Using generalized structured additive regression models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa.

BACKGROUND: The first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors. METHODS: COVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian. RESULTS: The risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR = 4.16; 95% Credible Interval: 4.05-4.27), being on oxygen (aOR = 1.49; 95% Credible Interval: 1.46-1.51) and on invasive mechanical ventilation (aOR = 3.74; 95% Credible Interval: 3.61-3.87). Being admitted in a public hospital (aOR = 3.16; 95% Credible Interval: 3.10-3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts. CONCLUSION: The results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts.

Understanding the Impact of Vaccination on Covid-19 in India Using Time-Interrupted Spatial Panel Data Models

COVID-19 is the biggest threat to the life of humankind around the globe. Vaccination became an important protective system against COVID-19 infection. The geographical aspect is an important factor in infection spreading. This study explores the effect of the vaccination on COVID-19 in India using the estimate of the spatial effects. Since the distribution of vaccination started in the middle of study period, time-interrupted spatial panel models were used. SDM model was selected as the best one. The spatial effect coefficients are statistically significant in SDM models (rho = 0.4057;p < 0.01 , rho = 0.3132;p < 0.01) and the spillover effect of second dose vaccination rate is statistically significant on both confirmed rate and deceased rate. The vaccination has a significant negative impact on deceased rate. There is a clear evidence for the requirement of second dose vaccination

Estimating the spatial correlation and convergence of China's healthcare resources allocation: evidence from the Yangtze River Delta Region.

BACKGROUND: China's imbalanced allocation of healthcare resources mainly arises from urban-rural and intercity differences, the solution of which has been the goal of reforms during the past decades. Estimating the spatial correlation and convergence could help to understand the impact of China's fast-evolving medical market and the latest healthcare reforms. METHODS: The entropy weight method was used to construct a healthcare resource supply index (HRS) by using data of 41cities in a cluster in the Yangtze River Delta (YRD) from 2007 to 2019. The Dagum Gini coefficient, kernel density estimation, Moran's I, and LISA cluster map were used to characterize the spatiotemporal evolution and agglomeration of healthcare resources, and then a spatial panel model was used to perform ß convergence estimation by incorporating the spatial effect, city heterogeneity, and healthcare reforms. RESULTS: Healthcare resources supply in the YRD region increases significantly and converges rapidly. There is a significant spatial correlation and agglomeration between provinces and cities, and a significant spatial spillover effect is also found in ß convergence. No evidence is found that the latest healthcare reforms have an impact on the balanced allocation and convergence of healthcare resources. CONCLUSION: China's long-term investment in past decades has yielded a more balanced allocation and intercity convergence of healthcare resources. However, the latest healthcare reforms do not contribute to the balanced allocation of healthcare resources from the supply-side, and demand-side analysis is needed in the future studies.

Tourism demand forecasting with spatiotemporal features

Tourism demand forecasting is a crucial prerequisite for effective and efficient tourism management. This study develops a novel model based on deep learning methods for precise demand forecasting, namely, spatial-temporal fused graph convolutional network (ST-FGCN). ST-FGCN generates forecasts based on spatial effects extracted using graph convolutional network and temporal dependency captured through long short-term memory. A data-driven spatial matrix is used in our model to strengthen forecasting performance further. Two markedly different forecasting experiments verify the effectiveness of our model. Empirical results suggest that incorporating spatial effects can remarkably reduce forecasting errors. Furthermore, our model shows good applicability for data with different time granularity and different periods: before and during the COVID-19 pandemic.

Modeling the spread of COVID-19 in New York City.

This paper proposes an ensemble predictor for the weekly increase in the number of confirmed COVID-19 cases in the city of New York at zip code level. Within a Bayesian model averaging framework, the baseline is a Poisson regression for count data. The set of covariates includes autoregressive terms, spatial effects, and demographic and socioeconomic variables. Our results for the second wave of the coronavirus pandemic show that these regressors are more significant to predict the number of new confirmed cases as the pandemic unfolds. Both pointwise and interval forecasts exhibit strong predictive ability in-sample and out-of-sample.

Este artículo propone un predictor de conjunto para el aumento semanal del número de casos confirmados de COVID­19 en la ciudad de Nueva York a nivel de código postal. Dentro de un marco de promediación de modelo bayesiano, la línea de base es una regresión de Poisson para datos de recuento. El conjunto de covariables incluye términos autorregresivos, efectos espaciales y variables demográficas y socioeconómicas. Los resultados para la segunda ola de la pandemia de coronavirus muestran que estos regresores son más significativos para predecir el número de nuevos casos confirmados a medida que se desarrolla la pandemia. Tanto las previsiones puntuales como las de intervalo muestran una fuerte capacidad de predicción, tanto dentro como fuera de la muestra.

On the relationship between COVID-19 reported fatalities early in the pandemic and national socio-economic status predating the pandemic.

This study investigates the relationship between socio-economic determinants pre-dating the pandemic and the reported number of cases, deaths, and the ratio of deaths/cases in 199 countries/regions during the first months of the COVID-19 pandemic. The analysis is performed by means of machine learning methods. It involves a portfolio/ensemble of 32 interpretable models and considers the case in which the outcome variables (number of cases, deaths, and their ratio) are independent and the case in which their dependence is weighted based on geographical proximity. We build two measures of variable importance, the Absolute Importance Index (AII) and the Signed Importance Index (SII) whose roles are to identify the most contributing socio-economic factors to the variability of the COVID-19 pandemic. Our results suggest that, together with the established influence on cases and deaths of the level of mobility, the specific features of the health care system (smart/poor allocation of resources), the economy of a country (equity/non-equity), and the society (religious/not religious or community-based vs not) might contribute to the number of COVID-19 cases and deaths heterogeneously across countries.

The impacts of human migration and city lockdowns on specific air pollutants during the COVID-19 outbreak: A spatial perspective.

The outbreak of COVID-19 continues to bring unprecedented shock to mankind's socioeconomic activities, and to the wider environment. China, as the early epicenter of the pandemic, locked down one-third of its cities in an attempt to prevent the rapid spread of the virus. Human migration patterns have subsequently been radically altered and many regions have seen perceived improvements in air quality during the lockdowns. This study empirically examines the relationship between human migration and air pollution and further evaluates the causal impacts of the lockdowns. A spatial econometric method and a spatial explicit counterfactual framework are employed in this study. The key findings are as follows: i) a considerable amount of variation in AQI, PM10, PM2.5, and NO2 concentration can be explained by human migration but we fail to find suggestive evidence in the cases of SO2 and CO; ii) the implementation of lockdown measures led to a significant reduction in AQI (18.1%), PM2.5 (22.2%), NO2 (20.5%), and PM10 (10.7%), but has no meaningful impacts on SO2, CO and O3 levels; iii) further analysis indicates that the impacts of lockdown policies varied by control stringency and by regional heterogeneity. Our findings are of great importance for the Chinese government to create a stronger and more coherent framework in its efforts to tackle air pollution.

Determining the spatial effects of COVID-19 using the spatial panel data model.

This study investigates the propagation power and effects of the coronavirus disease 2019 (COVID-19) in light of published data. We examine the factors affecting COVID-19 together with the spatial effects, and use spatial panel data models to determine the relationship among the variables including their spatial effects. Using spatial panel models, we analyse the relationship between confirmed cases of COVID-19, deaths thereof, and recovered cases due to treatment. We accordingly determine and include the spatial effects in this examination after establishing the appropriate model for COVID-19. The most efficient and consistent model is interpreted with direct and indirect spatial effects.