Spatial and Spatio-Temporal Epidemiology最新文献

{"title":"Predicting COVID-19 hospitalizations: The importance of healthcare hotlines, test positivity rates and vaccination coverage","authors":"Vera van Zoest ,&nbsp;Karl Lindberg ,&nbsp;Georgios Varotsis ,&nbsp;Frank Badu Osei ,&nbsp;Tove Fall","doi":"10.1016/j.sste.2024.100636","DOIUrl":"10.1016/j.sste.2024.100636","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"48 ","pages":"Article 100636"},"PeriodicalIF":3.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000030/pdfft?md5=e41ddfc5e71a08c21d18542145e8cd5c&pid=1-s2.0-S1877584524000030-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139496983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Significance of weather condition, human mobility, and vaccination on global COVID-19 transmission","authors":"Amandha Affa Auliya ,&nbsp;Inna Syafarina ,&nbsp;Arnida L. Latifah ,&nbsp;Wiharto","doi":"10.1016/j.sste.2024.100635","DOIUrl":"10.1016/j.sste.2024.100635","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"48 ","pages":"Article 100635"},"PeriodicalIF":3.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000029/pdfft?md5=34e1d7bcbf8a58cc080cb9844e6b7d74&pid=1-s2.0-S1877584524000029-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139518103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Taking cues from machine learning, compartmental and time series models for SARS-CoV-2 omicron infection in Indian provinces","authors":"Subhash Kumar Yadav ,&nbsp;Saif Ali Khan ,&nbsp;Mayank Tiwari ,&nbsp;Arun Kumar ,&nbsp;Vinit Kumar ,&nbsp;Yusuf Akhter","doi":"10.1016/j.sste.2024.100634","DOIUrl":"10.1016/j.sste.2024.100634","url":null,"abstract":"<div><p>SARS-CoV-2, the virus responsible for COVID-19, posed a significant threat to the world. We analyzed COVID-19 dissemination data in the top ten Indian provinces by infection incidences using the Susceptible-Infectious-Removed (SIR) model, an Autoregressive Integrated Moving Average (ARIMA) time series model, a machine learning model based on the Random Forest, and distribution fitting. Outbreaks are expected to continue if the Basic Reproduction Number (<span><math><msub><mi>R</mi><mrow><mn>0</mn><mspace></mspace></mrow></msub></math></span>) &gt; 1, and infection waves are anticipated to end if the <span><math><msub><mi>R</mi><mrow><mn>0</mn><mspace></mspace></mrow></msub></math></span> &lt; 1, as determined by the SIR model. Different parametric probability distributions are also fitted. Data collected from December 12, 2021, to March 31, 2022, encompassing data from both before and during the implementation of strict control measures. Based on the estimates of the model parameters, health agencies and government policymakers can develop strategies to combat the spread of the disease in the future, and the most effective technique can be recommended for real-world application for other outbreaks of COVID-19. The best method out of these could be also implemented further on the epidemiological data of other similar infectious agents.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"48 ","pages":"Article 100634"},"PeriodicalIF":3.4,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584524000017/pdfft?md5=22d7691a9fad641affb8e2a51c88b75d&pid=1-s2.0-S1877584524000017-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139518117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chronic back pain prevalence at small area level in England - the design and validation of a 2-stage static spatial microsimulation model","authors":"Harrison Smalley,&nbsp;Kimberley Edwards","doi":"10.1016/j.sste.2023.100633","DOIUrl":"10.1016/j.sste.2023.100633","url":null,"abstract":"<div><p>Spatially disaggregated estimates provide valuable insights into the nature of a disease. They highlight inequalities, aid public health planning and identify avenues for further research. Spatial microsimulation is advantageous in that it can be used to create large microdata sets with intact microlevel relationships between variables, which allows analysis of relationships between variables locally. This methodological paper outlines the design and validation of a 2-stage static spatial microsimulation model for chronic back pain prevalence across England, suitable for policy modelling. Data used was obtained from the Health Survey for England and the 2011 Census. Microsimulation was performed using SimObesity, a previously validated static deterministic program, and the synthetic chronic back pain microdataset was internally validated. The paper also highlights modelling considerations for researchers embarking on similar work, as well as future directions for research in this area of microsimulation.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"48 ","pages":"Article 100633"},"PeriodicalIF":3.4,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584523000709/pdfft?md5=3e985803b58d83b402dc2e07c3d49272&pid=1-s2.0-S1877584523000709-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139066440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Psychosis prevalence in London neighbourhoods; A case study in spatial confounding","authors":"Peter Congdon","doi":"10.1016/j.sste.2023.100631","DOIUrl":"10.1016/j.sste.2023.100631","url":null,"abstract":"<div><p>Analysis of impacts of neighbourhood risk factors on mental health outcomes frequently adopts a disease mapping approach, with unknown neighbourhood influences summarised by random effects. However, such effects may show confounding with observed predictors, especially when such predictors have a clear spatial pattern. Here, the standard disease mapping model is compared to methods which account and adjust for spatial confounding in an analysis of psychosis prevalence in London neighbourhoods. Established area risk factors such as area deprivation, non-white ethnicity, greenspace access and social fragmentation are considered as influences on psychosis. The results show evidence of spatial confounding in the standard disease mapping model. Impacts expected on substantive grounds and available evidence are either nullified or reversed in direction. It is argued that the potential for spatial confounding to affect inferences about geographic disease patterns and risk factors should be routinely considered in ecological studies of health based on disease mapping.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"48 ","pages":"Article 100631"},"PeriodicalIF":3.4,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584523000680/pdfft?md5=dda0e980c567d1c109bc5b42f55f59b4&pid=1-s2.0-S1877584523000680-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138683407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial distribution and determinants of tuberculosis incidence in Mozambique: A nationwide Bayesian disease mapping study","authors":"Nelson Cuboia ,&nbsp;Joana Reis-Pardal ,&nbsp;Isabel Pfumo-Cuboia ,&nbsp;Ivan Manhiça ,&nbsp;Cláudia Mutaquiha ,&nbsp;Luis Nitrogénio ,&nbsp;Pereira Zindoga ,&nbsp;Luís Azevedo","doi":"10.1016/j.sste.2023.100632","DOIUrl":"10.1016/j.sste.2023.100632","url":null,"abstract":"<div><h3>Introduction</h3><p>Mozambique is a high-burden country for tuberculosis (TB). International studies show that TB is a disease that tends to cluster in specific regions, and different risk factors (HIV prevalence, migration, overcrowding, poverty, house condition, temperature, altitude, undernutrition, urbanization, and inadequate access to TB diagnosis and treatment) are reported in the literature to be associated with TB incidence. Although Mozambique has a higher burden of TB, the spatial distribution, and determinants of TB incidence at the sub-national level have not been studied yet for the whole country. Therefore, we aimed to analyze the spatial distribution and determinants of tuberculosis incidence across all 154 districts of Mozambique and identify the hotspot areas.</p></div><div><h3>Method</h3><p>We conducted an ecological study with the district as our unit of analysis, where we included all cases of tuberculosis diagnosed in Mozambique between 2016 and 2020. We obtained the data from the Mozambique Ministry of Health and other publicly available open sources. The predictor variables were selected based on the literature review and data availability at the district level in Mozambique. The parameters were estimated through Bayesian hierarchical Poisson regression models using Markov Chain Monte Carlo simulation.</p></div><div><h3>Results</h3><p>A total of 512 877 people were diagnosed with tuberculosis in Mozambique during our five-year study period. We found high variability in the spatial distribution of tuberculosis incidence across the country. Sixty-two districts out of 154 were identified as hotspot areas. The districts with the highest incidence rate were concentrated in the south and the country's central regions. In contrast, those with lower incidence rates were mainly in the north. In the multivariate analysis, we found that TB incidence was positively associated with the prevalence of HIV (RR: 1.23; 95 % CrI 1.13 to 1.34) and negatively associated with the annual average temperature (RR: 0.83; 95 % CrI 0.74 to 0.94).</p></div><div><h3>Conclusion</h3><p>The incidence of tuberculosis is unevenly distributed across the country. Lower average temperature and high HIV prevalence seem to increase TB incidence. Targeting interventions in higher-risk areas and strengthening collaboration between HIV and TB programs is paramount to ending tuberculosis in Mozambique, as established by the WHO's End TB strategy and the Sustainable Development Goals.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"48 ","pages":"Article 100632"},"PeriodicalIF":3.4,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584523000692/pdfft?md5=e6f0668ba5b13059fa334b9819d335c3&pid=1-s2.0-S1877584523000692-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138683406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining associations between social vulnerability indices and COVID-19 incidence and mortality with spatial-temporal Bayesian modeling","authors":"Daniel P. Johnson ,&nbsp;Claudio Owusu","doi":"10.1016/j.sste.2023.100623","DOIUrl":"https://doi.org/10.1016/j.sste.2023.100623","url":null,"abstract":"<div><p>This study compares two social vulnerability indices, the U.S. CDC SVI and SoVI (the Social Vulnerability Index developed at the Hazards Vulnerability &amp; Resilience Institute at the University of South Carolina), on their ability to predict the risk of COVID-19 cases and deaths. We utilize COVID-19 cases and deaths data for the state of Indiana from the Regenstrief Institute in Indianapolis, Indiana, from March 1, 2020, to March 31, 2021. We then aggregate the COVID-19 data to the census tract level, obtain the input variables, domains (components), and composite measures of both CDC SVI and SoVI data to create a Bayesian spatial-temporal ecological regression model. We compare the resulting spatial-temporal patterns and relative risk (RR) of SARS-CoV-2 infection (COVID-19 cases) and associated death. Results show there are discernable spatial-temporal patterns for SARS-CoV-2 infections and deaths with the largest contiguous hotspot for SARS-CoV-2 infections found in the southwest of the Indianapolis metropolitan area. We also observed one large contiguous hotspot for deaths that stretches across Indiana from the Cincinnati area in the southeast to just east and north of Terre Haute (southeast to west central). The spatial-temporal Bayesian model shows that a 1-percentile increase in CDC SVI was significantly (<em>p</em> ≤ 0.05) associated with an increased risk of SARS-CoV-2 infection by 6 % (RR = 1.06, 95 %CI = 1.04 -1.08). Whereas a 1-percentile increase in SoVI was significantly predicted to increase the risk of COVID-19 death by 45 % (RR = 1.45, 95 %CI =1.38 – 1.53). Domain-specific variables related to socioeconomic status, age, and race/ethnicity were shown to increase the risk of SARS-CoV-2 infections and deaths. There were notable differences in the relative risk estimates for SARS-CoV-2 infections and deaths when each of the two indices were incorporated in the model. Observed differences between the two social vulnerability indices and infection and death are likely due to alternative methodologies of formation and differences in input variables. The findings add to the growing literature on the relationship between social vulnerability and COVID-19 and further the development of COVID-19-specific vulnerability indices by illustrating the utility of local spatial-temporal analysis.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"48 ","pages":"Article 100623"},"PeriodicalIF":3.4,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877584523000606/pdfft?md5=4ed3d0b4c67ef2905a525c5266f2e6c8&pid=1-s2.0-S1877584523000606-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138413267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal and spatial analysis of COVID-19 incidence hotspots in Pakistan: A spatio-statistical approach","authors":"Nayab Arif,&nbsp;Shakeel Mahmood","doi":"10.1016/j.sste.2023.100603","DOIUrl":"10.1016/j.sste.2023.100603","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"47 ","pages":"Article 100603"},"PeriodicalIF":3.4,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41410440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variable screening methods in spatial infectious disease transmission models","authors":"Tahmina Akter ,&nbsp;Rob Deardon","doi":"10.1016/j.sste.2023.100622","DOIUrl":"https://doi.org/10.1016/j.sste.2023.100622","url":null,"abstract":"<div><p><span>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 </span>least absolute shrinkage and selection operator<span> (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.</span></p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"47 ","pages":"Article 100622"},"PeriodicalIF":3.4,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91987303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The joint determination of morbidity and vaccination in the spatiotemporal epidemiology of COVID-19","authors":"Michael Beenstock ,&nbsp;Daniel Felsenstein ,&nbsp;Matan Gdaliahu","doi":"10.1016/j.sste.2023.100621","DOIUrl":"https://doi.org/10.1016/j.sste.2023.100621","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"47 ","pages":"Article 100621"},"PeriodicalIF":3.4,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49746763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}