A Bayesian hierarchical model for disease mapping that accounts for scaling and heavy-tailed latent effects.

In disease mapping, the relative risk of a disease is commonly estimated across different areas within a region of interest. The number of cases in an area is often assumed to follow a Poisson distribution whose mean is decomposed as the product between an offset and the logarithm of the disease's relative risk. The log risk may be written as the sum of fixed effects and latent random effects.

A Bayesian non-stationary heteroskedastic time series model for multivariate critical care data.

We propose a multivariate GARCH model for non-stationary health time series by modifying the observation-level variance of the standard state space model. The proposed model provides an intuitive and novel way of dealing with heteroskedastic data using the conditional nature of state-space models. We follow the Bayesian paradigm to perform the inference procedure.

Zika emergence, persistence, and transmission rate in Colombia: a nationwide application of a space-time Markov switching model.

Zika, a viral disease transmitted to humans by Aedes mosquitoes, emerged in the Americas in 2015, causing large-scale epidemics. Colombia alone reported over 72,000 Zika cases between 2015 and 2016. Using national surveillance data from 1121 municipalities over 70 weeks, we identified sociodemographic and environmental factors associated with Zika's emergence, re-emergence, persistence, and transmission intensity in Colombia.

Strangers in a strange land: mapping household and neighbourhood associations with improved wellbeing outcomes in Accra, Ghana.

Urban poverty is not limited to informal settlements, rather it extends throughout cities, with the poor and affluent often living in close proximity. Using a novel dataset derived from the full Ghanaian Census, we investigate how neighbourhood versus household socio-economic status (SES) relates to a set of household development outcomes (related to housing quality, energy, water and sanitation, and information technology) in Accra, Ghana. We then assess "stranger" households' outcomes within neighbourhoods: do poor households fare better in affluent neighbourhoods, and are affluent households negatively impacted by being in poor neighbourhoods? Through a simple generalized linear model we estimate the variance components associated with household and neighbourhood status for our outcome measures.

Identifying deprived "slum" neighbourhoods in the Greater Accra Metropolitan Area of Ghana using census and remote sensing data.

Background: Identifying urban deprived areas, including slums, can facilitate more targeted planning and development policies in cities to reduce socio-economic and health inequities, but methods to identify them are often ad-hoc, resource intensive, and cannot keep pace with rapidly urbanizing communities.

Objectives: We apply a spatial modelling approach to identify census enumeration areas (EAs) in the Greater Accra Metropolitan Area (GAMA) of Ghana with a high probability of being a deprived area using publicly available census and remote sensing data.

Methods: We obtained United Nations (UN) supported field mapping data that identified deprived "slum" areas in Accra's urban core, data on housing and population conditions from the most recent census, and remotely sensed data on environmental conditions in the GAMA.

Bayesian modeling of dynamic behavioral change during an epidemic.

For many infectious disease outbreaks, the at-risk population changes their behavior in response to the outbreak severity, causing the transmission dynamics to change in real-time. Behavioral change is often ignored in epidemic modeling efforts, making these models less useful than they could be. We address this by introducing a novel class of data-driven epidemic models which characterize and accurately estimate behavioral change.

Spatial modeling of ambient concentrations of volatile organic compounds in Montreal, Canada.

Unlabelled: Volatile organic compounds (VOCs) are components of the complex mixture of air pollutants within cities and can cause various adverse health effects. Therefore, it is necessary to understand their spatial distribution for exposure assessment in epidemiological studies.

Objectives: The objective was to model measured concentrations of five VOCs within the city of Montreal, Canada, developing spatial prediction models that can be used in health studies.

A process convolution model for crash count data on a network.

Crash data observed on a road network often exhibit spatial correlation due to unobserved effects with inherent spatial correlation following the structure of the road network. It is important to model this spatial correlation while accounting for the road network structure. In this study, we introduce the network process convolution (NPC) model.

Extended Bayesian endemic-epidemic models to incorporate mobility data into COVID-19 forecasting.

Forecasting the number of daily COVID-19 cases is critical in the short-term planning of hospital and other public resources. One potentially important piece of information for forecasting COVID-19 cases is mobile device location data that measure the amount of time an individual spends at home. Endemic-epidemic (EE) time series models are recently proposed autoregressive models where the current mean case count is modelled as a weighted average of past case counts multiplied by an autoregressive rate, plus an endemic component.

A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal.

As of July 2021, Montreal is the epicentre of the COVID-19 pandemic in Canada with highest number of deaths. We aim to investigate the spatial distribution of the number of cases and deaths due to COVID-19 across the boroughs of Montreal. To this end, we propose that the cumulative numbers of cases and deaths in the 33 boroughs of Montreal are modelled through a bivariate hierarchical Bayesian model using Poisson distributions.

Estimating the lagged effect of price discounting: a time-series study on sugar sweetened beverage purchasing in a supermarket.

Background: Price discount is an unregulated obesogenic environmental risk factor for the purchasing of unhealthy food, including Sugar Sweetened Beverages (SSB). Sales of price discounted food items are known to increase during the period of discounting. However, the presence and extent of the lagged effect of discounting, a sustained level of sales after discounting ends, is previously unaccounted for.

A joint spatial marked point process model for dengue and severe dengue in Medellin, Colombia.

The spatial distribution of surveillance-reported dengue cases and severity are usually analyzed separately, assuming independence between the spatial distribution of non-severe and severe cases. Given the availability of data for the individual geo-location of surveillance-notified dengue cases, we conducted a spatial analysis to model non-severe and severe dengue simultaneously, using a hierarchical Bayesian model. We fit a joint model to the spatial pattern formed by dengue cases as well as to the severity status of the cases.

A Poisson-multinomial spatial model for simultaneous outbreaks with application to arboviral diseases.

Dengue, Zika, and chikungunya are arboviral diseases (AVD) transmitted mainly by . Rio de Janeiro city, Brazil, has been endemic for dengue for over 30 years, and experienced the first joint epidemic of the three diseases between 2015-2016. They present similar symptoms and only a small proportion of cases are laboratory-confirmed.

Quantifying within-city inequalities in child mortality across neighbourhoods in Accra, Ghana: a Bayesian spatial analysis.

Objective: Countries in sub-Saharan Africa suffer the highest rates of child mortality worldwide. Urban areas tend to have lower mortality than rural areas, but these comparisons likely mask large within-city inequalities. We aimed to estimate rates of under-five mortality (U5M) at the neighbourhood level for Ghana's Greater Accra Metropolitan Area (GAMA) and measure the extent of intraurban inequalities.

Within city spatiotemporal variation of pollen concentration in the city of Toronto, Canada.

Background: The exacerbation of asthma and respiratory allergies has been associated with exposure to aeroallergens such as pollen. Within an urban area, tree cover, level of urbanization, atmospheric conditions, and the number of source plants can influence spatiotemporal variations in outdoor pollen concentrations.

Objective: We analyze weekly pollen measurements made between March and October 2018 over 17 sites in Toronto, Canada.

Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada.

Objective: Geographic measurement of diets is generally not available at areas smaller than a national or provincial (state) scale, as existing nutrition surveys cannot achieve sample sizes needed for an acceptable statistical precision for small geographic units such as city subdivisions.

Design: Using geocoded Nielsen grocery transaction data collected from supermarket, supercentre and pharmacy chains combined with a gravity model that transforms store-level sales into area-level purchasing, we developed small-area public health indicators of food purchasing for neighbourhood districts. We generated the area-level indicators measuring per-resident purchasing quantity for soda, diet soda, flavoured (sugar-added) yogurt and plain yogurt purchasing.

Spatio-temporal modelling of the first Chikungunya epidemic in an intra-urban setting: The role of socioeconomic status, environment and temperature.

Three key elements are the drivers of Aedes-borne disease: mosquito infestation, virus circulating, and susceptible human population. However, information on these aspects is not easily available in low- and middle-income countries. We analysed data on factors that influence one or more of those elements to study the first chikungunya epidemic in Rio de Janeiro city in 2016.

Estimating an individual-level deprivation index for HIV/HCV coinfected persons in Canada.

Background: HIV-HCV coinfected individuals are often more deprived than the general population. However, deprivation is difficult to measure, often relying on aggregate data which does not capture individual heterogeneity. We developed an individual-level deprivation index for HIV-HCV co-infected persons that encapsulated social, material, and lifestyle factors.

Price discounting as a hidden risk factor of energy drink consumption.

Objectives: Global consumption of caffeinated energy drinks (CED) has been increasing dramatically despite increasing evidence of their adverse health effects. Temporary price discounting is a rarely investigated but potentially powerful food marketing tactic influencing purchasing of CED. Using grocery transaction records generated by food stores in Montreal, we investigated the association between price discounting and purchasing of CED across socio-economic status operationalized by education and income levels in store neighbourhood.

Commentary: "How's It Going?" Training Experiences of Pediatric Postdoctoral Fellows During COVID-19.

The COVID-19 pandemic has impacted the lives and workplaces of individuals across the world substantially, in ways that are yet largely unknown. This commentary aims to provide an early snapshot of the experiences of pediatric postdoctoral fellows in academic medical settings; specifically, we will explore the impact of the pandemic on developing mastery within several competencies (e.g.

The role of case importation in explaining differences in early SARS-CoV-2 transmission dynamics in Canada-A mathematical modeling study of surveillance data.

Objective: The North American coronavirus disease-2019 (COVID-19) epidemic exhibited distinct early trajectories. In Canada, Quebec had the highest COVID-19 burden and its earlier March school break, taking place two weeks before those in other provinces, could have shaped early transmission dynamics.

Methods: We combined a semi-mechanistic model of SARS-CoV-2 transmission with detailed surveillance data from Quebec and Ontario (initially accounting for 85% of Canadian cases) to explore the impact of case importation and timing of control measures on cumulative hospitalizations.

Socioeconomic factors and bacillary dysentery risk in Jiangsu Province, China: a spatial investigation using Bayesian hierarchical models.

Bacillary dysentery (BD) is an acute diarrheal disease prevalent in areas affected by socioeconomic disparities. We investigated BD risk and its associations with socioeconomic factors at the county-level in Jiangsu province, China using epidemiological and socioeconomic data from 2011-2014. We fitted four Bayesian hierarchical models with various prior specifications for random effects.

Fluoroquinolone Use and Seasonal Patterns of Ciprofloxacin Resistance in Community-Acquired Urinary Escherichia coli Infection in a Large Urban Center.

Urinary tract infections caused by the bacterium Escherichia coli are among the most frequently encountered infections and are a common reason for antimicrobial prescriptions. Resistance to fluoroquinolone antimicrobial agents, particularly ciprofloxacin, has increased in recent decades. It is intuitive that variation in fluoroquinolone resistance is driven by changes in antimicrobial use, but careful study of this association requires the use of time-series methods.