Exploring uncertainty and risk in the accelerated response to a COVID-19 vaccine: Perspective from the pharmaceutical industry

Preparing for the upcoming 2022/23 influenza season: A modelling study of the susceptible population in Australia, France, Germany, Italy, Spain and the United Kingdom

We analysed the influenza epidemic that occurred in Australia during the 2022 winter using an age-structured dynamic transmission model, which accounts for past epidemics to estimate the population susceptibility to an influenza infection. We applied the same model to five European countries. Our analysis suggests Europe might experience an early and moderately large influenza epidemic. Also, differences may arise between countries, with Germany and Spain experiencing larger epidemics, than France, Italy and the United Kingdom, especially in children.

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support

The COVID-19 pandemic has highlighted the lack of preparedness of many healthcare systems against pandemic situations. In response, many population-level computational modeling approaches have been proposed for predicting outbreaks, spatiotemporally forecasting disease spread, and assessing as well as predicting the effectiveness of (non-) pharmaceutical interventions. However, in several countries, these modeling efforts have only limited impact on governmental decision-making so far. In light of this situation, the review aims to provide a critical review of existing modeling approaches and to discuss the potential for future developments.

A Bayesian predictive analytics model for improving long range epidemic forecasting during an infection wave

Following the outbreak of the coronavirus epidemic in early 2020, municipalities, regional governments and policymakers worldwide had to plan their Non-Pharmaceutical Interventions (NPIs) amidst a scenario of great uncertainty. At this early stage of an epidemic, where no vaccine or medical treatment is in sight, algorithmic prediction can become a powerful tool to inform local policymaking. However, when we replicated one prominent epidemiological model to inform health authorities in a region in the south of Brazil, we found that this model relied too heavily on manually predetermined covariates and was too reactive to changes in data trends. Our four proposed models access data of both daily reported deaths and infections as well as take into account missing data (e.g., the under-reporting of cases) more explicitly, with two of the proposed versions also attempting to model the delay in test reporting. We simulated weekly forecasting of deaths from the period from 31/05/2020 until 31/01/2021, with first week data being used as a cold-start to the algorithm, after which we use a lighter variant of the model for faster forecasting. Because our models are significantly more proactive in identifying trend changes, this has improved forecasting, especially in long-range predictions and after the peak of an infection wave, as they were quicker to adapt to scenarios after these peaks in reported deaths. Assuming reported cases were under-reported greatly benefited the model in its stability, and modelling retroactively-added data (due to the "hot" nature of the data used) had a negligible impact on performance.

Supply chain risk factor assessment of Indian pharmaceutical industry for performance improvement

Purpose

The purpose of the present work is to improve the industry performance by identifying and quantifying the risks faced by the Indian pharmaceutical industry (IPI). The risk values for the prominent risks and overall industry are determined based on the four risk parameters, which would help determine the most contributive risks for mitigation.

Design/methodology/approach

An extensive literature survey was done to identify the risks, which were also validated by industry experts. The finalized risks were then evaluated using the fuzzy synthetic evaluation (FSE) method, which is the most suitable approach for the risk assessment with parameters having a set of different risk levels.

Findings

The three most contributive sub-risks are counterfeit drugs, demand fluctuations and loss of customers due to partners' poor service performance, while the main risks obtained are demand, financial and logistics. Also, the overall risk value indicates that the industry faces medium to high risk.

Practical implications

The study identifies the critical risks which need to be mitigated for an efficient industry. The industry is most vulnerable to the demand risk category. Therefore, the managers should minimize this risk by mitigating its sub-risks, like demand fluctuations, bullwhip effect, etc. Another critical sub-risk, the counterfeit risk, should be managed by adopting advanced technologies like blockchain, artificial intelligence, etc.

Originality/value

There is insufficient literature focusing on risk quantification. Therefore, this work addresses this gap and obtains the industry's most critical risks. It also discusses suitable mitigation strategies for better industry performance.

COVID-19 exit strategy during vaccine implementation: a balance between social distancing and herd immunity

Currently, health authorities around the world are struggling to limit the spread of COVID-19. Since the beginning of the pandemic, social distancing has been the most important strategy used by most countries to control disease spread by flattening and elongating the epidemic curve. Another strategy, herd immunity, was also applied by some countries through relaxed control measures that allow the free spread of natural infection to build up solid immunity within the population. In 2021, COVID-19 vaccination was introduced with tremendous effort as a promising strategy for limiting the spread of disease. Therefore, in this review, we present the current knowledge about social distancing, herd immunity strategies, and aspects of their implementation to control the COVID-19 pandemic in the presence of the newly developed vaccines. Finally, we suggest a short-term option for controlling the pandemic during vaccine application.

Innovations and development of Covid-19 vaccines: A patent review

More than 125 million confirmed cases of COVID-19 have been reported globally with rising cases in all countries since the first case was reported. A vaccine is the best measure for the effective prevention and control of COVID-19. There are more than 292 COVID-19 candidates' vaccines being developed as of July 2021 of which 184 are in human preclinical trials. A patent provides protection and a marketing monopoly to the inventor of an invention for a specified period. Therefore, vaccine developers, including Moderna, BioNTech, Janssen, Inovio, and Gamaleya also filed patent applications for the protection of their vaccines. This review aims to provide an insight into the patent literature of COVID-19 vaccines. The patent search was done using Patentscope and Espacenet databases. The results have revealed that most of the key players have patented their inventive COVID-19 vaccine. Many patent applications related to COVID-19 vaccines developed via different technologies (DNA, RNA, virus, bacteria, and protein subunit) have also been filed. The publication of a normal patent application takes place after 18 months of its filing. Therefore, many patents/patent applications related to the COVID-19 vaccine developed through different technology may come into the public domain in the coming days.

Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics

There is a compelling and pressing need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February and late March 2021 shows that, despite the overall strength of positive sentiment and despite the increasing numbers of Americans being fully vaccinated, negative sentiment towards COVID-19 vaccines still persists among segments of people who are hesitant towards the vaccine. In this study, we perform sentiment analytics on vaccine tweets, monitor changes in public sentiment over time, contrast vaccination sentiment scores with actual vaccination data from the US CDC and the Household Pulse Survey (HPS), explore the influence of maturity of Twitter user-accounts and generate geographic mapping of tweet sentiments. We observe that fear sentiment remained unchanged in populous states, whereas trust sentiment declined slightly in these same states. Changes in sentiments were more notable among less populous states in the central sections of the US. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework, which was developed for COVID-19 sentiment analytics, to systematically posit implications for public policy processes with the aim of improving the positioning, messaging, and administration of vaccines. These insights are expected to contribute to policies that can expedite the vaccination program and move the nation closer to the cherished herd immunity goal.

A mathematical programming approach for equitable COVID-19 vaccine distribution in developing countries

Developing countries scramble to contain and mitigate the spread of coronavirus disease 2019 (COVID-19), and world leaders demand equitable distribution of vaccines to trigger economic recovery. Although numerous strategies, including education, quarantine, and immunization, have been used to control COVID-19, the best method to curb this disease is vaccination. Due to the high demand for COVID 19 vaccine, developing countries must carefully identify and prioritize vulnerable populations and rationalize the vaccine allocation process. This study presents a mixed-integer linear programming model for equitable COVID-19 vaccine distribution in developing countries. Vaccines are grouped into cold, very cold, and ultra-cold categories where specific refrigeration is required for their storage and distribution. The possibility of storage for future periods, facing a shortage, budgetary considerations, manufacturer selection, order allocation, time-dependent capacities, and grouping of the heterogeneous population are among the practical assumptions in the proposed approach. Real-world data is used to demonstrate the efficiency and effectiveness of the mathematical programming approach proposed in this study.

To vaccinate or not to vaccinate; that is the question! (New insights on COVID-19 Vaccination)

AIM

This is a mini-review of the literature; to discuss the obstacles and benefits of vaccination in the era of current pandemic, either the COVID-19 vaccines, which are on their way to be released or the influenza vaccines. There is much debate concerning their effectiveness on ameliorating the severity of the COVID-19 pandemic.

METHODOLOGY

Searching the literature till November 2020 in the PubMed database.

RESULTS

Pathophysiology behind the COVID-19 vaccination obstacles is discussed in detail with future hopes. Influenza vaccination during the debate of the pandemic is also discussed with the most recent guidelines.

CONCLUSIONS

During the COVID-19 pandemic, influenza vaccination is mandatory for all individuals provided no contraindications. Three SARS-CoV-2 vaccines are being released , while FDA approval for monoclonal antibodies for the treatment of at-risk outpatients to lower hospitalization rates is ongoing.

Potential impact of introducing vaccines against COVID-19 under supply and uptake constraints in France: A modelling study

BACKGROUND

The accelerated vaccine development in response to the COVID-19 pandemic should lead to a vaccine being available early 2021, albeit in limited supply and possibly without full vaccine acceptance. We assessed the short-term impact of a COVID-19 immunization program with varying constraints on population health and non-pharmaceutical interventions (NPIs) needs.

METHODS

A SARS-CoV-2 transmission model was calibrated to French epidemiological data. We defined several vaccine implementation scenarios starting in January 2021 based on timing of discontinuation of NPIs, supply and uptake constraints, and their relaxation. We assessed the number of COVID-19 hospitalizations averted, the need for and number of days with NPIs in place over the 2021-2022 period.

RESULTS

An immunisation program under constraints could reduce the burden of COVID-19 hospitalizations by 9-40% if the vaccine prevents against infections. Relaxation of constraints not only reduces further COVID-19 hospitalizations (30-39% incremental reduction), it also allows for NPIs to be discontinued post-2021 (0 days with NPIs in 2022 versus 11 to 125 days for vaccination programs under constraints and 327 in the absence of vaccination).

CONCLUSION

For 2021, COVID-19 control is expected to rely on a combination of NPIs and the outcome of early immunisation programs. The ability to overcome supply and uptake constraints will help prevent the need for further NPIs post-2021. As the programs expand, efficiency assessments will be needed to ensure optimisation of control policies post-emergency use.