The economic reaction to non-pharmaceutical interventions during Covid-19

Modeling Supply and Demand Dynamics of Vaccines against Epidemic-Prone Pathogens: Case Study of Ebola Virus Disease

Health emergencies caused by epidemic-prone pathogens (EPPs) have increased exponentially in recent decades. Although vaccines have proven beneficial, they are unavailable for many pathogens. Furthermore, achieving timely and equitable access to vaccines against EPPs is not trivial. It requires decision-makers to capture numerous interrelated factors across temporal and spatial scales, with significant uncertainties, variability, delays, and feedback loops that give rise to dynamic and unexpected behavior. Therefore, despite progress in filling R&D gaps, the path to licensure and the long-term viability of vaccines against EPPs continues to be unclear. This paper presents a quantitative system dynamics modeling framework to evaluate the long-term sustainability of vaccine supply under different vaccination strategies. Data from both literature and 50 expert interviews are used to model the supply and demand of a prototypical Ebolavirus Zaire (EBOV) vaccine. Specifically, the case study evaluates dynamics associated with proactive vaccination ahead of an outbreak of similar magnitude as the 2018-2020 epidemic in North Kivu, Democratic Republic of the Congo. The scenarios presented demonstrate how uncertainties (e.g., duration of vaccine-induced protection) and design criteria (e.g., priority geographies and groups, target coverage, frequency of boosters) lead to important tradeoffs across policy aims, public health outcomes, and feasibility (e.g., technical, operational, financial). With sufficient context and data, the framework provides a foundation to apply the model to a broad range of additional geographies and priority pathogens. Furthermore, the ability to identify leverage points for long-term preparedness offers directions for further research.

Individual preferences, government policy, and COVID-19: A game-theoretic epidemiological analysis

Purpose

The ongoing COVID-19 pandemic imposes serious short-term and long-term health costs on populations. Restrictive government policy measures decrease the risks of infection, but produce similarly serious social, mental health, and economic problems. Citizens have varying preferences about the desirability of restrictive policies, and governments are thus forced to navigate this tension in making pandemic policy. This paper analyses the situation facing government using a game-theoretic epidemiological model.

Methodology

We classify individuals into health-centered individuals and freedom-centered individuals to capture the heterogeneous preferences of citizens. We first use the extended Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) model (adding individual preferences) and the signaling game model (adding government) to analyze the strategic situation against the backdrop of a realistic model of COVID-19 infection.

Findings

We find the following: 1. There exists two pooling equilibria. When health-centered and freedom-centered individuals send anti-epidemic signals, the government will adopt strict restrictive policies under budget surplus or balance. When health-centered and freedom-centered individuals send freedom signals, the government chooses not to implement restrictive policies. 2. When governments choose not to impose restrictions, the extinction of an epidemic depends on whether it has a high infection transmission rate; when the government chooses to implement non-pharmacological interventions (NPIs), whether an epidemic will disappear depends on how strict the government's restrictions are.

Originality/value

Based on the existing literature, we add individual preferences and put the government into the game as a player. Our research extends the current form of combining epidemiology and game theory. By using both we get a more realistic understanding of the spread of the virus and combine that with a richer understanding of the strategic social dynamics enabled by game theoretic analysis. Our findings have important implications for public management and government decision-making in the context of COVID-19 and for potential future public health emergencies.

Impact of vaccination and non-pharmacological interventions on COVID-19: a review of simulation modeling studies in Asia

Introduction

Epidemiological modeling is widely used to offer insights into the COVID-19 pandemic situation in Asia. We reviewed published computational (mathematical/simulation) models conducted in Asia that assessed impacts of pharmacological and non-pharmacological interventions against COVID-19 and their implications for vaccination strategy.

Methods

A search of the PubMed database for peer-reviewed, published, and accessible articles in English was performed up to November 2022 to capture studies in Asian populations based on computational modeling of outcomes in the COVID-19 pandemic. Extracted data included model type (mechanistic compartmental/agent-based, statistical, both), intervention type (pharmacological, non-pharmacological), and procedures for parameterizing age. Findings are summarized with descriptive statistics and discussed in terms of the evolving COVID-19 situation.

Results

The literature search identified 378 results, of which 59 met criteria for data extraction. China, Japan, and South Korea accounted for approximately half of studies, with fewer from South and South-East Asia. Mechanistic models were most common, either compartmental (61.0%), agent-based (1.7%), or combination (18.6%) models. Statistical modeling was applied less frequently (11.9%). Pharmacological interventions were examined in 59.3% of studies, and most considered vaccination, except one study of an antiviral treatment. Non-pharmacological interventions were also considered in 84.7% of studies. Infection, hospitalization, and mortality were outcomes in 91.5%, 30.5%, and 30.5% of studies, respectively. Approximately a third of studies accounted for age, including 10 that also examined mortality. Four of these studies emphasized benefits in terms of mortality from prioritizing older adults for vaccination under conditions of a limited supply; however, one study noted potential benefits to infection rates from early vaccination of younger adults. Few studies (5.1%) considered the impact of vaccination among children.

Conclusion

Early in the COVID-19 pandemic, non-pharmacological interventions helped to mitigate the health burden of COVID-19; however, modeling indicates that high population coverage of effective vaccines will complement and reduce reliance on such interventions. Thus, increasing and maintaining immunity levels in populations through regular booster shots, particularly among at-risk and vulnerable groups, including older adults, might help to protect public health. Future modeling efforts should consider new vaccines and alternative therapies alongside an evolving virus in populations with varied vaccination histories.

Non-pharmaceutical interventions for COVID-19 reduced the incidence of infectious diseases: a controlled interrupted time-series study

BACKGROUND

Non-pharmaceutical interventions (NPIs) have been implemented worldwide to suppress the spread of coronavirus disease 2019 (COVID-19). However, few studies have evaluated the effect of NPIs on other infectious diseases and none has assessed the avoided disease burden associated with NPIs. We aimed to assess the effect of NPIs on the incidence of infectious diseases during the COVID-19 pandemic in 2020 and evaluate the health economic benefits related to the reduction in the incidence of infectious diseases.

METHODS

Data on 10 notifiable infectious diseases across China during 2010-2020 were extracted from the China Information System for Disease Control and Prevention. A two-stage controlled interrupted time-series design with a quasi-Poisson regression model was used to examine the impact of NPIs on the incidence of infectious diseases. The analysis was first performed at the provincial-level administrative divisions (PLADs) level in China, then the PLAD-specific estimates were pooled using a random-effect meta-analysis.

RESULTS

A total of 61,393,737 cases of 10 infectious diseases were identified. The implementation of NPIs was associated with 5.13 million (95% confidence interval [CI] 3.45‒7.42) avoided cases and USD 1.77 billion (95% CI 1.18‒2.57) avoided hospital expenditures in 2020. There were 4.52 million (95% CI 3.00‒6.63) avoided cases for children and adolescents, corresponding to 88.2% of total avoided cases. The top leading cause of avoided burden attributable to NPIs was influenza [avoided percentage (AP): 89.3%; 95% CI 84.5‒92.6]. Socioeconomic status and population density were effect modifiers.

CONCLUSIONS

NPIs for COVID-19 could effectively control the prevalence of infectious diseases, with patterns of risk varying by socioeconomic status. These findings have important implications for informing targeted strategies to prevent infectious diseases.

An optimal control policy in fighting COVID-19 and infectious diseases

When an outbreak starts spreading, policymakers have to make decisions that affect the health of their citizens and the economy. Some might induce harsh measures, such as a lockdown. Following a long, harsh lockdown, the recession forces policymakers to rethink reopening. To provide an effective strategy, here we propose a control strategy model. Our model assesses the trade-off between social performance and limited medical resources by determining individuals' propensities. The proposed strategy also helps decision-makers to find optimal lockdown and exit strategies for each region. Moreover, the financial loss is minimized. We use the public sentiment information during the pandemic to determine the percentage of individuals with high-risk behavior and the percentage of individuals with low-risk behavior. Hence, we propose an online platform using fear-sentiment information to estimate the personal protective equipment (PPE) burn rate overtime for the entire population. In addition, a study of a COVID-19 dataset for Los Angeles County is performed to validate our model and its results. The total social cost reduces by 18% compared with a control strategy where susceptible individuals are assumed to be homogeneous. We also reduce the total social costs by 26% and 22% compared to other strategies that consider the health-care cost or the social performance cost, respectively.

Coronavirus pandemic in the Nordic countries: Health policy and economy trade-off

Background

Countries making up the Nordic region - Denmark, Finland, Iceland, Norway, and Sweden - have minimal socioeconomic, cultural, and geographical differences between them, allowing for a fair comparative analysis of the health policy and economy trade-off in their national approaches towards mitigating the impact of the COVID-19 pandemic.

Methods

This study utilized publicly available COVID-19 data of the Nordic countries from January 2020 to January 3, 2021. COVID-19 epidemiology, public health and health policy, health system capacity, and macroeconomic data were analysed for each Nordic country. Joinpoint regression analysis was performed to identify changes in temporal trends using average monthly percent change (AMPC) and average weekly percent change (AWPC).

Results

Sweden's health policy, being by far the most relaxed response to COVID-19, was found to have the largest COVID-19 incidence and mortality, and the highest AWPC increases for both indicators (13.5, 95% CI = 5.6, 22.0, P < 0.001; 6.3, 95% CI = 3.5, 9.1, P < 0.001). Denmark had the highest number of COVID-19 tests per capita, consistent with their approach of increased testing as a preventive strategy for disease transmission. Iceland had the second-highest number of tests per capita due to their mass-testing, contact tracing, quarantine and isolation response. Only Norway had a significant increase in unemployment (AMPC = 2.8%, 95% CI = 0.7-4.9, P < 0.009) while the percentage change in real Gross Domestic Product (GDP) was insignificant for all countries.

Conclusions

There was no trade-off between public health policy and economy during the COVID-19 pandemic in the Nordic region. Sweden's relaxed and delayed COVID-19 health policy response did not benefit the economy in the short term, while leading to disproportionate COVID-19 hospitalizations and mortality.