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The study aims to carry out predictive modeling based on publicly available COVID-19 data for the duration April 01, 2020 to June 20, 2020 pertaining to India and five of its most infected states: Maharashtra, Tamil Nadu, Delhi, Gujarat and Rajasthan.

The study leverages the susceptible, infected, recovered and dead (SIRD) epidemiological framework for predictive modeling. The basic reproduction number R0 is derived by an exponential growth method using RStudio package R0. The differential equations reflecting the SIRD model have been solved using Python 3.7.4 on the Jupyter Notebook platform. For visualization, Python Matplotlib 3.2.1 package is used.

The study offers insights on peak-date, peak number of COVID-19 infections and end-date pertaining to India and five of its states.

The results subtly indicate toward the amount of effort required to completely eliminate the infection. It could be leveraged by the political leadership and industry doyens for economic policy planning and execution.

The emergence of a clear picture about COVID-19 lifecycle is impossible without integrating data science algorithms and epidemiology theoretical framework. This study amalgamates these two disciplines to undertake predictive modeling based on COVID-19 data from India and five of its states. Population-specific granular and objective assessment of key parameters such as reproduction number (R0), susceptible population (S), effective contact rate (ß) and case-fatality rate (s) have been used to generate a visualization of COVID-19 lifecycle pattern for a critically affected population.

The susceptible-infectious-susceptible (SIS) infectious disease models without spatial heterogeneity have limited applications, and the numerical simulation without considering models’ global existence and uniqueness of classical solutions might converge to an impractical solution. This paper aims to develop a robust and reliable numerical approach to the SIS epidemic model with spatial heterogeneity, which characterizes the horizontal and vertical transmission of the disease.

This study used stability analysis methods from nonlinear dynamics to evaluate the stability of SIS epidemic models. Additionally, the authors applied numerical solution methods from diffusion equations and heat conduction equations in fluid mechanics to infectious disease transmission models with spatial heterogeneity, which can guarantee a robustly stable and highly reliable numerical process. The findings revealed that this interdisciplinary approach not only provides a more comprehensive understanding of the propagation patterns of infectious diseases across various spatial environments but also offers new application directions in the fields of fluid mechanics and heat flow. The results of this study are highly significant for developing effective control strategies against infectious diseases while offering new ideas and methods for related fields of research.

Through theoretical analysis and numerical simulation, the distribution of infected persons in heterogeneous environments is closely related to the location parameters. The finding is suitable for clinical use.

The theoretical analysis of the stability theorem and the threshold dynamics guarantee robust stability and fast convergence of the numerical solution. It opens up a new window for a robust and reliable numerical study.

Traditional epidemic models, like the classical SIR, are fitted to real data using deterministic optimization techniques. As a consequence, their performances cannot be properly assessed and, more importantly, the estimates of the critical epidemic parameters (which are of dramatic importance in monitoring the epidemic evolution) cannot be complemented with the calculation of confidence intervals. The aim of the present work is to remove such limitations and to compare the results obtained using two stochastic versions of deterministic SIR models. We describe the two alternatives and the associated estimation procedures, and we apply the two methodologies to a set of COVID-19 data observed in Italy in the 2020 pandemic wave. Our estimates of the basic reproduction number are comparable with the official sources, but using our methods uncertainty can also be properly assessed.

The years 2011, 2012, and 2013 witnessed both significant developments and setbacks in global polio eradication efforts. On the positive side, January 13, 2012, marked a full year since India had detected a case of wild poliovirus. On the negative side, polio continued to be endemic in three countries-Pakistan, Afghanistan, and Nigeria-and in those countries the goal of eliminating polio seemed more challenging than ever. Between December 2012 and January 2013, sixteen polio workers were killed in Pakistan, and in February 2013, nine women vaccinating children against polio in Kano, Nigeria, were shot dead by gunmen suspected of belonging to a radical Islamist sect. In addition, after a 95 percent decline in polio cases in 2010, the number of cases in Nigeria rebounded in 2011. Recognizing that polio was unlikely to be eliminated in these countries in the near term, the Global Polio Eradication Initiative moved its target date for eradication from 2013 to 2018.

These setbacks sparked a debate about the appropriate strategy for global eradication of polio. Indeed, some experts believed that recent setbacks were not caused by poor management but were instead the result of epidemiological characteristics and preconditions that might render polio eradication unachievable. These experts argued that global health efforts should focus on the control or elimination of polio rather than on the eradication of the disease.

This case presents an overview of polio and the Global Polio Eradication Initiative and recounts the successful effort to eradicate smallpox. The case enables a rich discussion of the current global strategy to eradicate polio, as well as the issue of whether eradication is the appropriate global public health objective. More generally, the case provides a concrete example of a particular type of global public good, namely infectious disease eradication.

After analyzing and discussing the case, students will be able to:

• Understand the nature of a global public good

• Perform a back-of-the-envelope benefit-cost analysis of polio eradication

• Discuss the appropriate strategy for eradicating an infectious disease

• Apply game theory to analyzing which countries would be likely to contribute funds toward global polio eradication

• Discuss the role of private organizations in the provision of global public goods

Understand the nature of a global public good

Perform a back-of-the-envelope benefit-cost analysis of polio eradication

Discuss the appropriate strategy for eradicating an infectious disease

Apply game theory to analyzing which countries would be likely to contribute funds toward global polio eradication

Discuss the role of private organizations in the provision of global public goods

The authors investigate the effect of weather and mobility on the spread of the Covid-19 pandemic.

The authors first estimate the effective reproduction number (Rt) as a proxy of the spread of the Covid-19 pandemic and then study the relationship between the latter and weather and mobility in a panel data framework. The authors use US daily infections data between February and September of 2020 at the county level.

The authors find that lower temperatures are associated with a higher Rt, and this effect is greater at temperatures below 0°C. In addition, mobility reductions related to certain types of locations (retail and recreation, transit stations and workplaces) are effective at reducing Rt, but it is an increase in the time spent in parks that most helps reduce the spread of the pandemic.

The estimates imply that a 20°C fall in temperature from summer to winter would increase Rt by +0.35, which can be the difference between a well-controlled evolution and explosive behavior of the spread of the virus. Applying these coefficients estimated with US county data to aggregate series from other countries helps explain the resurgence of the pandemic in the Northern Hemisphere during the winter of 2020. The results show that mobility reduction and social distance are best policies to cope with the Covid-19 outbreak. This strong policy lesson will help facing similar outbreaks in the future.

Cryptocurrencies lack fundamental values and are often subject to behavioral bias leading to market bubbles. This study aims to investigate the contribution of the coronavirus pandemic to the creation of market bubbles.

This study identifies four major cryptocurrency market bubbles by using the Phillips et al. (2016) (hereafter PSY) test. Subsequently, the co-movements of the coronavirus proxies with PSY measurement using the wavelet approach were studied.

Short-lived bubbles are detected at the beginning of the studied period, and more extended bubble periods are identified at the end. Besides, the empirical results show evidence of significant negative co-movement between each pandemic proxy and each cryptocurrency bubble measurement.

Given the complex financial dynamics of the cryptocurrency markets due to some behavioral biases in some circumstances, investors can benefit from the date stamping of the bubbles bursting to make the best trading positions. In the same way, governments could support the healthy development of cryptocurrencies by preventing bubbles during such pandemics.

The financial bubble is commonly attributed to a change in investor behavior. Because traders and investors think they can resell the asset at a higher price in the future. This study explored the contribution of the COVID-19 pandemic in the creation of these bubbles by date stamping their occurrence and explosive periods. To the best of the authors’ knowledge, this study is the first attempt that explores the contribution of the COVID-19 pandemic to the creation of bubbles caused by a change in the investors’ behavior.

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.

Refugee camps can be severely struck by pandemics, like potential COVID-19 outbreaks, due to high population densities and often only base-level medical infrastructure. Fast responding medical systems can help to avoid spikes in infections and death rates as they allow the prompt isolation and treatment of patients. At the same time, the normal demand for emergency medical services has to be dealt with as well. The overall goal of this study is the design of an emergency service system that is appropriate for both types of demand.

A spatial hypercube queuing model (HQM) is developed that uses queuing-theory methods to determine locations for emergency medical vehicles (also called servers). Therefore, a general optimization approach is applied, and subsequently, virus outbreaks at various locations of the study areas are simulated to analyze and evaluate the solution proposed. The derived performance metrics offer insights into the behavior of the proposed emergency service system during pandemic outbreaks. The Za'atari refugee camp in Jordan is used as a case study.

The derived locations of the emergency medical system (EMS) can handle all non-virus-related emergency demands. If additional demand due to virus outbreaks is considered, the system becomes largely congested. The HQM shows that the actual congestion is highly dependent on the overall amount of outbreaks and the corresponding case numbers per outbreak. Multiple outbreaks are much harder to handle even if their cumulative average case number is lower than for one singular outbreak. Additional servers can mitigate the described effects and lead to enhanced resilience in the case of virus outbreaks and better values in all considered performance metrics.

Some parameters that were assumed for simplification purposes as well as the overall model should be verified in future studies with the relevant designers of EMSs in refugee camps. Moreover, from a practitioners perspective, the application of the model requires, at least some, training and knowledge in the overall field of optimization and queuing theory.

The model can be applied to different data sets, e.g. refugee camps or temporary shelters. The optimization model, as well as the subsequent simulation, can be used collectively or independently. It can support decision-makers in the general location decision as well as for the simulation of stress-tests, like virus outbreaks in the camp area.

The study addresses the research gap in an optimization-based design of emergency service systems for refugee camps. The queuing theory-based approach allows the calculation of precise (expected) performance metrics for both the optimization process and the subsequent analysis of the system. Applied to pandemic outbreaks, it allows for the simulation of the behavior of the system during stress-tests and adds a further tool for designing resilient emergency service systems.

Since joining Bennett College in 2008, Dr. Oh has directed 17 undergraduate students’ research projects in applied mathematics. The National Science Foundation (NSF) awarded Dr. Oh grants from the Historically Black Colleges and Universities – Undergraduate Program (HBCU-UP). The grants allowed her to mentor eight mathematics majors/minors in summer research for four years (2009–2012). Based on the four years of successful undergraduate research (UGR) experiences, she, together with Dr. Jan Rychtar from the University of North Carolina at Greensboro (UNCG), received funding for two summers National Research Experience for Undergraduates (NREUP), an activity of Mathematical Association of America (MAA), funded by the NSF in 2013 and 2014. During the six years of funded UGR, Bennett students made 33 presentations at regional, state, and national conferences; two teams won the outstanding student presentation award and first place for presentation. Three papers were published; two of them by Dr. Oh and one of them with a UGR coauthor. Three projects resulted in manuscripts. As a result of the UGR experiences in 2015, Dr. Oh received three more grants: the MAA NREUP, the NSF’s Center for Undergraduate Research in Mathematics (CURM), and the NSF’s Preparation for Industrial Careers in Mathematical Sciences (PIC Math) program awarded grants. A grant was also submitted to HBC-UP-Targeted Infusion Projects: Computational Mathematics at Bennett College.

Overall, the six years of UGR at Bennett College attained the three goals of: (1) enhancing the quality of undergraduate STEM education and research for a deeper appreciation in those disciplines; (2) supporting increased graduation rates in STEM undergraduate education of females; and (3) broadening participation in the nation’s STEM workforce as well as enrollments in graduate schools.

The purpose of this study is to understand the factors that contribute to the number of reported coronavirus (COVID-19) deaths among low-income and high-income countries, and to understand the sources of differences between these two groups of countries.

Multiple linear regression models evaluate the socio-economic factors that determine COVID-19 deaths in the two groups of countries. The Oaxaca–Blinder decomposition is used to examine sources of differences between these two groups.

Low-income countries report a significantly lower average number of COVID-19 deaths compared to high-income countries. Community mobility and the easiness of carrying the virus from one place to another are significant factors affecting the number of deaths, while life expectancy is only significant in high-income countries. Higher health expenditure is associated with more reported deaths in both high- and low-income countries. Factors such as the transport infrastructure system, life expectancy and the percent of expenditure on health lead to the differences in the number of deaths between high- and low-income countries.

Our study shows that mobility measures taken by individuals to limit the spread of the virus are important to prevent deaths in both high- and low-income countries. Additionally, our results suggest that countries with weak health institutions underestimate the number of deaths from COVID-19, especially low-income countries. The underestimation of COVID-19 deaths could be affecting a great number of people in poverty in low-income economies.

This paper contributes to the emerging literature on COVID-19 and its relation to socio-economic factors by examining the differences in reported between deaths between rates in low-income and high-income countries.