Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand.

A study of 2,569,617 Thailand citizens diagnosed with COVID-19 from January 2020 to March 2022 was conducted with the aim of identifying the spatial distribution pattern of incidence rate of COVID-19 during its five main waves in all 77 provinces of the country. Wave 4 had the highest incidence rate (9,007 cases per 100,000) followed by the Wave 5, with 8,460 cases per 100,000. We also determined the spatial autocorrelation between a set of five demographic and health care factors and the spread of the infection within the provinces using Local Indicators of Spatial Association (LISA) and univariate and bivariate analysis with Moran's I. The spatial autocorrelation between the variables examined and the incidence rates was particularly strong during the waves 3-5. All findings confirmed the existence of spatial autocorrelation and heterogenicity of COVID-19 with the distribution of cases with respect to one or several of the five factors examined. The study identified significant spatial autocorrelation with regard to the COVID-19 incidence rate with these variables in all five waves. Depending on which province that was investigated, strong spatial autocorrelation of the High-High pattern was observed in 3 to 9 clusters and of the Low-Low pattern in 4 to 17 clusters, whereas negative spatial autocorrelation was observed in 1 to 9 clusters of the High-Low pattern and in 1 to 6 clusters of Low-High pattern. These spatial data should support stakeholders and policymakers in their efforts to prevent, control, monitor and evaluate the multidimensional determinants of the COVID-19 pandemic.

Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln.

Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.

The utility of Zip4 codes in spatial epidemiological analysis.

There are many public health situations within the United States that require fine geographical scale data to effectively inform response and intervention strategies. However, a condition for accessing and analyzing such data, especially when multiple institutions are involved, is being able to preserve a degree of spatial privacy and confidentiality. Hospitals and state health departments, who are generally the custodians of these fine-scale health data, are sometimes understandably hesitant to collaborate with each other due to these concerns. This paper looks at the utility and pitfalls of using Zip4 codes, a data layer often included as it is believed to be "safe", as a source for sharing fine-scale spatial health data that enables privacy preservation while maintaining a suitable precision for spatial analysis. While the Zip4 is widely supplied, researchers seldom utilize it. Nor is its spatial characteristics known by data guardians. To address this gap, we use the context of a near-real time spatial response to an emerging health threat to show how the Zip4 aggregation preserves an underlying spatial structure making it potentially suitable dataset for analysis. Our results suggest that based on the density of urbanization, Zip4 centroids are within 150 meters of the real location almost 99% of the time. Spatial analysis experiments performed on these Zip4 data suggest a far more insightful geographic output than if using more commonly used aggregation units such as street lines and census block groups. However, this improvement in analytical output comes at a spatial privy cost as Zip4 centroids have a higher potential of compromising spatial anonymity with 73% of addresses having a spatial k anonymity value less than 5 when compared to other aggregations. We conclude that while offers an exciting opportunity to share data between organizations, researchers and analysts need to be made aware of the potential for serious confidentiality violations.

A global spatial analysis of factors associated with case and mortality rates for coronavirus disease 2019 during the first year of the pandemic.

BACKGROUND: A increasing number of studies have revealed associations between country-level determinants and coronavirus disease 2019 (COVID-19) outcomes. This ecological study was conducted to analyze country-level parameters related to COVID-19 infections and deaths during the first year of the pandemic. METHODS: The examined predictors comprised demographics, economic factors, disease prevalence and healthcare system status, and the relevant data were obtained from public databases. The index dates were set to 15 July 2020 (Time 1) and 15 December 2020 (Time 2). The adjusted spatial autoregression models used a first-order queen contiguity spatial weight for the main analysis and a second-order queen contiguity spatial weight for a sensitivity analysis to examine the predictors associated with COVID-19 case and mortality rates. RESULTS: Obesity was significantly and positively associated with COVID-19 case and mortality rates in both the main and sensitivity analyses. The sensitivity analysis revealed that a country's gross domestic product, population density, life expectancy and proportion of the population older than 65 y are positively associated with COVID-19 case and mortality rates. CONCLUSIONS: With the increasing global prevalence of obesity, the relationship between obesity and COVID-19 disease at the country level must be clarified and continually monitored.

Distribution of COVID-19 cases and health resources in Brazil's Amazon region: a spatial analysis. / Distribuição da COVID-19 e dos recursos de saúde na Amazônia Legal: uma análise espacial.

Spatial analysis can help measure the spatial accessibility of health services with a view to improving the allocation of health care resources. The objective of this study was to analyze the spatial distribution of COVID-19 detection rates and health care resources in Brazil's Amazon region. We conducted an ecological study using data on COVID-19 cases and the availability of health care resources in 772 municipalities during two waves of the pandemic. Local and global Bayesian estimation were used to construct choropleth maps. Moran's I was calculated to detect the presence of spatial dependence and Moran maps were used to identify disease clusters. In both periods, Moran's I values indicate the presence of positive spatial autocorrelation in distributions and spatial dependence between municipalities, with only a slight difference between the two estimators. The findings also reveal that case rates were highest in the states of Amapá, Amazonas, and Roraima. The data suggest that health care resources were inefficiently allocated, with higher concentrations of ventilators and ICU beds being found in state capitals.

O método de análise espacial permite mensurar a acessibilidade espacial dos serviços de saúde para alocação dos recursos de forma eficiente e eficaz. Diante disso, o objetivo deste estudo foi analisar a distribuição espacial das taxas de COVID-19 e dos recursos de saúde na Amazônia Legal. Estudo ecológico realizado com casos de COVID-19 e os recursos de saúde nos 772 municípios em dois picos da pandemia. Utilizou-se o método bayesiano global e local para elaboração de mapas coropléticos, com cálculo do índice de Moran para análise da dependência espacial e utilização do Moran map para identificação dos clusters da doença. Os índices de Moran calculados para os dois períodos demonstraram autocorrelação espacial positiva dessa distribuição e dependência espacial entre os municípios nos dois períodos, sem muita diferença entre os dois estimadores. Evidenciaram-se maiores taxas da doença nos estados do Amapá, Amazonas e Roraima. Em relação aos recursos de saúde, observou-se alocação de forma ineficiente, com maior concentração nas capitais.

Spatio-temporal relative risks and priority areas for visceral leishmaniasis control in Brazil, between 2001 and 2020.

Visceral leishmaniasis (VL) is a pressing public health problem in Brazil. The proper implementation of disease control programs in priority areas is a challenge for healthcare managers. The present study aimed to analyze the spatio-temporal distribution and identify high risk areas of VL occurrence in the Brazilian territory. We analyzed data regarding new cases with confirmed diagnosis of VL in Brazilian municipalities, from 2001 to 2020, extracted from the Brazilian Information System for Notifiable Diseases. The Local Index of Spatial Autocorrelation (LISA) was used to identify contiguous areas with high incidence rates in different periods of the temporal series. Clusters of high spatio-temporal relative risks were identified using the scan statistics. The accumulated incidence rate in the analyzed period was 33.53 cases per 100,000 inhabitants. The number of municipalities that reported cases showed an upward trend from 2001 onward, although there was a decrease in 2019 and 2020. According to LISA, the number of municipalities considered a priority increased in Brazil and in most states. Priority municipalities were predominantly concentrated in the states of Tocantins, Maranhão, Piauí, and Mato Grosso do Sul, in addition to more specific areas of Pará, Ceará, Piauí, Alagoas, Pernambuco, Bahia, São Paulo, Minas Gerais, and Roraima. The spatio-temporal clusters of high-risk areas varied throughout the time series and were relatively higher in the North and Northeast regions. Recent high-risk areas were found in Roraima and municipalities in northeastern states. VL expanded territorially in Brazil in the 21st century. However, there is still a considerable spatial concentration of cases. The areas identified in the present study should be prioritized for disease control actions.

Bayesian spatial modelling of tuberculosis-HIV co-infection in Ethiopia.

An in-depth analysis of the epidemiological patterns of TB/HIV co-infection is essential since it helps to target high-risk areas with effective control measures. The main objective of this study was to assess the spatial clustering of TB/HIV co-infection prevalence in Ethiopia for the year 2018 using district-level aggregated TB and HIV data obtained from the Ethiopian Federal Ministry of Health. The global Moran's index, Getis-Ord [Formula: see text] local statistic, and Bayesian spatial modeling techniques were applied to analyse the data. The result of the study shows that TB among people living with HIV (PLHIV) and HIV among TB patients prevalence were geographically heterogeneous. The highest prevalence of TB among PLHIV in 2018 was reported in the Gambella region (1.44%). The overall prevalence of TB among PLHIV in Ethiopia in the same year was 0.38% while the prevalence of HIV among TB patients was 6.88%. Both district-level prevalences of HIV among TB patients and TB among PLHIV were positively spatially autocorrelated, but the latter was not statistically significant. The local indicators of spatial analysis using the Getis-Ord statistic also identified hot-spots districts for both types of TB/HIV co-infection data. The results of Bayesian spatial logistic regression with spatially structured and unstructured random effects using the Besag, York, and Mollié prior showed that not all the heterogeneities in the prevalence of HIV among TB patients and TB among PLHIV were explained by the spatially structured random effects. This study expanded knowledge about the spatial clustering of TB among PLHIV and HIV among TB patients in Ethiopia at the district level in 2018. The findings provide information to health policymakers in the country to plan geographically targeted and integrated interventions to jointly control TB and HIV.

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies.

BACKGROUND: COVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection. METHODS: We present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination. RESULTS: Regression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions. CONCLUSIONS: This article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.

Geotechnologies applied in epidemiological studies on cases of covid-19: a narrative review / Geotecnologias aplicadas em estudos epidemiológicos sobre os casos de covid-19: revisão narrativa / Geotecnologías aplicadas en estudios epidemiológicos sobre casos de covid-19: revisión narrativa

Background and objectives: the applied geotechnologies are essential in helping the development of epidemiological studies that aim to identify and distribute health events in specific populations and territories, in addition to verifying the factors that influence the occurrence of these events, intending to apply the evidence in strategies of disease planning and control as in the covid-19 pandemic. This study aimed to present the scientific evidence that has been produced on geotechnologies applied in epidemiological studies on cases of covid-19. Methods: this is a descriptive narrative literature review (NLR). To guide the study, the following research question was elaborated: what has been studied about applied geotechnologies in epidemiological research on covid-19 cases? The search was carried out in October 2021, using the descriptors Geographic Information Systems AND Covid-19 OR SARS-CoV-2 AND Epidemiology AND Spatial Analysis, in Virtual Health Library, Scopus, Web of Science, Portal CAPES. Complementarily, a search was carried out for epidemiological bulletins and booklets on the Brazilian Ministry of Health website. Results: nineteen sources of information were selected that fit the objectives for the discussion construction, with three categories of analysis being listed: Geotechnology application; Information management; Challenges of epidemiological studies that use secondary data. Conclusion: geotechnology use in epidemiological studies on covid-19 in identifying areas at risk for the infection spread was such remarkable.(AU)

Justificativa e objetivos: as geotecnologias aplicadas são essenciais para auxiliar o desenvolvimento de estudos epidemiológicos que visam identificar e distribuir eventos de saúde em populações e territórios específicos, além de verificar os fatores que influenciam a ocorrência desses eventos, pretendendo aplicar as evidências em estratégias de planejamento e controle de doenças como na pandemia de covid-19. Este estudo teve como objetivo apresentar as evidências científicas que vêm sendo produzidas sobre geotecnologias aplicadas em estudos epidemiológicos de casos de covid-19. Métodos: trata-se de uma revisão de literatura narrativa descritiva (NLR). Para nortear o estudo, elaborou-se a seguinte questão de pesquisa: o que tem sido estudado sobre as geotecnologias aplicadas na pesquisa epidemiológica dos casos de covid-19? A busca foi realizada no mês de outubro de 2021, utilizando os descritores Geographic Information Systems AND Covid-19 OR SARS-CoV-2 AND Epidemiology AND Spatial Analysis, na Biblioteca Virtual em Saúde, Scopus, Web of Science, Portal CAPES. Complementarmente, foi realizada busca de boletins e cartilhas epidemiológicas no site do Ministério da Saúde do Brasil. Resultados: foram selecionadas dezenove fontes de informação que se enquadram nos objetivos para a construção da discussão, sendo elencadas três categorias de análise: Aplicação da geotecnologia; Gestão da informação; Desafios dos estudos epidemiológicos que utilizam dados secundários. Conclusão: o uso da geotecnologia em estudos epidemiológicos da covid-19 na identificação de áreas de risco para a propagação da infecção foi notável.(AU)

Justificación y objetivos: las geotecnologías aplicadas son esenciales para ayudar al desarrollo de estudios epidemiológicos que tengan como objetivo identificar y distribuir eventos de salud en poblaciones y territorios específicos, además de verificar los factores que influyen en la ocurrencia de estos eventos, con la intención de aplicar la evidencia en estrategias de planificación y control de enfermedades como en la pandemia de covid-19. Este estudio tuvo como objetivo presentar la evidencia científica que se ha producido sobre geotecnologías aplicadas en estudios epidemiológicos sobre casos de covid-19. Métodos: se trata de una revisión descriptiva narrativa de la literatura (NLR). Para orientar el estudio se elaboró la siguiente pregunta de investigación: ¿Qué se ha estudiado sobre geotecnologías aplicadas en la investigación epidemiológica de casos de covid-19? La búsqueda se realizó en octubre de 2021, utilizando los descriptores Sistemas de Información Geográfica Y Covid-19 O SARS-CoV-2 Y Epidemiología Y Análisis Espacial, en Biblioteca Virtual en Salud, Scopus, Web of Science, Portal CAPES. Complementariamente, se realizó una búsqueda de boletines y folletos epidemiológicos en el sitio web del Ministerio de Salud de Brasil. Resultados: fueron seleccionadas diecinueve fuentes de información que se ajustan a los objetivos para la construcción de la discusión, siendo enumeradas tres categorías de análisis: aplicación de la geotecnología; Gestión de la información; Retos de los estudios epidemiológicos que utilizan datos secundarios. Conclusión: el uso de geotecnología en estudios epidemiológicos sobre covid-19 para identificar áreas en riesgo de propagación de la infección fue tan notable.(AU)

Space-time cluster detection techniques for infectious diseases: A systematic review.

BACKGROUND: Public health organizations have increasingly harnessed geospatial technologies for disease surveillance, health services allocation, and targeting place-based health promotion initiatives. METHODS: We conducted a systematic review around the theme of space-time clustering detection techniques for infectious diseases using PubMed, Web of Science, and Scopus. Two reviewers independently determined inclusion and exclusion. RESULTS: Of 2,887 articles identified, 354 studies met inclusion criteria, the majority of which were application papers. Studies of airborne diseases were dominant, followed by vector-borne diseases. Most research used aggregated data instead of point data, and a significant proportion of articles used a repetition of a spatial clustering method, instead of using a "true" space-time detection approach, potentially leading to the detection of false positives. Noticeably, most articles did not make their data available, limiting replicability. CONCLUSION: This review underlines recent trends in the application of space-time clustering methods to the field of infectious disease, with a rapid increase during the COVID-19 pandemic.

Temporal trend, distribution and spatial autocorrelation of leprosy in Brazil: ecological study, 2011 to 2021.

OBJECTIVE: To characterize the temporal trend and spatial behavior of leprosy in Brazil, from 2011 to 2021. METHODS: This is an ecological study, with data from the Notifiable Diseases Information System, obtained in June 2022. The annual detection rate of new leprosy cases per 100 thousand inhabitants was calculated. To estimate the trend of the 2011-2019 and 2011-2021 series, the polynomial regression model was used, testing first-, second-, and third-order polynomials. For spatiality, natural breaks were used and, later, the univariate global and local Moran's indexes. A significance level of 5% was adopted and the analyses were performed using SPSS®, GeoDa®, and QGIS® software. RESULTS: The findings indicated an upward trend in the incidence of leprosy in Brazilian regions and in 20 federative units between 2011 and 2019. However, there was a decrease in most of the country when considering the COVID-19 pandemic years. Spatiality showed that the highest detection rates throughout the period were observed in the North, Midwest, and Northeast regions, with high-risk clusters, and the lowest detection rates in the South and Southeast regions, with low-risk clusters. CONCLUSION: The leprosy detection rate showed an upward trend in Brazil between 2011 and 2019, with greater spatial concentration in the North, Northeast, and Midwest regions. Nevertheless, the study raises an alert for the programmatic sustainability of leprosy control in Brazil, considering the drop in the COVID-19 pandemic, presumably due to the influence of the reorganization of the development of initiatives and provision of services in face of COVID-19.

A hybrid deep learning framework for air quality prediction with spatial autocorrelation during the COVID-19 pandemic.

China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as - 25.88 in Wuhan and - 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities.

Descriptive epidemiology and spatial analysis of African swine fever epidemics in Can Tho, Vietnam, 2019.

The objectives of this study were to describe the epidemiology of African swine fever (ASF) and to identify factors that increased commune-level risk for ASF in Can Tho, a province in the Mekong River Delta of Vietnam. In 2019, a total of 2377 of the 5220 pig farms in Can Tho were ASF positive, an incidence risk of 46 (95% CI 44-47) ASF positive farms for every 100 farms at risk. Throughout the outbreak ASF resulted in either the death or culling of 59,529 pigs out of a total population size of 124,516 (just under half of the total pig population, 48%). After the first detection in Can Tho in May 2019, ASF spread quickly across all districts with an estimated dissemination ratio (EDR) of greater than one up until the end of July 2019. A mixed-effects Poisson regression model was developed to identify risk factors for ASF. One hundred unit increases in the number of pigs per square kilometre was associated with a 1.28 (95% CrI 1.05-1.55) fold increase in commune-level ASF incidence rate. One unit increases in the number of pig farms per square kilometre was associated with a 0.91 (95% CrI 0.84-0.99) decrease in commune-level ASF incidence rate. Mapping spatially contiguous communes with elevated (unaccounted-for) ASF risk provide a means for generating hypotheses for continued disease transmission. We propose that the analyses described in this paper might be run on an ongoing basis during an outbreak and disease control efforts modified in light of the information provided.

Spatial, spatio-temporal, and origin-destination flow analyses of patients with severe acute respiratory syndrome hospitalized for COVID-19 in Southeastern Brazil, 2020-2021.

Brazil experienced one of the fastest increasing numbers of coronavirus disease (COVID-19) cases worldwide. The Sao Paulo State (SPS) reported a high incidence, particularly in Sao Paulo municipality. This study aimed to identify clusters of incidence and mortality of hospitalized patients with severe acute respiratory syndrome for COVID-19 in the SPS, in 2020-2021, and describe the origin flow pattern of the cases. Cases and mortality risk area clusters were identified through different analyses (spatial clusters, spatio-temporal clusters, and spatial variation in temporal trends) by weighting areas. Ripley's K12-function verified the spatial dependence between the cases and infrastructure. There were 517,935 reported cases, with 152,128 cases resulting in death. Of the 470,441 patients hospitalized and residing in the SPS, 357,526 remained in the original municipality, while 112,915 did not. Cases and death clusters were identified in the Sao Paulo metropolitan region (SPMR) and Baixada Santista region in the first study period, and in the SPMR and the Campinas, Sao Jose do Rio Preto, Barretos, and Sorocaba municipalities during the second period. We highlight the priority areas for control and surveillance actions for COVID-19, which could lead to better outcomes in future outbreaks.

Health behaviors and the risk of COVID-19 incidence: A Bayesian hierarchical spatial analysis.

OBJECTIVES: Effective infection control measures, based on a sound understanding of geographical community-specific health behavioral characteristics, should be implemented from the early stage of disease transmission. However, few studies have explored health behaviors as a possible contributor to COVID-19 infection in the spatial context. We investigated health behaviors as potential factors of COVID-19 incidence in the early phase of transmission in the spatial context. METHODS: We extracted COVID-19 cumulative case data as of February 25, 2021-one day prior to nationwide COVID-19 vaccination commencement-regarding health behaviors and covariates, including health condition and socio-economic factors, at the municipal level from publicly available datasets. The spatial autocorrelation of incidence was analyzed using Global Moran's I statistics. The associations between health behaviors and COVID-19 incidence were examined using Besag-York-Mollie models to deal with spatial autocorrelation of residuals. RESULTS: The COVID-19 incidence had positive spatial autocorrelation across South Korea (I = 0.584, p = 0.001). The results suggest that individuals vaccinated against influenza in the preceding year had a negative association with COVID-19 incidence (relative risk=0.913, 95 % Credible Interval=0.838-0.997), even after adjusting for covariates. CONCLUSIONS: Our ecological study suggests an association between COVID-19 infection and health behaviors, especially influenza vaccination, in the early stage of COVID-19 transmission at the municipal level.

Investigating the relationships between concentrated disadvantage, place connectivity, and COVID-19 fatality in the United States over time.

BACKGROUND: Concentrated disadvantaged areas have been disproportionately affected by COVID-19 outbreak in the United States (US). Meanwhile, highly connected areas may contribute to higher human movement, leading to higher COVID-19 cases and deaths. This study examined the associations between concentrated disadvantage, place connectivity, and COVID-19 fatality in the US over time. METHODS: Concentrated disadvantage was assessed based on the spatial concentration of residents with low socioeconomic status. Place connectivity was defined as the normalized number of shared Twitter users between the county and all other counties in the contiguous US in a year (Y = 2019). COVID-19 fatality was measured as the cumulative COVID-19 deaths divided by the cumulative COVID-19 cases. Using county-level (N = 3,091) COVID-19 fatality over four time periods (up to October 31, 2021), we performed mixed-effect negative binomial regressions to examine the association between concentrated disadvantage, place connectivity, and COVID-19 fatality, considering potential state-level variations. The moderation effects of county-level place connectivity and concentrated disadvantage were analyzed. Spatially lagged variables of COVID-19 fatality were added to the models to control for the effect of spatial autocorrelations in COVID-19 fatality. RESULTS: Concentrated disadvantage was significantly associated with an increased COVID-19 fatality in four time periods (p < 0.01). More importantly, moderation analysis suggested that place connectivity significantly exacerbated the harmful effect of concentrated disadvantage on COVID-19 fatality in three periods (p < 0.01), and this significant moderation effect increased over time. The moderation effects were also significant when using place connectivity data from the previous year. CONCLUSIONS: Populations living in counties with both high concentrated disadvantage and high place connectivity may be at risk of a higher COVID-19 fatality. Greater COVID-19 fatality that occurs in concentrated disadvantaged counties may be partially due to higher human movement through place connectivity. In response to COVID-19 and other future infectious disease outbreaks, policymakers are encouraged to take advantage of historical disadvantage and place connectivity data in epidemic monitoring and surveillance of the disadvantaged areas that are highly connected, as well as targeting vulnerable populations and communities for additional intervention.

Census Tract Patterns and Contextual Social Determinants of Health Associated With COVID-19 in a Hispanic Population From South Texas: A Spatiotemporal Perspective.

BACKGROUND: Previous studies have shown that various social determinants of health (SDOH) may have contributed to the disparities in COVID-19 incidence and mortality among minorities and underserved populations at the county or zip code level. OBJECTIVE: This analysis was carried out at a granular spatial resolution of census tracts to explore the spatial patterns and contextual SDOH associated with COVID-19 incidence from a Hispanic population mostly consisting of a Mexican American population living in Cameron County, Texas on the border of the United States and Mexico. We performed age-stratified analysis to identify different contributing SDOH and quantify their effects by age groups. METHODS: We included all reported COVID-19-positive cases confirmed by reverse transcription-polymerase chain reaction testing between March 18 (first case reported) and December 16, 2020, in Cameron County, Texas. Confirmed COVID-19 cases were aggregated to weekly counts by census tracts. We adopted a Bayesian spatiotemporal negative binomial model to investigate the COVID-19 incidence rate in relation to census tract demographics and SDOH obtained from the American Community Survey. Moreover, we investigated the impact of local mitigation policy on COVID-19 by creating the binary variable "shelter-in-place." The analysis was performed on all COVID-19-confirmed cases and age-stratified subgroups. RESULTS: Our analysis revealed that the relative incidence risk (RR) of COVID-19 was higher among census tracts with a higher percentage of single-parent households (RR=1.016, 95% posterior credible intervals [CIs] 1.005, 1.027) and a higher percentage of the population with limited English proficiency (RR=1.015, 95% CI 1.003, 1.028). Lower RR was associated with lower income (RR=0.972, 95% CI 0.953, 0.993) and the percentage of the population younger than 18 years (RR=0.976, 95% CI 0.959, 0.993). The most significant association was related to the "shelter-in-place" variable, where the incidence risk of COVID-19 was reduced by over 50%, comparing the time periods when the policy was present versus absent (RR=0.506, 95% CI 0.454, 0.563). Moreover, age-stratified analyses identified different significant contributing factors and a varying magnitude of the "shelter-in-place" effect. CONCLUSIONS: In our study, SDOH including social environment and local emergency measures were identified in relation to COVID-19 incidence risk at the census tract level in a highly disadvantaged population with limited health care access and a high prevalence of chronic conditions. Results from our analysis provide key knowledge to design efficient testing strategies and assist local public health departments in COVID-19 control, mitigation, and implementation of vaccine strategies.

[Spatiotemporal changes of COVID-19 outbreak in Shanghai].

Objective: To clarify the epidemiological characteristics and spatiotemporal clustering dynamics of COVID-19 in Shanghai in 2022. Methods: The COVID-19 data presented on the official websites of Municipal Health Commissions of Shanghai during March 1, 2022 and May 31, 2022 were collected for a spatial autocorrelation analysis by GeoDa software. A logistic growth model was used to fit the epidemic situation and make a comparison with the actual infection situation. Results: Pudong district had the highest number of symptomatic and asymptomatic infectants, accounting for 29.30% and 35.58% of the total infectants. Differences in cumulative attack rates and infection rates among 16 districts (P<0.001) were significant. The rates were significantly higher in Huangpu district than in other districts. The attack rate of COVID-19 from March 1, 2022 to May 31, 2022 had a global spatial positive correlation (P<0.05). Spatial distribution of COVID-19 attack rate was different at different periods. The global autocorrelation coefficient from March 16 to March 29, April 6 to April 12 and May 18 to May 24 had no statistical significance (P>0.05). Our local autocorrelation analysis showed that 22 high-high clustering areas were detected in eight periods.The high-risk hot-spot areas have experienced a "less-more-less" change process. The growth model fitting results were consistent with the actual infection situation. Conclusion: There was a clear spatiotemporal correlation in the distribution of COVID-19 in Shanghai. The comprehensive prevention and control measures of COVID-19 epidemic in Shanghai have effectively prohibited the growth of the epidemic, not only curbing the spatially spread of high-risk epidemic areas, but also reducing the risk of transmission to other cities.

Equality and social determinants of spatial accessibility, availability, and affordability to primary health care in Hong Kong, a descriptive study from the perspective of spatial analysis.

OBJECTIVE: Primary health care (PHC) is widely perceived to be the backbone of health care systems. Since the outbreak of COVID-19, PHC has not only provided primary medical services, but also served as a grassroots network for public health. Our research explored the accessibility, availability, and affordability of primary health care from a spatial perspective, to understand the social determinants affecting access to it in Hong Kong. METHOD: This constitutes a descriptive study from the perspective of spatial analysis. The nearest neighbor method was used to measure the geographic accessibility of PHC based on the road network. The 2SFCA method was used to measure spatial availability and affordability to primary health care, while the SARAR model, Spatial Error model, and Spatial Lag model were then constructed to explain potential factors influencing accessibility and availability of PHC. RESULTS: In terms of accessibility, 95% of residents in Hong Kong can reach a PHC institution within 15 minutes; in terms of availability, 83% of residents can receive PHC service within a month; while in terms of affordability, only 32% of residents can afford PHC services with the support of medical insurance and medical voucher. In Hong Kong, education status and household income show a significant impact on accessibility and availability of PHC. Regions with higher concentrations of residents with post-secondary education receive more PHC resources, while regions with higher concentrations of high-income households show poorer accessibility and poorer availability to PHC. CONCLUSION: The good accessibility and availability of primary health care reflects that the network layout of existing PHC systems in Hong Kong is reasonable and can meet the needs of most residents. No serious gap between social groups further shows equality in resource allocation of PHC in Hong Kong. However, affordability of PHC is not ideal. Indeed, narrowing the gap between availability and affordability is key to fully utilizing the capacity of the PHC system in Hong Kong. The private sector plays an important role in this, but the low coverage of medical insurance in outpatient services exacerbates the crowding of public PHC and underutilization of private PHC. We suggest diverting patients from public to private institutions through medical insurance, medical vouchers, or other ways, to relieve the pressure on the public health system and make full use of existing primary health care in Hong Kong.

The impact of built and socio-economic environment factors on Covid-19 transmission at the ZIP-code level in Florida.

Most studies have explored the Covid-19 outbreak by mainly focusing on restrictive public policies, human health, and behaviors at the macro level. However, the impacts of built and socio-economic environments, accounting for spatial effects on the spread at the local levels, have not been thoroughly studied. In this study, the relationships between the spatial spread of the virus and various indicators of the built and socio-economic environments are investigated, using Florida ZIP-code data on accumulated cases before large-scale vaccination campaigns began in 2021. Spatial regression models are used to account for the spatial dependencies and interactions that are core factors in Covid-19 spread. This study reveals both the spillover dynamics of the coronavirus spread at the ZIP code level and the existence of spatial dependencies among the unobserved variables represented by the error term. In addition, the findings show a positive association between the expected number of Covid-19 cases and specific land uses, such as education facilities and retail densities. Finally, the study highlights critical socio-economic characteristics causing a substantial increase in Covid-19 spread. Such results could help policymakers, public health experts, and urban planners design strategies to mitigate the spread of future Covid-19-like diseases.