Traditional Epidemiology Research Articles

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders.

The coronavirus disease of 2019 (COVID-19) pandemic is characterized by sequential emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, lineages, and sublineages, outcompeting previously circulating ones because of, among other factors, increased transmissibility and immune escape. We propose DeepAutoCoV, an unsupervised deep learning anomaly detection system to predict future dominant lineages (FDLs). We define FDLs as viral (sub)lineages that will constitute more than 10% of all the viral sequences added to the GISAID database on a given week. DeepAutoCoV is trained and validated by assembling global and country-specific data sets from over 16 million Spike protein sequences sampled over a period of about 4 years. DeepAutoCoV successfully flags FDLs at very low frequencies (0.01% - 3%), with median lead times of 4-17 weeks, and predicts FDLs ~5 and ~25 times better than a baseline approach For example, the B.1.617.2 vaccine reference strain was flagged as FDL when its frequency was only 0.01%, more than a year before it was considered for an updated COVID-19 vaccine. Furthermore, DeepAutoCoV outputs interpretable results by pinpointing specific mutations potentially linked to increased fitness, and may provide significant insights for the optimization of public health pre-emptive intervention strategies.

Supplementing an Epidemiology Course with Online Lectures

Background: Online distance education is proving to be an effective pedagogical vehicle for enhancing learning outcomes of medical students. Epidemiology is a subject area underexplored as an appropriate content area for online delivery. We sought to determine whether online epidemiology videos were an effective companion teaching tool that could result in measurable improvements in student performance. Methods: In partnership with the Lecturio Corporation in Germany, an online Epidemiology class created, featuring 11 recorded lectures tailored to the USMLE medical licensing requirements. Free access to the lectures was offered to the students of a 4th year undergraduate Epidemiology course at the University of Ottawa. Improvements in this group from the midterm examination baseline to the final examination performance were assessed relative to improvements experienced by students who did not watch the videos. Results: Students who watched the videos saw their average mark increased by 1.2%, while the mark in the control group decreased by 2.6%, though this difference was not statistically significant. Qualitative comments were universally positive with respect to the instructional usefulness of the videos. Conclusions: Augmenting traditional university epidemiology courses with an online video component is an effective strategy that can result in improved student learning outcomes, though further research is needed to determine how best to deploy such tools either in isolation or in partnership with in-person instruction.

Sensitive and modular amplicon sequencing of Plasmodium falciparum diversity and resistance for research and public health.

Targeted amplicon sequencing is a powerful and efficient tool to interrogate the P. falciparum genome and generate actionable data from infections to complement traditional malaria epidemiology. For maximum impact, genomic tools should be multi-purpose, robust, sensitive and reproducible. We developed, characterized, and implemented MAD4HatTeR, an amplicon sequencing panel based on Multiplex Amplicons for Drug, Diagnostic, Diversity, and Differentiation Haplotypes using Targeted Resequencing, along with a bioinformatic pipeline for data analysis. MAD4HatTeR targets 165 highly diverse loci, focusing on multiallelic microhaplotypes; key markers for drug and diagnostic resistance, including duplications and deletions; and csp and potential vaccine targets. In addition, it can detect non-falciparum Plasmodium species. We used laboratory control and field sample data to demonstrate the high sensitivity and robustness of the panel. The successful implementation of this method in five laboratories, including three in malaria-endemic African countries, showcases its feasibility in generating reproducible data across laboratories. Finally, we introduce an analytical approach to detect gene duplications and deletions from amplicon sequencing data. MAD4HatTeR is thus a powerful research tool and a robust resource for malaria public health surveillance and control.

Gene-vegetarianism interactions in calcium, estimated glomerular filtration rate, and testosterone identified in genome-wide analysis across 30 biomarkers.

We examined the associations of vegetarianism with metabolic biomarkers using traditional and genetic epidemiology. First, we addressed inconsistencies in self-reported vegetarianism among UK Biobank participants by utilizing data from two dietary surveys to find a cohort of strict European vegetarians (N = 2,312). Vegetarians were matched 1:4 with nonvegetarians for non-genetic association analyses, revealing significant effects of vegetarianism in 15 of 30 biomarkers. Cholesterol measures plus vitamin D were significantly lower in vegetarians, while triglycerides were higher. A genome-wide association study revealed no genome-wide significant (GWS; 5×10-8) associations with vegetarian behavior. We performed genome-wide gene-vegetarianism interaction analyses for the biomarkers, and detected a GWS interaction impacting calcium at rs72952628 (P = 4.47×10-8). rs72952628 is in MMAA, a B12 metabolic pathway gene; B12 has major deficiency potential in vegetarians. Gene-based interaction tests revealed two significant genes, RNF168 in testosterone (P = 1.45×10-6) and DOCK4 in estimated glomerular filtration rate (eGFR) (P = 6.76×10-7), which have previously been associated with testicular and renal traits, respectively. These nutrigenetic findings indicate genotype can modify the associations between vegetarianism and health outcomes.

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures.

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

Evolution of Liver Transplantation Indications: Expanding Horizons.

Liver transplantation (LT) has significantly transformed the prognosis of patients with end-stage liver disease and hepatocellular carcinoma (HCC). The traditional epidemiology of liver diseases has undergone a remarkable shift in indications for LT, marked by a decline in viral hepatitis and an increase in metabolic dysfunction-associated steatotic liver disease (MASLD), along with expanded indications for HCC. Recent advancements in surgical techniques, organ preservation and post-transplant patients' management have opened new possibilities for LT. Conditions that were historically considered absolute contraindications have emerged as potential new indications, demonstrating promising results in terms of patient survival. While these expanding indications provide newfound hope, the ethical dilemma of organ scarcity persists. Addressing this requires careful consideration and international collaboration to ensure equitable access to LT. Multidisciplinary approaches and ongoing research efforts are crucial to navigate the evolving landscape of LT. This review aims to offer a current overview of the primary emerging indications for LT, focusing on acute-on-chronic liver failure (ACLF), acute alcoholic hepatitis (AH), intrahepatic and perihilar cholangiocarcinoma (i- and p-CCA), colorectal liver metastasis (CRLM), and neuroendocrine tumor (NET) liver metastases.

Combined visualization of genomic and epidemiological data for outbreaks.

In epidemiological investigations, pathogen genomics can provide insights and test epidemiological hypotheses that would not have been possible through traditional epidemiology. Tools to synthesize genomic analysis with other types of data are a key requirement of genomic epidemiology. We propose a new 'phylepic' visualization that combines a phylogenomic tree with an epidemic curve. The combination visually links the molecular time represented in the tree to the calendar time in the epidemic curve, a correspondence that is not easily represented by existing tools. Using an example derived from a foodborne bacterial outbreak, we demonstrated that the phylepic chart communicates that what appeared to be a point-source outbreak was in fact composed of cases associated with two genetically distinct clades of bacteria. We provide an R package implementing the chart. We expect that visualizations that place genomic analyses within the epidemiological context will become increasingly important for outbreak investigations and public health surveillance of infectious diseases.

A systematic review of mixed studies on malaria in Colombia 1980–2022: what the “bifocal vision” discovers

Mixed methods are essential in public health research and malaria control, because they allow grasping part of the complexity and diversity of the factors that determine health-disease. This study analyzes the mixed studies on malaria in Colombia, 1980–2022, through a systematic review in 15 databases and institutional repositories. The methodological quality was assessed with Mixed Methods Appraisal Tool (MMAT), STrengthening the Reporting of OBservational studies in Epidemiology (STROBE), and Standards for Reporting Qualitative Research (SRQR). The qualitative and quantitative findings were grouped into a four-level hierarchical matrix. The epidemiological profile of malaria morbidity, from traditional epidemiology, has been sustained by environmental problems, armed conflict, individual risk behaviors, and low adherence to recommendations from health institutions. However, the qualitative component reveals deeper causes that are less studied, of greater theoretical complexity, and that reflect challenges to design and implement health interventions, such as socioeconomic and political crises, poverty, and the neoliberal orientation in the malaria control policy; the latter reflected in the change in the role of the State, the fragmentation of control actions, the predominance of insurance over social assistance, the privatization of the provision of health services, the individualistic and economistic predominance of health, and low connection with popular tradition and community initiatives. The above confirms the importance of expanding mixed studies as a source of evidence to improve malaria research and control models in Colombia, and to identify the underlying causes of the epidemiological profile.

COVID‐19: A multiwave SIR‐based model for learning waves

One of the greatest challenges of the COVID‐19 pandemic has been the way evolving regulation, information, and sentiment have driven waves of the disease. Traditional epidemiology models, such as the SIR model, are not equipped to handle these behavioral‐based changes. We propose a novel multiwave susceptible–infected–recovered (SIR) model, which can detect and model the waves of the disease. We bring together the SIR model's compartmental structure with a change‐point detection martingale process to identify new waves. We create a dynamic process where new waves can be flagged and learned in real time. We use this approach to extend the traditional susceptible–exposed–infected–recovered–dead (SEIRD) model into a multiwave SEIRD model and test it on forecasting COVID‐19 cases from the John Hopkins University data set for states in the United States. We find that compared to the traditional SEIRD model, the multiwave SEIRD model improves mean absolute percentage error (MAPE) by 15%–25% for the United States. We benchmark the multiwave SEIRD model against top performing Center for Disease Control (CDC) models for COVID‐19 and find that the multiwave SERID model is able to outperform the majority of CDC models in long‐term predictions.

A Privacy-Preserving Approach to Identify COVID-19 Infection Origins via Volunteered Share of Health Data Records by Mobile Users

Human-beings are suffering from the rapid spread of COVID-19 throughout the world. In order to quickly identify, quarantine and cure the infected people, and to stop further infections, it is crucial to expose those origins who have been infected but are asymptomatic. However, this task is not easy, especially when the rigid security and privacy constraints on health records are taken into consideration. In this paper, we develop a new method to solve this problem. In the outbreak of a disease like COVID-19, the proposed method can find hidden infected people (or communities) through volunteered share of health data by some mobile users. Such volunteers only reveal whether they are healthy or infected e.g. through they mobile apps. This approach minimises health data disclosure and preserves privacy for the others. There are three steps in the proposed method. First, we borrow the idea from traditional epidemiology and design a novel algorithm to estimate the number of infection origins based on a Susceptible-Infected model. Second, we introduce the concept of ‘heavy centre’ to locate those origins. The probability of each node being infected will then be derived by building a spreading model based on the origins. To evaluate our method, we conduct a series of experiments on various networks with different structures. We examine the performance in estimating the number of origins as well as their origins. The results show that the proposed method yields higher accuracies than the existing methods, even when the fraction of volunteers is small.

Climate change and health in Southeast Asia - defining research priorities and the role of the Wellcome Trust Africa Asia Programmes.

This article summarises a recent virtual meeting organised by the Oxford University Clinical Research Unit in Vietnam on the topic of climate change and health, bringing local partners, faculty and external collaborators together from across the Wellcome and Oxford networks. Attendees included invited local and global climate scientists, clinicians, modelers, epidemiologists and community engagement practitioners, with a view to setting priorities, identifying synergies and fostering collaborations to help define the regional climate and health research agenda. In this summary paper, we outline the major themes and topics that were identified and what will be needed to take forward this research for the next decade. We aim to take a broad, collaborative approach to including climate science in our current portfolio where it touches on infectious diseases now, and more broadly in our future research directions. We will focus on strengthening our research portfolio on climate-sensitive diseases, and supplement this with high quality data obtained from internal studies and external collaborations, obtained by multiple methods, ranging from traditional epidemiology to innovative technology and artificial intelligence and community-led research. Through timely agenda setting and involvement of local stakeholders, we aim to help support and shape research into global heating and health in the region.

Phylogeny and antimicrobial resistance of extended-spectrum beta-lactamaseproducing Escherichia coli from hospitalized oncology patients in Perú

ResumenIntroducción. Las infecciones asociadas con la atención en salud constituyen un problema de salud pública porque aumentan la morbimortalidad de los pacientes, sobre todo de aquellos con factores de riesgo, como la inmunosupresión debida a enfermedades oncológicas. Es importante conocer la diversidad genética de los principales microorganimos causantes de infecciones hospitalarias mediante la vigilancia epidemiológica tradicional y la epidemiología molecular, para hacer un mejor seguimiento y detectar brotes tempranamente.Objetivo. Determinar el grupo filogenético y la resistencia a antibióticos de las cepas de Escherichia coli aisladas de pacientes con cáncer hospitalizados.Materiales y métodos. Se hizo un estudio de tipo transversal que incluyó 67 cepas de Escherichia coli productoras de betalactamasas de espectro extendido (BLEE). Se determinó el grupo filogenético, el perfil de resistencia a los antibióticos, los genes de resistencia a betalactámicos, el tipo de las muestras y los servicios de hospitalización de donde fueron recuperadas.Resultados. El grupo filogenético más frecuente fue el B2 (36 %). El 57 % de las cepas B2 fueron aisladas de muestras de orina y el 33 % provenía del servicio de urología. La resistencia a ciprofloxacino y gentamicina fue de 91 y 53 %, respectivamente, y el 79 % de las cepas tenía el gen bla CTX-M. Se encontró una relación significativa (p<0,05) entre los grupos filogenéticos y la resistencia a ciprofloxacina, así como a la edad del paciente.Conclusión. El filogrupo de E. coli predominante fue el B2. Se evidenció una gran resistencia a ciprofloxacina y gentamicina, una proporción elevada de cepas BLEE con el bla CTX-M, y una relación entre el grupo filogenético y la resistencia a ciprofloxacino.

Climate change and health in Southeast Asia – defining research priorities and the role of the Wellcome Trust Africa Asia Programmes

This article summarises a recent virtual meeting organised by the Oxford University Clinical Research Unit in Vietnam on the topic of climate change and health, bringing local partners, faculty and external collaborators together from across the Wellcome and Oxford networks. Attendees included invited local and global climate scientists, clinicians, modelers, epidemiologists and community engagement practitioners, with a view to setting priorities, identifying synergies and fostering collaborations to help define the regional climate and health research agenda. In this summary paper, we outline the major themes and topics that were identified and what will be needed to take forward this research for the next decade. We aim to take a broad, collaborative approach to including climate science in our current portfolio where it touches on infectious diseases now, and more broadly in our future research directions. We will focus on strengthening our research portfolio on climate-sensitive diseases, and supplement this with high quality data obtained from internal studies and external collaborations, obtained by multiple methods, ranging from traditional epidemiology to innovative technology and artificial intelligence and community-led research. Through timely agenda setting and involvement of local stakeholders, we aim to help support and shape research into global heating and health in the region.

Circulating Isovalerylcarnitine and Lung Cancer Risk: Evidence from Mendelian Randomization and Prediagnostic Blood Measurements.

Tobacco exposure causes 8 of 10 lung cancers, and identifying additional risk factors is challenging due to confounding introduced by smoking in traditional observational studies. We used Mendelian randomization (MR) to screen 207 metabolites for their role in lung cancer predisposition using independent genome-wide association studies (GWAS) of blood metabolite levels (n = 7,824) and lung cancer risk (n = 29,266 cases/56,450 controls). A nested case-control study (656 cases and 1,296 matched controls) was subsequently performed using prediagnostic blood samples to validate MR association with lung cancer incidence data from population-based cohorts (EPIC and NSHDS). An MR-based scan of 207 circulating metabolites for lung cancer risk identified that blood isovalerylcarnitine (IVC) was associated with a decreased odds of lung cancer after accounting for multiple testing (log10-OR = 0.43; 95% CI, 0.29-0.63). Molar measurement of IVC in prediagnostic blood found similar results (log10-OR = 0.39; 95% CI, 0.21-0.72). Results were consistent across lung cancer subtypes. Independent lines of evidence support an inverse association of elevated circulating IVC with lung cancer risk through a novel methodologic approach that integrates genetic and traditional epidemiology to efficiently identify novel cancer biomarkers. Our results find compelling evidence in favor of a protective role for a circulating metabolite, IVC, in lung cancer etiology. From the treatment of a Mendelian disease, isovaleric acidemia, we know that circulating IVC is modifiable through a restricted protein diet or glycine and L-carnatine supplementation. IVC may represent a modifiable and inversely associated biomarker for lung cancer.

A nationwide wastewater-based assessment of metformin consumption across Australia.

Metformin is the most widely used drug to treat type 2 diabetes. Monitoring spatial patterns of metformin use could provide new insights into treatment of type 2 diabetes and the distribution among populations. This study applied a wastewater-based epidemiological (WBE) approach to estimate metformin use in different populations across Australia and compared these estimates with traditional approaches of surveys and prescription data. Twenty-four-hour influent samples were collected from 75 wastewater treatment plants (WWTPs) across Australia in 2016 and analysed for metformin. Metformin was detected in all samples ranging in concentration from 8.2 to 191µg/L (median 58µg/L). Concentrations were converted to population-weighted average consumption at the national level, resulting in an average consumption of 28.6g/day/1000 people across Australia, which was within 7% of estimates from national prescription statistics. In addition, results for five out of seven states had an estimated prevalence of type 2 diabetes within 20% compared to the traditional epidemiology surveys. Spatial patterns were also observed between urban and rural settings, with higher consumption rates of metformin found in Major Cities (22.5±10.9g/d/1000 people) and Inner Regional cities (25.4±13.4g/d/1000 people) than in Outer Regional (17.0±8.1g/d/1000 people) and Remote areas (15.1±7.4g/d/1000 people). Consumption estimates were also correlated against socioeconomic factors of the specific catchment areas. Greater metformin use was correlated with populations of lower education and income levels, while positive correlations were found between metformin consumption and consumption of allopurinol, caffeine and venlafaxine. Our study provides more evidence on the distribution of metformin use across Australia, which can be used to develop public health strategies to reduce the overall burden of type 2 diabetes in specific regions.

The Occurrence Birth-Death Process for Combined-Evidence Analysis in Macroevolution and Epidemiology.

Phylodynamic models generally aim at jointly inferring phylogenetic relationships, model parameters, and more recently, the number of lineages through time, based on molecular sequence data. In the fields of epidemiology and macroevolution, these models can be used to estimate, respectively, the past number of infected individuals (prevalence) or the past number of species (paleodiversity) through time. Recent years have seen the development of “total-evidence” analyses, which combine molecular and morphological data from extant and past sampled individuals in a unified Bayesian inference framework. Even sampled individuals characterized only by their sampling time, that is, lacking morphological and molecular data, which we call occurrences, provide invaluable information to estimate the past number of lineages. Here, we present new methodological developments around the fossilized birth–death process enabling us to (i) incorporate occurrence data in the likelihood function; (ii) consider piecewise-constant birth, death, and sampling rates; and (iii) estimate the past number of lineages, with or without knowledge of the underlying tree. We implement our method in the RevBayes software environment, enabling its use along with a large set of models of molecular and morphological evolution, and validate the inference workflow using simulations under a wide range of conditions. We finally illustrate our new implementation using two empirical data sets stemming from the fields of epidemiology and macroevolution. In epidemiology, we infer the prevalence of the coronavirus disease 2019 outbreak on the Diamond Princess ship, by taking into account jointly the case count record (occurrences) along with viral sequences for a fraction of infected individuals. In macroevolution, we infer the diversity trajectory of cetaceans using molecular and morphological data from extant taxa, morphological data from fossils, as well as numerous fossil occurrences. The joint modeling of occurrences and trees holds the promise to further bridge the gap between traditional epidemiology and pathogen genomics, as well as paleontology and molecular phylogenetics. [Birth–death model; epidemiology; fossils; macroevolution; occurrences; phylogenetics; skyline.]

Emulating a virtual digital cohort study based on social media data as a complementary approach to traditional epidemiology: When, what for, and how?

With an unprecedented volume of data generated online by individuals, it becomes more and more relevant to analyze them for health-related purposes. However, the field of digital epidemiology lacks standardization which prevents it from moving away from a reproducibility crisis. In this article, we introduce the concept of a virtual digital cohort study (VDCS), a standardized method that can mimic an actual cohort study design exclusively thanks to social media data. We draw parallels between principles in epidemiology and their adaptations for a VDCS, and we use two diabetes-related examples to illustrate how a VDCS can help address some research questions. We also argue that developing a VDCS is a valid alternative to a costly, real-life, cohort implementation for some but not all research questions. We provide guidelines and recommendations to future researchers willing to launch a VDCS and, finally, we demonstrate why combining both a virtual and a real-life cohort study, sequentially or in parallel, can help conduct more relevant, patient-centric, research.

Udder health, conceptual construct, and uses of the term: A systematic review from 1962 to 2019.

Background and Aim:Udder health management is essential for the further development of milk production systems and public health. This process depends on the generation of knowledge regarding control, prevention, and promotion of health. In scientific literature, it is impossible to find a synthesis of the categories that would allow comprehension of the complex phenomenon udder health. Different research approaches have allowed this polysemic concept, described by some researchers as multifactorial and by philosophical perspectives as a social phenomenon, to be further studied. Thus, the objective of this systematic review was to systematize the conceptual categories of udder health and the use of the term in the original articles published in the scientific literature from the period 1962 to 2019.Materials and Methods:A systematic review with a broad approach was designed by applying the phases of identification, screening, selection, and inclusion criteria described in the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes guide. An exhaustive search of original articles by specificity was carried out in the Science Direct, PubMed, Scielo, LILACS, and Google Scholar databases. The investigation was carried out on November 22, 2019. According to the inclusion criteria established, articles needed to be original studies, to be publications on bovine livestock, written in English, Spanish and Portuguese. Furthermore, the articles considered needed to tackle the term udder health so that its conceptual categorization could be extracted. Google Scholar patents and citations and articles removed from databases or not available were excluded from the study and those that, based on the reading of the complete text, considered the farming of animal species other than bovine. A qualitative synthesis of the year of publication, continent, approach, type of study, and conceptual category of udder health was carried out by calculating frequencies (Statistical Package for the Social Sciences version 24).Results:In total, 165 articles were included in the study. Eight conceptual categories, consolidated over time, were systematized, showing that udder health is not a static problem, and that science has been responding through the generation of new knowledge around conceptual categories as different udder health problems emerge.Conclusion:Culture and politics were two categories, related to all the others, that stood out in the results. These two categories were of great interest in countries advanced in milk production and in the implementation of udder health policies, which acknowledge the producer and other actors of the production chain as fundamental political actors for policies, decision-making processes, and public health care to be effective. The lack of synonyms for the term udder health (e.g., mastitis) may have led to the exclusion of important articles in each category. However, the constriction to the term udder health was intentional and aimed at constructing the concept. Udder health is hereby understood as a health-disease process, different from the term mastitis, which from its semantic origin, refers only to the disease process. According to this study, the concept can be understood through the categories of traditional epidemiology based on risk factors and disease; microbiology; genetics, resistance, and immunity; animal welfare; nutrition; organic production; culture; and politics.

Association of psoriasis with risk of COVID-19: A 2-sample Mendelian randomization study

With the growing pandemic of COVID-19, psoriasis has been reported to be linked with COVID-19 from genetic and epidemiological perspectives, especially in patients receiving systemic treatments.1-3 However, traditional epidemiology is inevitably affected by confounding bias. Mendelian randomization (MR) is an approach based on genome-wide association studies to construct instrumental variables (IVs) and can effectively control the confounding bias of observational studies. IVs refer to variables that only affect the outcome through risk factors, and MR uses single-nucleotide polymorphisms as IVs to identify risk factors.

Climate change and health in Southeast Asia – defining research priorities and the role of the Wellcome Trust Africa Asia Programmes

This article summarises a recent virtual meeting organised by the Oxford University Clinical Research Unit in Vietnam on the topic of climate change and health, bringing local partners, faculty and external collaborators together from across the Wellcome and Oxford networks. Attendees included invited local and global climate scientists, clinicians, modelers, epidemiologists and community engagement practitioners, with a view to setting priorities, identifying synergies and fostering collaborations to help define the regional climate and health research agenda. In this summary paper, we outline the major themes and topics that were identified and what will be needed to take forward this research for the next decade. We aim to take a broad, collaborative approach to including climate science in our current portfolio where it touches on infectious diseases now, and more broadly in our future research directions. We will focus on strengthening our research portfolio on climate-sensitive diseases, and supplement this with high quality data obtained from internal studies and external collaborations, obtained by multiple methods, ranging from traditional epidemiology to innovative technology and artificial intelligence and community-led research. Through timely agenda setting and involvement of local stakeholders, we aim to help support and shape research into global heating and health in the region.