, which led to a 0.1% death rate and 3.5% GDP loss, we could conclude that the death rate reduction is 0.07%. Approximately 378,000 Americans died because of COVID-19 during 2020, therefore, reducing the death rate to 0.03% means saving a significant proportion of the COVID-19 casualties, around 280,000 lives.

To the best of the author's knowledge, this paper is the first study to assess the economic impacts of COVID-controlling policies by using the multirisk SEQIHR model.

The purpose of this paper is to achieve effective governance of online rumors through the proposed rumor propagation model and immunization strategy.

The paper leverages the agent-based modeling (ABM) method to model individuals from two aspects, behavior and attitude. Based on the analysis and research of online data, we propose a rumor propagation model, namely the Untouched view transmit removed-Susceptible hesitate agree disagree (Unite-Shad), and devise an immunization strategy, namely the Gravity Immunization Strategy (GIS). A graph-based framework, namely Pregel, is used to carry out the rumor propagation simulation experiments. Through the experiments, the rationality of the Unite-Shad and the effectiveness of the GIS are verified.

The study discovers that the inconsistency between human behaviors and attitudes in rumor propagation can be explained by the Unite-shad model. Besides, the GIS, which shows better performance in small-world networks than in scale-free networks, can effectively suppress rumor propagation in the early stage.

This paper provides an effective immunization strategy for rumor governance. Specifically, the Unite-Shad model reveals the mechanism of rumor propagation, and the GIS provides an effective governance method for selecting immune nodes.

The inconsistency of human behaviors and attitudes in real scenes is modeled in the Unite-Shad model. Combined with the model, the definition of diffusion domain is proposed and a novel immunization strategy, namely GIS, is designed, which is significant for the social governance of rumor propagation.

Several national- and state-level studies have been predicting the course of the COVID-19 pandemic using supervised machine learning algorithms. However, the comparison of such models has not been discussed before. This is the first-ever research wherein the two leading models, susceptible-infected-recovered (SIR) and logistic are compared. The purpose of this study is to observe their utility, at both the National and Municipal Corporation level in India.

The modified SIR and the logistic were deployed for analysis of the COVID-19 patients’ database of India and three Municipal Corporations, namely, Akola, Kalyan-Dombivli and Mira-Bhayander. The data for the study was collected from the official notifications for COVID-19 released by respective government websites.

This study provides evidence to show the superiority of the modified SIR over the logistic model. The models give accurate predictions for a period up to 14 days. The prediction accuracy of the models is limited due to change in government policies. This can be observed by the drastic increase in the COVID-19 cases after Unlock 1.0 in India. The models have proven that they can effectively predict for both National and Municipal Corporation level database.

The modified SIR model can be used to signify the practicality and effectiveness of the decisions taken by the authorities to contain the spread of coronavirus.

This study modifies the SIR model and also identifies and fulfills the need to find a more accurate prediction algorithm to help curb the pandemic.

The purpose of this paper is to develop a system dynamics approach based on Susceptible, Exposed, Infected, Recovered (SEIR) model to investigate the coronavirus pandemic and the impact of therapeutic and preventive interventions on epidemic disaster.

To model the behavior of COVID-19 disease, a system dynamics model is developed in this paper based on SEIR model. In the proposed model, the impact of people's behavior, contact reduction, isolation of the sick people as well as public quarantine on the spread of diseases is analyzed. In this model, data collected by the Iran Ministry of Health have been used for modeling and verification of the results.

The results show that besides the intervening policies, early application of them is also of utmost priority and makes a significant difference in the result of the system. Also, if the number of patients with extreme conditions passes available hospital intensive care capacity, the death rate increases dramatically. Intervening policies play an important role in reducing the rate of infection, death and consequently control of pandemic. Also, results show that if proposed policies do not work before the violation of the hospital capacity, the best policy is to increase the hospital’s capacity by adding appropriate equipment.

The authors also had some limitations in the study including the lack of access to precise data regarding the epidemic of coronavirus, as well as accurate statistics of death rate and cases in the onset of the virus due to the lack of diagnostic kits in Iran. These parameters are still part of the problem and can negatively influence the effectiveness of intervening policies introduced in this paper.

The contribution of this paper includes the development of SEIR model by adding more policymaking details and considering the constraint of the hospital and public health capacity in the rate of coronavirus infection and death within a system dynamics modeling framework.