Estimation of the spatial pattern of gout prevalence across China by wastewater-based epidemiology.

Gout is a metabolic arthritis caused by hyperuricemia. In recent years, the prevalence of gout has been increased significantly in China due to the improvement of the living standards, and gout has become another common metabolic disease following diabetes mellitus. Gout severely affects the health status and life quality of human. In order to monitor the near real-time prevalence of gout, a wastewater-based epidemiology (WBE) approach was carried out in 257 Chinese cities using febuxostat as the biomarker. Febuxostat in wastewater was measured by a LC-MS/MS method with satisfactory results of method validation. The average concentration of febuxostat in wastewater was 53.05 ± 31.76 ng/L, with the estimated per capita consumption of 124.40 ± 73.37 mg/day/1000 inhabitant. The calculated prevalence of febuxostat was 0.41 % ± 0.24 %, and the prevalence of gout was finally estimated to be 1.30 % ± 0.77 % (0.60 % to 2.11 %), which was nearly consistent with value of 1.10 % obtained from the Guideline for the diagnosis and management of hyperuricemia and gout in China (2019). The results indicated that the febuxostat-based WBE approach might be reasonable to assess the near real-time gout prevalence in China.

Temporal gene signature of myofibroblast transformation in Peyronie's disease: first insights into the molecular mechanisms of irreversibility.

BACKGROUND: Transformation of resident fibroblasts to profibrotic myofibroblasts in the tunica albuginea is a critical step in the pathophysiology of Peyronie's disease (PD). We have previously shown that myofibroblasts do not revert to the fibroblast phenotype and we suggested that there is a point of no return at 36 hours after induction of the transformation. However, the molecular mechanisms that drive this proposed irreversibility are not known. AIM: Identify molecular pathways that drive the irreversibility of myofibroblast transformation by analyzing the expression of the genes involved in the process in a temporal fashion. METHODS: Human primary fibroblasts obtained from tunica albuginea of patients with Peyronie's disease were transformed to myofibroblasts using transforming growth factor beta 1 (TGF-ß1). The mRNA of the cells was collected at 0, 24, 36, 48, and 72 hours after stimulation with TGF-ß1 and then analyzed using a Nanostring nCounter Fibrosis panel. The gene expression results were analyzed using Reactome pathway analysis database and ANNi, a deep learning-based inference algorithm based on a swarm approach. OUTCOMES: The study outcome was the time course of changes in gene expression during transformation of PD-derived fibroblasts to myofibroblasts. RESULTS: The temporal analysis of the gene expression revealed that the majority of the changes at the gene expression level happened within the first 24 hours and remained so throughout the 72-hour period. At 36 hours, significant changes were observed in genes involved in MAPK-Hedgehog signaling pathways. CLINICAL TRANSLATION: This study highlights the importance of early intervention in clinical management of PD and the future potential of new drugs targeting the point of no return. STRENGTHS AND LIMITATIONS: The use of human primary cells and confirmation of results with further RNA analysis are the strengths of this study. The study was limited to 760 genes rather than the whole transcriptome. CONCLUSION: This study is to our knowledge the first analysis of temporal gene expression associated with the regulation of the transformation of resident fibroblasts to profibrotic myofibroblasts in PD. Further research is warranted to investigate the role of the MAPK-Hedgehog signaling pathways in reversibility of PD.

Spatio-temporal analysis and risk modeling of foot-and-mouth disease outbreaks in China.

FMD is an acute contagious disease that poses a significant threat to the health and safety of cloven-hoofed animals in Asia, Europe, and Africa. The impact of FMD exhibits geographical disparities within different regions of China. The present investigation undertook an exhaustive analysis of documented occurrences of bovine FMD in China, spanning the temporal range from 2011 to 2020. The overarching objective was to elucidate the temporal and spatial dynamics underpinning these outbreaks. Acknowledging the pivotal role of global factors in FMD outbreaks, advanced machine learning techniques were harnessed to formulate an optimal prediction model by integrating comprehensive meteorological data pertinent to global FMD. Random Forest algorithm was employed with top three contributing factors including Isothermality(bio3), Annual average temperature(bio1) and Minimum temperature in the coldest month(bio6), all relevant to temperature. By encompassing both local and global factors, our study provides a comprehensive framework for understanding and predicting FMD outbreaks. Furthermore, we conducted a phylogenetic analysis to trace the origin of Foot-and-mouth disease virus (FMDV), pinpointing India as the country posing the greatest potential hazard by leveraging the spatio-temporal attributes of the collected data. Based on this finding, a quantitative risk model was developed for the legal importation of live cattle from India to China. The model estimated an average probability of 0.002254% for FMDV-infected cattle imported from India to China. TA sensitivity analysis identified two critical nodes within the model: he possibility of false negative clinical examination in infected cattle at destination (P5) and he possibility of false negative clinical examination in infected cattle at source(P3). This comprehensive approach offers a thorough evaluation of FMD landscape within China, considering both domestic and global perspectives, thereby augmenting the efficacy of early warning mechanisms.

Spatial and temporal trends of dengue infections in Curaçao: A 21-year analysis.

Dengue viruses are a significant global health concern, causing millions of infections annually and putting approximately half of the world's population at risk, as reported by the World Health Organization (WHO). Understanding the spatial and temporal patterns of dengue virus spread is crucial for effective prevention of future outbreaks. By investigating these patterns, targeted dengue surveillance and control measures can be improved, aiding in the management of outbreaks in dengue-affected regions. Curaçao, where dengue is endemic, has experienced frequent outbreaks over the past 25 years. To examine the spatial and temporal trends of dengue outbreaks in Curaçao, this study employs an interdisciplinary and multi-method approach. Data on >6500 cases of dengue infections in Curaçao between the years 1995 and 2016 were used. Temporal and spatial statistics were applied. The Moran's I index identified the presence of spatial autocorrelation for incident locations, allowing us to reject the null hypothesis of spatial randomness. The majority of cases were recorded in highly populated areas and a relationship was observed between population density and dengue cases. Temporal analysis demonstrated that cases mostly occurred from October to January, during the rainy season. Lower average temperatures, higher precipitation and a lower sea surface temperature appear to be related to an increase in dengue cases. This effect has a direct link to La Niña episodes, which is the cooling phase of El Niño Southern Oscillation. The spatial and temporal analyses conducted in this study are fundamental to understanding the timing and locations of outbreaks, and ultimately improving dengue outbreak management.

Spatial spillover effect of environmental factors on the tuberculosis occurrence among the elderly: a surveillance analysis for nearly a dozen years in eastern China.

BACKGROUND: In many areas of China, over 30% of tuberculosis cases occur among the elderly. We aimed to investigate the spatial distribution and environmental factors that predicted the occurence of tuberculosis in this group. METHODS: Data were collected on notified pulmonary tuberculosis (PTB) cases aged ≥ 65 years in Zhejiang Province from 2010 to 2021. We performed spatial autocorrelation and spatial-temporal scan statistics to determine the clusters of epidemics. Spatial Durbin Model (SDM) analysis was used to identify significant environmental factors and their spatial spillover effects. RESULTS: 77,405 cases of PTB among the elderly were notified, showing a decreasing trend in the notification rate. Spatial-temporal analysis showed clustering of epidemics in the western area of Zhejiang Province. The results of the SDM indicated that a one-unit increase in PM2.5 led to a 0.396% increase in the local notification rate. The annual mean temperature and precipitation had direct effects and spatial spillover effects on the rate, while complexity of the shape of the greenspace (SHAPE_AM) and SO2 had negative spatial spillover effects. CONCLUSION: Targeted interventions among the elderly in Western Zhejiang may be more efficient than broad, province-wide interventions. Low annual mean temperature and high annual mean precipitation in local and neighboring areas tend to have higher PTB onset among the elderly.

Spatio-temporal effects of digital inclusive finance on the synergy between CO2 and air pollution emissions in 251 Chinese cities.

Achieving the synergistic reduction of CO2 and air pollution emissions (SRCAPEs) holds great significance in promoting the green transformation. However, limited research has been conducted on the spatio-temporal impact of digital inclusive finance (DIF) on the synergy between CO2 and air pollution emissions (SCAPEs). To address this gap, we comprehensively employ the linear regression model, geographically and the temporally weighted regression (GTWR) model, and the ordered probit model to empirically analyze the influence of DIF on SCAPE. Our research reveals the following: (1) The linear regression model demonstrates that, on average, DIF can achieve a weak synergistic emission reduction effect. This result remains robust after a battery of robustness tests. (2) The GTWR model reveals that the impact of DIF on both emissions exhibits evident spatio-temporal characteristics. Its emission reduction effect gradually increases, especially after 2014. (3) On the basis of the estimates from the GTWR model, we can identify four distinct synergy types driven by DIF. The number of cities with the preferred type (i.e., achieving SRCAPE) increases the most, from 59 in 2011 to 233 in 2019. (4) On the basis of the built ordered probit models, green technology innovation is an important path for DIF to achieve synergistic emission reduction. The synergistic emission reduction effect is also significantly moderated by the regional economic level and environmental regulation intensity. Our findings have policy implications for central and local governments in achieving SRCAPE and support efforts to achieve sustainable development.

Temporal cytokine storm dynamics in dengue infection predicts severity.

The immunopathogenesis of dengue severity is convoluted. The primary objective of the research was to examine the dynamics of cytokine storm and its correlation with disease development in individuals affected by DENV infection. Additionally, the study aimed to discover potential biomarkers that could indicate severe dengue infection and determine the most suitable timeframe for predicting the severity of these biomarkers during the acute stage of dengue infections. We conducted a temporal analysis of the daily viral load and cytokine levels in 60 hospitalized dengue patients until discharge. Our findings reveal a distinct cytokine profile (elevated IL-8, IL-10, IL-6, GM-CSF, MCP-1, IL-13, and IL-4 and decreased IL-12, MIP-1ß) on the third day after symptom onset is predictive of severe dengue in secondary dengue infection. The imbalanced cytokine signature may inform clinical decision-making in treating severe dengue infections.

Fundamentals of Unanticipated Efficiency of Gd2O3-based Catalysts in Oxidative Coupling of Methane.

Improving the selectivity in the oxidative coupling of methane to ethane/ethylene poses a significant challenge for commercialization. The required improvements are hampered by the uncertainties associated with the reaction mechanism due to its complexity. Herein, we report about 90 % selectivity to the target products at 11 % methane conversion over Gd2O3-based catalysts at 700 °C using N2O as the oxidant. Sophisticated kinetic studies have suggested the nature of adsorbed oxygen species and their binding strength as key parameters for undesired methane oxidation to carbon oxides. These descriptors can be controlled by a metal oxide promoter for Gd2O3.

Effects of scheduled school breaks on the circulation of influenza in children, school-aged population, and adults in China: A spatio-temporal analysis.

OBJECTIVES: To investigate the effect of scheduled school break on the circulation of influenza in young children, school-aged population, and adults. METHODS: In a spatial-temporal analysis using influenza activity, school break dates, and meteorological covariates across mainland China during 2015-2018, we estimated age-specific, province-specific, and overall relative risk (RR) and effectiveness of school break on influenza. RESULTS: We included data in 24, 25, and 17 provinces for individuals aged 0-4 years, 5-19 years and 20+ years. We estimated a RR meta-estimate of 0.34 (95% confidence interval 0.29-0.40) and an effectiveness of 66% for school break in those aged 5-19 years. School break showed a lagged and smaller mitigation effect in those aged 0-4 years (RR meta-estimate: 0.73, 0.68-0.79) and 20+ years (RR meta-estimate: 0.89, 0.78-1.01) versus those aged 5-19 years. CONCLUSION: The results show heterogeneous effects of school break between population subgroups, a pattern likely to hold for other respiratory infectious diseases. Our study highlights the importance of anticipating age-specific effects of implementing school closure interventions and provides evidence for rational use of school closure interventions in future epidemics.

Spatial-Temporal Analysis of Drinking Water Type of Endemic Fluorosis - China, 2009-2022.

What is already known about this topic?: Endemic fluorosis, caused by high fluoride levels in drinking water, has been a significant health issue in rural areas of China for many decades. What is added by this report?: There has been a notable decline in the detection rate of dental fluorosis in children aged 8-12 years in drinking water fluorosis areas across the country from 2009 to 2022. While 14 provincial-level administrative divisions are classified as low-probability clusters, Tianjin remains classified as a high-probability cluster. What are the implications for public health practice?: The current policy for preventing and controlling endemic fluorosis in China needs adjustment. Rather than focusing solely on regions with high incidence, there should be a shift towards monitoring and early warning of fluoride exposure. Additionally, local containment measures should be intensified.

Spatio-temporal dynamics and distributional trend analysis of African swine fever outbreaks (2020-2021) in North-East India.

African swine fever (ASF) is a highly contagious, notifiable, and fatal hemorrhagic viral disease affecting domestic and wild pigs. The disease was reported for the first time in India during 2020, resulted in serious outbreaks and economic loss in North-Eastern (NE) parts, since 47% of the Indian pig population is distributed in the NE region. The present study focused on analyzing the spatial autocorrelation, spatio-temporal patterns, and directional trend of the disease in NE India during 2020-2021. The ASF outbreak data (2020-2021) were collected from the offices of the Department of Animal Husbandry and Veterinary Services in seven NE states of India to identify the potential clusters, spatio-temporal aggregation, temporal distribution, disease spread, density maps, and risk zones. Between 2020 and 2021, a total of 321 ASF outbreaks were recorded, resulting in 59,377 deaths. The spatial pattern analysis of the outbreak data (2020-2021) revealed that ASF outbreaks were clustered in 2020 (z score = 2.20, p < .01) and 2021 (z score = 4.89, p < .01). Spatial autocorrelation and Moran's I value (0.05-0.06 in 2020 and 2021) revealed the spatial clustering and spatial relationship between the outbreaks. The hotspot analysis identified districts of Arunachal Pradesh, Assam and districts of Mizoram, Tripura as significant hotspots in 2020 and 2021, respectively. The spatial-scan statistics with a purely spatial and purely temporal analysis revealed six and one significant clusters, respectively. Retrospective unadjusted, temporal, and spatially adjusted space-time analysis detected five, five, and two statistically significant (p < .01) clusters, respectively. The directional trend analysis identified the direction of disease distribution as northeast-southwest (2020) and north-south (2021), indicate the possibility of ASF introduction to India from China. The high-risk zones and spatio-temporal pattern of ASF outbreaks identified in the present study can be used as a guide for deploying proper prevention, optimizing resource allocation and disease control measures in NE Indian states.

Multiscale spatial-temporal evolution of energy carbon footprint in the Yellow River Basin of China based on DMSP/OLS and NPP/VIIRS integrated data.

Increased CO2 emissions from urban energy consumption pose a significant challenge to regional carbon mitigation policies. In this paper, we integrated two nighttime light (NTL) data: the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (NPP) composite data to estimate the energy carbon emissions from 2000 to 2019. Then the spatiotemporal dynamics of carbon footprint and deficit in the Yellow River Basin were analyzed at the provincial, municipal, and county scales combined with NPP data. The study shows that (1) the total amount of energy consumption CO2 emissions in the Yellow River Basin had increased from 1332 Mt in 2000 to 6469 Mt in 2019, but the average annual growth rate slowed down after 2010 from 11.5 to 5.61%. (2) From 2000 to 2018, the provinces with the highest carbon footprint and carbon deficit were concentrated in Inner Mongolia and Shanxi. In 2018, Inner Mongolia's carbon footprint was 1366.91 × 104 km2, accounting for 22.8% of the total. Cities with high carbon footprint were mainly economic centers and energy-intensive areas of various provinces. High-carbon deficit counties were mainly distributed in the western region. In 2018, 954 counties exhibited carbon deficits. (3) The carbon footprint in the Yellow River Basin at the municipal and county scales have a significant spatial correlation. The H-H clusters of the carbon footprint on the municipal scale were distributed in the middle reaches of the Yellow River Basin. At the county scale, the L-L clusters were mainly in Sichuan and eastern Henan regions. Through the analysis of the spatial and temporal evolution of carbon footprint and carbon deficit in the Yellow River Basin, it is significant to measure the degree of comprehensive coordination of carbon sources and sinks in the basin, to grasp the differences in the level of regional carbon emissions, and to promote synergistic regional governance, assist in the formulation of more precise carbon emission reduction policies, and to promote green and high-quality development.

Spatio-temporal analysis of the visceral leishmaniasis in the state of Alagoas, Brazil

Abstract Visceral leishmaniosis is a neglected tropical disease. We evaluated the spatial distribution of cases of visceral leishmaniosis in the state of Alagoas, Brazil. All cases of VL, registered by the health department, were analyzed and georeferenced. Results: Between 2008 and 2017, 97.1% of the municipalities presented sporadic classification of transmission. With temporal evolution, the incidence of cases of visceral leishmaniosis was concentrated in most municipalities in the microregion of Santana do Ipanema-AL. Space-time analysis, if considered, may promote the improvement of surveillance and control actions of visceral leishmaniosis.

Resumo A leishmaniose visceral é uma doença tropical negligenciada. Foram avaliadas a distribuição espacial dos casos de leishmaniose visceral no estado de Alagoas. Todos os casos de LV, registrados pela secretaria de saúde, foram analisados e georreferenciados. Entre 2008 e 2017, 97,1% dos municípios apresentaram classificação esporádica de transmissão. Com a evolução temporal, a incidência de casos de leishmaniose visceral se concentrou na maioria dos municípios da microrregião de Santana do Ipanema-AL. A análise espaço-tempo, se considerada, pode promover o aprimoramento das ações de vigilância e controle da leishmaniose visceral.

Spatio-temporal analysis of the visceral leishmaniasis in the state of Alagoas, Brazil / Análise espacial-temporal da leishmaniose visceral no estado de Alagoas

Visceral leishmaniosis is a neglected tropical disease. We evaluated the spatial distribution of cases of visceral leishmaniosis in the state of Alagoas, Brazil. All cases of VL, registered by the health department, were analyzed and georeferenced. Results: Between 2008 and 2017, 97.1% of the municipalities presented sporadic classification of transmission. With temporal evolution, the incidence of cases of visceral leishmaniosis was concentrated in most municipalities in the microregion of Santana do Ipanema-AL. Space-time analysis, if considered, may promote the improvement of surveillance and control actions of visceral leishmaniosis.

A leishmaniose visceral é uma doença tropical negligenciada. Foram avaliadas a distribuição espacial dos casos de leishmaniose visceral no estado de Alagoas. Todos os casos de LV, registrados pela secretaria de saúde, foram analisados e georreferenciados. Entre 2008 e 2017, 97,1% dos municípios apresentaram classificação esporádica de transmissão. Com a evolução temporal, a incidência de casos de leishmaniose visceral se concentrou na maioria dos municípios da microrregião de Santana do Ipanema-AL. A análise espaço-tempo, se considerada, pode promover o aprimoramento das ações de vigilância e controle da leishmaniose visceral.

Was the COVID-19 epidemic synchronous in space? An analysis in the health regions of the Rio de Janeiro state, 2020-2022 / A epidemia de COVID-19 foi síncrona no espaço? Uma análise nas regiões de saúde do estado do Rio de Janeiro, 2020-2022

ABSTRACT Objective: To analyze the spatio-temporal dynamics of COVID-19 in the Rio de Janeiro state within the nine health regions, between March 2020 and December 2022. Methods: The Poisson model with random effects was used to smooth and estimate the incidence of COVID-19 hospitalizations reported in the Influenza Epidemiological Surveillance Information System (SIVEP-Gripe) to verify the synchronicity of the epidemic in the state. Results: The COVID-19 epidemic in the state is characterized by the presence of seven peaks during the analyzed period corresponding to seven found. An asynchrony in hospitalizations was identified, varying according to the different virus variants in the nine health regions of the state. The incidence peaks of hospitalizations ranged from 1 to 12 cases per 100,000 inhabitants during the pandemic. Conclusion: This spatio-temporal analysis is applicable to other scenarios, enabling monitoring and decision-making for the control of epidemic diseases in different areas.

RESUMO Objetivo: Analisar a dinâmica espaço-temporal de COVID-19 no estado do Rio de Janeiro nas nove regiões de saúde, entre março de 2020 e dezembro de 2022. Métodos: Utilizou-se o modelo de Poisson com efeitos aleatórios para suavizar a curva de incidência de hospitalizações por COVID-19 notificadas no Sistema de Informação da Vigilância Epidemiológica da Gripe (Sivep-Gripe) para verificar a sincronicidade da epidemia no estado. Resultados: A epidemia de COVID-19 no estado é caracterizada pela presença de sete picos no período analisado correspondentes a sete variantes encontradas. Identificou-se uma assincronicidade nas hospitalizações, variando de acordo com as diferentes variantes do vírus nas nove regiões de saúde do estado. Os picos de incidência das hospitalizações variaram de 1 a 12 casos por 100 mil habitantes no decorrer da pandemia. Conclusão: Essa análise espaço-temporal é extensível em outros cenários, sendo possível o monitoramento e a tomada de decisões de controle de doenças epidêmicas em várias áreas.

Spatio-temporal evolutionary analysis of surface ecological quality in Pingshuo open-cast mine area, China.

The open-pit mining area is highly affected by human activities, which aggravate soil erosion and disturb surface ecology, bringing many problems and challenges to its environmental management and restoration, which has received widespread attention. The establishment of an objective, timely and quantitative remote sensing monitoring, and evaluation system for the spatio-temporal evolution of the surface ecological environment in the open-pit mining area is of great significance for its environmental protection, management decisions, and sustainable social development. Based on the Google Earth Engine (GEE) platform, this paper uses Landsat images to construct and calculate the remote sensing ecological index (RSEI) of the Pingshuo open-cast mine area (POMA) from 1990 to 2020 and monitor and evaluate its surface ecological environment. Combined with the Theil-Sen median, Mann-Kendall test, and Hurst index, the spatio-temporal process was analyzed. The results showed that the ecological environmental quality of the mining area first decreased and then increased from 1990 to 2020. 1990-2000 was a period of serious ecological degradation, followed by improvement. The overall improvement area reached 87.03%, and the degradation was concentrated in the coal mining area. Between 1990 and 2020, the Hurst index of the mining area was 0.452, indicating that the region has a fragile ecological environment and has difficult maintaining its stability. The global Moran's I mean value of the RSEI of the study area is 0.92, which combined with Moran's scatter plot to indicate that there is a strong positive spatial correlation rather than a random distribution of its ecological environment. During the study period, the impact on the climate of the ecological environmental change of POMA was weak, and human factors such as coal mining, land reclamation, and social construction were the main driving forces for the change in ecological quality. The results of this study reveal the changing trend of surface ecology in the mining area over the past 30 years, which is helpful for understanding its impact mechanism on ecological quality and provides support for the management of the region.

Social disparities and inequalities in healthcare access and expenditures among Iranians exposed to sulfur mustard: a national study using spatio-temporal analysis.

BACKGROUND: Sulfur Mustard (SM) is a chemical warfare agent that has serious short-term and long-term effects on health. Thousands of Iranians were exposed to SM during the eight-year Iran-Iraq conflict and permanently injured while the socioeconomic imbalance in their healthcare utilization (HCU) and health expenditures remains. This study aims to describe the HCU of SM-exposed survivors in Iran from 2018 to 2021; identify high-risk areas; and apply an inequality analysis of utilization regarding the socioeconomic groups to reduce the gap by controlling crucial determinants. METHODS: From Oct 2018 to June 2021, the Veterans and Martyrs Affairs Foundation (VMAF) recorded 58,888 living war survivors with eye, lung, and skin ailments. After cleaning the dataset and removing junk codes, we defined 11 HCU-related variables and predicted the HCU for the upcoming years using Bayesian spatio-temporal models. We explored the association of individual-level HCU and determinants using a Zero-inflated Poisson (ZIP) model and also investigated the provincial hotspots using Local Moran's I. RESULTS: With ≥ 90% confidence, we discovered eleven HCU clusters in Iran. We discovered that the expected number of HCU 1) rises with increasing age, severity of complications in survivors' eyes and lungs, wealth index (WI), life expectancy (LE), and hospital beds ratio; and 2) decreases with growing skin complications, years of schooling (YOS), urbanization, number of hospital beds, length of stay (LOS) in bed, and bed occupancy rate (BOR). The concentration index (CInd) of HCU and associated costs in age and wealth groups were all positive, however, the signs of CInd values for HCU and total cost in YOS, urbanization, LOS, and Hospital beds ratio groups were not identical. CONCLUSIONS: We observed a tendency of pro-rich inequity and also higher HCU and expenditures for the elderly population. Finally, health policies should tackle potential socioeconomic inequities to reduce HCU gaps in the SM-exposed population. Also, policymakers should allocate the resources according to the hotspots of HCU.

The emergence and successful elimination of SARS-CoV-2 dominant strains with increasing epidemic potential in Taiwan's 2021 outbreak.

Taiwan's experience with severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003 guided its development of strategies to defend against SARS-CoV-2 in 2020, which enabled the successful control of Coronavirus disease 2019 (COVID-19) cases from 2020 through March 2021. However, in late-April 2021, the imported Alpha variant began to cause COVID-19 outbreaks at an exceptional rate in Taiwan. In this study, we aimed to determine what epidemiological conditions enabled the SARS-CoV-2 Alpha variant strains to become dominant and decline later during a surge in the outbreak. In conjunction with contact-tracing investigations, we used our bioinformatics software, CoVConvert and IniCoV, to analyze whole-genome sequences of 101 Taiwan Alpha strains. Univariate and multivariable regression analyses revealed the epidemiological factors associated with viral dominance. Univariate analysis showed the dominant Alpha strains were preferentially selected in the surge's epicenter (p = 0.0024) through intensive human-to-human contact and maintained their dominance for 1.5 months until the Zero-COVID Policy was implemented. Multivariable regression found that the epidemic periods (p = 0.007) and epicenter (p = 0.001) were two significant factors associated with the dominant virus strains spread in the community. These dominant virus strains emerged at the outbreak's epicenter with frequent human-to-human contact and low vaccination coverage. The Level 3 Restrictions and Zero-COVID policy successfully controlled the outbreak in the community without city lockdowns. Our integrated method can identify the epidemiological conditions for emerging dominant virus with increasing epidemiological potential and support decision makers in rapidly containing outbreaks using public health measures that target fast-spreading virus strains.

Variegated spatial-temporal landscape of COVID-19 infection in England: findings from spatially filtered multilevel models.

BACKGROUND: Although there are empirical studies examining COVID-19 infection from a spatial perspective, majority of them focused on the USA and China, and there has been a lacuna of systematic research to unpack the spatial landscape of infection in the UK and its related factors. METHODS: England's spatial-temporal patterns of COVID-19 infection levels in 2020 were examined via spatial clustering analysis. Spatially filtered multilevel models (SFMLM), capturing both hierarchical and horizontal spatial interactive effects, were applied to identify how different demographic, socio-economic, built environment and spatial contextual variables were associated with varied infection levels over the two waves in 2020. RESULTS: The fragmented spatial distribution of COVID incidence in the first wave has made a rural-urban shift and resulted in a clearer north-south divide in England throughout 2020. The SFMLM results do not only identify the association between variables at different spatial scales with COVID-19 infection level but also highlight the increasing importance of spatial-dependent effect of the pandemic over time and that the locational spatial contexts also help explain variations in infection rates.