Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward

Innovative strategies for the surveillance, prevention, and management of pediatric infections applied to low-income settings

INTRODUCTION

Infectious diseases still cause a significant burden of morbidity and mortality among children in low- and middle-income countries (LMICs). There are ample opportunities for innovation in surveillance, prevention, and management, with the ultimate goal of improving survival.

AREAS COVERED

This review discusses the current status in the use and development of innovative strategies for pediatric infectious diseases in LMICs by focusing on surveillance, diagnosis, prevention, and management. Topics covered are: Minimally Invasive Tissue Sampling as a technique to accurately ascertain the cause of death; Genetic Surveillance to trace the pathogen genomic diversity and emergence of resistance; Artificial Intelligence as a multidisciplinary tool; Portable noninvasive imaging methods; and Prognostic Biomarkers to triage and risk stratify pediatric patients.

EXPERT OPINION

To overcome the specific hurdles in child health for LMICs, some innovative strategies appear at the forefront of research. If the development of these next-generation tools remains focused on accessibility, sustainability and capacity building, reshaping epidemiological surveillance, diagnosis, and treatment in LMICs, can become a reality and result in a significant public health impact. Their integration with existing healthcare infrastructures may revolutionize disease detection and surveillance, and improve child health and survival.

A mixed-method approach to determining contact matrices in the Cox's Bazar refugee settlement

Contact matrices are an important ingredient in age-structured epidemic models to inform the simulated spread of the disease between subgroups of the population. These matrices are generally derived using resource-intensive diary-based surveys and few exist in the Global South or tailored to vulnerable populations. In particular, no contact matrices exist for refugee settlements-locations under-served by epidemic models in general. In this paper, we present a novel, mixed-method approach for deriving contact matrices in populations, which combines a lightweight, rapidly deployable survey with an agent-based model of the population informed by census and behavioural data. We use this method to derive the first set of contact matrices for the Cox's Bazar refugee settlement in Bangladesh. To validate our approach, we apply it to the UK population and compare our derived matrices with well-known contact matrices collected using traditional methods. Our findings demonstrate that our mixed-method approach successfully addresses some of the challenges faced by traditional and agent-based approaches to deriving contact matrices. It also shows potential for implementation in resource-constrained environments. This work therefore contributes to a broader aim of developing new methods and mechanisms of data collection for modelling disease spread in refugee and internally displaced person (IDP) settlements and better serving these vulnerable communities.

Participatory Mathematical Modeling Approach for Policymaking during the First Year of the COVID-19 Crisis, Jordan

We engaged in a participatory modeling approach with health sector stakeholders in Jordan to support government decision-making regarding implementing public health measures to mitigate COVID-19 disease burden. We considered the effect of 4 physical distancing strategies on reducing COVID-19 transmission and mortality in Jordan during March 2020-January 2021: no physical distancing; intermittent physical distancing where all but essential services are closed once a week; intermittent physical distancing where all but essential services are closed twice a week; and a permanent physical distancing intervention. Modeling showed that the fourth strategy would be most effective in reducing cases and deaths; however, this approach was only marginally beneficial to reducing COVID-19 disease compared with an intermittently enforced physical distancing intervention. Scenario-based model influenced policy-making and the evolution of the pandemic in Jordan confirmed the forecasting provided by the modeling exercise and helped confirm the effectiveness of the policy adopted by the government of Jordan.

Persistence of the Omicron variant of SARS-CoV-2 in Australia: The impact of fluctuating social distancing

We modelled emergence and spread of the Omicron variant of SARS-CoV-2 in Australia between December 2021 and June 2022. This pandemic stage exhibited a diverse epidemiological profile with emergence of co-circulating sub-lineages of Omicron, further complicated by differences in social distancing behaviour which varied over time. Our study delineated distinct phases of the Omicron-associated pandemic stage, and retrospectively quantified the adoption of social distancing measures, fluctuating over different time periods in response to the observable incidence dynamics. We also modelled the corresponding disease burden, in terms of hospitalisations, intensive care unit occupancy, and mortality. Supported by good agreement between simulated and actual health data, our study revealed that the nonlinear dynamics observed in the daily incidence and disease burden were determined not only by introduction of sub-lineages of Omicron, but also by the fluctuating adoption of social distancing measures. Our high-resolution model can be used in design and evaluation of public health interventions during future crises.