Epidemiological Applications Research Articles

Application and development of wastewater epidemiology in public health current status and future trends

Abstract. Wastewater epidemiology has emerged as a powerful tool for public health surveillance, enabling the monitoring of infectious diseases at the community level. This paper provides an overview of wastewater epidemiology, including its methodology, applications, current status, and future trends. The challenges and ethical considerations associated with its implementation are also discussed. The methodology section explores the various techniques used in wastewater epidemiology, such as analyzing viral RNA and biomarkers in wastewater samples. These methods offer valuable insights into disease prevalence, transmission patterns, and trends in a population. The applications section highlights the wide range of public health applications for wastewater epidemiology. It discusses how this approach can contribute to disease surveillance, early detection of outbreaks, and monitoring the effectiveness of interventions. The current status section examines the progress made in wastewater epidemiology. It discusses the standardization of methodologies and the need for research collaboration to ensure reliable and comparable results. The future trends and developments section explores potential advancements in wastewater epidemiology. It discusses emerging technologies, such as next-generation sequencing and advanced data analysis techniques, that could enhance the capabilities of this approach. The challenges and ethical considerations section addresses the hurdles in implementing wastewater epidemiology. It emphasizes the importance of standardization, logistical considerations, privacy protection, transparent reporting, and equitable distribution of benefits. In conclusion, wastewater epidemiology holds great promise for public health surveillance. However, addressing the challenges and ethical considerations is crucial for its responsible and effective implementation. By proactively overcoming these obstacles and upholding ethical principles, wastewater epidemiology can continue to evolve as a valuable tool for improving public health outcomes.

Pervasive biases in proxy genome-wide association studies based on parental history of Alzheimer's disease.

Almost every recent Alzheimer's disease (AD) genome-wide association study (GWAS) has performed meta-analysis to combine studies with clinical diagnosis of AD with studies that use proxy phenotypes based on parental disease history. Here, we report major limitations in current GWAS-by-proxy (GWAX) practices due to uncorrected survival bias and nonrandom participation in parental illness surveys, which cause substantial discrepancies between AD GWAS and GWAX results. We demonstrate that the current AD GWAX provide highly misleading genetic correlations between AD risk and higher education, which subsequently affects a variety of genetic epidemiological applications involving AD and cognition. Our study sheds light on potential issues in the design and analysis of middle-aged biobank cohorts and underscores the need for caution when interpreting genetic association results based on proxy-reported parental disease history.

Methods for Quantifying Source-Specific Air Pollution Exposure to Serve Epidemiology, Risk Assessment, and Environmental Justice.

Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source-specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source-specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data-Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on-road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult-since source-specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models.

Salivary antigens rPagSP02 and rPagSP06 are a reliable composite biomarker for evaluating exposure to Phlebotomus argentipes in Sri Lanka

Phlebotomus argentipes is the established vector of leishmaniasis in the Indian sub-continent. Antibodies to sand fly salivary antigens are biomarkers for vector-host exposure in leishmaniasis-endemic regions. Ph. argentipes transmits Leishmania donovani in Sri Lanka, primarily causing cutaneous leishmaniasis (CL). Our study compared the performance of salivary gland homogenate (SGH) from a lab-reared local strain of Ph. argentipes females to a composite recombinant salivary biomarker (rPagSP02 + rPagSP06) in a CL-endemic population. Sera from 546 healthy individuals, 30 CL patients, and 15 non-endemic individuals were collected. Western blot analysis of Ph. argentipes SGH identified immunogenic bands between 15 kDa and 67 kDa, with bands of predicted molecular weight ∼of 15 kDa (SP02) and ∼28–30 kDa (SP06) as the major antibody targets. Indirect ELISAs using SGH or rPagSP02 + rPagSP06 antigens showed high sensitivity (96.7%) and specificity (100%), detecting comparable seropositivity in endemic populations. rPagSP02 + rPagSP06 exhibited enhanced discriminatory ability, supported by a strong positive correlation (r = 0.869) with SGH. Our findings indicate that the composite rPagSP02 + rPagSP06 salivary biomarker effectively identifies Ph. argentipes exposure in individuals living in Sri Lanka, showing promising potential for use in surveillance. These findings should be further validated to confirm the epidemiological applications in leishmaniasis-endemic regions.

Microbiome Geographic Population Structure (mGPS) Detects Fine-Scale Geography.

Over the past decade, sequencing data generated by large microbiome projects showed that taxa exhibit patchy geographical distribution, raising questions about the geospatial dynamics that shape natural microbiomes and the spread of antimicrobial resistance genes. Answering these questions requires distinguishing between local and nonlocal microorganisms and identifying the source sites for the latter. Predicting the source sites and migration routes of microbiota has been envisioned for decades but was hampered by the lack of data, tools, and understanding of the processes governing biodiversity. State-of-the-art biogeographical tools suffer from low resolution and cannot predict biogeographical patterns at a scale relevant to ecological, medical, or epidemiological applications. Analyzing urban, soil, and marine microorganisms, we found that some taxa exhibit regional-specific composition and abundance, suggesting they can be used as biogeographical biomarkers. We developed the microbiome geographic population structure, a machine learning-based tool that utilizes microbial relative sequence abundances to yield a fine-scale source site for microorganisms. Microbiome geographic population structure predicted the source city for 92% of the samples and the within-city source for 82% of the samples, though they were often only a few hundred meters apart. Microbiome geographic population structure also predicted soil and marine sampling sites for 86% and 74% of the samples, respectively. We demonstrated that microbiome geographic population structure differentiated local from nonlocal microorganisms and used it to trace the global spread of antimicrobial resistance genes. Microbiome geographic population structure's ability to localize samples to their water body, country, city, and transit stations opens new possibilities in tracing microbiomes and has applications in forensics, medicine, and epidemiology.

A Coupled Spatial-Network Model: A Mathematical Framework for Applications in Epidemiology.

There is extensive evidence that network structure (e.g., air transport, rivers, or roads) may significantly enhance the spread of epidemics into the surrounding geographical area. A new compartmental modeling framework is proposed which couples well-mixed (ODE in time) population centers at the vertices, 1D travel routes on the graph's edges, and a 2D continuum containing the rest of the population to simulate how an infection spreads through a population. The edge equations are coupled to the vertex ODEs through junction conditions, while the domain equations are coupled to the edges through boundary conditions. A numerical method based on spatial finite differences for the edges and finite elements in the 2D domain is described to approximate the model, and numerical verification of the method is provided. The model is illustrated on two simple and one complex example geometries, and a parameter study example is performed. The observed solutions exhibit exponential decay after a certain time has passed, and the cumulative infected population over the vertices, edges, and domain tends to a constant in time but varying in space, i.e., a steady state solution.

Towards a comprehensive COVID-19 non-pharmaceutical interventions’ index for the province of Québec

ObjectivesThe primary objective of this project was to develop a comprehensive COVID-19 non-pharmaceutical interventions’ index for the province of Québec (QCnPI-Index). The resulting database systematically categorizes, multiple non-pharmaceutical interventions implemented in the 17 administrative regions (AR) of the province of Québec to mitigate the spread of COVID-19 in the form of an index.Data descriptionData represent interventions and groups of interventions implemented during the COVID-19 period in Québec. They are a compilation of policies, guidelines, and governmental interventions related to COVID-19, considering temporal and geographical dimensions. Data were collected for all 17 AR of Québec using dates as unit of analysis, from March 2020 to April 2022. They were first collected and then coded by an interdisciplinary research team to form the foundation of the QCnPI-Index.ContributionThis quantitative instrument offers the necessary granularity for nuanced spatial and temporal studies within the province of Québec, using AR, for instance, as unit of analysis. With this database, pre-, during-, and post-COVID periods can thus be better analyzed. Additionally, the innovative methodologies employed for data collection, coding, and weighting offer valuable insights that may have broader applications in public health, epidemiology, and other research domains. The QCnPI-Index could be instrumental for public health, epidemiology, and transportation researchers investigating the multifaceted impacts of non-pharmaceutical interventions on various societal domains, such as road safety, alcohol and cannabis consumption, and/or mental health, in the province of Québec.

Epidemiological investigation support application and user evaluation based on infectious disease self-management model in the endemic era.

Objectives: Rapid epidemiological investigations are fundamental to prevent the spread of infectious diseases such as coronavirus disease 2019. An epidemiological investigation presents significant challenges for both epidemiologists and infected individuals. It requires creating an environment that enables people to independently manage infectious diseases and voluntarily participate in epidemiological investigations. Methods: We developed the KODARI application, an epidemiological investigation support system that users can voluntarily use. We developed the questionnaires based on literature reviews. We evaluated the application through an online survey from December 2 to 14, 2022. Results: The application automatically or manually collect epidemiological investigation information. The application improved data accuracy through accurate information collection. It voluntarily can transmit self-management information to epidemiologist terminals or users in real time. We collected 248 users from an online survey. Most users had high ratings and willingness to use. They have willingness to manage infectious patients was substantial. The application was evaluated as helpful for epidemiological investigations and could shorten the time required for epidemiological investigations by more than 30min. Conclusion: The application proposes a model based on people's voluntary participation. We demonstrated that the application could enhance epidemiological investigations and diminish the duration of existing epidemiological investigation processes.

Spatial Epidemiology and Its Role in Prevention and Control of Swine Viral Disease

Spatial epidemiology offers a comprehensive framework for analyzing the spatial distribution and transmission of diseases, leveraging advanced technical tools and software, including Geographic Information Systems (GISs), remote sensing technology, statistical and mathematical software, and spatial analysis tools. Despite its increasing application to swine viral diseases (SVDs), certain challenges arise from its interdisciplinary nature. To support novices, frontline veterinarians, and public health policymakers in navigating its complexities, we provide a comprehensive overview of the common applications of spatial epidemiology in SVD. These applications are classified into four categories based on their objectives: visualizing and elucidating spatiotemporal distribution patterns, identifying risk factors, risk mapping, and tracing the spatiotemporal evolution of pathogens. We further elucidate the technical methods, software, and considerations necessary to accomplish these objectives. Additionally, we address critical issues such as the ecological fallacy and hypothesis generation in geographic correlation analysis. Finally, we explore the future prospects of spatial epidemiology in SVD within the One Health framework, offering a valuable reference for researchers engaged in the spatial analysis of SVD and other epidemics.

A GPU-accelerated Lagrangian method for solving the Liouville equation in random differential equation systems

This work presents and analyzes a numerical approach to efficiently solve the Liouville equation in the context of random ODEs using GPGPUs. Our method combines wavelet compression-based adaptive mesh refinement, Lagrangian particle methods, and radial basis function interpolation to create a versatile algorithm applicable in multiple dimensions. We discuss the advantages and limitations of this algorithm. To demonstrate its effectiveness, we compute the probability density function for various 2D and 3D random ODE systems with applications in physics and epidemiology.

Effect of SARS-CoV-2 shedding rate distribution of individuals during their disease days on the estimation of the number of infected people. Application of wastewater-based epidemiology to the city of Thessaloniki, Greece

During the COVID-19 pandemic, wastewater-based epidemiology has proved to be an important tool for monitoring the spread of a disease in a population. Indeed, wastewater surveillance was successfully used as a complementary approach to support public health monitoring schemes and decision-making policies. An essential feature for the estimation of a disease transmission using wastewater data is the distribution of viral shedding rate of individuals in their personal human wastes as a function of the days of their infection. Several candidate shapes for this function have been proposed in literature for SARS-CoV-2. The purpose of the present work is to explore the proposed function shapes and examine their significance on analyzing wastewater SARS-CoV-2 shedding rate data. For this purpose, a simple model is employed applying to medical surveillance and wastewater data of the city of Thessaloniki during a period of Omicron variant domination in 2022. The distribution shapes are normalized with respect to the total virus shedding and then their basic features are investigated. Detailed analysis reveals that the main parameter determining the results of the model is the difference between the day of maximum shedding rate and the day of infection reporting. Since the latter is not part of the distribution shape, the major feature of the distribution affecting the estimation of the number of infected people is the day of maximum shedding rate with respect to the initial infection day. On the contrary, the duration of shedding (total number of disease days) as well as the exact shape of the distribution are by far less important. The incorporation of such wastewater surveillance models in conventional epidemiological models - based on recorded disease transmission data- may improve predictions for disease spread during outbreaks.

Unveiling the Quest: Crafting an Enzyme-Linked Immunosorbent Assay (ELISA) Technique to Uncover COVID-19 Antibodies.

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has had a profound impact on global health. Rapid and accurate diagnostic tools are crucial for effective disease control and management. The enzyme-linked immunosorbent assay (ELISA) has emerged as a reliable and widely used method for detecting COVID-19 antibodies in patients, which develop in response to SARS-CoV-2 infection. While the ELISA technique is effective in identifying the presence of antibodies and thus confirming exposure to the virus, its role in predicting the clinical course and severity of the disease is limited. ELISA primarily confirms prior exposure to the virus or vaccination status, but it does not directly correlate antibody levels with the severity or progression of the disease. The variability in clinical outcomes is influenced by factors such as viral load, patient co-morbidities, genetic predispositions, and the timing of the immune response. ELISA has diverse applications in epidemiology, vaccination assessment, and therapeutic development. It determines antibody prevalence, aids in surveillance, and evaluates vaccine effectiveness and antibody protection duration. ELISA quantitatively measures antibody levels, providing insights into the immune response and treatment efficacy. Challenges include specialized facilities and personnel, cross-reactivity, and false results. Multiplex assays and integration with other diagnostics are future directions. In summary, ELISA is an essential tool in COVID-19 diagnostics, enabling precise assessment of the immune response and contributing to effective strategies. The development of point-of-care devices that integrate ELISA technology could enable rapid and accessible testing in various settings. Additionally, integrating ELISA with other diagnostic platforms could enhance the overall diagnostic capabilities for COVID-19. Despite challenges, ongoing advancements in ELISA technology, and its integration with other diagnostic approaches, hold promise for further improving COVID-19 diagnostics and management strategies.

Gambaran Kompetensi Mahasiswa Rekam Medis dan Informasi Kesehatan Universitas Esa Unggul

Knowledge is the result of curiosity through the process of perception, especially the sense of sight and hearing of certain objects. The D-III Medical Records and Health Information Study Program is a program that prepares students skilled in managing health information management. The aim of the research is to determine the competency description of Esa Unggul University Medical Records and Health Information students. Descriptive research method with a quantitative analysis approach. The population in this study were Esa Unggul University Medical Records and Health Information students from the 2020 and 2019 regular and parallel classes with a total of 94 students. Research results: the characteristics of the majority of students were female (73.6%), student age ≤ 22 years (63.7%), the largest campus base was regular Kebon Jeruk (51.6%), the largest class of 2020 (73.6%). The results for competency in Clinical Classification Skills, Codification of Diseases and other Health Problems, and Clinical Procedures showed that the percentage of competent was 51.3%, while the percentage of incompetent was 48.7%. Competency in the Application of Health Statistics, Basic Epidemiology and Biomedicine obtained a competent percentage of 49.9% while the percentage of incompetent was 50.1%. RMIK Service Management Competency obtained a competent percentage of 50.3% while the percentage of incompetent was 49.7%. Conclusion: The number of competent Medical Records and Health Information students was 55 students and 36 students who were not competent. Suggestion: Registration officers improve and understand more in serving and conveying more precise and clear information. It is recommended to maintain and improve the quality of learning and increase student learning motivation in order to increase the percentage of competency.

The measurement of "high-impact chronic pain": Limitations and alternative methods.

Chronic pain is known to be an important construct in clinical practice and a particular form of chronic pain, high-impact chronic pain (HICP), has gained recent interest and attention by pain clinicians, epidemiologists, and clinical researchers. The purpose of our Topical Review is to describe the historical development of measures of HICP and to explore the psychometric properties of HICP as well as to present alternative measurement methods. We identified strengths and weaknesses of the psychometric characteristics of HICP measures. Limitations of existing HICP measures were discussed and summarized and alternatives to current methods were proposed. HICP operational definitions show variability across studies. All definitions cannot be correct, but which ones are incorrect cannot be determined as there is no gold standard. Random measurement error and recall bias are among the other limitations of current HICP measures. Model-based definitions of HICP, the discrete (for epidemiologic applications) and continuous (for clinical applications) latent variable models are discussed as likely superior alternatives to current methods. Limitations of existing HICP methods are discussed and alternative development approaches to HICP measures are presented. The use of either discrete or continuous latent variable models would improve upon the psychometric characteristics of current HICP evidence. Examples are used to illustrate the benefits of latent variable models over traditional observed variable conceptualizations as the measurement of HICP continues to develop. This work takes the position that current methods of measuring high impact chronic pain (HICP) likely contain substantial error. We have endorsed an alternative approach for several psychometrically grounded reasons. We recommend that future work consider the discrete latent variable framework for dichotomous measures of HICP and the continuous latent variable framework for continuous measures of HICP. The paper provides illustrative examples of these methods for a different patient reported measure that is lacking a gold standard, much like HICP measures.

Application of molecular epidemiology in revealing HIV-1 transmission network and recombination patterns in Tianjin, China.

Using a comprehensive molecular epidemiological approach, we characterized the transmission dynamics of HIV-1 among the MSM population in Tianjin, China. Our findings revealed that 38.56% (386/1001) of individuals clustered across 109 molecular transmission clusters (TCs), with MSM aged 50 and below being the group most commonly transmitting HIV-1. Among the identified TCs, CRF01_AE predominated, followed by CRF07_BC. Notably, CRF07_BC demonstrated a higher propensity for forming large clusters compared to CRF01_AE. Birth-death skyline analyses of the two largest clusters indicated that the HIV/AIDS transmission may be at a critical point, nearly all had Re approximately 1 by now. A retrospective analysis revealed that the rapid expansion of these large clusters was primarily driven by the introduction of viruses in 2021, highlighting the crucial importance of continuous molecular surveillance in identifying newly emerging high-risk transmission chains and adapting measures to address evolving epidemic dynamics. Furthermore, we detected the transmission of drug-resistant mutations (DRMs) within the TCs, particularly in the CRF07_BC clusters (K103N, Y181C, and K101E) and CRF01_AE clusters (P225H and K219R), emphasizing the importance of monitoring to support the continued efficacy of first-line therapies and pre-exposure prophylaxis (PrEP). Recombination analyses indicated that complex recombinant patterns, associated with increased amino acid variability, could confer adaptive traits to the viruses, potentially providing a competitive advantage in certain host populations or regions. Our study highlights the potential of integrating molecular epidemiological and phylodynamic approaches to inform targeted interventions.

Change of measure in a Heston–Hawkes stochastic volatility model

AbstractWe consider the stochastic volatility model obtained by adding a compound Hawkes process to the volatility of the well-known Heston model. A Hawkes process is a self-exciting counting process with many applications in mathematical finance, insurance, epidemiology, seismology, and other fields. We prove a general result on the existence of a family of equivalent (local) martingale measures. We apply this result to a particular example where the sizes of the jumps are exponentially distributed. Finally, a practical application to efficient computation of exposures is discussed.

RPagSP02+rPagSP06 recombinant salivary antigen is a reliable biomarker for evaluating exposure to Phlebotomus argentipes in Sri Lanka.

Phlebotomus argentipes is the established vector of leishmaniasis in the Indian sub-continent. Antibodies to sand fly salivary antigens are biomarkers for vector-host exposure in leishmaniasis-endemic regions. Ph. argentipes transmits Leishmania donovani in Sri Lanka, primarily causing cutaneous leishmaniasis (CL). Our study compared the performance of salivary gland homogenate (SGH) from a lab-reared local strain of Ph. argentipes females to a composite recombinant salivary biomarker (rPagSP02 + rPagSP06) in a CL-endemic population. Sera from 546 healthy individuals, 30 CL patients, and 15 non-endemic individuals were collected. Western blot analysis of Ph. argentipes SGH identified immunogenic bands between 15 kDa and 67 kDa, with bands of predicted molecular weight õf 15 kDa (SP02) and ~28-30 kDa (SP06) as the major antibody targets. Indirect ELISAs using SGH or rPagSP02 + rPagSP06 antigens showed high sensitivity (96.7%) and specificity (100%), detecting comparable seropositivity in endemic populations. rPagSP02 + rPagSP06 exhibited enhanced discriminatory ability, supported by a strong positive correlation (r = 0.869) with SGH. Our findings indicate that the composite rPagSP02 + rPagSP06 salivary biomarker effectively identifies Ph. argentipes exposure in individuals living in Sri Lanka, showing promising potential for use in surveillance. These findings should be further validated to confirm the epidemiological applications in leishmaniasis-endemic regions.

Collecting routine and timely cancer stage at diagnosis by implementing a cancer staging tiered framework: the Western Australian Cancer Registry experience

BackgroundCurrent processes collecting cancer stage data in population-based cancer registries (PBCRs) lack standardisation, resulting in difficulty utilising diverse data sources and incomplete, low-quality data. Implementing a cancer staging tiered framework aims to improve stage collection and facilitate inter-PBCR benchmarking.ObjectiveDemonstrate the application of a cancer staging tiered framework in the Western Australian Cancer Staging Project to establish a standardised method for collecting cancer stage at diagnosis data in PBCRs.MethodsThe tiered framework, developed in collaboration with a Project Advisory Group and applied to breast, colorectal, and melanoma cancers, provides business rules – procedures for stage collection. Tier 1 represents the highest staging level, involving complete American Joint Committee on Cancer (AJCC) tumour–node–metastasis (TNM) data collection and other critical staging information. Tier 2 (registry-derived stage) relies on supplementary data, including hospital admission data, to make assumptions based on data availability. Tier 3 (pathology stage) solely uses pathology reports.FindingsThe tiered framework promotes flexible utilisation of staging data, recognising various levels of data completeness. Tier 1 is suitable for all purposes, including clinical and epidemiological applications. Tiers 2 and 3 are recommended for epidemiological analysis alone. Lower tiers provide valuable insights into disease patterns, risk factors, and overall disease burden for public health planning and policy decisions. Capture of staging at each tier depends on data availability, with potential shifts to higher tiers as new data sources are acquired.ConclusionsThe tiered framework offers a dynamic approach for PBCRs to record stage at diagnosis, promoting consistency in population-level staging data and enabling practical use for benchmarking across jurisdictions, public health planning, policy development, epidemiological analyses, and assessing cancer outcomes. Evolution with staging classifications and data variable changes will futureproof the tiered framework. Its adaptability fosters continuous refinement of data collection processes and encourages improvements in data quality.

A Bayesian high-dimensional mediation analysis for multilevel genome-wide epigenetic data

Causal mediation analysis has increasingly become a popular practice in various clinical trials and epidemiological applications to evaluate whether an intermediate variable is on the pathway between the exposure of interest and a response. Previous mediation analyses in the literature mainly focused on settings with a single or low-dimensional mediators and single-level data. In this article, we propose a Bayesian causal mediation analysis method that can handle our multilevel intergenerational epigenetic mechanisms study (IEMS) with high-dimensional mediators. Specifically, we develop a Bayesian hierarchical model for data with such complexity, and then employ the Bayesian spike-and-slab priors on the exposure-mediator-outcome effect pathway to identify active mediators involved in mediation. We derive the natural indirect and direct effects based on our hierarchical model and provide statistical inference based on Markov chain Monte Carlo (MCMC) methods. Our simulation study demonstrates that our proposed Bayesian method outperforms other alternative methods in various scenarios. We further illustrate the utility of our method to IEMS to assess the causal mechanisms between maternal exposure to climate extremes and offspring's growth outcomes through DNA methylation.

High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city

The development of high-resolution spatial and spatiotemporal models of air pollutants is essential for exposure science and epidemiological applications. While fixed-site sampling has conventionally provided input data for statistical predictive models, the evolving mobile monitoring method offers improved spatial resolution, ideal for measuring pollutants with high spatial variability such as ultrafine particles (UFP).The Quebec Air Pollution Exposure and Epidemiology (QAPEE) study measured and modelled the spatial and spatiotemporal distributions of understudied pollutants, such as UFPs, black carbon (BC), and brown carbon (BrC), along with fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in Quebec City, Canada. We conducted a combined fixed-site (NO2 and O3) and mobile monitoring (PM2.5, BC, BrC, and UFPs) campaign over 10-months. Mobile monitoring routes were monitored on a weekly basis between 8am–10am and designed using location/allocation modelling. Seasonal fixed-site sampling campaigns captured continuous 24-h measurements over two-week periods. Generalized Additive Models (GAMs), which combined data on pollution concentrations with spatial, temporal, and spatiotemporal predictor variables were used to model and predict concentration surfaces.Annual models for PM2.5, NO2, O3 as well as seven of the smallest size fractions in the UFP range, had high out of sample predictive accuracy (range r2: 0.54–0.86). Varying spatial patterns were observed across UFP size ranges measured as Particle Number Counts (PNC). The monthly spatiotemporal models for PM2.5 (r2 = 0.49), BC (r2 = 0.27), BrC (r2 = 0.29), and PNC (r2 = 0.49) had moderate or moderate-low out of sample predictive accuracy. We conducted a sensitivity analysis and found that the minimum number of ‘n visits’ (mobile monitoring sessions) required to model annually representative air pollution concentrations was between 24 and 32 visits dependent on the pollutant.This study provides a single source of exposure models for a comprehensive set of air pollutants in Quebec City, Canada. These exposure models will feed into epidemiological research on the health impacts of ambient UFPs and other pollutants.