Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh

Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19

According to the World Health Organization (WHO), COVID-19 has caused more than 6.5 million deaths, while over 600 million people are infected. With regard to the tools and techniques of disease analysis, spatial analysis is increasingly being used to analyze the impact of COVID-19. The present review offers an assessment of research that used regional data systems to study the COVID-19 epidemic published between 2020 and 2022. The research focuses on: categories of the area, authors, methods, and procedures used by the authors and the results of their findings. This input will enable the contrast of different spatial models used for regional data systems with COVID-19. Our outcomes showed increased use of geographically weighted regression and Moran I spatial statistical tools applied to better spatial and time-based gauges. We have also found an increase in the use of local models compared to other spatial statistics models/methods.

The role of vaccination and environmental factors on outbreaks of high pathogenicity avian influenza H5N1 in Bangladesh

High Pathogenicity Avian Influenza (HPAI) H5N1 outbreaks continue to wreak havoc on the global poultry industry and threaten the health of wild bird populations, with sporadic spillover in humans and other mammals, resulting in widespread calls to vaccinate poultry. Bangladesh has been vaccinating poultry since 2012, presenting a prime opportunity to study the effects of vaccination on HPAI H5N1circulation in both poultry and wild birds. We investigated the efficacy of vaccinating commercial poultry against HPAI H5N1 along with climatic and socio-economic factors considered potential drivers of HPAI H5N1 outbreak risk in Bangladesh. Using a multivariate modeling approach, we estimated that the rate of outbreaks was 18 times higher before compared to after vaccination, with winter months having a three times higher chance of outbreaks than summer months. Variables resulting in small but significant increases in outbreak rate were relatively low ambient temperatures for the time of year, literacy rate, chicken and duck density, crop density, and presence of highways; this may be attributable to low temperatures supporting viral survival outside the host, higher literacy driving reporting rate, density of the host reservoir, and spread of the virus through increased connectivity. Despite the substantial impact of vaccination on outbreaks, we note that HPAI H5N1 is still enzootic in Bangladesh; vaccinated poultry flocks have high rates of H5N1 prevalence, and spillover to wild birds has increased. Vaccination in Bangladesh thus bears the risk of supporting "silent spread," where the vaccine only provides protection against disease and not also infection. Our findings underscore that poultry vaccination can be part of holistic HPAI mitigation strategies when accompanied by monitoring to avoid silent spread.

Annual trading patterns and risk factors of avian influenza A/H5 and A/H9 virus circulation in turkey birds (Meleagris gallopavo) at live bird markets in Dhaka city, Bangladesh

The impacts of the avian influenza virus (AIV) on farmed poultry and wild birds affect human health, livelihoods, food security, and international trade. The movement patterns of turkey birds from farms to live bird markets (LBMs) and infection of AIV are poorly understood in Bangladesh. Thus, we conducted weekly longitudinal surveillance in LBMs to understand the trading patterns, temporal trends, and risk factors of AIV circulation in turkey birds. We sampled a total of 423 turkeys from two LBMs in Dhaka between May 2018 and September 2019. We tested the swab samples for the AIV matrix gene (M-gene) followed by H5, H7, and H9 subtypes using real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). We used exploratory analysis to investigate trading patterns, annual cyclic trends of AIV and its subtypes, and a generalized estimating equation (GEE) logistic model to determine the factors that influence the infection of H5 and H9 in turkeys. Furthermore, we conducted an observational study and informal interviews with traders and vendors to record turkey trading patterns, demand, and supply and turkey handling practices in LBM. We found that all trade routes of turkey birds to northern Dhaka are unidirectional and originate from the northwestern and southern regions of Bangladesh. The number of trades from the source district to Dhaka depends on the turkey density. The median distance that turkey was traded from its source district to Dhaka was 188 km (Q1 = 165, Q3 = 210, IQR = 45.5). We observed seasonal variation in the median and average distance of turkey. The qualitative findings revealed that turkey farming initially became reasonably profitable in 2018 and at the beginning of 2019. However, the fall in demand and production in the middle of 2019 may be related to unstable market pricing, high feed costs, a shortfall of adequate marketing facilities, poor consumer knowledge, and a lack of advertising. The overall prevalence of AIV, H5, and H9 subtypes in turkeys was 31% (95% CI: 26.6-35.4), 16.3% (95% CI: 12.8-19.8), and 10.2% (95% CI: 7.3-13.1) respectively. None of the samples were positive for H7. The circulation of AIV and H9 across the annual cycle showed no seasonality, whereas the circulation of H5 showed significant seasonality. The GEE revealed that detection of AIV increases in retail vendor business (OR: 1.71; 95% CI: 1.12-2.62) and the bird's health status is sick (OR: 10.77; 95% CI: 4.31-26.94) or dead (OR: 11.33; 95% CI: 4.30-29.89). We also observed that winter season (OR: 5.83; 95% CI: 2.80-12.14) than summer season, dead birds (OR: 61.71; 95% CI: 25.78-147.75) and sick birds (OR 8.33; 95% CI: 3.36-20.64) compared to healthy birds has a higher risk of H5 infection in turkeys. This study revealed that the turkeys movements vary by time and season from the farm to the LBM. This surveillance indicated year-round circulation of AIV with H5 and H9 subtypes in turkey birds in LBMs. The seasonality and health condition of birds influence H5 infection in birds. The trading pattern of turkey may play a role in the transmission of AIV viruses in the birds. The selling of sick turkeys infected with H5 and H9 highlights the possibility of virus transmission to other species of birds sold at LBMs and to people.

Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States

This article examines three spatiotemporal methods used for analyzing of infectious diseases, with a focus on COVID-19 in the United States. The methods considered include inverse distance weighting (IDW) interpolation, retrospective spatiotemporal scan statistics and Bayesian spatiotemporal models. The study covers a 12-month period from May 2020 to April 2021, including monthly data from 49 states or regions in the United States. The results show that the spread of COVID-19 pandemic increased rapidly to a high value in winter of 2020, followed by a brief decline that later reverted into another increase. Spatially, the COVID-19 epidemic in the United States exhibited a multi-centre, rapid spread character, with clustering areas represented by states such as New York, North Dakota, Texas and California. By demonstrating the applicability and limitations of different analytical tools in investigating the spatiotemporal dynamics of disease outbreaks, this study contributes to the broader field of epidemiology and helps improve strategies for responding to future major public health events.

Hotspots of un-iodized salt availability among Ethiopian households, evidence from the national survey data

BACKGROUND

Universal salt iodization was started before decades but there are communities using the un-iodized salt till now. More than one-tenth of the Ethiopian community uses un-iodized salt.

OBJECTIVE

This study aimed to identify the hotspots and associate factors of un-iodized salt availability in Ethiopia based on Ethiopian national household survey data.

METHODS

We conducted an in-depth analysis of the Ethiopian Demographic and Health Survey 2016 data. A total of 15,567 households were included in the final analysis. We cleaned and weighed the data using Stata version 16 software and descriptive outputs were reported in graphs and tables. We computed the weighted prevalence of un-iodized salt and prepared it for spatial analysis. Global-level spatial autocorrelation, hotspot analysis using the Getis-Ord Gi* statistics, and spatial interpolation using empirical Bayesian interpolation were executed using ArcGIS 10.3 to predict the magnitude of un-iodized salt at the national level. The binary logistics regression model was used to identify the contributing factors of un-iodized salt utilization. Model goodness of fit was tested with Hosmer and Lemeshow goodness-of-fit test (P = 0.96). Finally, the adjusted odds ratio (AOR) with 95% CI was reported to identify significant factors.

RESULTS

The magnitude of un-iodized salt availability was 14.19% (95% CI: 13.65, 14.75) among Ethiopian households. Un-iodized salt hotspots were found in Afar, Somalia, and Benishangul Gumuz regions. Compared to poorest wealth index: poorer (AOR = 0.55, 95% CI: 0.48, 0.64), middle (AOR = 0.51, 95% CI: 0.44, 0.60), richer (AOR = 0.55, 95% CI: 0.47, 0.64), and richest (AOR = 0.61, 95% CI: 0.50, 0.75); compared to uneducated household head: heads with secondary (AOR = 0.72, 95% CI: 0.60, 0.67) and above secondary (AOR = 0.54, 95% CI: 0.43, 0.67) education reduced the odds of un-iodized salt viability, while households living in highland (AOR = 1.16, 95% CI: 1.05, 1.29) had increased the odds of un-iodized salt availability.

CONCLUSION

More than a tenth of the households in Ethiopia uses un-iodized salt. Hotspots of un-iodized salt availability were found in Somali and Afar regions of Ethiopia. Better wealth index and education of the household heads reduces the odds of un-iodized salt availability while living in a high altitude above 2200 m increases the odds of un-iodized salt availability in Ethiopia.

A Statistical Synopsis of COVID-19 Components and Descriptive Analysis of Their Socio-Economic and Healthcare Aspects in Bangladesh Perspective

The aim of the work is to analyze the socio-economic and healthcare aspects that arise in the contemporary COVID-19 situation from Bangladesh perspective. We elaborately discuss the successive COVID-19 occurrences in Bangladesh with consequential information. The components associated with the COVID-19 commencement and treatment policy with corresponding features and their consequences are patently delineated. The effect of troublesome issues related to the treatment is detailed with supporting real-time data. We elucidate the applications of modern technologies advancement in epidemiological aspects and their existent compatibility in Bangladesh. We statistically analyze the real-time data through figurative and tabular approaches. Some relevant measures of central tendency and dispersion are utilized to explore the data structure and its observable specifications. For a clear manifestation, Z- scores of the COVID-19 components are analyzed through the Box-Whisker plot. We have discovered that the gathered data exhibit features that are unsatisfactory for the normal distribution, are highly positively skewed, and are predominated by the earliest occurrences. Infections and deaths were initially lower than the global average, but they drastically rose in the first quarter of 2021 and persisted for the remainder of the year. Substantial preventive results were produced by the region-wisetime-worthy moves. In the fourth quarter of 2021, the infections and deaths noticeably decreased, and the number of recoveries was highly significant. In the middle of 2022, a lethal rise in infections was observed in Bangladesh and that was quickly stabilized, and the pandemic ingredients were under control. According to our assessment, some concluding remarks are made at the end of this work.

Revealing the spatiotemporal characteristics of the general public's panic levels during the pandemic crisis in China

The existing crisis management research mostly reveals the patterns of the public's panic levels from the perspectives of public management, sociology, and psychology, only a few studies have revealed the spatiotemporal characteristics. Therefore, this study investigates the spatial distribution and temporal patterns and influencing factors on the general public's panic levels using the Baidu Index data from a geographic perspective. The results show that: (1) The public's panic levels were significantly correlated with the spatial distance between the epicenter and the region of investigation, and with the number of confirmed cases in different regions when the pandemic began to spread. (2) Based on the spatial distance between the epicenter and the region, the public's panic levels in different regions could be divided into three segments: core segment (0-500 km), buffer segment (500-1300 km), and peripheral segment (>1300 km). The panic levels of different people in the three segments were consistent with the Psychological Typhoon Eye Effect and the Ripple Effect can be detected in the buffer segment. (3) The public's panic levels were strongly correlated with whether the spread of the infectious disease crisis occurred and how long it lasted. It is suggested that crisis information management in the future needs to pay more attention to the spatial division of control measures. The type of crisis information released to the general public should depend on the spatial relationship associated with the place where the crisis breaks out.

Coupling coordination development of energy-economy-carbon emissions in China under the background of "double carbon"

Based on the panel data of 30 provinces in China from 2010 to 2019, this paper measured the coupling coordination development of energy-economy-carbon emissions and investigated its regional differences and spatial convergence. The research methods in this paper include entropy weight technique method for order preference by similarity to an ideal solution, coupling coordination degree model, Dagum Gini coefficient and decomposition method, Moran's I index, σ convergence model and β convergence model. The study found that the coupling coordination degree of energy-economy-carbon emissions in China has been continuously improved and has obvious regional and stage characteristics, but it is still on the verge of imminent disorder; the overall difference in the coupling coordination degree of energy-economy-carbon emissions shows a decreasing and then increasing trend, the main source of which is inter-regional differences; the coupling coordination degree of energy-economy-carbon emissions has a positive spatial correlation; except for the Southern Coastal Economic Zone and the Middle Yangtze River Economic Zone, there is no significant σ-convergence and β-convergence in the coupling coordination degree of energy-economy-carbon emissions system in other economic zones; the coupling coordination degree of energy-economy-carbon emissions changes fastest in the Middle Yangtze River Economic Zone. The innovation of this paper is to measure the coupling coordination degree of energy-economy-carbon emissions and to analyse its regional differences and spatial effects. It is of great practical significance to promote the coupling coordination development and regional balanced development of energy-economy-carbon emissions in China under the background of "dual carbon".

Epidemiology and molecular characterization of avian influenza virus in backyard poultry of Chattogram, Bangladesh

Ducks, the natural reservoir of avian influenza virus (AIV), act as reassortment vessels for HPAI and low pathogenic avian influenza (LPAI) virus for domestic and wild bird species. In Bangladesh, earlier research was mainly focused on AIV in commercial poultry and live bird markets, where there is scanty literature reported on AIV in apparently healthy backyard poultry at the household level. The present cross-sectional study was carried out to reveal the genomic epidemiology of AIV of backyard poultry in coastal (Anowara) and plain land (Rangunia) areas of Bangladesh. We randomly selected a total of 292 households' poultry (having both chicken and duck) for sampling. We administered structured pre-tested questionnaires to farmers through direct interviews. We tested cloacal samples from birds for the matrix gene (M gene) followed by H5 and H9 subtypes using real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). All AIV-positive samples were subjected to four-gene segment sequencing (M, PB1, HA, and NA gene). We found that the prevalence of AIV RNA at the household level was 6.2% (n = 18; N = 292), whereas duck and chicken prevalence was 3.6% and 3.2%, respectively. Prevalence varied with season, ranging from 3.1% in the summer to 8.2% in the winter. The prevalence of subtypes H5 and H9 in backyard poultry was 2.7% and 3.3%, respectively. The phylogenetic analysis of M, HA, NA, and PB1 genes revealed intra-genomic similarity, and they are closely related to previously reported AIV strains in Bangladesh and Southeast Asia. The findings indicate that H5 and H9 subtypes of AIV are circulating in the backyard poultry with or without clinical symptoms. Moreover, we revealed the circulation of 2.3.2.1a (new) clade among the chicken and duck population without occurring outbreak which might be due to vaccination. In addition to routine surveillance, molecular epidemiology of AIV will assist to gain a clear understanding of the genomic evolution of the AIV virus in the backyard poultry rearing system, thereby facilitating the implementation of effective preventive measures to control infection and prevent the potential spillover to humans.

Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada

The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19. We also used global (spatial regression models), and local geographically weighted regression (GWR) and Multiscale Geographically weighted regression (MGWR) models to identify the globally and locally varying socioeconomic drivers of COVID-19. The global regression model identified a lower percentage of educated people and a higher percentage of immigrants in the neighbourhoods as significant predictors of COVID-19. MGWR shows the best fit model to explain the variables affecting COVID-19. The findings imply that a single intervention package for the entire area would not be an effective strategy for controlling COVID-19; a locally adaptable intervention package would be beneficial.

Impact of COVID-19 on mode choice behavior: A case study for Dhaka, Bangladesh

To ensure safety against the COVID-19, along with all other countries, Bangladesh as a least-developed country needs to deal with the changes in travel behavior, particularly changes in mode choice behavior. As Dhaka has been marked as a hotspot for the virus contagion, this paper has focused on the changes in mode choice behavior of Dhaka people due to the COVID-19 pandemic while they are on the road. A web-based questionnaire survey was conducted to capture the information on mode preferences and perspectives on travel characteristics for commute and discretionary trips before and during COVID-19. Multinomial Logit (MNL) model based on a utility function has been used to investigate the significance of the socio-demographic attributes and travel characteristics of the trips on the mode choice behavior and to calculate the maximum utility of the mode choice. This study highlighted some noticeable changes in perspective towards mode choice. People prefer walking, private cars, and rickshaw more during the pandemic as they feel these modes are more reliable, available, and cost-effective in this crucial time. Usage of public transportation dropped drastically for discretionary purposes. Additionally, usage of the on-demand vehicle increased during the pandemic as a large portion of commuters shifted to on-demand vehicles from public transportation. Furthermore, this paper suggested some viable policy-making implications to cope with the current pandemic and relatable future national and global crises. Finally, the paper concludes by suggesting some future research insights.

Transmission dynamics and susceptibility patterns of SARS-CoV-2 in domestic, farmed and wild animals: Sustainable One Health surveillance for conservation and public health to prevent future epidemics and pandemics

The exact origin of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and source of introduction into humans has not been established yet, though it might be originated from animals. Therefore, we conducted a study to understand the putative reservoirs, transmission dynamics, and susceptibility patterns of SARS-CoV-2 in animals. Rhinolophus bats are presumed to be natural progenitors of SARS-CoV-2-related viruses. Initially, pangolin was thought to be the source of spillover to humans, but they might be infected by human or other animal species. So, the virus spillover pathways to humans remain unknown. Human-to-animal transmission has been testified in pet, farmed, zoo and free-ranging wild animals. Infected animals can transmit the virus to other animals in natural settings like mink-to-mink and mink-to-cat transmission. Animal-to-human transmission is not a persistent pathway, while mink-to-human transmission continues to be illuminated. Multiple companions and captive wild animals were infected by an emerging alpha variant of concern (B.1.1.7 lineage) whereas Asiatic lions were infected by delta variant, (B.1.617.2). To date, multiple animal species - cat, ferrets, non-human primates, hamsters and bats - showed high susceptibility to SARS-CoV-2 in the experimental condition, while swine, poultry, cattle showed no susceptibility. The founding of SARS-CoV-2 in wild animal reservoirs can confront the control of the virus in humans and might carry a risk to the welfare and conservation of wildlife as well. We suggest vaccinating pets and captive animals to stop spillovers and spillback events. We recommend sustainable One Health surveillance at the animal-human-environmental interface to detect and prevent future epidemics and pandemics by Disease X.

Spatial Heterogeneity and Its Influencing Factors of Syphilis in Ningxia, Northwest China, from 2004 to 2017: A Spatial Analysis

Syphilis remains a growing and resurging infectious disease in China. However, exploring the influence of environmental factors on the spatiotemporal distribution of syphilis remains under explore. This study aims to analyze the spatiotemporal distribution characteristics of syphilis in Ningxia, Northwest China, and its potential environmental influencing factors. Based on the standardized incidence ratio of syphilis for 22 administrative areas in Ningxia from 2004 to 2017, spatiotemporal autocorrelation and scan analyses were employed to analyze the spatial and temporal distribution characteristics of syphilis incidence, while a fixed-effect spatial panel regression model identified the potential factors affecting syphilis incidence. Syphilis incidence increased from 3.78/100,000 in 2004 to 54.69/100,000 in 2017 with significant spatial clustering in 2007 and 2009-2013. The "high-high" and "low-low" clusters were mainly distributed in northern and southern Ningxia, respectively. The spatial error panel model demonstrated that the syphilis incidence may be positively correlated with the per capita GDP and tertiary industry GDP and negatively correlated with the number of health facilities and healthcare personnel. Sex ratio and meteorological factors were not significantly associated with syphilis incidence. These results show that the syphilis incidence in Ningxia is still increasing and has significant spatial distribution differences and clustering. Socio-economic and health-resource factors could affect the incidence; therefore, strengthening syphilis surveillance of migrants in the economically developed region and allocating health resources to economically underdeveloped areas may effectively help prevent and control syphilis outbreaks in high-risk cluster areas of Ningxia.

Transmission Dynamics and Genomic Epidemiology of Emerging Variants of SARS-CoV-2 in Bangladesh

With the progression of the global SARS-CoV-2 pandemic, the new variants have become more infectious and continue spreading at a higher rate than pre-existing ones. Thus, we conducted a study to explore the epidemiology of emerging variants of SARS-CoV-2 that circulated in Bangladesh from December 2020 to September 2021, representing the 2nd and 3rd waves. We collected new cases and deaths per million daily data with the reproduction rate. We retrieved 928 SARS-CoV-2 sequences from GISAID and performed phylogenetic tree construction and mutation analysis. Case counts were lower initially at the end of 2020, during January-February and April-May 2021, whereas the death toll reached the highest value of 1.587 per million on the first week of August and then started to decline. All the variants (α, β, δ, η) were prevalent in the capital city, Dhaka, with dispersion to large cities, such as Sylhet and Chattogram. The B.1.1.25 lineage was prevalent during December 2020, but the B.1.617.2/δ variant was later followed by the B.1.351/β variant. The phylogeny revealed that the various strains found in Bangladesh could be from numerous countries. The intra-cluster and inter-cluster communication began in Bangladesh soon after the virus arrived. The prominent amino acid substitution was D614G from December 2020 to July 2021 (93.5 to 100%). From February-April, one of the VOC's important mutations, N501Y substitution, was also estimated at 51.8%, 76.1%, and 65.1% for the α, β and γ variants, respectively. The γ variant's unique mutation K417T was detected only at 1.8% in February. Another frequent mutation was P681R, a salient feature of the δ variant, detected in June (88.2%) and July (100%). Furthermore, only one γ variant was detected during the entire second and third wave, whereas no η variant was observed in this period. This rapid growth in the number of variants identified across Bangladesh shows virus adaptation and a lack of strict quarantine, prompting periodic genomic surveillance to foresee the spread of new variants, if any, and to take preventive measures as soon as possible.

Effectiveness of controlling COVID-19 epidemic by implementing soft lockdown policy and extensive community screening in Taiwan

Strict and repeated lockdowns have caused public fatigue regarding policy compliance and had a large impact on several countries’ economies. We aimed to evaluate the effectiveness of a soft lockdown policy and the strategy of active community screening for controlling COVID-19 in Taiwan. We used village-based daily confirmed COVID-19 statistics in Taipei City and New Taipei City, between May 2, 2021, and July 17, 2021. The temporal Gi* statistic was used to compute the spatiotemporal hotspots. Simple linear regression was used to evaluate the trend of the epidemic, positivity rate from community screening, and mobility changes in COVID-19 cases and incidence before and after a level three alert in both cities. We used a Bayesian hierarchical zero-inflated Poisson model to estimate the daily infection risk. The cities accounted for 11,403 (81.17%) of 14,048 locally confirmed cases. The mean effective reproduction number (Re) surged before the level three alert and peaked on May 16, 2021, the day after the level three alert in Taipei City (Re = 3.66) and New Taipei City (Re = 3.37). Mobility reduction and a lower positive rate were positively associated with a lower number of cases and incidence. In the spatiotemporal view, seven major districts were identified with a radial spreading pattern from one hard-hit district. Villages with a higher inflow degree centrality among people aged ≥ 60 years, having confirmed cases, specific land-use types, and with a higher aging index had higher infection risks than other villages. Early soft lockdown policy and detection of infected patients showed an effective strategy to control COVID-19 in Taiwan.

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.

Exploring the Temporal and Spatial Evolution Laws of County Green Land-Use Efficiency: Evidence from 11 Counties in Sichuan Province

With rapid urbanisation in China, sustainable urban development faces a major obstacle due to insufficient consideration of land-use efficiency. Currently, despite progress in analysing land-use efficiency, not every land manager has enough knowledge of green land use from a county perspective. Therefore, the objective of this research is to explore the spatiotemporal evolution law focused on county green land-use efficiency (CGLUE), which can support sustainable county development. Based on 10 specific CGLUE factors identified through a content-mining tool, this study explored the temporal and spatial evolution law of 11 counties in Sichuan Province using the ultra-efficient slacks-based measure (SBM), kernel density estimation, and Moran’s I statistic. The study found that (1) CGLUE factors cover the administrative area, total investment in fixed assets by region, the number of employed persons in secondary and tertiary industries, gross domestic product in secondary and tertiary industries, the average wage of staff and workers, basic statistics on per capita park green area, carbon emissions of land, the volume of industrial wastewater discharged, the volume of industrial sulphur dioxide emission, and the volume of industrial soot (dust) emission; (2) from a time-evolution perspective, CGLUE shows an increasing trend of time series evolution as a whole, and its dynamic evolution process has obvious differences in time. CGLUE increased, and the difference in CGLUE became larger from 2010 to 2012. CGLUE also increased, and the difference in CGLUE became smaller from 2013 to 2016. CGLUE also increased, and the difference in CGLUE became larger from 2017 to 2020; (3) from a spatial evolution perspective, the global spatial evolution laws of CGLUE show that the spatial agglomeration state has gone from strong to weak. Overall, however, Sichuan Province CGLUE maintains a high spatial agglomeration effect. The local spatial evolution laws show that the CGLUE of the 11 counties is positively correlated. The high–low CGLUE agglomeration areas are mainly distributed in Chengdu, Mianyang, Meishan and Yibin; the low–low CGLUE agglomeration areas are mainly distributed in Deyang, Yaan, and Zigong. The novelty of the research lies in these aspects: (1) the carbon emissions of land should be considered the undesired output of CGLUE; (2) CGLUE in Sichuan Province has various growing stages from a time perspective; (3) CGLUE in Sichuan Province has a high spatial concentration in Chengdu from spatial view, and these counties’ resources flow and interact at high speed. These findings offer a solid reference for the sustainable development of these 11 counties in Sichuan Province.

Prediction of COVID-19 Pandemic in Bangladesh: Dual Application of Susceptible-Infective-Recovered (SIR) and Machine Learning Approach

The outbreak of COVID-19 is a global problem today, and, to reduce infectious cases and increase recovered cases, it is relevant to estimate the future movement and pattern of the disease. To identify the hotspot for COVID-19 in Bangladesh, we performed a cluster analysis based on the hierarchical k-means approach. A well-known epidemiological model named "susceptible-infectious-recovered (SIR)" and an additive regression model named "Facebook PROPHET Procedure" were used to predict the future direction of COVID-19 using data from IEDCR. Here we compare the results of the optimized SIR model and a well-known machine learning algorithm (PROPHET algorithm) for the forecasting trend of the COVID-19 pandemic. The result of the cluster analysis demonstrates that Dhaka city is now a hotspot for the COVID-19 pandemic. The basic reproduction ratio value was 2.1, which indicates that the infection rate would be greater than the recovery rate. In terms of the SIR model, the result showed that the virus might be slightly under control only after August 2022. Furthermore, the PROPHET algorithm observed an altered result from SIR, implying that all confirmed, death, and recovered cases in Bangladesh are increasing on a daily basis. As a result, it appears that the PROPHET algorithm is appropriate for pandemic data with a growing trend. Based on the findings, the study recommended that the pandemic is not under control and ensured that if Bangladesh continues the current pattern of infectious rate, the spread of the pandemic in Bangladesh next year will increase.

Clustering of countries according to the COVID-19 incidence and mortality rates

Background

Two years after the beginning of the COVID-19 pandemic on December 29, 2021, there have been 281,808,270 confirmed cases of COVID-19, including 5,411,759 deaths. This information belongs to almost 216 Countries, areas, or territories facing COVID-19. The disease trend was not homogeneous across these locations, and studying this variation is a crucial source of information for policymakers and researchers. Therefore, we address different patterns in mortality and incidence of COVID-19 across countries using a clustering approach.

Methods

The daily records of new cases and deaths of 216 countries were available on the WHO online COVID-19 dashboard. We used a three-step approach for identifying longitudinal patterns of change in quantitative COVID-19 incidence and mortality rates. At the first, we calculated 27 summary measurements for each trajectory. Then we used factor analysis as a dimension reduction method to capture the correlation between measurements. Finally, we applied a K-means algorithm on the factor scores and clustered the trajectories.

Results

We determined three different patterns for the trajectories of COVID-19 incidence and the three different ones for mortality rates. According to incidence rates, among 206 countries the 133 (64.56) countries belong to the second cluster, and 15 (7.28%) and 58 (28.16%) belong to the first and 3rd clusters, respectively. All clusters seem to show an increased rate in the study period, but there are several different patterns. The first one exhibited a mild increasing trend; however, the 3rd and the second clusters followed the severe and moderate increasing trend. According to mortality clusters, the frequency of sets is 37 (18.22%) for the first cluster with moderate increases, 157 (77.34%) for the second one with a mild rise, and 9 (4.34%) for the 3rd one with severe increase.

Conclusions

We determined that besides all variations within the countries, the pattern of a contagious disease follows three different trajectories. This variation looks to be a function of the government’s health policies more than geographical distribution. Comparing this trajectory to others declares that death is highly related to the nature of epidemy.

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.

Deep recurrent reinforced learning model to compare the efficacy of targeted local versus national measures on the spread of COVID-19 in the UK

OBJECTIVES

To prevent the emergence of new waves of COVID-19 caseload and associated mortalities, it is imperative to understand better the efficacy of various control measures on the national and local development of this pandemic in space-time, characterise hotspot regions of high risk, quantify the impact of under-reported measures such as international travel and project the likely effect of control measures in the coming weeks.

METHODS

We applied a deep recurrent reinforced learning based model to evaluate and predict the spatiotemporal effect of a combination of control measures on COVID-19 cases and mortality at the local authority (LA) and national scale in England, using data from week 5 to 46 of 2020, including an expert curated control measure matrix, official statistics/government data and a secure web dashboard to vary magnitude of control measures.

RESULTS

Model predictions of the number of cases and mortality of COVID-19 in the upcoming 5 weeks closely matched the actual values (cases: root mean squared error (RMSE): 700.88, mean absolute error (MAE): 453.05, mean absolute percentage error (MAPE): 0.46, correlation coefficient 0.42; mortality: RMSE 14.91, MAE 10.05, MAPE 0.39, correlation coefficient 0.68). Local lockdown with social distancing (LD_SD) (overall rank 3) was found to be ineffective in preventing outbreak rebound following lockdown easing compared with national lockdown (overall rank 2), based on prediction using simulated control measures. The ranking of the effectiveness of adjunctive measures for LD_SD were found to be consistent across hotspot and non-hotspot regions. Adjunctive measures found to be most effective were international travel and quarantine restrictions.

CONCLUSIONS

This study highlights the importance of using adjunctive measures in addition to LD_SD following lockdown easing and suggests the potential importance of controlling international travel and applying travel quarantines. Further work is required to assess the effect of variant strains and vaccination measures.

Spatio-temporal estimation of basic and effective reproduction number of COVID-19 and post-lockdown transmissibility in Bangladesh

The ongoing COVID-19 pandemic has caused unprecedented public health concern in Bangladesh. This study investigated the role of Non-Pharmaceutical Interventions on COVID-19 transmission and post-lockdown scenarios of 64 administrative districts and the country as a whole based on the spatiotemporal variations of effective reproduction number (R t) of COVID-19 incidences. The daily confirmed COVID-19 data of Bangladesh and its administrative districts from March 8, 2020, to March 10, 2021, were used to estimate R t. This study finds that the maximum value of R t reached 4.15 (3.43, 4.97, 95% CI) in late March 2020, which remained above 1 afterwards in most of the districts. Containment measures are moderately effective in reducing transmission by 24.03%. The R t was established below 1 from early December 2020 for overall Bangladesh and a gradual increase of R t above 1 has been seen from early February 2021. The basic reproduction number (R 0) in Bangladesh probably varied around 2.02 (1.33–3.28, 95% CI). This study finds a significant positive correlation (r = 0.75) between population density and COVID-19 incidence and explaining 56% variation in Bangladesh. The findings of this study are expected to support the policymakers to adopt appropriate measures for curbing the COVID-19 transmission effectively.

Spatial epidemiology and genetic diversity of SARS-CoV-2 and related coronaviruses in domestic and wild animals

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) showed susceptibility to diverse animal species. We conducted this study to understand the spatial epidemiology, genetic diversity, and statistically significant genetic similarity along with per-gene recombination events of SARS-CoV-2 and related viruses (SC2r-CoVs) in animals globally. We collected a number of different animal species infected with SARS-CoV-2 and its related viruses. Then, we retrieved genome sequences of SARS-CoV-2 and SC2r-CoVs from GISAID and NCBI GenBank for genomic and mutational analysis. Although the evolutionary origin of SARS-CoV-2 remains elusive, the diverse SC2r-CoV have been detected in multiple Rhinolophus bat species and in Malayan pangolin. To date, human-to-animal spillover events have been reported in cat, dog, tiger, lion, gorilla, leopard, ferret, puma, cougar, otter, and mink in 25 countries. Phylogeny and genetic recombination events of SC2r-CoVs showed higher similarity to the bat coronavirus RaTG13 and BANAL-103 for most of the genes and to some Malayan pangolin coronavirus (CoV) strains for the N protein from bats and pangolin showed close resemblance to SARS-CoV-2. The clustering of animal and human strains from the same geographical area has proved human-to-animal transmission of the virus. The Alpha, Delta and Mu-variant of SARS-CoV-2 was detected in dog, gorilla, lion, tiger, otter, and cat in the USA, India, Czech Republic, Belgium, and France with momentous genetic similarity with human SARS-CoV-2 sequences. The mink variant mutation (spike_Y453F) was detected in both humans and domestic cats. Moreover, the dog was affected mostly by clade O (66.7%), whereas cat and American mink were affected by clade GR (31.6 and 49.7%, respectively). The α-variant was detected as 2.6% in cat, 4.8% in dog, 14.3% in tiger, 66.7% in gorilla, and 77.3% in lion. The highest mutations observed in mink where the substitution of D614G in spike (95.2%) and P323L in NSP12 (95.2%) protein. In dog, cat, gorilla, lion, and tiger, Y505H and Y453F were the common mutations followed by Y145del, Y144del, and V70I in S protein. We recommend vaccine provision for pet and zoo animals to reduce the chance of transmission in animals. Besides, continuous epidemiological and genomic surveillance of coronaviruses in animal host is crucial to find out the immediate ancestor of SARS-CoV-2 and to prevent future CoVs threats to humans.

Local Characteristics Related to SARS-CoV-2 Transmissions in the Seoul Metropolitan Area, South Korea

The Seoul metropolitan area is one of the most populated metropolitan areas in the world; hence, Seoul's COVID-19 cases are highly concentrated. This study identified local demographic and socio-economic characteristics that affected SARS-CoV-2 transmission to provide locally targeted intervention policies. For the effective control of outbreaks, locally targeted intervention policies are required since the SARS-CoV-2 transmission process is heterogeneous over space. To identify the local COVID-19 characteristics, this study applied the geographically weighted lasso (GWL). GWL provides local regression coefficients, which were used to account for the spatial heterogeneity of SARS-CoV-2 outbreaks. In particular, the GWL pinpoints statistically significant regions with specific local characteristics. The applied explanatory variables involving demographic and socio-economic characteristics that were associated with higher SARS-CoV-2 transmission in the Seoul metropolitan area were as follows: young adults (19~34 years), older population, Christian population, foreign-born population, low-income households, and subway commuters. The COVID-19 case data were classified into three periods: the first period (from January 2020 to July 2021), the second period (from August to November 2020), and the third period (from December 2020 to February 2021), and the GWL was fitted for the entire period (from January 2020 to February 2021). The result showed that young adults, the Christian population, and subway commuters were the most significant local characteristics that influenced SARS-CoV-2 transmissions in the Seoul metropolitan area.

Escalating SARS-CoV-2 circulation in environment and tracking waste management in South Asia

The novel coronavirus disease of 2019 (COVID-19) pandemic has caused an exceptional drift of production, utilization, and disposal of personal protective equipment (PPE) and different microplastic objects for safety against the virus. Hence, we reviewed related literature on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA detected from household, biomedical waste, and sewage to identify possible health risks and status of existing laws, regulations, and policies regarding waste disposal in South Asian (SA) countries. The SARS-CoV-2 RNA was detected in sewage and wastewater samples of Nepal, India, Pakistan, and Bangladesh. Besides, this review reiterates the enormous amounts of PPE and other single-use plastic wastes generated from healthcare facilities and households in the SA region with inappropriate disposal, landfilling, and/or incineration techniques wind-up polluting the environment. Consequently, the Delta variant (B.1.617.2) of SARS-CoV-2 has been detected in sewer treatment plant in India. Moreover, the overuse of non-biodegradable plastics during the pandemic is deteriorating plastic pollution condition and causes a substantial health risk to the terrestrial and aquatic ecosystems. We recommend making necessary adjustments, adopting measures and strategies, and enforcement of the existing biomedical waste management and sanitation-related policy in SA countries. We propose to adopt the knowledge gaps to improve COVID-19-associated waste management and legislation to prevent further environmental pollution. Besides, the citizens should follow proper disposal procedures of COVID-19 waste to control the environmental pollution.

Evolutionary Dynamics and Epidemiology of Endemic and Emerging Coronaviruses in Humans, Domestic Animals, and Wildlife

Diverse coronavirus (CoV) strains can infect both humans and animals and produce various diseases. CoVs have caused three epidemics and pandemics in the last two decades, and caused a severe impact on public health and the global economy. Therefore, it is of utmost importance to understand the emergence and evolution of endemic and emerging CoV diversity in humans and animals. For diverse bird species, the Infectious Bronchitis Virus is a significant one, whereas feline enteric and canine coronavirus, recombined to produce feline infectious peritonitis virus, infects wild cats. Bovine and canine CoVs have ancestral relationships, while porcine CoVs, especially SADS-CoV, can cross species barriers. Bats are considered as the natural host of diverse strains of alpha and beta coronaviruses. Though MERS-CoV is significant for both camels and humans, humans are nonetheless affected more severely. MERS-CoV cases have been reported mainly in the Arabic peninsula since 2012. To date, seven CoV strains have infected humans, all descended from animals. The severe acute respiratory syndrome coronaviruses (SARS-CoV and SARS-CoV-2) are presumed to be originated in Rhinolopoid bats that severely infect humans with spillover to multiple domestic and wild animals. Emerging alpha and delta variants of SARS-CoV-2 were detected in pets and wild animals. Still, the intermediate hosts and all susceptible animal species remain unknown. SARS-CoV-2 might not be the last CoV to cross the species barrier. Hence, we recommend developing a universal CoV vaccine for humans so that any future outbreak can be prevented effectively. Furthermore, a One Health approach coronavirus surveillance should be implemented at human-animal interfaces to detect novel coronaviruses before emerging to humans and to prevent future epidemics and pandemics.

Molecular Epidemiology of SARS-CoV-2 in Diverse Environmental Samples Globally

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has swamped the global environment greatly in the current pandemic. Wastewater-based epidemiology (WBE) effectively forecasts the surge of COVID-19 cases in humans in a particular region. To understand the genomic characteristics/footprints and diversity of SARS-CoV-2 in the environment, we analyzed 807 SARS-CoV-2 sequences from 20 countries deposited in GISAID till 22 May 2021. The highest number of sequences (n = 638) were reported in Austria, followed by the Netherlands, China, and Bangladesh. Wastewater samples were highest (40.0%) to successfully yield the virus genome followed by a 24 h composite wastewater sample (32.6%) and sewage (18.5%). Phylogenetic analysis revealed that SARS-CoV-2 environmental strains are a close congener with the strains mostly circulating in the human population from the same region. Clade GRY (32.7%), G (29.2%), GR (25.3%), O (7.2%), GH (3.4%), GV (1.4%), S (0.5%), and L (0.4%) were found in environmental samples. Various lineages were identified in environmental samples; nevertheless, the highest percentages (49.4%) of the alpha variant (B.1.1.7) were detected in Austria, Liechtenstein, Slovenia, Czech Republic, Switzerland, Germany, and Italy. Other prevalent lineages were B.1 (18.2%), B.1.1 (9.2%), and B.1.160 (3.9%). Furthermore, a significant number of amino acid substitutions were found in environmental strains where the D614G was found in 83.8% of the sequences. However, the key mutations-N501Y (44.6%), S982A (44.4%), A570D (43.3%), T716I (40.4%), and P681H (40.1%) were also recorded in spike protein. The identification of the environmental belvedere of SARS-CoV-2 and its genetic signature is crucial to detect outbreaks, forecast pandemic harshness, and prepare with the appropriate tools to control any impending pandemic. We recommend genomic environmental surveillance to trace the emerging variants and diversity of SARS-CoV-2 viruses circulating in the community. Additionally, proper disposal and treatment of wastewater, sewage, and medical wastes are important to prevent environmental contamination.