Identification of risk factors contributing to COVID-19 incidence rates in Bangladesh: A GIS-based spatial modeling approach

Exploring the Spatial and Temporal Patterns of Children and Adolescents with COVID-19 Infections in Slovakia during March 2020 to July 2022

Background and Objectives: The COVID-19 pandemic has had a significant global impact, necessitating a comprehensive understanding of its spatiotemporal patterns. The objective of this study is to explore the spatial and temporal patterns of COVID-19 infections among five age groups (<1, 1-4, 5-9, 10-14, and 15-19 years) in 72 districts of Slovakia on a quarterly basis from March 2020 to July 2022. Material and Methods: During the study period, a total of 393,429 confirmed PCR cases of COVID-19 or positive antigen tests were recorded across all studied age groups. The analysis examined the spatiotemporal spread of COVID infections per quarter, from September 2021 to May 2022. Additionally, data on hospitalizations, intensive care unit (ICU) admissions, pulmonary ventilation (PV), and death cases were analyzed. Results: The highest number of COVID-19 infections occurred between September 2021 and May 2022, particularly in the 10-14-year-old group (68,695 cases), followed by the 15-19-year-old group (62,232 cases), while the lowest incidence was observed in the <1-year-old group (1235 cases). Out of the total confirmed PCR cases, 18,886 individuals required hospitalization, 456 needed ICU admission, 402 received pulmonary ventilation, and only 16 died. The analysis of total daily confirmed PCR cases for all regions showed two major peaks on 12 December 2021 (6114 cases) and 1 February 2022 (3889 cases). Spatial mapping revealed that during December 2021 to February 2022, the highest number of infections in all age groups were concentrated mainly in Bratislava. Moreover, temporal trends of infections within each age group, considering monthly and yearly variations, exhibited distinct spatial patterns, indicating localized outbreaks in specific regions. Conclusions: The spatial and temporal patterns of COVID-19 infections among different age groups in Slovakia showed a higher number of infections in the 10-14-year-old age group, mainly occurring in urban districts. The temporal pattern of the spread of the virus to neighboring urban and rural districts reflected the movement of infected individuals. Hospitalizations, ICU and PV admissions, and deaths were relatively low. The study highlights the need for more proactive measures to contain outbreaks promptly and ensure the resilience of healthcare systems against future pandemics.

Spatiotemporal hierarchical Bayesian analysis to identify factors associated with COVID-19 in suburban areas in Colombia

Introduction

The pandemic had a profound impact on the provision of health services in Cúcuta, Colombia where the neighbourhood-level risk of Covid-19 has not been investigated. Identifying the sociodemographic and environmental risk factors of Covid-19 in large cities is key to better estimate its morbidity risk and support health strategies targeting specific suburban areas. This study aims to identify the risk factors associated with the risk of Covid-19 in Cúcuta considering inter -spatial and temporal variations of the disease in the city's neighbourhoods between 2020 and 2022.

Methods

Age-adjusted rate of Covid-19 were calculated in each Cúcuta neighbourhood and each quarter between 2020 and 2022. A hierarchical spatial Bayesian model was used to estimate the risk of Covid-19 adjusting for socioenvironmental factors per neighbourhood across the study period. Two spatiotemporal specifications were compared (a nonparametric temporal trend; with and without space-time interaction). The posterior mean of the spatial and spatiotemporal effects was used to map the Covid-19 risk.

Results

There were 65,949 Covid-19 cases in the study period with a varying standardized Covid-19 rate that peaked in October-December 2020 and April-June 2021. Both models identified an association of the poverty and stringency indexes, education level and PM10 with Covid-19 although the best fit model with a space-time interaction estimated a strong association with the number of high-traffic roads only. The highest risk of Covid-19 was found in neighbourhoods in west, central, and east Cúcuta.

Conclusions

The number of high-traffic roads is the most important risk factor of Covid-19 infection in Cucuta. This indicator of mobility and connectivity overrules other socioenvironmental factors when Bayesian models include a space-time interaction. Bayesian spatial models are important tools to identify significant determinants of Covid-19 and identifying at-risk neighbourhoods in large cities. Further research is needed to establish causal links between these factors and Covid-19.

Tokyo's COVID-19: An urban perspective on factors influencing infection rates in a global city

This research investigates the relationship between COVID-19 and urban factors in Tokyo. To understand the spread dynamics of COVID-19, the study examined 53 urban variables (including population density, socio-economic status, housing conditions, transportation, and land use) in 53 municipalities of Tokyo prefecture. Using spatial models, the study analysed the patterns and predictors of COVID-19 infection rates. The findings revealed that COVID-19 cases were concentrated in central Tokyo, with clustering levels decreasing after the outbreaks. COVID-19 infection rates were higher in areas with a greater density of retail stores, restaurants, health facilities, workers in those sectors, public transit use, and telecommuting. However, household crowding was negatively associated. The study also found that telecommuting rate and housing crowding were the strongest predictors of COVID-19 infection rates in Tokyo, according to the regression model with time-fixed effects, which had the best validation and stability. This study's results could be useful for researchers and policymakers, particularly because Japan and Tokyo have unique circumstances, as there was no mandatory lockdown during the pandemic.

Geographical Distribution and Social Justice of the COVID-19 Pandemic: The Case of Palma (Balearic Islands)

The spatial distribution of the COVID-19 infection rate in the city of Palma (Balearic Islands) is analyzed from the geolocation of positive cases by census tract and its relationship with socioeconomic variables is evaluated. Data on infections have been provided by the Health Service of the Ministry of Health and Consumption of the Government of the Balearic Islands. The study combines several methods of analysis: spatial autocorrelation, calculation of the Gini index and least squares regression, and weighted geographical regression. The results show that the pandemic comprised five waves in the March 2020-March 2022 period, corresponding to the months of April 2020, August 2020, December 2020, July 2021, and January 2022. Each wave shows a particular geographical distribution pattern, however, the second and third waves show higher levels of spatial concentration. In this sense, the second wave, affecting the peripheral neighborhoods of the eastern part of the city. The Gini index confirms geographical imbalances in the distribution of infections in the first waves of the pandemic. In addition, the regression models indicate that the most significant socioeconomic variables in the prediction of COVID-19 infection are average income, percentage of children under 18 years of age, average size of the household, and percentage of single-person households. The study shows that economic imbalances in the city have had a clear influence on the spatial pattern of pandemic distribution. It shows the need to implement spatial justice policies in income distribution to balance the effects of the pandemic.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

Soon after its emergence, COVID-19 became a global problem. While different types of vaccines and treatments are now available, still non-pharmacological policies play a critical role in managing the pandemic. The literature is enriched enough to provide comprehensive, practical, and scientific insights to better deal with the pandemic. This research aims to find out how the built environment and human factors have affected the transmission of COVID-19 on different scales, including country, state, county, city, and urban district. This is done through a systematic literature review of papers indexed on the Web of Science and Scopus. Initially, these databases returned 4264 papers, and after different stages of screening, we found 166 relevant papers and reviewed them. The empirical papers that had at least one case study and analyzed the effects of at least one built environment factor on the spread of COVID-19 were selected. Results showed that the driving forces can be divided into seven main categories: density, land use, transportation and mobility, housing conditions, demographic factors, socio-economic factors, and health-related factors. We found that among other things, overcrowding, public transport use, proximity to public spaces, the share of health and services workers, levels of poverty, and the share of minorities and vulnerable populations are major predictors of the spread of the pandemic. As the most studied factor, density was associated with mixed results on different scales, but about 58 % of the papers reported that it is linked with a higher number of cases. This study provides insights for policymakers and academics to better understand the dynamic roles of the non-pharmacological driving forces of COVID-19 at different levels.

What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review

This article aims to provide a better understanding of the associations between groups of socioeconomic variables and confirmed cases of COVID-19. The focus is on cross-continental differences of reported positive, negative, unclear, or no associations. A systematic review of the literature is conducted on the Web of Science and SCOPUS databases. Our search identifies 314 eligible studies published on or before 31 December 2021. We detect nine groups of frequently used socioeconomic variables and results are presented by region of the world (Africa, Asia, Europe, Middle East, North American and South America). The review expands to describe the most used statistical and modelling techniques as well as inclusion of additional dimensions such as demographic, healthcare weather and mobility. Meanwhile findings agree on the generalized positive impact of population density, per capita GDP and urban areas on transmission of infections, contradictory results have been found concerning to educational level and income.

Spatial Associations of Long-term Exposure to Diesel Particulate Matter with Seasonal and Annual Mortality Due to COVID-19 in the Contiguous United States

Background People with certain underlying respiratory and cardiovascular conditions might be at an increased risk for severe illness from COVID-19. Diesel Particulate Matter (DPM) exposure may affect the pulmonary and cardiovascular systems. The study aims to assess if DPM was spatially associated with COVID-19 mortality across three waves of the disease and throughout 2020. Methods We tested an ordinary least square (OLS) model, then two global models, spatial lag model (SLM) and spatial error model (SEM), designed to explore spatial dependence, and a geographically weighted regression (GWR) model designed to explore local associations. Results The GWR model found that associations between COVID-19 deaths and DPM concentrations may increase up to 57, 36, 43, and 58 deaths per 100,000 people in some US counties for every 1 µg/m ³ increase in DPM concentration. Relative significant positive association are observed in New York, New Jersey, eastern Pennsylvania, and western Connecticut for the wave from January to May, and in southern Florida and southern Texas for June to September. The period from October to December exhibit a negative association in most parts of the US, which seems to have influenced the year-long relationship due to the large number of deaths during that wave of the disease. Conclusions Our models provided a picture in which long-term DPM exposure may have influenced COVID-19 mortality during the early stages of the disease, but that influence appears to have waned over time as transmission patterns evolved.

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 clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland

This study aims to elucidate the variations in spatiotemporal patterns and sociodemographic determinants of SARS-CoV-2 infections in Helsinki, Finland. Global and local spatial autocorrelation were inspected with Moran's I and LISA statistics, and Getis-Ord Gi* statistics was used to identify the hot spot areas. Space-time statistics were used to detect clusters of high relative risk and regression models were implemented to explain sociodemographic determinants for the clusters. The findings revealed the presence of spatial autocorrelation and clustering of COVID-19 cases. High-high clusters and high relative risk areas emerged primarily in Helsinki's eastern neighborhoods, which are socioeconomically vulnerable, with a few exceptions revealing local outbreaks in other areas. The variation in COVID-19 rates was largely explained by median income and the number of foreign citizens in the population. Furthermore, the use of multiple spatiotemporal analysis methods are recommended to gain deeper insights into the complex spatiotemporal clustering patterns and sociodemographic determinants of the COVID-19 cases.

Spatio-temporal cluster analysis and transmission drivers for Peste des Petits Ruminants in Uganda

Peste des Petits Ruminants (PPR) is a transboundary, highly contagious, and fatal disease of small ruminants. PPR causes global annual economic losses of between USD 1.5-2.0 billion across more than 70 affected countries. Despite the commercial availability of effective PPR vaccines, lack of financial and technical commitment to PPR control coupled with a dearth of refined PPR risk profiling data in different endemic countries has perpetuated PPR virus transmission. In Uganda, over the past five years, PPR has extended from north-eastern Uganda (Karamoja) with sporadic incursions in other districts /regions. To identify disease cluster hotspot trends that would facilitate the design and implementation of PPR risk-based control methods (including vaccination), we employed the space-time cube approach to identify trends in the clustering of outbreaks in neighbouring space-time cells using confirmed PPR outbreak report data (2007-2020). We also used negative binomial and logistic regression models and identified high small ruminant density, extended road length, low annual precipitation and high soil water index as the most important drivers of PPR in Uganda. The study identified (with 90 - 99% confidence) five PPR disease hotspot trend categories across subregions of Uganda. Diminishing hotspots were identified in the Karamoja region whereas consecutive, sporadic, new, and emerging hotspots were identified in central and southwestern districts of Uganda. Inter-district and cross-border small ruminant movement facilitated by longer road stretches and animal comingling precipitate PPR outbreaks as well as PPR virus spread from its initial Karamoja focus to the central and south-western Uganda. There is therefore urgent need to prioritize considerable vaccination coverage to obtain the required herd immunity among small ruminants in the new hotspot areas to block transmission to further emerging hotspots. Findings of this study provide a basis for more robust timing and prioritization of control measures including vaccination. This article is protected by copyright. All rights reserved.

Service contracting as a policy response for public transport recovery during the Covid-19 Pandemic: A preliminary evaluation

We examine and assess the service contracting (SC) program implemented for the first time in Metro Manila, Philippines as a response to the impact of the pandemic on road-based public transport sector. We develop an evaluation framework, consisting of three indicators: social amelioration, increase in transport supply and performance improvement. These indicators are the purported objectives of SC. Using a mix of qualitative and quantitative methods, our evaluation suggests that although SC has brought positive impact in terms of the first two indicators, there is no robust evidence so far that may suggest that SC has improved the performance of public transport service delivery. We also find that while the primary objective of providing social amelioration to affected operators is appropriate during the time of the pandemic, this has also brought challenges in financially sustaining the program and in effecting improvements to public transport services. Our work aims to contribute as an empirical case study on the upsides and downsides of service contracting implemented as a business model for public transport provision during the pandemic.

COVID-19 Variability Within European Countries Sourced From ECDC Data. Is Variability Explained by Specific Country Policies?

Background

Europe has had a large variability in COVID-19 incidence between and within countries, particularly after June 2020. We aim to assess the variability between European countries and regions located in a given country.

Methods

We used ECDC information including countries having 7 regions or more. The metric used to assess the regional variability within a country was the intercuartilic range in a weekly basis for 32 weeks between June 29th 2020 and February 1st 2021. We also calculated each country's overall variability across the 32 weeks using the distances from the regional curves of the 14-day incidence rates to the corresponding national curve, using the L2 metric for functional data. We afterwards standardised this metric to a scale from 0 to 100 points. We repeated the calculations excluding island regions.

Results

The variability between and within countries was large. Slovenia, Spain and Portugal have the greatest variability. Spain and Slovenia held also the top three places for the greatest number of weeks (Spain for 19 weeks and Slovenia for 10) with the highest variability. For variability among the incidence curves across the 32-week period, Slovenia, Portugal and Spain ranked first in functional variability, when all the regions were analysed but also when the island regions were excluded.

Conclusions

These differences might be due to how countries tackled the epidemiological situation. The persistent variability in COVID-19 incidence between regions of a given country suggests that governmental action may have an important role in applying epidemiological control measures.

COVID-19, the firestone in 21st century: a review on coronavirus disease and its clinical perspectives

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak began in late 2019 in Wuhan, China, and have since spread globally. Deep sequencing analysis identified the disease within a few weeks, and on February 11, the World Health Organization (WHO) named it "COVID-19 caused by SARS-CoV-2." SARS-CoV-2 was declared a global pandemic by the WHO in March 2020. Coronavirus disease has become a global challenge for researchers and health care workers, affecting over 174 million people and causing over 3 million deaths. Because of the widespread nature, extensive measures are being taken to reduce person-to-person contact, and special precautions are being taken to prevent the transmission of this infection to vulnerable populations such as geriatrics, pediatrics, and health care professionals. We summarized the genesis of COVID-19 spread, its pathology, clinical perspectives, and the use of natural ingredients as a possible cure for COVID-19 in this review. This article has highlighted information about current vaccines approved for emergency use as well as those in various stages of clinical trials. Vaccine availability around the world is a promising development in the fight against the SARS-CoV-2 virus. We conducted a narrative review to present the current state and research on this situation, specific diagnosis, clinical manifestation, emergency approaches, herbal-based remedies, and COVID vaccines.

Assessment of COVID-19 induced travel pattern changes in Dhaka City

In response to Coronavirus 2019 (COVID-19) pandemic, Bangladesh enforced social distancing measures to mitigate the virus transmission rate through lockdowns. However, it is challenging for people to follow through and stay home in developing nations where socio-economic conditions are divergent from developed countries. This research aims to investigate COVID-19 induced travel pattern changes of residents and significant demographic factors affecting the trip generation in Dhaka City, the most densely populated Bangladeshi city. A questionnaire survey was used to extract information on demographic characteristics of respondents in Dhaka City and their travel patterns in the pre-pandemic era and during the pandemic. Analyses reveal striking differences in work trips except for workers and craftsmen. The use of telemedicine facilities is noticeable. Preference for public transport has decreased yet a decent percentage (9%) of people use buses during the pandemic. However, non-motorized modes are also very popular (19.93%) in the pandemic. The findings offer major implications for transportation planners and policymakers on how to dynamically plan for such crisis by combining a range of strategies so that safe and sustainable urban mobility and reduction of unnecessary travel demand can be ensured.

Spatial pattern of COVID-19 in Bangladesh: an ecological study

Objective

To analyse the spatial clustering of COVID-19 case fatality risks in the districts of Bangladesh and to explore the association of sociodemographic indicators with these risks.

Study design

Ecological study.

Study setting

Secondary data were collected for a total of 64 districts of Bangladesh.

Methods

The data for district-wise COVID-19 cases were collected from the Ministry of Health and Family Welfare, Bangladesh from March 2020 to June 2020. Socioeconomic and demographic data were collected from National Census Data, 2011. Retrospective spatial analysis was conducted based on district-wise COVID-19 cases in Bangladesh. Global Moran’s I was adopted to find out the significance of the clusters. Furthermore, generalised linear model was conducted to find out the association of COVID-19 cases with sociodemographic variables.

Results

Total 87 054 COVID-19 cases were included in this study. The epidemic hotspots were distributed in the 11 most populous cities. The most likely clusters are primarily situated in the central, south-eastern and north-western regions of the country. High-risk clusters were found in Dhaka (Relative Risk (RR): 5.22), Narayanganj (RR: 2.70), Chittagong (RR: 1.69), Munshiganj (RR: 2.31) Cox’s Bazar (RR: 1.63), Faridpur (RR: 1.65), Gazipur (RR: 1.33), Bogra (RR: 1.35), Khulna (RR: 1.22), Barishal (RR: 1.07) and Noakhali (RR: 1.06). Weekly progression of COVID-19 cases showed spatially clustered by Moran’s I statistics (p value ranging from 0.013 to 0.436). After fitting a Poisson linear model, we found a positive association of COVID-19 with floating population rate (RR=1.542, 95% CI 1.520 to 1.564), and urban population rate (RR=1.027, 95% CI 1.026 to 1.028).

Conclusion

This study found the high-risk cluster areas in Bangladesh and analysed the basic epidemiological issues; further study is needed to find out the common risk behaviour of the patients and other relative issues that involve the spreading of this infectious disease.

Spatial association of mobility and COVID-19 infection rate in the USA: A county-level study using mobile phone location data

INTRODUCTION

Human mobility has been a central issue in the discussion from the beginning of COVID-19. While the body of literature on the relationship of COVID transmission and mobility is large, studies mostly captured a relatively short timeframe. Moreover, spatial non-stationarity has garnered less attention in these explorative models. Therefore, the major concern of this study is to see the relationship of mobility and COVID on a broader temporal scale and after mitigating this methodological gap.

OBJECTIVE

In response to this concern, this study first explores the spatiotemporal pattern of mobility indicators. Secondly, it attempts to understand how mobility is related to COVID infection rate and how this relationship has been changed over time and space after controlling several sociodemographic characteristics, spatial heterogeneity, and policy-related changes during different phases of Coronavirus.

DATA AND METHOD

This study uses GPS-based mobility data for a wider time frame of six months (March 20-August'20) divided into four tiers and carries analysis for all the US counties (N = 3142). Space-time cube is used to generate the spatiotemporal pattern. For the second objective, Ordinary Least Square (OLS), Spatial Error Model (SEM), and Geographically Weighted Regression (GWR) were used.

RESULT

The spatial-temporal pattern suggests that the trip rate, out-of-county trip rate, and miles/person traveled were mostly plummeted till the first wave reached its peak, and subsequently, all of these mobility matrices started to rise. From spatial models, infection rates were found negatively correlated with miles traveled and out-of-county trips. Highly COVID infected areas mostly had more people working from home, low percentages of aged people and educated people, and high percentages of poor people.

CONCLUSION

This study, with necessary policy implications, provides a comprehensive understanding of the shifting pattern of mobility and COVID. Spatial models outperform OLS with better fits and non-clustered residuals.

Understanding the nexus between public risk perception of COVID-19 and evacuation behavior during cyclone Amphan in Bangladesh

In May 2020, when Bangladesh was struggling with community transmission of COVID-19, the country had to face the strongest tropical storm- Cyclone Amphan -which puts the evacuation process in jeopardy. Thus, it is crucial to measure the public risk perception about COVID-19 and its influence on the evacuation decision. This study explores the nexus between COVID-19 risk perception and coastal peoples' evacuation decisions during cyclone Amphan. With an analysis of 378 sample households survey data of the Satkhira district, this study developed the COVID-19 risk perception index using Principal Component Analysis (PCA) and categorized the respondents based on the score. The result shows that 1.85 %, 21.43 %, 45.77 %, 25.13 %, and 5.82 % have very low, low, moderate, high, and very high-risk perceptions, respectively. The analysis also reveals that 96.6 % of the respondents received an evacuation order during Amphan, but only 42 % complied with the order. The t-test analysis and common language effect size test of the survey data reveal that the respondents with a high perception score are 65 % less likely to evacuate than the respondents with low perception scores. This study has important implications in guiding concerned authorities to combat natural disasters during COVID-19 and other similar public health emergencies in the future.