How are mathematical models and results from mathematical models of vaccine-preventable diseases used, or not, by global health organisations?

Recent approaches in computational modelling for controlling pathogen threats

In this review, we assess the status of computational modelling of pathogens. We focus on three disparate but interlinked research areas that produce models with very different spatial and temporal scope. First, we examine antimicrobial resistance (AMR). Many mechanisms of AMR are not well understood. As a result, it is hard to measure the current incidence of AMR, predict the future incidence, and design strategies to preserve existing antibiotic effectiveness. Next, we look at how to choose the finite number of bacterial strains that can be included in a vaccine. To do this, we need to understand what happens to vaccine and non-vaccine strains after vaccination programmes. Finally, we look at within-host modelling of antibody dynamics. The SARS-CoV-2 pandemic produced huge amounts of antibody data, prompting improvements in this area of modelling. We finish by discussing the challenges that persist in understanding these complex biological systems.

Defining a malaria diagnostic pathway from innovation to adoption: Stakeholder perspectives on data and evidence gaps

Malaria, a major global health concern, requires effective diagnostic tools for patient care, disease control, and elimination. The pathway from concept to the adoption of diagnostic products is complex, involving multiple steps and stakeholders. To map this process, our study introduces a malaria-specific diagnostic pathway, synthesising existing frameworks with expert insights. Comprising six major stages and 31 related activities, the pathway retains the core stages from existing frameworks and integrates essential malaria diagnostic activities, such as WHO prequalification processes, global stakeholder involvement, and broader health systems considerations. To understand the scope and availability of evidence guiding the activities along this pathway, we conducted an online survey with 113 participants from various stages of the malaria diagnostic pathway. The survey assessed perceptions on four critical attributes of evidence: clear requirements, alignment with user needs, accuracy and reliability, and public and free availability. It also explored the types of evidence used and the challenges and potential solutions related to evidence generation and use. Respondents reported using a broad range of formal and informal data sources. Findings indicated differing levels of agreement on the attributes across pathway stages, with notable challenges in the Approvals and Manufacturing stage and consistent concerns regarding the public availability of data/evidence. The study offers valuable insights for optimising evidence generation and utilisation across the malaria diagnostic pathway. It highlights the need for enhanced stakeholder collaboration, improved data availability, and increased funding to support effective evidence generation, sharing, and use. We propose actionable solutions, including the use of public data repositories, progressive data sharing policies, open-access publishing, capacity-building initiatives, stakeholder engagement forums, and innovative funding solutions. The developed framework and study insights have broader applications, offering a model adaptable for other diseases, particularly for neglected tropical diseases, which face similar diagnostic challenges.

A Framework for Assessing the Impact of Outbreak Response Immunization Programs

The impact of outbreak response immunization (ORI) can be estimated by comparing observed outcomes to modelled counterfactual scenarios without ORI, but the most appropriate metrics depend on stakeholder needs and data availability. This study developed a framework for using mathematical models to assess the impact of ORI for vaccine-preventable diseases. Framework development involved (1) the assessment of impact metrics based on stakeholder interviews and literature reviews determining data availability and capacity to capture as model outcomes; (2) mapping investment in ORI elements to model parameters to define scenarios; (3) developing a system for engaging stakeholders and formulating model questions, performing analyses, and interpreting results; and (4) example applications for different settings and pathogens. The metrics identified as most useful were health impacts, economic impacts, and the risk of severe outbreaks. Scenario categories included investment in the response scale, response speed, and vaccine targeting. The framework defines four phases: (1) problem framing and data sourcing (identification of stakeholder needs, metrics, and scenarios); (2) model choice; (3) model implementation; and (4) interpretation and communication. The use of the framework is demonstrated by application to two outbreaks, measles in Papua New Guinea and Ebola in the Democratic Republic of the Congo. The framework is a systematic way to engage with stakeholders and ensure that an analysis is fit for purpose, makes the best use of available data, and uses suitable modelling methodology.

Evaluation of the use of modelling in resource allocation decisions for HIV and TB

INTRODUCTION

Globally, resources for health spending, including HIV and tuberculosis (TB), are constrained, and a substantial gap exists between spending and estimated needs. Optima is an allocative efficiency modelling tool that has been used since 2010 in over 50 settings to generate evidence for country-level HIV and TB resource allocation decisions. This evaluation assessed the utilisation of modelling to inform financing priorities from the perspective of country stakeholders and their international partners.

METHODS

In October to December 2021, the World Bank and Burnet Institute led 16 semi-structured small-group virtual interviews with 54 representatives from national governments and international health and funding organisations. Interviews probed participants' roles and satisfaction with Optima analyses and how model findings have had been used and impacted resource allocation. Interviewed stakeholders represented nine countries and 11 different disease programme-country contexts with prior Optima modelling analyses. Interview notes were thematically analysed to assess factors influencing the utilisation of modelling evidence in health policy and outcomes.

RESULTS

Common influences on utilisation of Optima findings encompassed the perceived validity of findings, health system financing mechanisms, the extent of stakeholder participation in the modelling process-including engagement of funding organisations, sociopolitical context and timeliness of the analysis. Using workshops can facilitate effective stakeholder engagement and collaboration. Model findings were often used conceptually to localise global evidence and facilitate discussion. Secondary outputs included informing strategic and financial planning, funding advocacy, grant proposals and influencing investment shifts.

CONCLUSION

Allocative efficiency modelling has supported evidence-informed decision-making in numerous contexts and enhanced the conceptual and practical understanding of allocative efficiency. Most immediately, greater involvement of country stakeholders in modelling studies and timing studies to key strategic and financial planning decisions may increase the impact on decision-making. Better consideration for integrated disease modelling, equity goals and financing constraints may improve relevance and utilisation of modelling findings.

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

METHODOLOGY

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

FINDINGS

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

INTERPRETATION

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

A review and analysis of accountability in global health funding, research collaborations and training: towards conceptual clarity and better practice

INTRODUCTION

Accountability is a complex idea to unpack and involves different processes in global health practice. Calls for accountability in global health would be better translated to action through a better understanding of the concept and practice of accountability in global health. We sought to analyse accountability processes in practice in global health funding, research collaborations and training.

METHODS

This study is a literature review that systematically searched PubMed and Scopus for articles on formal accountability processes in global health. We charted information on processes based on accountability lines ('who is accountable to whom') and the outcomes the processes were intended for ('accountability for what'). We visualised the representation of accountability in the articles by mapping the processes according to their intended outcomes and the levels where processes were implemented.

RESULTS

We included 53 articles representing a wide range of contexts and identified 19 specific accountability processes for various outcomes in global health funding, research collaborations and training. Target setting and monitoring were the most common accountability processes. Other processes included interinstitutional networks for peer checking, litigation strategies to enforce health-related rights, special bodies that bring actors to account for commitments, self-accountability through internal organisational processes and multipolar accountability involving different types of institutional actors. Our mapping identified gaps at the institutional, interinstitutional and broader system levels where accountability processes could be enhanced.

CONCLUSION

To rebalance power in global health, our review has shown that analysing information on existing accountability processes regarding 'who is accountable to whom' and 'accountability for what' would be useful to characterise existing lines of accountability and create lines where there are gaps. However, we also suggest that institutional and systems processes for accountability must be accompanied by political engagement to mobilise collective action and create conditions where a culture of accountability thrives in global health.