Colleen L. Lau

Description:Background: To demonstrate the application and utility of geostatistical modelling to provide comprehensive high-resolution understanding of the population's protective immunity during a pandemic and identify pockets with sub-optimal protection. Methods: Using data from a national cross-sectional household survey of 6620 individuals in the Dominican Republic (DR) from June to October 2021, we developed and applied geostatistical regression models to estimate and predict Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spike (anti-S) antibodies (Ab) seroprevalence at high resolution (1 km) across heterogeneous areas. Results: Spatial patterns in population immunity to SARS-CoV-2 varied across the DR. In urban areas, a one-unit increase in the number of primary healthcare units per population and 1% increase in the proportion of the population aged under 20 years were associated with higher odds ratios of being anti-S Ab positive of 1.38 (95% confidence interval [CI]: 1.35-1.39) and 1.35 (95% CI: 1.32-1.33), respectively. In rural areas, higher odds of anti-S Ab positivity, 1.45 (95% CI: 1.39-1.51), were observed with increasing temperature in the hottest month (per°C), and 1.51 (95% CI: 1.43-1.60) with increasing precipitation in the wettest month (per mm). Conclusions: A geostatistical model that integrates contextually important socioeconomic and environmental factors can be used to create robust and reliable predictive maps of immune protection during a pandemic at high spatial resolution and will assist in the identification of highly vulnerable areas.

Description:Zoonotic diseases whose transmission processes are sensitive to environmental characteristics represent an important public health burden in Australia, particularly in Queensland. This study aimed to analyse the epidemiology of hospitalisations from the four main environmental zoonotic diseases-leptospirosis, melioidosis, Q fever, and Ross River virus-from 2012 to 2019 in Queensland. Our analyses reveal an increasing trend of hospitalisation incidence for melioidosis, stable incidence for Q fever and Ross River virus infection, and a declining trend for leptospirosis. We identified sex and age disparities in hospitalisations, with males being more likely to be hospitalised for leptospirosis, melioidosis, and Q fever compared to females. We also uncovered discrepancies between hospitalisation and notification data, which could be attributed to diagnostic and reporting criteria. The findings of this study show that the epidemiological patterns of hospitalisation are different to the notification for the same diseases and underscore the importance of accurate recording and reporting of zoonoses-related hospitalisations to inform environmental public health interventions.

Description:Spatial epidemiology, defined as the study of spatial patterns in disease burdens or health outcomes, aims to estimate disease risk or incidence by identifying geographical risk factors and populations at risk (Morrison et al., 2024). Research in spatial epidemiology relies on both conventional approaches and Machine- Learning (ML) algorithms to explore geographic patterns of diseases and identify influential factors (Pfeiffer & Stevens, 2015). Traditional spatial techniques, including spatial autocorrelation using global Moran's I, Geary's C (Amgalan et al., 2022), and Ripley's K Function (Kan et al., 2022), Local Indicators of Spatial Association (LISA) (Sansuk et al., 2023), hotspot analysis by Getis-Ord Gi* (Lun et al., 2022), spatial lag models (Rey & Franklin, 2022), and Geographically Weighted Regression (GWR) (Kiani et al., 2024) are designed to explicitly incorporate the spatial structure of data into spatial modelling, often referred to as spatially aware models (Reich et al., 2021). Beyond these models, several other spatially aware approaches that have been widely applied in epidemiological studies include but are not limited to Bayesian spatial models that account for spatial uncertainty in disease mapping, such as Bayesian Hierarchical models, Conditional Autoregressive (CAR), and Besage, York, and Mollie' (BYM) models (Louzada et al., 2021). Bayesian methods are statistically rigorous techniques that assume neighboring regions share similar values. Kulldorff's Spatial Scan Statistic is another traditional spatial technique that uses a moving circular window to extract significant disease clusters (Tango, 2021). Moreover, geostatistical models such as Kriging and Inverse Distance Weighting (IDW) allow for continuous spatial interpolation of health data (Nayak et al., 2021). [...].

Description:Background: Drivers of leptospirosis transmission can vary across regions, leading to spatial clustering of infections. This study aims to identify clusters of leptospirosis seroprevalence in the Dominican Republic (DR) and factors associated with high-risk areas. Results: We analysed data from two provinces, Espaillat and San Pedro de Macoris (SPM), obtained on a national survey conducted in 2021 (n = 2,078). Samples were tested by microscopic agglutination testing (MAT) to detect leptospirosis antibodies. We used flexible spatial scan statistics to locate significant clusters for seropositive individuals (all serogroups combined) in each province and calculated risk ratios (RR) at the household and community level. Environmental and sociodemographic risk factors associated with clusters were assessed by logistic regression. One cluster was identified in each province. Participants living inside a cluster were more likely to live further from health facilities (OR 1.86, p < 0.001 and OR 4.41, p = 0.044 by motorized travel time in Espaillat and SPM, respectively). Cluster participants were also less likely to live in areas of higher population density (OR 0.76, p < 0.01 and OR 0.29, p < 0.001 in Espaillat and SPM, respectively) and in communities with higher gross domestic product (GDP) (OR 0.70, p < 0.001 and OR 0.42, p < 0.001 in Espaillat and SPM, respectively). Additional risk factors varied between Espaillat and SPM. Conclusions: Our findings confirm the clustered spatial pattern of leptospirosis and highlight that transmission drivers vary by province. While both provinces show higher transmission in impoverished areas, modifiable factors differ, requiring tailored public health interventions.

Description:Infectious diseases of poverty (IDoP) affect disproportionately resource-limited and marginalized populations, resulting in spatial patterns of vulnerability across various geographical areas. Currently, no spatial indices exist to quantify vulnerability to IDoP at a fine geographical level within countries, such as municipalities or provinces. Without such an index, policymakers cannot effectively allocate resources or target interventions in the most vulnerable areas. This protocol aims to specify a methodological approach to measure spatial variation in vulnerability to IDoP. We will evaluate this methodological approach using surveillance and seroprevalence data from the Dominican Republic (DR) as part of a broader effort to develop a regional index for the Caribbean region. The study will consist of three main components. The first component involves identifying the relevant factors associated with IDoP in the Caribbean region through a scoping review, supplemented by expert-elicited opinion. The second component will apply a Fuzzy Analytic Hierarchy Process to weigh the aforementioned factors and develop a spatial composite index, using open data and available national surveys in the DR. In the final component, we will evaluate and validate the index by analysing the prevalence of at least three IDoPs at a fine-grained municipal level in the DR, using seroprevalence data from a 2021 national field study and other national surveillance programs. The spatial vulnerability index framework developed in this study will assess the degree of vulnerability to IDoP across different geographical scales, depending on data availability in each country.