Spatial and Spatio-Temporal Epidemiology最新文献

Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that was first detected in captive cervids in Colorado, United States (US) in 1967, but has since spread into free-ranging white-tailed deer (Odocoileus virginianus) across the US and Canada as well as to Scandinavia and South Korea. In some areas, the disease is considered endemic in wild deer populations, and governmental wildlife agencies have employed epidemiological models to understand long-term environmental risk. However, continued rapid spread of CWD into new regions of the continent has underscored the need for extension of these models into broader tools applicable for wide use by wildlife agencies. Additionally, efforts to semi-automate models will facilitate access of technical scientific methods to broader users. We introduce software (Habitat Risk) designed to link a previously published epidemiological model with spatially referenced environmental and disease testing data to enable agency personnel to make up-to-date, localized, data-driven predictions regarding the odds of CWD detection in surrounding areas after an outbreak is discovered. Habitat Risk requires pre-processing publicly available environmental datasets and standardization of disease testing (surveillance) data, after which an autonomous computational workflow terminates in a user interface that displays an interactive map of disease risk. We demonstrated the use of the Habitat Risk software with surveillance data of white-tailed deer from Tennessee, USA.

The incidence of low birthweight is a common measure of public health due to the increased risk of complications associated with infants having low and very low birthweights. Moreover, many factors that increase the risk of an infant having a low birthweight can be linked to the mother’s socioeconomic status, leading to large racial/ethnic disparities in its incidence. Our objective is thus to analyze the incidence of low and very low birthweight in Pennsylvania counties by race/ethnicity. Due to the small number of births in many Pennsylvania counties when stratified by race/ethnicity, our methods must walk a fine line: While we wish to leverage spatial structure to improve the precision of our estimates, we also wish to avoid oversmoothing the data, which can yield spurious conclusions. As such, we develop a framework by which we can measure (and control) the informativeness of our spatial model. To analyze the data, we first model the Pennsylvania birth data using the conditional autoregressive model to demonstrate the extent to which it can lead to oversmoothing. We then reanalyze the data using our proposed framework and highlight its ability to detect (or not detect) evidence of racial/ethnic disparities in the incidence of low birthweight.

This ecological study assesses the association between the incidence rate of COVID-19 confirmed cases and socioeconomic deprivation in the Catalan small areas for the first six waves of the pandemic. The association is estimated using Poisson regressions and, in contrast to previous studies, considering that the relationship is not linear but rather depends on the degree of deprivation. The results show that the association between deprivation and incidence varied between waves, not only in intensity but also in its sign. Although it was insignificant in the first, third and fourth waves, the association was positive and significant in the second, becoming significantly negative in the fifth and sixth waves. Interestingly, the evidence suggests that the link between both magnitudes was not homogeneous throughout the distribution of deprivation, the pattern also varying between waves. The results are discussed in view of the role of non-pharmacological interventions and vaccination, as well as potential biases (for example that associated with differences between population groups in the propensity to be tested in each wave).

A factor constraining the elimination of dog-mediated human rabies is limited information on the size and spatial distribution of free-roaming dog populations (FRDPs). The aim of this study was to develop a statistical model to predict the size of free-roaming dog populations and the spatial distribution of free-roaming dogs in urban areas of Nepal, based on real-world dog census data from the Himalayan Animal Rescue Trust (HART) and Animal Nepal. Candidate explanatory variables included proximity to roads, building density, specific building types, human population density and normalised difference vegetation index (NDVI). A multivariable Poisson point process model was developed to estimate dog population size in four study locations in urban Nepal, with building density and distance from nearest retail food establishment or lodgings as explanatory variables. The proposed model accurately predicted, within a 95 % confidence interval, the surveyed FRDP size and spatial distribution for all four study locations. This model is proposed for further testing and refinement in other locations as a decision-support tool alongside observational dog population size estimates, to inform dog health and public health initiatives including rabies elimination efforts to support the ‘zero by 30′ global mission.

In practice, survival analyses appear in pharmaceutical testing, procedural recovery environments, and registry-based epidemiological studies, each reasonably assuming a known patient population. Less commonly discussed is the additional complexity introduced by non-registry and spatially-referenced data with time-dependent covariates in observational settings. In this short report we discuss residual diagnostics and interpretation from an extended Cox proportional hazard model intended to assess the effects of wildfire evacuation on risk of a secondary cardiovascular events for patients of a specific healthcare system on the California’s central coast. We describe how traditional residuals obscure important spatial patterns indicative of true geographical variation, and their impacts on model parameter estimates. We briefly discuss alternative approaches to dealing with spatial correlation in the context of Bayesian hierarchical models. Our findings/experience suggest that careful attention is needed in observational healthcare data and survival analysis contexts, but also highlights potential applications for detecting observed hospital service areas.

Bayesian inference in modelling infectious diseases using Bayesian inference using Gibbs Sampling (BUGS) is notable in the last two decades in parallel with the advancements in computing and model development. The ability of BUGS to easily implement the Markov chain Monte Carlo (MCMC) method brought Bayesian analysis to the mainstream of infectious disease modelling. However, with the existing software that runs MCMC to make Bayesian inferences, it is challenging, especially in terms of computational complexity, when infectious disease models become more complex with spatial and temporal components, in addition to the increasing number of parameters and large datasets. This study investigates two alternative subscripting strategies for creating models in Just Another Gibbs Sampler (JAGS) environment and their performance in terms of run times. Our results are useful for practitioners to ensure the efficiency and timely implementation of Bayesian spatiotemporal infectious disease modelling.

Dementia is a major global public health concern that is increasingly leading to morbidity and mortality among older adults. While studies have focused on the risk factors and care provision, there is currently limited knowledge about the spatial risk pattern of the disease. In this study, we employ Bayesian spatial modelling with a stochastic partial differential equation (SPDE) approach to model the spatial risk using complete residential history data from the Danish population and health registers. The study cohort consisted of 1.6 million people aged 65 years and above from 2005 to 2018. The results of the spatial risk map indicate high-risk areas in Copenhagen, southern Jutland and Funen. Individual socioeconomic factors and population density reduce the intensity of high-risk patterns across Denmark. The findings of this study call for the critical examination of the contribution of place of residence in the susceptibility of the global ageing population to dementia.

Anaemia remains a major nutritional-related health concern for women under reproductive age (WRA) in developing nations like India as well as the Indian EAG states. According to NFHS round-5, EAG states constitute 57% of WRA having any form of anaemia, higher than many other states of India and other developed and developing nations. This study aimed to assess the frequency of anaemia among the WRA in India's eight EAG states. Also, it attempts to analyse the causes associated with anaemia by the women's background characteristics with spatial correlation with its co-variates across 291 districts of the EAG states. One of the most current Demographic and Health Survey's (DHS) cross-sectional data is the NFHS-5th (2019–21) round taken, conducted by the IIPS under the administration of MoHFW, India. This study only included 315,069 women under reproductive age (WRA). The variables related to anaemia among women's (WRA) background socio-demographic characteristics were assessed using bivariate statistics and multinominal logistic regression analysis to comprehend the spatial correlation between women and their determinant factors. Among the EAG states, the overall prevalence of anaemia was 57%, varying from 42.6% in Uttarakhand to 65.3% in Jharkhand. Multinominal logistic regression analyses reveal that the chances of anaemia are remarkably more prevalent in younger women (15–19 years of age), women living in rural areas, no educated and primary level educated women, women belonging to the middle to poorest wealth quintile, women no longer living together, women of the Christian religion, women who are not exposed to reading newspapers, underweight BMI women, and scheduled tribe women. Mainly, the prevalence is observed in the North-eastern and southeastern states of Bihar, Jharkhand, Odisha, Chhattisgarh, some parts of Madhya Pradesh, Uttar Pradesh, and Rajasthan, which is shown by the hotspot map. According to the findings of this study, numerous factors like family, socioeconomic, educational, awareness, and individual characteristics such as caste and domicile all lead to a risk of anaemia. The WRA suffers from anaemia as a result of their socioeconomic background and awareness, which leads to a lack of nourishment, and they seek nutrient deficiencies. To overcome this anaemia, multiple discipline policies and initiatives need to be taken targeting women's wellness and nutritional status by increasing women's education and socioeconomic status.

In this study, we developed a negative binomial regression model for one-week ahead spatio-temporal predictions of the number of COVID-19 hospitalizations in Uppsala County, Sweden. Our model utilized weekly aggregated data on testing, vaccination, and calls to the national healthcare hotline. Variable importance analysis revealed that calls to the national healthcare hotline were the most important contributor to prediction performance when predicting COVID-19 hospitalizations. Our results support the importance of early testing, systematic registration of test results, and the value of healthcare hotline data in predicting hospitalizations. The proposed models may be applied to studies modeling hospitalizations of other viral respiratory infections in space and time assuming count data are overdispersed. Our suggested variable importance analysis enables the calculation of the effects on the predictive performance of each covariate. This can inform decisions about which types of data should be prioritized, thereby facilitating the allocation of healthcare resources.

The transmission growth rate of infectious diseases, particularly COVID-19, has forced governments to take immediate control decisions. Previous studies have shown that human mobility, weather condition, and vaccination are potential factors influencing virus transmission. This study investigates the contribution of weather conditions, namely temperature and precipitation, human mobility, and vaccination to coronavirus transmission. Three machine learning models: random forest (RF), XGBoost, and neural networks, are applied to predict the confirmed cases based on three aforementioned variables. All models’ prediction are evaluated via spatial and temporal analysis. The spatial analysis observes the model performance over countries on certain times. The temporal analysis looks at the model prediction of each country during the specified period. The models’ prediction results effectively indicate the transmission trend. The RF model performs best with a coefficient of determination of up to 89%. Meanwhile, all models confirm that vaccination is most significantly associated with COVID-19 cases.