Spatiotemporal Assessment of COVID-19 Spread over Oman Using GIS Techniques

Province-level distribution and drivers of infant mortality in mainland China: a Geodetector-based analysis of data from 2020

OBJECTIVE

The present study investigated the province-level distribution and drivers of infant mortality rate (IMR) in mainland China.

DESIGN

Ecological analysis based on publicly available data for all 31 provinces in mainland China.

DATA SOURCES

Data on province-level IMRs in 2020 were obtained from the official websites of the healthcare commissions within each province and from the China Health Statistics Yearbook 2021. Data on potential IMR drivers were retrieved from the China Statistical Yearbook 2021.

DATA ANALYSIS

GeoDa V.1.12.1 and ArcMap V.10.2 software were used to examine province-level distribution of IMR. Global and local spatial autocorrelations were performed, and Getis-ord G* hotspots and coldspots were identified. Geodetector was used to analyse the individual and joint influence of drivers on IMR.

RESULTS

IMRs in 2020 varied from 1.91 to 7.60 per 1000 live births across provinces. The following statistically significant drivers with q values >0.5 were identified: health literacy of the population (0.6673), male illiteracy rate (0.6433), proportion of the population older than >65 years (0.6369), per capita government health expenditure (0.6216), forest coverage rate (0.5820), per capita disposable income (0.5785), per capita number of hospitals (0.5592), per capita gross regional product (0.5410) and sulfur dioxide concentration in the atmosphere (0.5158). The following three interactions among these drivers emerged as strongest influences on province-level IMR: proportion of population >65 years ∩ per capita gross regional product (q=0.9653), forest coverage rate ∩ per capita gross regional product (0.9610) and per capita government health expenditure ∩ sulfur dioxide (0.9295).

CONCLUSION

IMR in mainland China varies substantially across the country, being generally high-west and low-east. Several factors, on their own and interacting together, contribute to IMR. Policies and programmes to reduce IMR should be formulated according to local conditions and should focus on western provinces of the country.

Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia

BACKGROUND

In Sarawak, 252 300 coronavirus disease 2019 (COVID-19) cases have been recorded with 1 619 fatalities in 2021, compared to only 1 117 cases in 2020. Since Sarawak is geographically separated from Peninsular Malaysia and half of its population resides in rural districts where medical resources are limited, the analysis of spatiotemporal heterogeneity of disease incidence rates and their relationship with socio-demographic factors are crucial in understanding the spread of the disease in Sarawak.

METHODS

The spatial dependence of district-wise incidence rates is investigated using spatial autocorrelation analysis with two orders of contiguity weights for various pandemic waves. Nine determinants are chosen from 14 covariates of socio-demographic factors via elastic net regression and recursive partitioning. The relationships between incidence rates and socio-demographic factors are examined using ordinary least squares, spatial lag and spatial error models, and geographically weighted regression.

RESULTS

In the first 8 months of 2021, COVID-19 severely affected Sarawak's central region, which was followed by the southern region in the next 2 months. In the third wave, based on second-order spatial weights, the incidence rate in a district is most strongly influenced by its neighboring districts' rate, although the variance of incidence rates is best explained by local regression coefficient estimates of socio-demographic factors in the first wave. It is discovered that the percentage of households with garbage collection facilities, population density and the proportion of male in the population are positively associated with the increase in COVID-19 incidence rates.

CONCLUSION

This research provides useful insights for the State Government and public health authorities to critically incorporate socio-demographic characteristics of local communities into evidence-based decision-making for altering disease monitoring and response plans. Policymakers can make well-informed judgments and implement targeted interventions by having an in-depth understanding of the spatial patterns and relationships between COVID-19 incidence rates and socio-demographic characteristics. This will effectively help in mitigating the spread of the disease.

One health system supporting surveillance during COVID-19 epidemic in Abruzzo region, southern Italy

The Istituti Zooprofilattici Sperimentali (IZSs) are public health institutes dealing with the aetiology and pathogenesis of infectious diseases of domestic and wild animals. During Coronavirus Disease 2019 epidemic, the Italian Ministry of Health appointed the IZSs to carry out diagnostic tests for the detection of SARS-CoV-2 in human samples. In particular, the IZS of Abruzzo and Molise (IZS-Teramo) was involved in the diagnosis of SARS-CoV-2 through testing nasopharyngeal swabs by Real Time RT-PCR. Activities and infrastructures were reorganised to the new priorities, in a "One Health" framework, based on interdisciplinary, laboratory promptness, accreditation of the test for the detection of the RNA of SARS-CoV-2 in human samples, and management of confidentiality of sensitive data. The laboratory information system - SILAB - was implemented with a One Health module for managing data of human origin, with tools for the automatic registration of information improving the quality of the data. Moreover, the "National Reference Centre for Whole Genome Sequencing of microbial pathogens - database and bioinformatics analysis" - GENPAT - formally established at the IZS-Teramo, developed bioinformatics workflows and IT dashboard with ad hoc surveillance tools to support the metagenomics-based SARS-CoV-2 surveillance, providing molecular sequencing analysis to quickly intercept the variants circulating in the area. This manuscript describes the One Health system developed by adapting and integrating both SILAB and GENPAT tools for supporting surveillance during COVID-19 epidemic in the Abruzzo region, southern Italy. The developed dashboard permits the health authorities to observe the SARS-CoV-2 spread in the region, and by combining spatio-temporal information with metagenomics provides early evidence for the identification of emerging space-time clusters of variants at the municipality level. The implementation of the One Health module was designed to be easily modelled and adapted for the management of other diseases and future hypothetical events of pandemic nature.

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.

Alcohol consumption: context and association with mortality in Switzerland

PURPOSE

Non-communicable diseases generate the largest number of avoidable deaths often caused by risk factors such as alcohol, smoking, and unhealthy diets. Our study investigates the association between amount and context of alcohol consumption and mortality from major non-communicable diseases in Switzerland.

METHODS

Generalized linear regression models were fitted on data of the cross-sectional population-based National Nutrition Survey menuCH (2014-2015, n = 2057). Mortality rates based on the Swiss mortality data (2015-2018) were modeled by the alcohol consumption group considering the amount and context (i.e., during or outside mealtime) of alcohol consumption and potential confounders. The models were checked for spatial autocorrelation using Moran's I statistic. Integrated nested Laplace approximation (INLA) models were fitted when evidence for missing spatial information was found.

RESULTS

Higher mortality rates were detected among drinkers compared to non-drinkers for all-cancer (rate ratio (RR) ranging from 1.01 to 1.07) and upper aero-digestive tract cancer (RR ranging from 1.15 to 1.20) mortality. Global Moran's I statistic revealed spatial autocorrelation at the Swiss district level for all-cancer mortality. An INLA model led to the identification of three districts with a significant decrease and four districts with a significant increase in all-cancer mortality.

CONCLUSION

Significant associations of alcohol consumption with all-cancer and upper aero-digestive tract cancer mortality were detected. Our study results indicate the need for further studies to improve the next alcohol-prevention scheme and to lower the number of avoidable deaths in Switzerland.

Is Following a Cancer-Protective Lifestyle Linked to Reduced Cancer Mortality Risk?

Objectives: This study investigates the association between a cancer protective lifestyle (defined based on the revised World Cancer Research Fund (WCRF) and the American Institute for Cancer Research (AICR) cancer prevention recommendations) and mortality in Switzerland. Methods: Based on the cross-sectional, population-based National Nutrition Survey, menuCH (n = 2057), adherence to the WCRF/AICR recommendations was assessed via a score. Quasipoisson regression models were fitted to examine the association of adherence to the WCRF/AICR recommendations with mortality at the Swiss district-level. Spatial autocorrelation was tested with global Moran's I. Integrated nested Laplace approximation models were fitted when significant spatial autocorrelation was detected. Results: Participants with higher cancer prevention scores had a significant decrease in all-cause (relative risk 0.95; 95% confidence interval 0.92, 0.99), all-cancer (0.93; 0.89, 0.97), upper aero-digestive tract cancer (0.87; 0.78, 0.97), and prostate cancer (0.81; 0.68, 0.94) mortality, compared to those with lower scores. Conclusion: The inverse association between adherence to the WCRF/AICR recommendations and mortality points out the potential of the lifestyle recommendations to decrease mortality and especially the burden of cancer in Switzerland.

COVID-19 Restriction Movement Control Order (MCO) Impacted Emissions of Peninsular Malaysia Using Sentinel-2a and Sentinel-5p Satellite

The unprecedented outbreak of Coronavirus Disease 2019 (COVID-19) has impacted the whole world in every aspect including health, social life, economic activity, education, and the environment. The pandemic has led to an improvement in air quality all around the world, including in Malaysia. Lockdowns have resulted in industry shutting down and road travel decreasing which can reduce the emission of Greenhouse Gases (GHG) and air pollution. This research assesses the impact of the COVID-19 lockdown on emissions using the Air Pollution Index (API), aerosols, and GHG which is Nitrogen Dioxide (NO₂) in Malaysia. The data used is from Sentinel-5p and Sentinel-2A which monitor the air quality based on Ozone (O₃) and NO₂ concentration. Using an interpolated API Index Map comparing 2019, before the implementation of a Movement Control Order (MCO), and 2020, after the MCO period we examine the impact on pollution during and after the COVID-19 lockdown. Data used Sentinel-5p, Sentinel-2A, and Air Pollution Index of Malaysia (APIMS) to monitor the air quality that contains NO₂ concentration. The result has shown the recovery in air quality during the MCO implementation which indirectly shows anthropogenic activities towards the environmental condition. The study will help to enhance and support the policy and scope for air pollution management strategies as well as raise public awareness of the main causes that contribute to air pollution.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

Spatiotemporal pattern of Covid-19 outbreak in Turkey

The earliest case of Covid-19 was documented in Wuhan city of China and since then the virus has been spreading throughout the globe. The aim of this study is to evaluate the clusters of Covid-19 among the provinces in Turkey and to examine whether the clustering pattern has changed after the country's lockdown strategy. The spatial dependence of Covid-19 in 81 provinces of Turkey was examined by spatial analysis between February 8 and June 28, 2021. Global and Local Moran's I and Gi* were employed to measure the global and local spatial autocorrelation degrees. The geographical distribution of Covid-19 in the provinces of Turkey showed a strong spatial autocorrelation while the spatial structure of the clusters varied by weeks. The findings of the study show that the complete lockdown carried out in Turkey has been quite effective in mitigating Covid-19. The importance of spatial relations in preventing the spread of the disease in Turkey has also been demonstrated in this context.

Lower COVID-19 Incidence in Low-Continentality West-Coast Areas of Europe

In March 2020, the first known cases of COVID-19 occurred in Europe. Subsequently, the pandemic developed a seasonal pattern. The incidence of COVID-19 comprises spatial heterogeneity and seasonal variations, with lower and/or shorter peaks resulting in lower total incidence and higher and/or longer peaks resulting higher total incidence. The reason behind this phenomena is still unclear. Unraveling factors that explain why certain places have higher versus lower total COVID-19 incidence can help health decision makers understand and plan for future waves of the pandemic. We test whether differences in the total incidence of COVID-19 within five European countries (Norway, Sweden, Germany, Italy, and Spain), correlate with two environmental factors: the Köppen-Geiger climate zones and the Continentality Index, while statistically controlling for crowding. Our results show that during the first 16 months of the pandemic (March 2020 to July 2021), climate zones with larger annual differences in temperature and annually distributed precipitation show a higher total incidence than climate zones with smaller differences in temperature and dry seasons. This coincides with lower continentality values. Total incidence increases with continentality, up to a Continentality Index value of 19, where a peak is reached in the semicontinental zone. Low continentality (high oceanic influence) appears to be a strong suppressing factor for COVID-19 spread. The incidence in our study area is lowest at open low continentality west coast areas.

The ecology of COVID-19 and related environmental and sustainability issues

Around the globe, human behavior and ecosystem health have been extensively and sometimes severely affected by the unprecedented COVID-19 pandemic. Most efforts to study these complex and heterogenous effects to date have focused on public health and economics. Some studies have evaluated the pandemic's influences on the environment, but often on a single aspect such as air or water pollution. The related research opportunities are relatively rare, and the approaches are unique in multiple aspects and mostly retrospective. Here, we focus on the diverse research opportunities in disease ecology and ecosystem sustainability related to the (intermittent) lockdowns that drastically reduced human activities. We discuss several key knowledge gaps and questions to address amid the ongoing pandemic. In principle, the common knowledge accumulated from invasion biology could also be effectively applied to COVID-19, and the findings could offer much-needed information for future pandemic prevention and management.

Spatial Analysis of Inequality in Thailand: Applications of Satellite Data and Spatial Statistics/Econometrics

To formulate and monitor the progress of development policies, acquiring data with sufficient spatiotemporal details is inevitable. With the increasing availability of open remote-sensing data and open-source software packages, this research suggested the novelty integration of satellite data and spatial analytical methods, enabling a timely and costless framework for assessing the nationwide socioeconomic condition. Specifically, the spatial statistical and spatial econometrical methods were applied to geospatial data to identify the clustering patterns and the localized associations of inequality in Thailand. The spatial statistical results showed that Bangkok and its vicinity had been a cluster of high socioeconomic conditions, representing the spatial inequality of development. In addition, results of the spatial econometrical models showed that the satellite-based indicators could identify the socioeconomic condition (with p-value < 0.010 and R-squared ranging between 0.345 and 0.657). Inequality indicators (i.e., Gini, Thiel and Atkinson) were then constructed by using survey-based and satellite-based data, informing that spatial inequality has been slowly declining. These findings recommended the new establishment of polycentric growth poles that offer economic opportunities and reduce spatial inequality. In addition, in accordance with Sustainable Development Goal 10 (reduced inequalities), this analytical framework can be applied to country-specific implications along with the global scale extensions.

Spatial association between COVID-19 incidence rate and nighttime light index

This study statistically identified the localised association between socioeconomic conditions and the coronavirus disease 2019 (COVID-19) incidence rate in Thailand on the basis of the 1,727,336 confirmed cases reported nationwide during the first major wave of the pandemic (March-May 2020) and the second one (July 2021-September 2021). The nighttime light (NTL) index, formulated using satellite imagery, was used as a provincial proxy of monthly socioeconomic conditions. Local indicators of spatial association statistics were applied to identify the localised bivariate association between COVID-19 incidence rate and the year-on-year change of NTL index. A statistically significant negative association was observed between the COVID-19 incidence rate and the NTL index in some central and southern provinces in both major pandemic waves. Regression analyses were also conducted using the spatial lag model (SLM) and the spatial error model (SEM). The obtained slope coefficient, for both major waves of the pandemic, revealed a statistically significant negative association between the year-on-year change of NTL index and COVID-19 incidence rate (SLM: coefficient= âˆ'0.0078 and âˆ'0.0064 with P<0.001 and 0.056, respectively; and SEM: coefficient= âˆ'0.0086 and âˆ'0.0083 with P=0.067 and 0.056, respectively). All of the obtained results confirmed the negative association between the COVID-19 pandemic and socioeconomic activity revealing the future extensive applications of satellite imagery as an alternative data source for the timely monitoring of the multidimensional impacts of the pandemic.

Spatial Analysis of COVID-19 Vaccine Centers Distribution: A Case Study of the City of Jeddah, Saudi Arabia

The COVID-19 pandemic is one of the most devastating public health emergencies in history. In late 2020 and after almost a year from the initial outbreak of the novel coronavirus (SARS-CoV-2), several vaccines were approved and administered in most countries. Saudi Arabia has established COVID-19 vaccination centers in all regions. Various facilities were selected to set up these vaccination centers, including conference and exhibition centers, old airport terminals, pre-existing medical facilities, and primary healthcare centers. Deciding the number and locations of these facilities is a fundamental objective for successful epidemic responses to ensure the delivery of vaccines and other health services to the entire population. This study analyzed the spatial distribution of COVID-19 vaccination centers in Jeddah, a major city in Saudi Arabia, by using GIS tools and methods to provide insight on the effectiveness of the selection and distribution of the COVID-19 vaccination centers in terms of accessibility and coverage. Based on a spatial analysis of vaccine centers’ coverage in 2020 and 2021 in Jeddah presented in this study, coverage deficiency would have been addressed earlier if the applied GIS analysis methods had been used by authorities while gradually increasing the number of vaccination centers. This study recommends that the Ministry of Health in Saudi Arabia evaluated the assigned vaccination centers to include the less-populated regions and to ensure equity and fairness in vaccine distribution. Adding more vaccine centers or reallocating some existing centers in the denser districts to increase the coverage in the uncovered sparse regions in Jeddah is also recommended. The methods applied in this study could be part of a strategic vaccination administration program for future public health emergencies and other vaccination campaigns.

Increasing risks for emerging infectious diseases within a rapidly changing High Asia

The cold and arid mountains and plateaus of High Asia, inhabited by a relatively sparse human population, a high density of livestock, and wildlife such as the iconic snow leopard Panthera uncia, are usually considered low risk for disease outbreaks. However, based on current knowledge about drivers of disease emergence, we show that High Asia is rapidly developing conditions that favor increased emergence of infectious diseases and zoonoses. This is because of the existing prevalence of potentially serious pathogens in the system; intensifying environmental degradation; rapid changes in local ecological, socio-ecological, and socio-economic factors; and global risk intensifiers such as climate change and globalization. To better understand and manage the risks posed by diseases to humans, livestock, and wildlife, there is an urgent need for establishing a disease surveillance system and improving human and animal health care. Public health must be integrated with conservation programs, more ecologically sustainable development efforts and long-term disease surveillance.

Deaths due to COVID-19 in a state of northeastern Brazil: spatiotemporal distribution, sociodemographic and clinical and operational characteristics

BACKGROUND

The detection of spatiotemporal clusters of deaths by coronavirus disease 2019 (COVID-19) is essential for health systems and services, as it contributes to the allocation of resources and helps in effective decision making aimed at disease control and surveillance. Thus we aim to analyse the spatiotemporal distribution and describe sociodemographic and clinical and operational characteristics of COVID-19-related deaths in a Brazilian state.

METHODS

A descriptive and ecological study was carried out in the state of Maranhão. The study population consisted of deaths by COVID-19 in the period from 29 March to 31 July 2020. The detection of spatiotemporal clusters was performed by spatiotemporal scan analysis.

RESULTS

A total of 3001 deaths were analysed with an average age of 69 y, predominantly in males, of brown ethnicity, with arterial hypertension and diabetes, diagnosed mainly by reverse transcription polymerase chain reaction in public laboratories. The crude mortality rates the municipalities ranged from 0.00 to 102.24 deaths per 100 000 inhabitants and three spatiotemporal clusters of high relative risk were detected, with a mortality rate ranging from 20.25 to 91.49 deaths per 100 000 inhabitants per month. The headquarters was the metropolitan region of São Luís and municipalities with better socio-economic and health development.

CONCLUSIONS

The heterogeneous spatiotemporal distribution and the sociodemographic and clinical and operational characteristics of deaths by COVID-19 point to the need for interventions.

Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling

The coronavirus disease of 2019 (COVID-19) pandemic is currently a global challenge, with 210 countries, including Indonesia, seeking to minimize its spread. Therefore, this study aims to determine the spatiotemporal spread pattern of this virus in Surabaya using various data on confirmed cases from 28 April to 26 October 2021. It also aims to determine the relationship between pollutant parameters, such as carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃), as well as the government’s high social restrictions policy in Java-Bali. Several methods, such as the weighted mean center, directional distribution, Getis–Ord Gi*, Moran’s I, and geographically weighted regression, were used to identify the spatial spread pattern of the virus. The weighted mean center indicated that the epicenter location of the outbreak moved randomly. The directional distribution demonstrated a decrease of 21 km² at the end of the study phase, which proved that its spread has significantly reduced in Surabaya. Meanwhile, the Getis–Ord Gi* results demonstrated that the eastern and southern parts of the study region were highly infected. Moran’s I demonstrate that COVID-19 cases clustered during the spike. The geographically weighted regression model indicated a number of influence zones in the northeast, northwest, and a few in the southwest parts at the peak of R² 0.55. The relationship between COVID-19 cases and air pollution parameters proved that people living at the outbreak’s center have low pollution levels due to lockdown. Furthermore, the lockdown policy reduced CO, NO₂, SO₂, and O₃. In addition, increase in air pollutants; namely, NO₂, CO, SO₂ and O₃, was recorded after 7 weeks of lockdown implementation (started from 18 August).

Effects of L-carnitine supplementation in patients with mild-to-moderate COVID-19 disease: a pilot study

BACKGROUND

The present single-center clinical trial was designed to evaluate the potential benefits of L-carnitine supplementation in patients with COVID-19 disease.

METHODS AND PATIENTS

The study was conducted on 75 patients with mild-to-moderate COVID-19 hospitalized in Shahid Beheshti Hospital-Hamadan, IRAN. The participants were randomly divided into intervention (n = 32) and control groups (n = 43). The control group received their standard hospital treatment only. In addition to standard medications, the intervention group received 3000 mg oral L-carnitine daily in three divided doses for five days. The blood samples were collected and para-clinical parameters were measured at the beginning and end of the treatment. Clinical outcomes were also recorded, and data were analyzed using χ² and t-tests.

RESULTS

Higher means of O2 saturation were observed in the intervention rather than in the control group. Mean erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) were significantly lower in the intervention group. Furthermore, mean alkaline phosphatase (ALP) activity and lactate dehydrogenase (LDH) were lower in the intervention group. Also, lower mean serum creatine phosphokinase (CPK) was observed in the intervention group. No significant differences were observed in terms of clinical symptoms; however, six patients (14%) in the control group died due to the complications of COVID-19, while all patients in the intervention group survived.

CONCLUSION

Taken together, L-carnitine can be considered as a drug supplement in patients with COVID-19.

Spatiotemporal Analysis of Covid-19 in Turkey

The Covid-19 pandemic continues to threaten public health around the world. Understanding the spatial dimension of this impact is very important in terms of controlling and reducing the spread of the pandemic. This study measures the spatial association of the Covid-19 outbreak in Turkey between February 8 and May 28, 2021 and reveals its spatiotemporal pattern. In this context, global and local spatial autocorrelation was used to determine whether there is a spatial association of Covid-19 infections, while the spatial regression model was employed to reveal the geographical relationship of the potential factors affecting the number of Covid-19 cases. As a result of the analyzes made in this context, it has been observed that there are spatial associations and distinct spatial clusters in Covid-19 cases at the provincial level in Turkey. The results of the spatial regression model showed that population density and elderly dependency ratio are very important in explaining the model of Covid-19 case numbers. Additionally, it has been revealed that Covid-19 is affected by the Covid-19 numbers of neighboring provinces, apart from the said explanatory variables. The findings of the study revealed that spatial analysis is helpful in understanding the spread of the pandemic in Turkey. It has been determined that geographical location is an important factor to be considered in the investigation of the factors affecting Covid-19.

Spatiotemporal analysis of water-electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors in Doha City, Qatar

This study investigates the water - electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors. Due to inadequate research on spatial modelling of water - electricity consumption in the context of the COVID-19 pandemic, this study investigated geographical block-level variation in water and electricity consumption in Doha city of Qatar. Spatial analyses were performed to investigate the spatial differences in each sector. Five geospatial techniques in a Geographical Information System (GIS) context were used in the study. Moran's I, Anselin Local Moran's I, and Getis-Ord G i ∗ statistics tools were used to identify the hot spots and cold spots of water and electricity consumption in each sector. Furthermore, Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models were employed to investigate the spatial relationship between water and electricity consumption during the pandemic year. The findings show that there is a distinction in water and electricity consumption at the block level across all sectors and over time. Hot spot and spatial regression analysis reveal spatial and temporal heterogeneities in the study area across the six socioeconomic sectors. The intensity of hot spots of water and electricity consumption are found in the southern and western parts of the city due to high population density and the concentration of the commercial and industrial areas. Furthermore, analyzing the spatiotemporal correlation between the water and electricity consumption across the six sectors shows variation within and between these sectors over space and time. The results show a positive relationship between water and electricity consumption in some blocks and over time of each sector. During the lockdown phase, strong positive correlation between water and electricity consumption have exist in the residential sector due to extra water and electricity footprints in this sector. Conversely, the water and electricity consumption were positively correlated but declined in the industrial and commercial sector due to the curtailment in production, economic activities, and reduction in people's mobility. Mapping the hot spot blocks and the blocks with high relationship between water and electricity consumption could provide useful insight to decision-makers for targeted interventions.

Modeling electricity consumption patterns during the COVID-19 pandemic across six socioeconomic sectors in the State of Qatar

The propagation of the COVID-19 pandemic, and the associated measures taken by many countries to slow down the spread of the disease, has significantly affected all aspects of people's lives, including the global energy sector. This study aims to investigate the impact of the pandemic on the spatial patterns of electricity consumption in six socioeconomic sectors (residential (villa and flat), industrial, commercial, government, and productive farms) in the State of Qatar. The spatiotemporal patterns of electricity consumption have been assessed using various Geographic Information Systems (GIS) and spatial statistical modeling prior and during the pandemic. The results demonstrate variations in electricity consumption within and between the six sectors. The main changes in the electricity consumption within sectors during the pandemic year is during the lockdown phase. Spatially, some sectors are affected by the pandemic, and hence the pattern and the spatial and temporal distribution of electricity consumption has changed during the pandemic year compared to pre-pandemic years. The results also show that there were variations of spatial clustering of electricity consumption among these sectors. Most of the high-high clustering patterns are located in the mid-eastern and northeastern parts of Qatar. The highest variation in electricity consumption between sectors occurred in the productive farms due to its massive development during the pre-pandemic period and were not affected by the pandemic. There is a sharp decline in electricity consumption in both the industrial and commercial sectors during the pandemic year. Other sectors witnessed an increase in electricity consumption during the summer months, which was mainly due to travel restrictions imposed by many countries around the world. This analysis is vital for policymakers to detect the changes in electricity consumption patterns in the context of emergencies such as the pandemic.

A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020

COVID-19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID-19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID-19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID-19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID-19 transmission and the associated risk factors.

Physics-Informed Neural Networks and Functional Interpolation for Data-Driven Parameters Discovery of Epidemiological Compartmental Models

In this work, we apply a novel and accurate Physics-Informed Neural Network Theory of Functional Connections (PINN-TFC) based framework, called Extreme Theory of Functional Connections (X-TFC), for data-physics-driven parameters’ discovery of problems modeled via Ordinary Differential Equations (ODEs). The proposed method merges the standard PINNs with a functional interpolation technique named Theory of Functional Connections (TFC). In particular, this work focuses on the capability of X-TFC in solving inverse problems to estimate the parameters governing the epidemiological compartmental models via a deterministic approach. The epidemiological compartmental models treated in this work are Susceptible-Infectious-Recovered (SIR), Susceptible-Exposed-Infectious-Recovered (SEIR), and Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS). The results show the low computational times, the high accuracy, and effectiveness of the X-TFC method in performing data-driven parameters’ discovery systems modeled via parametric ODEs using unperturbed and perturbed data.

Spatial Layout and Coupling of Urban Cultural Relics: Analyzing Historical Sites and Commercial Facilities in District III of Shaoxing

Exploring the spatial coupling relationship between cultural relics and historic sites and their surroundings can provide reasonable suggestions for the layout and development of commercial facilities and hold crucial significance for improving the management and maintenance of cultural relics and historical sites, as well as enhancing their attractiveness to the public. We chose District III of Shaoxing City as the research area based on the point of interest and road network data. This study analyzed the scale and accessibility of cultural relics and historic sites (CRHSs) as well as their surrounding commercial facilities, and then objectively evaluated their spatial layout and coupling relationship by employing kernel density estimation, standard deviation ellipse, network analysis, inverse distance weight and the spatial correlation analysis method. The results show that: (1) from the perspective of spatial layout, the distribution of CRHSs has a positive and strong correlation with the distribution of road networks; (2) there are noticeable variations in the number of industrial facilities surrounding various CRHSs, closely related to the protection grade of CRHSs; (3) the accessibility of commercial facilities surrounding CRHS varies significantly—commercial facilities surrounding CRHSs located within central District III of Shaoxing City have good accessibility, whereas those of the peripheral areas have comparatively poor accessibility; and (4) the accessibility of commercial facilities surrounding CRHSs in different administrative districts varies, showing an extremely uneven pattern.

NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient's symptoms

Nowadays, humanity is facing one of the most dangerous pandemics known as COVID-19. Due to its high inter-person contagiousness, COVID-19 is rapidly spreading across the world. Positive patients are often suffering from different symptoms that can vary from mild to severe including cough, fever, sore throat, and body aches. In more dire cases, infected patients can experience severe symptoms that can cause breathing difficulties which lead to stern organ failure and die. The medical corps all over the world are overloaded because of the exponentially myriad number of contagions. Therefore, screening for the disease becomes overwrought with the limited tools of test. Additionally, test results may take a long time to acquire, leaving behind a higher potential for the prevalence of the virus among other individuals by the patients. To reduce the chances of infection, we suggest a prediction model that distinguishes the infected COVID-19 cases based on clinical symptoms and features. This model can be helpful for citizens to catch their infection without the need for visiting the hospital. Also, it helps the medical staff in triaging patients in case of a deficiency of medical amenities. In this paper, we use the non-dominated sorting genetic algorithm (NSGA-II) to select the interesting features by finding the best trade-offs between two conflicting objectives: minimizing the number of features and maximizing the weights of selected features. Then, a classification phase is conducted using an AdaBoost classifier. The proposed model is evaluated using two different datasets. To maximize results, we performed a natural selection of hyper-parameters of the classifier using the genetic algorithm. The obtained results prove the efficiency of NSGA-II as a feature selection algorithm combined with AdaBoost classifier. It exhibits higher classification results that outperformed the existing methods.

Demographic and socioeconomic determinants of COVID-19 across Oman - A geospatial modelling approach

Local, bivariate relationships between coronavirus 2019 (COVID-19) infection rates and a set of demographic and socioeconomic variables were explored at the district level in Oman. To limit multicollinearity a principal component analysis was conducted, the results of which showed that three components together could explain 65% of the total variance that were therefore subjected to further study. Comparison of a generalized linear model (GLM) and geographically weighted regression (GWR) indicated an improvement in model performance using GWR (goodness of fit=93%) compared to GLM (goodness of fit=86%). The local coefficient of determination (R2) showed a significant influence of specific demographic and socioeconomic factors on COVID-19, including percentages of Omani and non-Omani population at various age levels; spatial interaction; population density; number of hospital beds; total number of households; purchasing power; and purchasing power per km2. No direct correlation between COVID- 19 rates and health facilities distribution or tobacco usage. This study suggests that Poisson regression using GWR and GLM can address unobserved spatial non-stationary relationships. Findings of this study can promote current understanding of the demographic and socioeconomic variables impacting the spatial patterns of COVID-19 in Oman, allowing local and national authorities to adopt more appropriate strategies to cope with this pandemic in the future and also to allocate more effective prevention resources.

Does the medical insurance system play a real role in reducing catastrophic economic burden in elderly patients with cardiovascular disease in China? Implication for accurately targeting vulnerable characteristics

BACKGROUND

The vulnerability of cardiovascular disease (CVD) patients' health abilities, combined with the severity of the disease and the overlapping risk factors, leads such people to bear the economic burden of the disease due to the medical services. We estimated the economic burden of CVD and identified the weak link in the design of the medical insurance.

METHODS

Data from 5610 middle-aged and elderly with CVD were drawn from the 2015 wave of "China Health and Retirement Longitudinal Study" (CHARLS). The recommended method of the "World Health Organization" (WHO) was adopted to calculate "catastrophic health expenditure" (CHE), "impoverishment by medical expenses" (IME), and applied the treatment-effect model to analyze the determinants of CHE.

RESULTS

The incidence of CHE was 19.9% for the elderly families with CVD members, which was 3.6% higher than for uninsured families (16.3%). Families with CVD combined with > 3 other chronic diseases (38.88%) were the riskiest factor for the high CHE in the new rural cooperative medical system (NCMS). Moreover, families with members > 75 years old (33.33%), having two chronic disease (30.74%), and families having disabled members (33.33%), hospitalization members (32.41%) were identified as the high risky determinants for the high CHE in NCMS.

CONCLUSIONS

Elderly with physical vulnerabilities were more prone to CHE. The medical insurance only reduced barriers to accessing health resources for elderly with CVD; however it lacked the policy inclination for high-utilization populations, and had poorly accurate identification of the vulnerable characteristics of CVD, which in turn affects the economic protection ability of the medical insurance. The dispersion between the multiple medical security schemes leads to the existence of blind spots in the economic risk protection of individuals and families.

The Arab region's contribution to global COVID-19 research: Bibliometric and visualization analysis

BACKGROUND

At the global level and in the Arab world, particularly in low-income countries, COVID-19 remains a major public health issue. As demonstrated by an incredible number of COVID-19-related publications, the research science community responded rapidly. Therefore, this study was intended to assess the growing contribution of the Arab world to global research on COVID-19.

METHODS

For the period between December 2019 and March 2021, the search for publications was conducted via the Scopus database using terms linked to COVID-19. VOSviewer 1.6.16 software was applied to generate a network map to assess hot topics in this area and determine the collaboration patterns between different countries. Furthermore, the research output of Arab countries was adjusted in relation to population size and gross domestic product (GDP).

RESULTS

A total of 143,975 publications reflecting the global overall COVID-19 research output were retrieved. By restricting analysis to the publications published by the Arab countries, the research production was 6131 documents, representing 4.26% of the global research output regarding COVID-19. Of all these publications, 3990 (65.08%) were original journal articles, 980 (15.98%) were review articles, 514 (8.38%) were letters and 647 (10.55%) were others, such as editorials or notes. The highest number of COVID-19 publications was published by Saudi Arabia (n = 2186, 35.65%), followed by Egypt (n = 1281, 20.78%) and the United Arab Emirates (UAE), (n = 719, 11.73%). After standardization by population size and GDP, Saudi Arabia, UAE and Lebanon had the highest publication productivity. The collaborations were mostly with researchers from the United States (n = 968), followed by the United Kingdom (n = 661). The main research lines identified in COVID-19 from the Arab world are related to: public health and epidemiology; immunological and pharmaceutical research; signs, symptoms and clinical diagnosis; and virus detection.

CONCLUSIONS

A novel analysis of the latest Arab COVID-19-related studies is discussed in the current study and how these findings are connected to global production. Continuing and improving future collaboration between developing and developed countries will also help to facilitate the sharing of responsibilities for COVID-19 in research results and the implementation of policies for COVID-19.

Engineering Graphics for Thermal Assessment: 3D Thermal Data Visualisation Based on Infrared Thermography, GIS and 3D Point Cloud Processing Software

Engineering graphics are present in the design stage, but also constitute a way to communicate, analyse, and synthesise. In the Architecture-Engineering-Construction sector, graphical data become essential in analysing buildings and constructions throughout their lifecycles, such as in the thermal behaviour assessment of building envelopes. Scientific research has addressed the thermal image mapping onto three-dimensional (3D) models for visualisation and analysis. However, the 3D point cloud data creation of buildings’ thermal behaviour directly from rectified infrared thermography (IRT) thermograms is yet to be investigated. Therefore, this paper develops an open-source software graphical method to produce 3D thermal data from IRT images for temperature visualisation and subsequent analysis. This low-cost approach uses both a geographic information system for the thermographic image rectification and the point clouds production, and 3D point cloud processing software. The methodology has been proven useful to obtain, without perspective distortions, 3D thermograms even from non-radiometric raster images. The results also revealed that non-rectangular thermograms enable over 95% of the 3D thermal data generated from IRT against rectangular shapes (over 85%). Finally, the 3D thermal data produced allow further thermal behaviour assessment, including calculating the object’s heat loss and thermal transmittance for diverse applications such as energy audits, restoration, monitoring, or product quality control.

Transmission of SARS-Cov-2 and other enveloped viruses to the environment through protective gear: a brief review

Over the past two decades, several deadly viral epidemics have emerged, which have placed humanity in danger. Previous investigations have suggested that viral diseases can spread through contaminants or contaminated surfaces. The transmission of viruses via polluted surfaces relies upon their capacity to maintain their infectivity while they are in the environment. Here, a range of materials that are widely used to manufacture personal protective equipment (PPE) are summarized, as these offer effective disinfection solutions and are the environmental variables that influence virus survival. Infection modes and prevention as well as disinfection and PPE disposal strategies are discussed. A coronavirus-like enveloped virus can live in the environment after being discharged from a host organism until it infects another healthy individual. Transmission of enveloped viruses such as SARS-CoV-2 can occur even without direct contact, although detailed knowledge of airborne routes and other indirect transmission paths is still lacking. Ground transmission of viruses is also possible via wastewater discharges. While enveloped viruses can contaminate potable water and wastewater through human excretions such as feces and droplets, careless PPE disposal can also lead to their transmission into our environment. This paper also highlights the possibility that viruses can be transmitted into the environment from PPE kits used by healthcare and emergency service personnel. A simulation-based approach was developed to understand the transport mechanism for coronavirus and similar enveloped viruses in the environment through porous media, and preliminary results from this model are presented here. Those results indicate that viruses can move through porous soil and eventually contaminate groundwater. This paper therefore underlines the importance of proper PPE disposal by healthcare workers in the Mediterranean region and around the world.

Mathematical modelling of the second wave of COVID-19 infections using deterministic and stochastic SIDR models

Recently, various countries from across the globe have been facing the second wave of COVID-19 infections. In order to understand the dynamics of the spread of the disease, much effort has been made in terms of mathematical modeling. In this scenario, compartmental models are widely used to simulate epidemics under various conditions. In general, there are uncertainties associated with the reported data, which must be considered when estimating the parameters of the model. In this work, we propose an effective methodology for estimating parameters of compartmental models in multiple wave scenarios by means of a dynamic data segmentation approach. This robust technique allows the description of the dynamics of the disease without arbitrary choices for the end of the first wave and the start of the second. Furthermore, we adopt a time-dependent function to describe the probability of transmission by contact for each wave. We also assess the uncertainties of the parameters and their influence on the simulations using a stochastic strategy. In order to obtain realistic results in terms of the basic reproduction number, a constraint is incorporated into the problem. We adopt data from Germany and Italy, two of the first countries to experience the second wave of infections. Using the proposed methodology, the end of the first wave (and also the start of the second wave) occurred on 166 and 187 days from the beginning of the epidemic, for Germany and Italy, respectively. The estimated effective reproduction number for the first wave is close to that obtained by other approaches, for both countries. The results demonstrate that the proposed methodology is able to find good estimates for all parameters. In relation to uncertainties, we show that slight variations in the design variables can give rise to significant changes in the value of the effective reproduction number. The results provide information on the characteristics of the epidemic for each country, as well as elements for decision-making in the economic and governmental spheres.