Macroeconomic consequences of the COVID-19 pandemic

Efficacy of host cell serine protease inhibitor MM3122 against SARS-CoV-2 for treatment and prevention of COVID-19

We developed a novel class of peptidomimetic inhibitors targeting several host cell human serine proteases, including transmembrane protease serine 2 (TMPRSS2), matriptase, and hepsin. TMPRSS2 is a membrane-associated protease that is highly expressed in the upper and lower respiratory tracts and is utilized by SARS-CoV-2 and other viruses to proteolytically process their glycoproteins, enabling host cell entry, replication, and dissemination of new virus particles. We have previously shown that compound MM3122 exhibited subnanomolar potency against all three proteases and displayed potent antiviral effects against SARS-CoV-2 in a cell viability assay. Herein, we demonstrate that MM3122 potently inhibits viral replication in human lung epithelial cells and is also effective against the EG.5.1 variant of SARS-CoV-2. Furthermore, we evaluated MM3122 in a mouse model of COVID-19 and demonstrated that MM3122 administered intraperitoneally (IP) before (prophylactic) or after (therapeutic) SARS-CoV-2 infection had significant protective effects against weight loss and lung congestion and reduced pathology. Amelioration of COVID-19 disease was associated with a reduction in proinflammatory cytokine and chemokine production after SARS-CoV-2 infection. Prophylactic, but not therapeutic, administration of MM3122 also reduced virus titers in the lungs of SARS-CoV-2-infected mice. Therefore, MM3122 is a promising lead candidate small-molecule drug for the treatment and prevention of infections caused by SARS-CoV-2 and other coronaviruses.

IMPORTANCE

SARS-CoV-2 and other emerging RNA coronaviruses are a present and future threat in causing widespread endemic and pandemic infection and disease. In this paper, we have shown that the novel host cell protease inhibitor, MM3122, blocks SARS-CoV-2 viral replication and is efficacious as both a prophylactic and a therapeutic drug for the treatment of COVID-19 given intraperitoneally in mice. Targeting host proteins and pathways in antiviral therapy is an underexplored area of research, but this approach promises to avoid drug resistance by the virus, which is common in current antiviral treatments.

Economic impact of government health expenditure: An application of the computable general equilibrium model to the Iran

BACKGROUND

Considering the increase in health expenses and the government's role in health financing, this study investigated the economic impact of increases in the share of the health sector in the government budget while taxes remain unchanged and government spending is fixed.

MATERIAL AND METHODS

The economic model used in this study was a macroeconomic Computable General Equilibrium (CGE) model. This model was calibrated using a 2011 Social Accounting Matrix (SAM) Of Iran. The CGE model was solved with non-linear programming using the General Algebraic Modeling System package, version 2.50. The effect of this simulation on the government budget deficit, the production of different sectors of the economy, and the employment rate was investigated.

RESULTS

Based on our fundings the elasticity of substitution in the agricultural and industrial sectors is higher than in the health and service sector. Also, the biggest decrease in production occurred in the industry, agriculture, and service sectors, respectively. With the doubling of the share of government spending in the health sector, the employment rate of this sector has increased by 40.9%, but the highest decrease in the ignition rate is related to the service sectors (-2.7%), agriculture (-0.23%), and industry (-0.14%).

CONCLUSION

Increasing the share of government spending in the health sector in comparison with other sectors of the economy, provided that government spending is maintained in general, leads to a decrease in production and economic welfare. It seems that the Iranian government should seek to increase the sources of health financing and the share of government expenditures in the health sector with other ways in order to improve the health level of the society and have a positive effect on other economic sectors.

Overview of U.S. COVID-19 vaccine safety surveillance systems

The U.S. COVID-19 vaccination program, which commenced in December 2020, has been instrumental in preventing morbidity and mortality from COVID-19 disease. Safety monitoring has been an essential component of the program. The federal government undertook a comprehensive and coordinated approach to implement complementary safety monitoring systems and to communicate findings in a timely and transparent way to healthcare providers, policymakers, and the public. Monitoring involved both well-established and newly developed systems that relied on both spontaneous (passive) and active surveillance methods. Clinical consultation for individual cases of adverse events following vaccination was performed, and monitoring of special populations, such as pregnant persons, was conducted. This report describes the U.S. government's COVID-19 vaccine safety monitoring systems and programs used by the Centers for Disease Control and Prevention, the U.S. Food and Drug Administration, the Department of Defense, the Department of Veterans Affairs, and the Indian Health Service. Using the adverse event of myocarditis following mRNA COVID-19 vaccination as a model, we demonstrate how the multiple, complementary monitoring systems worked to rapidly detect, assess, and verify a vaccine safety signal. In addition, longer-term follow-up was conducted to evaluate the recovery status of myocarditis cases following vaccination. Finally, the process for timely and transparent communication and dissemination of COVID-19 vaccine safety data is described, highlighting the responsiveness and robustness of the U.S. vaccine safety monitoring infrastructure during the national COVID-19 vaccination program.

The impact of COVID-19 pandemic on the world's major economies: based on a multi-country and multi-sector CGE model

Objective

To quantitatively assess the impact of COVID-19 pandemic on public health, as well as its economic and social consequences in major economies, which is an international public health concern. The objective is to provide a scientific basis for policy interventions.

Subject and methods

This study utilizes a multi-country, multi-sector CGE-COVID-19 model to analyze the repercussions of the pandemic in 2022. The re-search focuses on quantifying the effects of COVID-19 on the macroeconomy and various industry sectors within six economies: the United States, China, the EU, the United Kingdom, Japan, and South Korea.

Results

The COVID-19 pandemic shock had the most significant impact on China and the EU, followed by notable effects observed in the United States and the United Kingdom. In contrast, South Korea and Japan experienced relatively minimal effects. The reduction in output caused by the pandemic has affected major economies in multiple sectors, including real industries such as forestry and fisheries, and the services such as hotels and restaurants.

Conclusion

The overall negative macroeconomic impact of the epidemic on major economies has been significant. Strategic interventions encompassing initiatives like augmenting capital supply, diminishing corporate taxes and fees, offering individual subsidies, and nurturing international cooperation held the potential to mitigate the detrimental economic consequences and enhance the global-economic amid the pan-demic. Consequently, this study contributes to the advancement of global anti-epidemic policies targeting economic recovery. Moreover, using the CGE-COVID-19 model has enriched the exploration of general equilibrium models in PHEIC events.

Assessing the Burden and Cost of COVID-19 Across Variants in Commercially Insured Immunocompromised Populations in the United States: Updated Results and Trends from the Ongoing EPOCH-US Study

INTRODUCTION/METHODS

EPOCH-US is an ongoing, retrospective, observational cohort study among individuals identified in the Healthcare Integrated Research Database (HIRD®) with ≥ 12 months of continuous health plan enrollment. Data were collected for the HIRD population (containing immunocompetent and immunocompromised [IC] individuals), individual IC cohorts (non-mutually exclusive cohorts based on immunocompromising condition and/or immunosuppressive [IS] treatment), and the composite IC population (all unique IC individuals). This study updates previous results with addition of the general population cohort and data specifically for the year of 2022 (i.e., Omicron wave period). To provide healthcare decision-makers the most recent trends, this study reports incidence rates (IR) and severity of first SARS-CoV-2 infection; and relative risk, healthcare utilization, and costs related to first COVID-19 hospitalizations in the full year of 2022 and overall between April 2020 and December 2022.

RESULTS

These updated results showed a 2.9% prevalence of immune compromise in the population. From April 2020 through December 2022, the overall IR of COVID-19 was 115.7 per 1000 patient-years in the composite IC cohort and 77.8 per 1000 patient-years in the HIRD cohort. The composite IC cohort had a 15.4% hospitalization rate with an average cost of $42,719 for first COVID-19 hospitalization. Comparatively, the HIRD cohort had a 3.7% hospitalization rate with an average cost of $28,848 for first COVID-19 hospitalization. Compared to the general population, IC individuals had 4.3 to 23 times greater risk of hospitalization with first diagnosis of COVID-19. Between January and December 2022, hospitalizations associated with first COVID-19 diagnosis cost over $1 billion, with IC individuals (~ 3% of the population) generating $310 million (31%) of these costs.

CONCLUSION

While only 2.9% of the population, IC individuals had a higher risk of COVID-19 hospitalization and incurred higher healthcare costs across variants. They also disproportionately accounted for over 30% of total costs for first COVID-19 hospitalization in 2022, amounting to ~ $310 million. These data highlight the need for additional preventive measures to decrease the risk of developing severe COVID-19 outcomes in vulnerable IC populations.

Efficacy of Host Cell Serine Protease Inhibitor MM3122 against SARS-CoV-2 for Treatment and Prevention of COVID-19

We have developed a novel class of peptidomimetic inhibitors targeting several host cell human serine proteases including transmembrane protease serine 2 (TMPRSS2), matriptase and hepsin. TMPRSS2 is a membrane associated protease which is highly expressed in the upper and lower respiratory tract and is utilized by SARS-CoV-2 and other viruses to proteolytically process their glycoproteins, enabling host cell receptor binding, entry, replication, and dissemination of new virion particles. We have previously shown that compound MM3122 exhibited sub nanomolar potency against all three proteases and displayed potent antiviral effects against SARS-CoV-2 in a cell-viability assay. Herein, we demonstrate that MM3122 potently inhibits viral replication in human lung epithelial cells and is also effective against the EG.5.1 variant of SARS-CoV-2. Further, we have evaluated MM3122 in a mouse model of COVID-19 and have demonstrated that MM3122 administered intraperitoneally (IP) before (prophylactic) or after (therapeutic) SARS-CoV-2 infection had significant protective effects against weight loss and lung congestion, and reduced pathology. Amelioration of COVID-19 disease was associated with a reduction in pro-inflammatory cytokines and chemokines production after SARS-CoV-2 infection. Prophylactic, but not therapeutic, administration of MM3122 also reduced virus titers in the lungs of SARS-CoV-2 infected mice. Therefore, MM3122 is a promising lead candidate small molecule drug for the treatment and prevention of infections caused by SARS-CoV-2 and other coronaviruses.

IMPORTANCE

SARS-CoV-2 and other emerging RNA coronaviruses are a present and future threat in causing widespread endemic and pandemic infection and disease. In this paper, we have shown that the novel host-cell protease inhibitor, MM3122, blocks SARS-CoV-2 viral replication and is efficacious as both a prophylactic and therapeutic drug for the treatment of COVID-19 in mice. Targeting host proteins and pathways in antiviral therapy is an underexplored area of research but this approach promises to avoid drug resistance by the virus, which is common in current antiviral treatments.

Identification of SARS-CoV-2 variants in indoor dust

Environmental surveillance of pathogens underlying infectious disease is critical to ensure public health. Recent efforts to track SARS-CoV-2 have utilized wastewater sampling to infer community trends in viral abundance and variant composition. Indoor dust has also been used for building-level inferences, though to date no sequencing data providing variant-scale resolution have been reported from dust samples, and strategies to monitor circulating variants in dust are needed to help inform public health decisions. In this study, we demonstrate that SARS-CoV-2 lineages can be detected and sequenced from indoor bulk dust samples. We collected 93 vacuum bags from April 2021 to March 2022 from buildings on The Ohio State University's (OSU) Columbus campus, and the dust was used to develop and apply an amplicon-based whole-genome sequencing protocol to identify the variants present and estimate their relative abundances. Three variants of concern were detected in the dust: Alpha, Delta, and Omicron. Alpha was found in our earliest sample in April 2021 with an estimated frequency of 100%. Delta was the primary variant present from October of 2021 to January 2022, with an average estimated frequency of 91% (±1.3%). Omicron became the primary variant in January 2022 and was the dominant strain in circulation through March with an estimated frequency of 87% (±3.2%). The detection of these variants on OSU's campus correlates with the circulation of these variants in the surrounding population (Delta p<0.0001 and Omicron p = 0.02). Overall, these results support the hypothesis that dust can be used to track COVID-19 variants in buildings.

Embracing dynamic public health policy impacts in infectious diseases responses: leveraging implementation science to improve practice

Rationale

The host-pathogen relationship is inherently dynamic and constantly evolving. Applying an implementation science lens to policy evaluation suggests that policy impacts are variable depending upon key implementation outcomes (feasibility, acceptability, appropriateness costs) and conditions and contexts.

COVID-19 case study

Experiences with non-pharmaceutical interventions (NPIs) including masking, testing, and social distancing/business and school closures during the COVID-19 pandemic response highlight the importance of considering public health policy impacts through an implementation science lens of constantly evolving contexts, conditions, evidence, and public perceptions. As implementation outcomes (feasibility, acceptability) changed, the effectiveness of these interventions changed thereby altering public health policy impact. Sustainment of behavioral change may be a key factor determining the duration of effectiveness and ultimate impact of pandemic policy recommendations, particularly for interventions that require ongoing compliance at the level of the individual.

Practical framework for assessing and evaluating pandemic policy

Updating public health policy recommendations as more data and alternative interventions become available is the evidence-based policy approach and grounded in principles of implementation science and dynamic sustainability. Achieving the ideal of real-time policy updates requires improvements in public health data collection and analysis infrastructure and a shift in public health messaging to incorporate uncertainty and the necessity of ongoing changes. In this review, the Dynamic Infectious Diseases Public Health Response Framework is presented as a model with a practical tool for iteratively incorporating implementation outcomes into public health policy design with the aim of sustaining benefits and identifying when policies are no longer functioning as intended and need to be adapted or de-implemented.

Conclusions and implications

Real-time decision making requires sensitivity to conditions on the ground and adaptation of interventions at all levels. When asking about the public health effectiveness and impact of non-pharmaceutical interventions, the focus should be on when, how, and for how long they can achieve public health impact. In the future, rather than focusing on models of public health intervention effectiveness that assume static impacts, policy impacts should be considered as dynamic with ongoing re-evaluation as conditions change to meet the ongoing needs of the ultimate end-user of the intervention: the public.

Direct medical cost of COVID-19 in children hospitalized at a tertiary referral healthcare center in Mexico City

Introduction

Despite the end of the COVID-19 pandemic being declared by the WHO, the economic consequences are far from over. One of these implications was the cost of inpatient care for health institutions. To date, some studies have examined the economic burden of COVID-19 in the adult population but only a few have focused on child populations.

Objective

To estimate the direct medical costs of COVID-19, focusing on children in Mexico.

Method

Data about resources consumed during hospital stays were extracted from the medical records of patients hospitalized at a Mexican tertiary healthcare institution. Other sources of information were the unit prices of inputs and the salaries of health personnel. A micro-costing methodology was used to obtain cost results by age group over different hospital areas. Data analysis was performed with descriptive statistics and regression models to evaluate the predictors of total cost.

Results

One hundred and ten medical records were reviewed of which 57.3% corresponded to male patients and the mean age was 7.2 years old. The estimated average cost per patient was US$5,943 (95% CI: US$4,249-7,637). When the costs of the three clinical areas were summed, only the 5-10 years old group showed a maximum cost of US$14,000. The regression analysis revealed the following factors as significant: sex, age, staying at an emergency room, having a positive bacterial culture, and having comorbidities.

Discussion

The cost results were somewhat similar to those reported in children from the USA, but only regarding low severity COVID-19 cases. However, comparability between these types of studies should be done with caution due to the huge differences between the healthcare systems of countries. The study cost results may help public decision-makers in budget planning and as inputs for future cost-effectiveness studies about interventions regarding COVID-19.

The effectiveness of COVID deaths to COVID policies: A robust conditional approach

This paper examines the effectiveness of four major COVID-19 social distancing policies, (i) shelter-in-place orders (SIPO), (ii) non-essential business closures, (iii) mandatory quarantine for travelers, and (iv) bans on large gatherings, on both COVID cases and COVID deaths. Results indicate that states are highly ineffective in producing the fraction of the population that does not have COVID-19 or the fraction of the population that does not die from COVID-19. We find that having any form of social distancing policies increases the fraction of the population not considered a positive COVID-19 case by 23.5 percentage points. Results also show that having any of the four major social distancing policies reduces the fraction of the population who has died of COVID-19 by 1.3 percentage points between March 1, 2020 and September 1, 2020; during the first 100 days, effectiveness would improve by 2.1 percentage points. Evidence suggests that there is no effective uniform national COVID-19 social distancing policy. Furthermore, conditional efficiency regressions after 100 days suggest that behavioral noncompliance and premature expiration of social distancing policies both negatively impact effectiveness. Partial regression plots suggest that bans on large gatherings and the closure of non-essential businesses were the two most impactful COVID-19 social distancing policies.