Spatial Epidemiology and GeoInformatics

Dysentery is still a serious global public health problem. In Chongqing, China, there were 37,140 reported cases of dysentery from 2015 to 2021. However, previous research has relied on statistical data of dysentery incidence rate data based on administrative regions, while grained scale products are lacking. Thus, an initialized gradient-boosted decision trees (IGBDT) hybrid machine learning model was constructed to fill this gap in grained scale products. Socioeconomic factors, meteorological factors, topographic factors, and air quality factors were used as inputs of the IGBDT to map the statistical dysentery incidence rate data of Chongqing, China, from 2015 to 2021 on the grid scale. Then, dysentery incidence rate grained scale products (1 km) were generated. The products were evaluated using the total incidence of Chongqing and its districts, with resulting R² values of 0.7369 and 0.5439, indicating the suitable prediction performance of the model. The importance and correlation of factors related to the dysentery incidence rate were investigated. The results showed that socioeconomic factors had the main impact (43.32%) on the dysentery incidence rate, followed by meteorological factors (33.47%). The Nighttime light, normalized difference vegetation index, and maximum temperature showed negative correlations, while the population, minimum and mean temperature, precipitation, and relative humidity showed positive correlations. The impacts of topographic factors and air quality factors were relatively weak. Full article

Undernutrition is a key driver of the global tuberculosis (TB) epidemic, yet there is limited understanding regarding the spatial overlap of both diseases. This study aimed to determine the geographical co-distribution and socio-climatic factors of undernutrition and TB in Ethiopia. Data on undernutrition were found from the Ethiopian Demographic and Health Survey (EDHS). Data on TB were obtained from the Ethiopia national TB prevalence survey. We applied a geostatistical model using a Bayesian framework to predict the prevalence of undernutrition and TB. Spatial overlap of undernutrition and TB prevalence was detected in the Afar and Somali regions. Population density was associated with the spatial distribution of TB [β: 0.008; 95% CrI: 0.001, 0.014], wasting [β: −0.017; 95% CrI: −0.032, −0.004], underweight [β: −0.02; 95% CrI: −0.031, −0.011], stunting [β: −0.012; 95% CrI: −0.017, −0.006], and adult undernutrition [β: −0.007; 95% CrI: −0.01, −0.005]. Distance to a health facility was associated with the spatial distribution of stunting [β: 0.269; 95% CrI: 0.08, 0.46] and adult undernutrition [β: 0.176; 95% CrI: 0.044, 0.308]. Healthcare access and demographic factors were associated with the spatial distribution of TB and undernutrition. Therefore, geographically targeted service integration may be more effective than nationwide service integration. Full article

Earlier studies have examined various factors that may contribute to the contagion rate of COVID-19, such as urban climatic and socioeconomic characteristics. However, there is a lack of studies at the township level detailing the spatiotemporal settings of built environment attributes, especially in the context of lockdown as a response to the global Omicron outbreak. In this study, we extended the existing literature by relating the initial-stage Omicron pandemic conditions with more comprehensive measures of the built environment, including density, diversity, design, distance to transit, and destination accessibility. The variations from the confirmed clusters of COVID-19 and asymptomatic infected cases before, during, and after the lockdown throughout the Omicron outbreak were identified geographically using GIS methods in 218 township-level divisions across Shanghai during the lockdown period. We also compared the regression results of the ordinary least-squares regression, geographically weighted regression, and geographically and temporally weighted regression. Our results show that (1) among all the built environment variables, metro line length, walking accessibility, hotel and inn density, and population exhibited positive significance in influencing pandemic prevalence; (2) spatial and temporal variations were evident in the association between accessibility, mobility, density-related built environment variables, and COVID-19 transmission across three phases: pre-lockdown, during lockdown, and post-lockdown. This study highlights the importance of targeted public health interventions in densely populated areas with high demand for public transit. It emphasizes the significance of transportation network layout and walking accessibility in controlling the spread of infectious diseases in specific urban contexts. By considering these factors, policymakers and stakeholders can foster urban resilience and effectively mitigate the impact of outbreaks, aligning with the objectives of the 2030 UN Sustainable Development Goals. Full article

This paper addresses two common challenges in analyzing spatial epidemiological data, specifically disease incidence rates recorded over small areas: filtering noise caused by small local population sizes and deriving estimates at different spatial scales. Geostatistical techniques, including Poisson kriging (PK), have been used to address these issues by accounting for spatial correlation patterns and neighboring observations in smoothing and changing spatial support. However, PK has a limitation in that it can generate unrealistic rates that are either negative or greater than 100%. To overcome this limitation, an alternative method that relies on soft indicator kriging (IK) is presented. The performance of this method is compared to PK using daily COVID-19 incidence rates recorded in 2020–2021 for each of the 581 municipalities in Belgium. Both approaches are used to derive noise-filtered incidence rates for four different dates of the pandemic at the municipality level and at the nodes of a 1 km spacing grid covering the country. The IK approach has several attractive features: (1) the lack of negative kriging estimates, (2) the smaller smoothing effect, and (3) the better agreement with observed municipality-level rates after aggregation, in particular when the original rate was zero. Full article