Spatial and Spatio-Temporal Epidemiology最新文献

This research paper analyzes the spread of COVID-19 in Pakistan using geo-statistical approach to geo-visualize the spatio-temporal pattern hotspots of active cases. The study is based on secondary data, collected from concerned Government Department. Getis-Ord-Gi* statistical model was used to estimate Z score and P score values representing the intensity of active cases in each location. The results indicate that the high intensity of active cases in the selected period is spatially distributed in Punjab and Sindh provinces and extending towards the west. The capital territory also experiences a slight increase in active cases rate. However, the rate of active cases decreases in Khyber Pakhtunkhwa (KP), Balochistan, Gilgit Baltistan (GB) and Azad Jammu and Kashmir with some fluctuations. Overall, this research highlights the usefulness of geo-statistical modeling for identifying hotspots of any epidemic or pandemic. By knowing the hotspots of a disease, policy makers can easily identify the reasons for its spread, trends, and distribution patterns, making it easier to develop management policies to tackle any pandemic situation in the future.

Data-driven mathematical modelling can enrich our understanding of infectious disease spread enormously. Individual-level models of infectious disease transmission allow the incorporation of different individual-level covariates, such as spatial location, vaccination status, etc. This study aims to explore and develop methods for fitting such models when we have many potential covariates to include in the model. The aim is to enhance the performance and interpretability of models and ease the computational burden of fitting these models to data. We have applied and compared multiple variable selection methods in the context of spatial epidemic data. These include a Bayesian two-stage least absolute shrinkage and selection operator (Lasso), forward and backward stepwise selection based on the Akaike information criterion (AIC), spike-and-slab priors, and random variable selection (boosting) methods. We discuss and compare the performance of these methods via simulated datasets and UK 2001 foot-and-mouth disease data. While comparing the variable selection methods all performed consistently well except the two-stage Lasso. We conclude that the spike-and-slab prior method is to be recommended, consistently resulting in high accuracy and short computational time.

This paper examines the mutual dependence between COVID-19 morbidity and vaccination rollout. A theory of endogenous immunization is proposed in which the decision to become vaccinated varies directly with the risks of contagion, and the public self-selects into self-protection. Hence, COVID-19 morbidity varies inversely with vaccination rollout, and vaccination rollout varies directly with COVID-19 morbidity. The paper leverages the natural sequencing between morbidity and immunization to identify the causal order in the dynamics of this relationship. A modified SIR model is estimated using spatial econometric methods for weekly panel data for Israel at a high level of spatial granularity. Connectivity between spatial units is measured using physical proximity and a unique mobility-based measure. Spatiotemporal models for morbidity and vaccination rollout show that not only does morbidity vary inversely with vaccination rollout, vaccination rollout varies directly with morbidity. The utility of the model for public health policy targeting, is highlighted.

This study explores the spatio-temporal behavior of mortality due to multiple causes associated with several diseases and their relationship with the physical availability of food. We analyze data for the 2010–2020 period at the municipality level in Mexico. After collecting and standardizing national databases for each disease, we perform SATSCAN temporal and FleXScan spatial cluster analyses. We use the he Kruskal-Wallis test to analyze the differences between municipalities with high relative risk of mortality and their relationship with food retail units and food establishments. We found statistically significant relationships between clusters by disease and the physical availability of food per hundred thousand inhabitants. The main pattern is a higher average density of convenience stores, supermarkets, fast food chains and franchises, and Mexican snack restaurants in high-risk municipalities, while a higher density of grocery stores and inns, cheap kitchens, and menu restaurants exists in the municipalities with low risk. The density of convenience stores, fast food chains and franchises, and Mexican snack restaurants plays a very important role in mortality behavior, so measures must exist to regulate them and encourage and protect convenience stores, grocery stores, and local food preparation units.

The effects extreme air temperature events are related with an increase in cardiovascular mortality among vulnerable groups worldwide. Therefore, we identify spatiotemporal mortality clusters associated with diseases of the cardiovascular system among people ≥ 65 years in São Paulo, from 2006 to 2015, and investigate whether high-risk mortality clusters occurred during or following extreme air temperature events. To detect the clusters, we used daily mortality data and a retrospective space-time scan analysis with a discrete Poisson model. Extreme air temperature events were defined by daily mean temperatures, below the 10th percentile for cold spells and above the 90th percentile for heatwaves, with two or more consecutive days. We found statistically significant high-risk mortality clusters located in the peripheral areas. The spatiotemporal clusters of risk areas for cardiovascular and ischemic heart disease occurred during or following cold spell events, whereas those for stroke and ischemic stroke events were related to heatwaves.

A steep increase of small papillary thyroid cancers (sPTCs) has been observed globally. A major risk factor for developing PTC is ionizing radiation. The aim of this study is to investigate the spatial distribution of sPTC in Sweden and the extent to which prevalence is correlated to gamma radiation levels (Caesium-137 (Cs-137), Thorium-232 (Th-232), Uranium-238 (U-238) and Potassium-40 (K-40)) using multiple geospatial and geostatistical methods. The prevalence of metastatic sPTC was associated with significantly higher levels of Gamma radiation from Th-232, U-238 and K-40. The association is, however, inconsistent and the prevalence is higher in densely populated areas. The results clearly indicate that sPTC has causative factors that are neither evenly distributed among the population, nor geographically, calling for further studies with bigger cohorts. Environmental factors are believed to play a major role in the pathogenesis of the disease.

This study proposes to use exceedance posterior probabilities of a space-time random-effects model to study the temporal dynamics of clusters. The local time trends specified for each area is further smoothed over space. We modelled the common spatial and the space-varying temporal trend using a multivariate Markov Random field to incorporate within-area correlations. We estimate the model parameters within a fully Bayesian framework. The exceedance posterior probabilities are further used to classify the common spatial trend into hot-spots, cold-spots, and neutral-spots. The local time trends are classified into increasing, decreasing, and stable trends. The results is a $3\times 3$ table depicting the time trends within clusters. As a demonstration, we apply the proposed methodology to study the evolution of spatial clustering of intestinal parasite infections in Ghana. We find the methodology presented in this paper applicable and extendable to other or multiple tropical diseases which may have different space-time conceptualizations.

Mosquito-borne diseases such as dengue and chikungunya have been co-circulating in the Americas, causing great damage to the population. In 2021, for instance, almost 1.5 million cases were reported on the continent, being Brazil the responsible for most of them. Even though they are transmitted by the same mosquito, it remains unclear whether there exists a relationship between both diseases. In this paper, we model the geographic distributions of dengue and chikungunya over the years 2016 to 2021 in the Brazilian state of Ceará. We use a Bayesian hierarchical spatial model for the joint analysis of two arboviruses that includes spatial covariates as well as specific and shared spatial effects that take into account the potential autocorrelation between the two diseases. Our findings allow us to identify areas with high risk of one or both diseases. Only 7% of the areas present high relative risk for both diseases, which suggests a competition between viruses. This study advances the understanding of the geographic patterns and the identification of risk factors of dengue and chikungunya being able to help health decision-making.

Tegumentary (TL) and visceral (VL) leishmaniasis are neglected zoonotic diseases in Brazil, caused by different parasites and transmitted by various vector species. This study investigated and compared spatio-temporal patterns of TL and VL from 2007 to 2020 in the state of Bahia, Brazil, and their correlations with extrinsic factors. The results showed that the total number of cases of both TL and VL were decreasing. The number of municipalities with reported cases reduced for TL over time but remained almost unchanged for VL. There were few municipalities with reported both diseases. Statistical analysis showed that local TL incidence was associated positively with natural forest. Local VL incidence was associated positively with Cerrado (Brazilian savannah) vegetation. This study identified different patterns of occurrence of VL and TL and the risk areas that could be prioritized for epidemiological surveillance.

The United States experienced at least five COVID-19 waves linked with different mutated SARS-CoV-2 variants including Alpha, Delta and Omicron. In addition to the variants, the intensity, geographical distribution, and risk factors related to those waves also vary within socio-demographic characteristics and timeframes. In this project, we have examined the spatial and temporal pattern of COVID-19 in the USA and its associations with Social Determinants of Health (SDoH) by utilizing the County Health Rankings & Roadmaps (CHRR) dataset. Our epidemiologic investigation at the county level showed that the burden of COVID-19 cases and deaths is higher in counties with high percentages of smoking, number of preventable hospital stays, primary care physician rate, the average daily density of PM_2.5 and percentages of high proportions of Hispanic residents. In addition, the analysis also demonstrated that COVID-19 incidence and mortality had distinct characteristics in their association with SDoH variables. For example, the percentages of the population 65 and older had negative associations with incidence while a significant positive association with mortality. In addition to the elderly population, median household income, unemployment, and number of drug overdose deaths showed a mixed association with COVID-19 incidence and mortality. Our findings validate several influential factors found in the existing social epidemiology literature and highlight temporal associations between SDoH variables and COVID-19 incidence and mortality not yet frequently studied.