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As of July 30, 2020, more than 17 million novel coronavirus disease 2019 (COVID-19) cases were registered including 671,500 deaths. Yet, there is no immediate medicine or vaccination for control this dangerous pandemic and researchers are trying to implement mathematical or time series epidemic models to predict the disease severity with national wide data.

In this study, the authors considered COVID-19 daily infection data four most COVID-19 affected nations (such as the USA, Brazil, India and Russia) to conduct 60-day forecasting of total infections. To do that, the authors adopted a machine learning (ML) model called Fb-Prophet and the results confirmed that the total number of confirmed cases in four countries till the end of July were collected and projections were made by employing Prophet logistic growth model.

Results highlighted that by late September, the estimated outbreak can reach 7.56, 4.65, 3.01 and 1.22 million cases in the USA, Brazil, India and Russia, respectively. The authors found some underestimation and overestimation of daily cases, and the linear model of actual vs predicted cases found a p-value (<2.2e-16) lower than the R2 value of 0.995.

In this paper, the authors adopted the Fb-Prophet ML model because it can predict the epidemic trend and derive an epidemic curve.

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 analyze the epidemic modeling applications and policy‐making strategies for six different infectious diseases in a number of countries, thus comparing and contrasting research in underdeveloped, developing, and developed countries.

A systematic review has been conducted by identifying relevant studies for six diseases from different sources and selecting 74 publications for inclusion. These selected publications are classified and analyzed based on infectious disease, control policies, theme and objective, methodology, origin of population data, publication year and results.

Review results indicate that disaster preparedness and surveillance plans for epidemics are available mostly for developed countries. There is a need for further research in both developing and developed countries because of the ease of dispersion, which constitutes a universal threat. Analysis of the publications suggests that epidemic disasters are mostly studied by researchers in the field of medicine or biology with the aim of assessing the potential impact of an epidemic. The authors highlight the need for further research in operations research and disaster management fields and propose further research directions in the area of disaster management.

This review emphasizes the importance of epidemic disaster modeling for the preparedness stage of disaster management and policy making. Disease and population‐specific intervention policies (e.g. vaccination) reported in this review should set an example and help policy makers during their decision making.

Potential use of the epidemiological modeling on further planning and decision‐making issues in the context of disaster management is studied for the first time.

We discuss the connections between epidemiology models and the search and matching (SAM) approach and draw conclusions about modeling the trade-offs between lockdowns and disease spread. We review the pre-COVID epidemics literature, which was mainly by epidemiologists, and the post-COVID surge in economics papers that use meeting technologies to model the trade-offs. We argue that modeling the decentralized equilibrium with economic trade-offs gives rise to substantially different results from the earlier epidemics literature, but policy action is still welfare-improving because of several externalities.

The goals of this chapter are three-fold: (1) to outline some broad empirical regularities concerning how drug problems evolve over time, (2) to sketch some plausible mechanisms for ways in which aspects of that variation might be endogenous, and (3) to review two classes of dynamic models of drug use that have implications for how policy should vary over a drug epidemic.

Under current conditions, strong sustainable socio-economic development of major metropolitan areas in separate regions and separate countries comparable to them in size is possible with the optimal management of a set of anti-epidemic measures to combat the COVID-19 pandemic. This chapter constructs the first numerical model of the quasi-periodic dynamics of the COVID-19 pandemic. It was created based on the innovative model of the Kondratiev waves developed by the authors in their previous works. The authors found a close approximation between the model and the actual data for the four waves of development of the COVID-19 pandemic in Moscow. It was also noted that this model applies to small countries close in population to Moscow when comparing the correlation and autocorrelation curves. The data calculated by the models indicate the possibility of the practical application of the developed model for metropolitan areas and small countries comparable to them in size and population. Additionally, the model showed the accuracy of the results for such large countries as Russia and the United States.

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.

This chapter argues that models trying to explain the spread of drug use should not be based on standard epidemiological models developed to describe the spread of infectious diseases. The main weaknesses of the standard model are the lack of attention to micro-foundations and the inappropriateness of several of its assumptions in the context of drug use. An approach based on mechanisms and social interaction is argued to provide a promising alternative to the standard approach. To illustrate this, a model of the spread of drugs based on two mechanisms has been developed (observational learning and social stigma). Lastly, some of the difficulties in testing and deriving policy implications in these models are discussed.

Epidemics not only affect the public health but also are a threat to a nation's growth and economy as well. Early prediction of epidemic can be beneficial to take preventive measures and to reduce the impact of epidemic in an area.

A deep neural network (DNN) based context aware smart epidemic system has been proposed to prevent and monitor epidemic spread in a geographical area. Various neural networks (NNs) have been used: LSTM, RNN, BPNN to detect the level of disease, direction of spread of disease in a geographical area and marking the high-risk areas. Multiple DNNs collect and process various data points and these DNNs are decided based on type of data points. Output of one DNN is used by another DNN to reach to final prediction.

The experimental evaluation of the proposed framework achieved the accuracy of 87% for the synthetic dataset generated for Zika epidemic in Brazil in 2016.

The proposed framework is designed in a way that every data point is carefully processed and contributes to the final decision. These multiple DNNs will act as a single DNN for the end user.