Influences of climatic and non-climatic factors on COVID-19 outbreak: A review of existing literature

Simultaneous COVID-19 Pandemic and Dengue Epidemic: A Double Challenge to Geriatric Health Security in Bangladesh

Bangladesh faces distinct challenges as a resource-poor country due to the combined effects of the COVID-19 pandemic and simultaneous dengue outbreaks. Older adults are particularly vulnerable to infection and death from COVID-19. While overall health and life expectancy in the general population have improved substantially in Bangladesh, health services for older adults are still lacking. No specialized geriatric units have been established in hospitals, and no home care programs have been established for older adults. COVID-19 mortality rates were highest among older adults ages 61 to 70 years (35%), and 71 to 80 years (20%) in 2022. Although the country's average COVID-19 mortality rate was low at 1.76%, in older adults, it was much higher (55%), accounting for 14,797 deaths, despite that most cases (55%) were recorded in young adults. During the COVID-19 pandemic, Bangladesh also experienced a dengue epidemic. Around 21,193 dengue patients were admitted to hospitals between January 1 and October 8, 2022. Without a well-established and all-encompassing social care program, the indirect socioeconomic burden of COVID-19 continues to fall on older adults. There is an immediate need for robust healthcare and support services, especially for older adults in Bangladesh, which are particularly susceptible to the dual threats posed by the COVID-19 pandemic and the dengue epidemic. Recommendations are made to protect older adults from the devastating effects of the 2 simultaneous epidemics.

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.

Exploring the spatial pattern of community urban green spaces and COVID-19 risk in Wuhan based on a random forest model

Since 2019, COVID-19 has triggered a renewed investigation of the urban environment and disease outbreak. While the results have been inconsistent, it has been observed that the quantity of urban green spaces (UGS) is correlated with the risk of COVID-19. However, the spatial pattern has largely been ignored, especially on the community scale. In high-density communities where it is difficult to increase UGS quantity, UGS spatial pattern could be a crucial predictive variable. Thus, this study investigated the relative contribution of quantity and spatial patterns of UGS on COVID-19 risk at the community scale using a random forest (RF) regression model based on (n = 44) communities in Wuhan. Findings suggested that 8 UGS indicators can explain 35% of the risk of COVID-19, and the four spatial pattern metrics that contributed most were core, edge, loop, and branch whereas UGS quantity contributed least. The potential mechanisms between UGS and COVID-19 are discussed, including the influence of UGS on residents' social distance and environmental factors in the community. This study offers a new perspective on optimizing UGS for public health and sustainable city design to combat pandemics and inspire future research on the specific relationship between UGS spatial patterns and pandemics and therefore help establish mechanisms of UGS and pandemics.

A Statistical Investigation into the COVID-19 Outbreak Spread

Objective

Coronavirus-19 (COVID-19) outbreaks have been reported in a range of climates worldwide, including Bangladesh. There is less evidence of a link between the COVID-19 pandemic and climatic variables. This research article's purpose is to examine the relationship between COVID-19 outbreaks and climatic factors in Dhaka, Bangladesh.

Methods

The daily time series COVID-19 data used in this study span from May 1, 2020, to April 14, 2021, for the study area, Dhaka, Bangladesh. The Climatic factors included in this study were average temperature, particulate matter ( P M 2 . 5 ), humidity, carbon emissions, and wind speed within the same timeframe and location. The strength and direction of the relationship between meteorological factors and COVID-19 positive cases are examined using the Spearman correlation. This study examines the asymmetric effect of climatic factors on the COVID-19 pandemic in Dhaka, Bangladesh, using the Nonlinear Autoregressive Distributed Lag (NARDL) model.

Results

COVID-19 widespread has a substantial positive association with wind speed (r = .781), temperature (r = .599), and carbon emissions (r = .309), whereas P M 2 . 5 (r = -.178) has a negative relationship at the 1% level of significance. Furthermore, with a 1% change in temperature, the incidence of COVID-19 increased by 1.23% in the short run and 1.53% in the long run, with the remaining variables remaining constant. Similarly, in the short-term, humidity was not significantly related to the COVID-19 pandemic. However, in the long term, it increased 1.13% because of a 1% change in humidity. The changes in PM2.5 level and wind speed are significantly associated with COVID-19 new cases after adjusting population density and the human development index.

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.

Vulnerabilities of older adults and mitigation measures to address COVID-19 outbreak in Bangladesh: A review

The infection with coronavirus disease (COVID-19) had an extremely negative influence on public health and the global economy. Covid-19 infection is more likely to affect the elderly than younger people, and pre-existing medical conditions, such as cardiovascular disease, diabetes, high blood pressure, and respiratory diseases, might lead to death due to COVID-19 infection. In low-income, developing, and highly dense countries like Bangladesh, the aging population is particularly vulnerable to the pandemic due to inadequate health services, socio-economic circumstances, environmental settings, religious and cultural beliefs, personal cleanliness habits, and a contemplative approach to infectious disease. Besides, recent cyclones and floods have combined effects on older people's increasing vulnerabilities. In this study, we reviewed and examined the vulnerabilities of older adults to the COVID-19 outbreak in Bangladesh. Different mitigation measures are discussed to protect the elderly from the adverse effect of the pandemic. This study proposes several steps to reinforce the commitment to social care and health care services to guarantee well-being, encourage preventive measures, and increase access to older people's health services in Bangladesh. The core findings will provide a valuable guideline for older adults, scientists, and policymakers to take effective long-term measures to mitigate the pandemic's risk.

The Relative Contribution of Climatic, Demographic Factors, Disease Control Measures and Spatiotemporal Heterogeneity to Variation of Global COVID-19 Transmission

Despite a substantial number of COVID-19 related research papers published, it remains unclear as to which factors are associated with the observed variation in global transmission and what are their relative levels of importance. This study applies a rigorous statistical framework to provide robust estimations of the factor effects for a global and integrated perspective on this issue. We developed a mixed effect model exploring the relative importance of potential factors driving COVID-19 transmission while incorporating spatial and temporal heterogeneity of spread. We use an integrated data set for 87 countries across six continents for model specification and fitting. The best model accounts for 70.4% of the variance in the data analyzed: 10 fixed effect factors explain 20.5% of the variance, random temporal and spatial effects account for 50% of the variance. The fixed effect factors are classified into climatic, demographic and disease control groups. The explained variance in global transmission by the three groups are 0.6%, 1.1%, and 4.4% respectively. The high proportion of variance accounted for by random effects indicated striking differences in temporal transmission trajectories and effects of population mobility among the countries. In particular, the country-specific mobility-transmission relationship turns out to be the most important factor in explaining the observed global variation of transmission in the early phase of COVID-19 pandemic.

Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore

Geographical weighted regression (GWR) can be used to explore the COVID-19 transmission pattern between cases. This study aimed to explore the influence from environmental and urbanisation factors, and the spatial relationship between epidemiologically-linked, unlinked and imported cases during the early phase of the epidemic in Singapore. Spatial relationships were evaluated with GWR modelling. Community COVID-19 cases with residential location reported from 21st January 2020 till 17th March 2020 were considered for analyses. Temperature, relative humidity, population density and urbanisation are the variables used as exploratory variables for analysis. ArcGIS was used to process the data and perform geospatial analyses. During the early phase of COVID-19 epidemic in Singapore, significant but weak correlation of temperature with COVID-19 incidence (significance 0.5-1.5) was observed in several sub-zones of Singapore. Correlations between humidity and incidence could not be established. Across sub-zones, high residential population density and high levels of urbanisation were associated with COVID-19 incidence. The incidence of COVID-19 case types (linked, unlinked and imported) within sub-zones varied differently, especially those in the western and north-eastern regions of Singapore. Areas with both high residential population density and high levels of urbanisation are potential risk factors for COVID-19 transmission. These findings provide further insights for directing appropriate resources to enhance infection prevention and control strategies to contain COVID-19 transmission.