A synthetic control analysis of U.S. state level COVID-19 stay-at-home orders on new jobless claims

There is an ongoing debate regarding the economic consequences of public policies designed to curb public health crises, such as the COVID-19 pandemic. Many opponents of such policies claim that their economic costs may outweigh their health benefits. In this paper, we use synthetic control analysis to determine the impact of stay-at-home orders on weekly new jobless claims during the initial phase of the COVID-19 pandemic. Our analysis reveals that while new jobless claims spike following the stay-at-home orders, similar spikes are observed within our synthetic control. Specifically, we find that stay-at-home orders account for only 32 percent of the increase in new jobless claims, with the majority of the increase being driven by factors outside of the policy, such as the general spread of the virus and waning consumer confidence. © 2022, Academy of Economics and Finance.

Mathematical model of COVID-19 transmission dynamics incorporating booster vaccine program and environmental contamination.

COVID-19 is one of the greatest human global health challenges that causes economic meltdown of many nations. In this study, we develop an SIR-type model which captures both human-to-human and environment-to-human-to-environment transmissions that allows the recruitment of corona viruses in the environment in the midst of booster vaccine program. Theoretically, we prove some basic properties of the full model as well as investigate the existence of SARS-CoV-2-free and endemic equilibria. The SARS-CoV-2-free equilibrium for the special case, where the constant inflow of corona virus into the environment by any other means, Ω is suspended ( Ω = 0 ) is globally asymptotically stable when the effective reproduction number R 0 c < 1 and unstable if otherwise. Whereas in the presence of free-living Corona viruses in the environment ( Ω > 0 ), the endemic equilibrium using the centre manifold theory is shown to be stable globally whenever R 0 c > 1 . The model is extended into optimal control system and analyzed analytically using Pontryagin's Maximum Principle. Results from the optimal control simulations show that strategy E for implementing the public health advocacy, booster vaccine program, treatment of isolated people and disinfecting or fumigating of surfaces and dead bodies before burial is the most effective control intervention for mitigating the spread of Corona virus. Importantly, based on the available data used, the study also revealed that if at least 70% of the constituents followed the aforementioned public health policies, then herd immunity could be achieved for COVID-19 pandemic in the community.

Application of fractional optimal control theory for the mitigating of novel coronavirus in Algeria.

In this paper, we investigate the dynamics of novel coronavirus infection (COVID-19) using a fractional mathematical model in Caputo sense. Based on the spread of COVID-19 virus observed in Algeria, we formulate the model by dividing the infected population into two sub-classes namely the reported and unreported infective individuals. The existence and uniqueness of the model solution are given by using the well-known Picard-Lindelöf approach. The basic reproduction number R 0 is obtained and its value is estimated from the actual cases reported in Algeria. The model equilibriums and their stability analysis are analyzed. The impact of various constant control parameters is depicted for integer and fractional values of α . Further, we perform the sensitivity analysis showing the most sensitive parameters of the model versus R 0 to predict the incidence of the infection in the population. Further, based on the sensitivity analysis, the Caputo model with constant controls is extended to time-dependent variable controls in order obtain a fractional optimal control problem. The associated four time-dependent control variables are considered for the prevention, treatment, testing and vaccination. The fractional optimality condition for the control COVID-19 transmission model is presented. The existence of the Caputo optimal control model is studied and necessary condition for optimality in the Caputo case is derived from Pontryagin's Maximum Principle. Finally, the effectiveness of the proposed control strategies are demonstrated through numerical simulations. The graphical results revealed that the implantation of time-dependent controls significantly reduces the number of infective cases and are useful in mitigating the infection.

Energy-food nexus scarcity risk and the synergic impact of climate policy: A global production network perspective

Carbon neutrality has been a global consensus to navigate away from catastrophic climate change. In particular, such climate changes also generate inevitable influences on economic securities, such as energy security and food security, through energy structure transformation etc. Energy and food are essential elements for human beings, and they are naturally linked to sustainable development. Usually, emergency events, such as the COVID-19 pandemic, may threaten energy security or food security in a region, the risk of which would be amplified due to the energy-food nexus effect. This is no doubt also a challenge and an opportunity for countries to achieve carbon neutrality. To realize the stable pathways to carbon neutrality, it is important to analyze the energy scarcity risk and food scarcity risk of each industry and country as well as the nexus effect between energy and food. In this paper, we combine multi-regional input-output (MRIO) analysis with network control analysis (NCA) to investigate the dependence degree of each country and region on energy and food resources as well as the risk transmission network of the energy-food scarcity nexus. Base on this, the impact of climate policy on energy-food nexus scarcity risk is analyzed. We found some interesting conclusions. First, regarding the risk transmission network of the energy-food scarcity nexus, China, Germany and the US are the main generators, and the main receptors are Taiwan, Mexico and the Netherlands. These results imply that international trade transfers energy/food scarcity to geographically distant regions via the international supply chain. Second, as for the scarcity risk per unit of output, small economies that rely heavily on imported energy or food (such as Cyprus and Luxemburg) have the highest scarcity risk and are among the top receptors of transmitted risks. We suggest collaborative conservation and management of energy and food resources. Third, the analyses that assess the emission intensity and scarcity risk find that implementation of emission control policies could significantly decrease initial energy scarcity risk and energy-food nexus scarcity risk. This implies that besides emission reduction achievement, climate policies bring co-benefits of energy-food nexus security. Moreover, the co-benefit of energy and food nexus security for low income economies associated with climate policy is much higher than that for high income economies.

Exploring the Impact of the US’s Lottery Incentives on COVID-19 Vaccination Rates

Many states in the US have carried out lotteries with tantalizing prizes to reduce the covid-19 vaccine hesitancy. However, there has yet been a consensus regarding the effectiveness of such incentives. This study conducts a synthetic control analysis for each treated state, to provide a better understanding of the influence that the lottery programs have made on the vaccination rates across different states. However, for all treated states, no evidence is found for the effects of the lotteries. Next, the article investigates the impact of people's policy ideology on the prediction of the vaccination rates under the synthetic control models. Within each treated state, counties are categorized into two regions according to their political affiliation. The comparison of the treatment effects between the two regions indicates that there is no relationship between people's policy ideology and the vaccination rates. © 2021 ACM.

Mathematical assessment of the role of denial on COVID-19 transmission with non-linear incidence and treatment functions.

A mathematical model describing the dynamics of Corona virus disease 2019 (COVID-19) is constructed and studied. The model assessed the role of denial on the spread of the pandemic in the world. Dynamic stability analyzes show that the equilibria, disease-free equilibrium (DFE) and endemic equilibrium point (EEP) of the model are globally asymptotically stable for R 0 < 1 and R 0 > 1 , respectively. Again, the model is shown via numerical simulations to possess the backward bifurcation, where a stable DFE co-exists with one or more stable endemic equilibria when the control reproduction number, R 0 is less than unity and the rate of denial of COVID-19 is above its upper bound. We then apply the optimal control strategy for controlling the spread of the disease using the controllable variables such as COVID-19 prevention, hospitalization and maximum treatment efforts. Using the Pontryagin maximum principle, we derive analytically the optimal controls of the model. The aforementioned control strategies are performed numerically in the presence of denial and without denial rate. Among such experiments, results without denial have shown to be more productive in ending the pandemic than others where the denial of the disease invalidates the effectiveness of the controls causing the disease to continue ravaging the globe.

HLA-A*11:01:01:01, HLA-C*12:02:02:01-HLA-B*52:01:02:02, Age and Sex Are Associated With Severity of Japanese COVID-19 With Respiratory Failure.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 2019 (COVID-19) was announced as an outbreak by the World Health Organization (WHO) in January 2020 and as a pandemic in March 2020. The majority of infected individuals have experienced no or only mild symptoms, ranging from fully asymptomatic cases to mild pneumonic disease. However, a minority of infected individuals develop severe respiratory symptoms. The objective of this study was to identify susceptible HLA alleles and clinical markers that can be used in risk prediction model for the early identification of severe COVID-19 among hospitalized COVID-19 patients. A total of 137 patients with mild COVID-19 (mCOVID-19) and 53 patients with severe COVID-19 (sCOVID-19) were recruited from the Center Hospital of the National Center for Global Health and Medicine (NCGM), Tokyo, Japan for the period of February-August 2020. High-resolution sequencing-based typing for eight HLA genes was performed using next-generation sequencing. In the HLA association studies, HLA-A*11:01:01:01 [Pc = 0.013, OR = 2.26 (1.27-3.91)] and HLA-C*12:02:02:01-HLA-B*52:01:01:02 [Pc = 0.020, OR = 2.25 (1.24-3.92)] were found to be significantly associated with the severity of COVID-19. After multivariate analysis controlling for other confounding factors and comorbidities, HLA-A*11:01:01:01 [P = 3.34E-03, OR = 3.41 (1.50-7.73)], age at diagnosis [P = 1.29E-02, OR = 1.04 (1.01-1.07)] and sex at birth [P = 8.88E-03, OR = 2.92 (1.31-6.54)] remained significant. The area under the curve of the risk prediction model utilizing HLA-A*11:01:01:01, age at diagnosis, and sex at birth was 0.772, with sensitivity of 0.715 and specificity of 0.717. To the best of our knowledge, this is the first article that describes associations of HLA alleles with COVID-19 at the 4-field (highest) resolution level. Early identification of potential sCOVID-19 could help clinicians prioritize medical utility and significantly decrease mortality from COVID-19.

Sensitivity assessment and optimal economic evaluation of a new COVID-19 compartmental epidemic model with control interventions.

Optimal economic evaluation is pivotal in prioritising the implementation of non-pharmaceutical and pharmaceutical interventions in the control of diseases. Governments, decision-makers and policy-makers broadly need information about the effectiveness of a control intervention concerning its cost-benefit to evaluate whether a control intervention offers the best value for money. The outbreak of COVID-19 in December 2019, and the eventual spread to other parts of the world, have pushed governments and health authorities to take drastic socioeconomic, sociocultural and sociopolitical measures to curb the spread of the virus, SARS-CoV-2. To help policy-makers, health authorities and governments, we propose a Susceptible, Exposed, Asymptomatic, Quarantined asymptomatic, Severely infected, Hospitalized, Recovered, Recovered asymptomatic, Deceased, and Protective susceptible (individuals who observe health protocols) compartmental structure to describe the dynamics of COVID-19. We fit the model to real data from Ghana and Egypt to estimate model parameters using standard incidence rate. Projections for disease control and sensitivity analysis are presented using MATLAB. We noticed that multiple peaks (waves) of COVID-19 for Ghana and Egypt can be prevented if stringent health protocols are implemented for a long time and/or the reluctant behaviour on the use of protective equipment by individuals are minimized. The sensitivity analysis suggests that: the rate of diagnoses and testing, the rate of quarantine through doubling enhanced contact tracing, adhering to physical distancing, adhering to wearing of nose masks, sanitizing-washing hands, media education remains the most effective measures in reducing the control reproduction number R c , to less than unity in the absence of vaccines and therapeutic drugs in Ghana and Egypt. Optimal control and cost-effectiveness analysis are rigorously studied. The main finding is that having two controls (transmission reduction and case isolation) is better than having one control, but is economically expensive. In case only one control is affordable, then transmission reduction is better than case isolation. Hopefully, the results of this research should help policy-makers when dealing with multiple waves of COVID-19.

Modeling the pandemic trend of 2019 Coronavirus with optimal control analysis.

In this work, we propose a mathematical model to analyze the outbreak of the Coronavirus disease (COVID-19). The proposed model portrays the multiple transmission pathways in the infection dynamics and stresses the role of the environmental reservoir in the transmission of the disease. The basic reproduction number R 0 is calculated from the model to assess the transmissibility of the COVID-19. We discuss sensitivity analysis to clarify the importance of epidemic parameters. The stability theory is used to discuss the local as well as the global properties of the proposed model. The problem is formulated as an optimal control one to minimize the number of infected people and keep the intervention cost as low as possible. Medical mask, isolation, treatment, detergent spray will be involved in the model as time dependent control variables. Finally, we present and discuss results by using numerical simulations.

Optimal control and sensitivity analysis for transmission dynamics of Coronavirus.

Analysis of mathematical models designed for COVID-19 results in several important outputs that may help stakeholders to answer disease control policy questions. A mathematical model for COVID-19 is developed and equilibrium points are shown to be locally and globally stable. Sensitivity analysis of the basic reproductive number (R0) showed that the rate of transmission from asymptomatically infected cases to susceptible cases is the most sensitive parameter. Numerical simulation indicated that a 10% reduction of R0 by reducing the most sensitive parameter results in a 24% reduction of the size of exposed cases. Optimal control analysis revealed that the optimal practice of combining all three (public health education, personal protective measure, and treating COVID-19 patients) intervention strategies or combination of any two of them leads to the required mitigation of transmission of the pandemic.