Impact of population density on Covid-19 infected and mortality rate in India

Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: A Bayesian spatiotemporal modelling study

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximation (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the [Formula: see text] of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

Optimizing the Use of SARS-CoV-2 Antigen Rapid Diagnostic Tests for the Timely Detection of and Response to COVID-19 in Schools and Markets in Uganda

The early detection and management of infections is crucial to control epidemics. We evaluated the feasibility and utility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen rapid diagnostic tests (Ag-RDTs) for the timely detection of and response to coronavirus disease 2019 in high-risk border communities in Uganda. Between May and September 2022, monthly cross-sectional surveys were conducted in 11 schools and two markets in two border districts. Only baseline and end-line testing were also performed in matched control communities. Antigen rapid diagnostic test results and demographic and clinical data were collected, and contacts of patients were traced and tested. All patients were advised to self-isolate, and compliance was assessed on day 5. We enrolled 10,406 participants out of 10,472 screened individuals. The participants had a 1.3% test positivity rate, with schools recording higher, but non-significant, positivity rates than markets (1.4% versus 0.9%; P = 0.149). We tracked 556 contacts, and 536 (96.4%) agreed to test. The test positivity rate was significantly higher among contacts than the index participants (8.8% versus 1.3%; P <0.001). Only 55 (29.7%) of the index participants self-isolated effectively. Settings that received monthly testing had lower end-line positivity rates than controls (0.3% versus 1.4%; P = 0.001). Repeated SARS-CoV-2 Ag-RDT testing is feasible and could reduce SARS-CoV-2 infections. However, the participation in testing may have been enhanced by the compensation provided. Also, isolation was limited, which may reduce the impact of the intervention when rolled out on a large scale. Innovative strategies to increase the isolation of patients could improve the utility of early testing for transmission reduction during epidemics.

Potential physical distance in the open urban grey space of city counties in Poland and COVID-19 cases and deaths throughout the pandemic

BACKGROUND

This paper reports the structure of grey space and the number of cases and deaths throughout the COVID-19 pandemic (from March 2020 to June 2023) in 66 city counties in Poland.

METHODS

Three main components of urban grey space (built-up areas, transport areas, and industrial areas) and the potential physical distance between residents in the open grey space - was determined. The total number of COVID-19 cases and deaths covered the entire period of the pandemic (totalling 1,214 days) was identified. The incidence and mortality density rates and the case fatality ratio were calculated. Simple and multiple linear regression models were developed to predict the quantitative characteristics of COVID-19 independent of city size.

RESULTS

Within the open spaces of cities, the average distance between residents was 17.7 m and was several times greater than that in closed spaces (1.5-2.0 m), which significantly reduced the risk of COVID-19 infection. Strong relationships were observed between the potential physical distance in the grey space structure and the total number of COVID-19 cases and deaths. The coefficient of determination (R2) for these relationships in the eight city groups by population was 0.90 for cases and 0.88 for deaths (significance level p = 0.001).

CONCLUSION

The study contributes to understanding how potential physical distance based on population density in grey space, might have influenced the course of COVID-19 during the pandemic. These findings can be applied to planning antiviral protection and to implementing future multilevel restrictions aimed at reducing the reproduction of SARS-CoV-2 in cities of various sizes.

Geospatial modelling of COVID19 mortality in Oman using geographically weighted Poisson regression GWPR

The year 2020 witnessed the arrival of the global COVID-19 pandemic, which became the most devastating public health disaster in the last decade. Understanding the underlying spatial variations of the consequences of the pandemic, particularly mortality, is crucial for plans and policies. Nevertheless, few studies have been conducted on the key determinants of COVID-19 mortality and how these might vary geographically across developing nations. Therefore, this research aims to address these gaps by adopting the Geographically Weighted Poisson Regression (GWPR) model to investigate spatial heterogeneity of COVID-19 mortality in Oman. The findings indicated that local GWPR performed better than global Ordinary Least Square (OLS) model, and the relationship between risk factors and mortality cases varied geographically at a subnational scale. The local parameter estimates of the model revealed that elderly populations, respiratory diseases, and population density were significant in predicting mortality cases. The elderly population variable was the most influential regressor, followed by respiratory diseases. The formulated policy recommendations will provide decision-makers and practitioners with key factors related to pandemic mortality so that future interventions and preventive measures can mitigate high fatality risks.

Deciphering the COVID-19 density puzzle: A meta-analysis approach

The COVID-19 pandemic has sparked widespread efforts to mitigate its transmission, raising questions about the role of urban density in the spread of the virus. Understanding how city density affects the severity of communicable diseases like COVID-19 is crucial for designing sustainable, pandemic-resilient cities. However, recent studies on this issue have yielded inconsistent and conflicting results. This study addresses this gap by employing a comprehensive meta-analytic approach, synthesizing data across diverse regions and urban contexts to offer a broader, more nuanced perspective on the impact of city density. A systematic meta-analysis was conducted, initially screening 2,452 studies from Google Scholar, Scopus, and Avery Index databases (up to August 31, 2023), and narrowing down to 63 eligible studies. Using the restricted maximum likelihood (REML) method with a random effects model, the study accounted for variations across different studies. Statistical tests, file drawer analysis, and influence measure analysis were performed, along with assessments of heterogeneity and publication bias through forest and funnel plots. Despite this extensive analysis, the findings indicate that city density has a negligible effect on the severity of COVID-19, challenging the prevailing assumptions in the literature.

Urban density, household overcrowding and the spread of COVID-19 in Australian cities

The UN-Habitat World Cities Report 2020 highlighted that overcrowded housing, not urban density, is the major contributing factor to the spread of COVID-19. The relatively successful ability of densely populated cities such as Seoul, Singapore, Tokyo and New York City to manage virus spread supports this. We hypothesise that, given the complexity of the interaction between people and place, the relative contribution of density and crowding to the spread of infectious diseases may be contingent on local factors. To directly compare the role of urban density and household overcrowding, we examine each in relation to COVID-19 incidence in the three largest cities in Australia, Sydney, Melbourne and Brisbane, as the pandemic unfolded from July 2021 to January 2022. Using ecological models adjusted for spatial autocorrelation and area-level measures of age and socio-economic factors, we assess the association between population density, overcrowding in homes, and COVID-19 infections in local neighbourhoods. Challenging prevailing assumptions, we find evidence for an effect of both density and overcrowding on COVID-19 infections depending on the city and area within cities; that is, depending on the local context. For example, in the southwestern suburbs of Sydney, the case rate decreases by between 0.4 and 6.4 with every one-unit increase in gross density however the case rate increases by between 0.01 and 9.6 with every one-unit increase in total overcrowding. These findings have important implications for developing pandemic response strategies: public health measures that target either density (e.g., lockdowns and restricted range of travel) or overcrowding (e.g., restricting number of people relative to dwelling, mask-wearing indoors, vaccination prioritisation) must be cognisant of the geographically local contexts in which they are implemented.

Spatial, temporal, and demographic nonstationary dynamics of COVID-19 exposure among older adults in the U.S

This study examines demographic disparities in COVID-19 exposures across older adults age 60-79 and older adults age 80 and over, and explores the factors driving these dynamics in the United States (U.S.) from January 2020 to July 2022. Spatial clusters were identified, and 14 main health determinants were synthesized from 62 pre-existing county-level variables. The study also assessed the correlation between these health determinants and COVID-19 incidence rates for both age groups during the pandemic years. Further examination of incidence rates in relation to health determinants was carried out through statistical and spatial regression models. Results show that individuals aged 80 and over had much higher hospitalization rates, death rates, and case-fatality rates in 2020-2022. Spatial results indicate that the geographical cluster of high incidence rates for both groups shifted from the Midwest at the beginning of the pandemic to the Southwest in 2022. The study revealed marked spatial, temporal, and demographic nonstationary dynamics in COVID-19 exposures, indicating that the health effects of contextual factors vary across age groups. COVID-19 incidence rates in older adults were strongly influenced by race, healthcare access, social capital, environment, household composition, and mobility. Future public health policies and mitigations should further their efforts by considering temporal and demographic nonstationarity as well as local conditions.

Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: a Bayesian spatiotemporal modelling study

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

Assessing eco-geographic influences on COVID-19 transmission: a global analysis

COVID-19 has been massively transmitted for almost 3 years, and its multiple variants have caused serious health problems and an economic crisis. Our goal was to identify the influencing factors that reduce the threshold of disease transmission and to analyze the epidemiological patterns of COVID-19. This study served as an early assessment of the epidemiological characteristics of COVID-19 using the MaxEnt species distribution algorithm using the maximum entropy model. The transmission of COVID-19 was evaluated based on human factors and environmental variables, including climate, terrain and vegetation, along with COVID-19 daily confirmed case location data. The results of the SDM model indicate that population density was the major factor influencing the spread of COVID-19. Altitude, land cover and climatic factor showed low impact. We identified a set of practical, high-resolution, multi-factor-based maximum entropy ecological niche risk prediction systems to assess the transmission risk of the COVID-19 epidemic globally. This study provided a comprehensive analysis of various factors influencing the transmission of COVID-19, incorporating both human and environmental variables. These findings emphasize the role of different types of influencing variables in disease transmission, which could have implications for global health regulations and preparedness strategies for future outbreaks.

Association of socio-demographic factors with clinical outcome among hospitalized patients in first and second waves of COVID-19 pandemic: Study from the developing world

Background

Recent disease resurgence in China indicates that corona virus infectious disease is still a pertinent public health problem. We stand at a juncture where we are still unsure about the initial dilemmas regarding its birth, therapies, and the emerging novel strains. Medical literature has focused on the clinical, laboratory, radiological, and therapeutic aspects of disease management. There is paucity of literature on the association between socio-demographic variables on disease severity and clinical outcome.

Materials and Methods

This retrospective observational study analyzing the socio-demographic variables was performed at a dedicated COVID care center in western Maharashtra, India. Electronic records of all individuals who were admitted to this hospital from July 29 2020, to June 14, 2021, and diagnosed COVID-19 positive by reverse transcriptase polymerase chain reaction (RT-PCR) were identified after due institutional ethical clearance. Patients admitted from July 29, 2020, to February 27, 2021, were categorized as patients presenting during the 'first wave of viral pandemic'. Those admitted from March 01, 2021, to June 14, 2021, have been included as patients admitted during 'second wave of viral pandemic'. The following outcome parameters were collected (presenting symptoms, duration of symptoms before the individual presented for diagnostic RT-PCR, total duration of symptoms, severity of disease at onset, duration of hospital stay, the final outcome (discharge/death) and Charlson's comorbidity index). The linear regression model was used to establish association between socio-demographic factors and disease severity at onset (mild/moderate/severe/critical).

Results

A total of 37033 patients were screened, and the positivity rate with RT-PCR was 16.99% (n = 6275) during the study period. Out of which 45% (n = 2824) of the patients had mild disease requiring home isolation and the remaining 55% of patients required admission. 1590 patients from the first wave and 910 from the second wave of COVID-19 were hospitalized and included in the study after exclusion. The mean age of patients in first wave was 49 years and that in second wave was 54 years with 77.6% and 70.6% males in two waves, respectively. The burden of critical cases was higher in second wave as computed to first wave (10% vs 8%). The second wave had more outreach in the rural population as compared to second one (17.8% vs 12.2%). The mean duration from the onset of symptoms to hospitalization was 03 and 04 days, respectively, in two waves. Mortality associated in two waves was 11.9% and 24%, respectively (P < 0.05). Higher Charlson's comorbidity index was associated with higher mortality, and the cumulative survival from urban area was more as compared to the rural population (log rank - 9.148, P = 0.0002).

Conclusion

The second COVID-19 wave had significantly higher case mortality. It affected elderly patients and those with rural background. The factors associated with higher mortality during COVID-19 pandemic were rural background, higher Charlson's comorbidity index and late presentation to the hospital. Ongoing vaccine campaigns, thus, should focus on rural areas and individuals with comorbidities especially in developing and least developed countries.

Influence of Environmental Factors and Genome Diversity on Cumulative COVID-19 Cases in the Highland Region of China: Comparative Correlational Study

BACKGROUND

The novel coronavirus SARS-CoV-2 caused the global COVID-19 pandemic. Emerging reports support lower mortality and reduced case numbers in highland areas; however, comparative studies on the cumulative impact of environmental factors and viral genetic diversity on COVID-19 infection rates have not been performed to date.

OBJECTIVE

The aims of this study were to determine the difference in COVID-19 infection rates between high and low altitudes, and to explore whether the difference in the pandemic trend in the high-altitude region of China compared to that of the lowlands is influenced by environmental factors, population density, and biological mechanisms.

METHODS

We examined the correlation between population density and COVID-19 cases through linear regression. A zero-shot model was applied to identify possible factors correlated to COVID-19 infection. We further analyzed the correlation of meteorological and air quality factors with infection cases using the Spearman correlation coefficient. Mixed-effects multiple linear regression was applied to evaluate the associations between selected factors and COVID-19 cases adjusting for covariates. Lastly, the relationship between environmental factors and mutation frequency was evaluated using the same correlation techniques mentioned above.

RESULTS

Among the 24,826 confirmed COVID-19 cases reported from 40 cities in China from January 23, 2020, to July 7, 2022, 98.4% (n=24,430) were found in the lowlands. Population density was positively correlated with COVID-19 cases in all regions (ρ=0.641, P=.003). In high-altitude areas, the number of COVID-19 cases was negatively associated with temperature, sunlight hours, and UV index (P=.003, P=.001, and P=.009, respectively) and was positively associated with wind speed (ρ=0.388, P<.001), whereas no correlation was found between meteorological factors and COVID-19 cases in the lowlands. After controlling for covariates, the mixed-effects model also showed positive associations of fine particulate matter (PM2.5) and carbon monoxide (CO) with COVID-19 cases (P=.002 and P<.001, respectively). Sequence variant analysis showed lower genetic diversity among nucleotides for each SARS-CoV-2 genome (P<.001) and three open reading frames (P<.001) in high altitudes compared to 300 sequences analyzed from low altitudes. Moreover, the frequencies of 44 nonsynonymous mutations and 32 synonymous mutations were significantly different between the high- and low-altitude groups (P<.001, mutation frequency>0.1). Key nonsynonymous mutations showed positive correlations with altitude, wind speed, and air pressure and showed negative correlations with temperature, UV index, and sunlight hours.

CONCLUSIONS

By comparison with the lowlands, the number of confirmed COVID-19 cases was substantially lower in high-altitude regions of China, and the population density, temperature, sunlight hours, UV index, wind speed, PM2.5, and CO influenced the cumulative pandemic trend in the highlands. The identified influence of environmental factors on SARS-CoV-2 sequence variants adds knowledge of the impact of altitude on COVID-19 infection, offering novel suggestions for preventive intervention.

A proposed analytical approach to estimate excess daily mortality rates in Ecuador

Background

The COVID-19 pandemic has proved deadly all over the globe; however, one of the most lethal outbreaks occurred in Ecuador.

Aims

This study aims to highlight the pandemic's impact on the most affected countries worldwide in terms of excess deaths per capita and per day.

Methods

An ecological study of all-cause mortality recorded in Ecuador was performed. To calculate the excess deaths relative to the historical average for the same dates in 2017, 2018, and 2019, we developed a bootstrap method based on the central tendency measure of mean. A Poisson fitting analysis was used to identify trends on officially recorded all-cause deaths and COVID-19 deaths. A bootstrapping technique was used to emulate the sampling distribution of our expected deaths estimator μ ⌢ d e a t h s by simulating the data generation and model fitting processes daily since the first confirmed case.

Results

In Ecuador, during 2020, 115,070 deaths were reported and 42,453 were cataloged as excess mortality when compared to 2017-2019 period. Ecuador is the country with the highest recorded excess mortality in the world within the shortest timespan. In one single day, Ecuador recorded 1,120 deaths (6/100,000), which represents an additional 408% of the expected fatalities.

Conclusion

Adjusting for population size and time, the hardest-hit country due to the COVID-19 pandemic was Ecuador. The mortality excess rate shows that the SARS-CoV-2 virus spread rapidly in Ecuador, especially in the coastal region. Our results and the proposed new methodology could help to address the real situation of the number of deaths during the initial phase of pandemics.

Seroprevalence of anti-SARS-CoV-2 antibodies and risk of viral exposure among healthcare workers in the South Kivu province, eastern Democratic Republic of the Congo: a cross-sectional study

OBJECTIVES

Healthcare workers (HCWs) are on the frontline of combating COVID-19, hence are at elevated risk of contracting an infection with SARS-CoV-2. The present study aims to measure the impact of SARS-CoV-2 on HCWs in central sub-Saharan Africa.

SETTING

A cross-sectional serological study was conducted at six urban and five rural hospitals during the first pandemic wave in the South Kivu province, Democratic Republic of the Congo (DRC).

PARTICIPANTS

Serum specimens from 1029 HCWs employed during the first pandemic wave were collected between August and October 2020, and data on demographics and work-related factors were recorded during structured interviews.

PRIMARY AND SECONDARY OUTCOME MEASURES

The presence of IgG antibodies against SARS-CoV-2 was examined by ELISA. Positive specimens were further tested using a micro-neutralisation assay. Factors driving SARS-CoV-2 seropositivity were assessed by multivariable analysis.

RESULTS

Overall SARS-CoV-2 seroprevalence was high among HCWs (33.1%), and significantly higher in urban (41.5%) compared with rural (19.8%) hospitals. Having had presented with COVID-19-like symptoms before was a strong predictor of seropositivity (31.5%). Personal protective equipment (PPE, 88.1% and 11.9%) and alcohol-based hand sanitizer (71.1% and 28.9%) were more often available, and hand hygiene was more often reported after patient contact (63.0% and 37.0%) in urban compared with rural hospitals, respectively. This may suggest that higher exposure during non-work times in high incidence urban areas counteracts higher work protection levels of HCWs.

CONCLUSIONS

High SARS-CoV-2 seropositivity indicates widespread transmission of the virus in this region of DRC. Given the absence of publicly reported cases during the same time period at the rural sites, serological studies are very relevant in revealing infection dynamics especially in regions with low diagnostic capacities. This, and discrepancies in the application of PPE between urban and rural sites, should be considered in future pandemic response programmes.

Climate Changes and COVID-19

Climatic change, which influences population growth and land usage, has been theorized to be linked to the emergence and spread of new viruses like the currently unfolding COVID-19 pandemic. In this chapter, we explain how climate change may have altered the beginning, transmission, and maybe even the sickness consequences of the COVID-19 pandemic. Where possible, we also provide mechanistic explanations for how this may have occurred. We have presented evidence that suggests climate change may have had a role in the establishment and transmission of SARS-CoV-2 infection, and most possibly even in some of its clinical effects. Human activities bringing people into closer contact with bats and animals like pangolins that potentially represent the intermediate hosts, and evidence that climate-induced changes in vegetation are the main reservoir source of coronaviruses for human infection, are among the explanations. Although there are still unsubstantiated indications that the first viral pathogen may have escaped from a laboratory, it is possible that this encounter took place in the field or in marketplaces in the instance of COVID-19. We also present the argument that climate change is working to enhance transmission between diseased and uninfected humans, and this is true regardless of the source of the original development of the disease. Changes in temperature and humidity make it easier for viruses to survive, and the impacts of industrial pollution induce people to cough and sneeze, which releases highly infectious aerosols into the air. These three factors combine to make this a more likely scenario than it would otherwise be. We suggest that changes in climate are contributing to create conditions that are favorable for the development of more severe symptoms of illness. It is more difficult to build the argument for this circumstance, and much of it is indirect. However, climate change has caused some communities to adjust their nutritional habits, both in terms of the quantity of food they eat and the quality of the foods they consume. The effects frequently become apparent as a result of alterations that are imposed on the microbiome of the gut, which, in turn, influence the types of immune responses that are produced. The incidence of comorbidities like diabetes and animal vectors like bats that transmit other illnesses that modify vulnerability to SARS-CoV-2 are also two examples of the factors that have been affected by climate change. In order to curb the development of infectious illnesses caused by new viruses, it is necessary to understand the connection between environmental dynamics and the emergence of new coronaviruses. This knowledge should lead to initiatives aimed at reducing global greenhouse gas emissions.

The effect of the urban exposome on COVID-19 health outcomes: A systematic review and meta-analysis

BACKGROUND

The global severity of SARS-CoV-2 illness has been associated with various urban characteristics, including exposure to ambient air pollutants. This systematic review and meta-analysis aims to synthesize findings from ecological and non-ecological studies to investigate the impact of multiple urban-related features on a variety of COVID-19 health outcomes.

METHODS

On December 5, 2022, PubMed was searched to identify all types of observational studies that examined one or more urban exposome characteristics in relation to various COVID-19 health outcomes such as infection severity, the need for hospitalization, ICU admission, COVID pneumonia, and mortality.

RESULTS

A total of 38 non-ecological and 241 ecological studies were included in this review. Non-ecological studies highlighted the significant effects of population density, urbanization, and exposure to ambient air pollutants, particularly PM2.5. The meta-analyses revealed that a 1 μg/m3 increase in PM2.5 was associated with a higher likelihood of COVID-19 hospitalization (pooled OR 1.08 (95% CI:1.02-1.14)) and death (pooled OR 1.06 (95% CI:1.03-1.09)). Ecological studies, in addition to confirming the findings of non-ecological studies, also indicated that higher exposure to nitrogen dioxide (NO2), ozone (O3), sulphur dioxide (SO2), and carbon monoxide (CO), as well as lower ambient temperature, humidity, ultraviolet (UV) radiation, and less green and blue space exposure, were associated with increased COVID-19 morbidity and mortality.

CONCLUSION

This systematic review has identified several key vulnerability features related to urban areas in the context of the recent COVID-19 pandemic. The findings underscore the importance of improving policies related to urban exposures and implementing measures to protect individuals from these harmful environmental stressors.

Araníbar J, Munayco Escate CV. High altitudes, population density, and poverty: Unraveling the complexities of COVID-19 in Peru during the years 2020-2022

Background

Several factors related to hospitalizations, morbidity, and mortality from COVID-19 have been identified. However, limited exploration has been done on geographic and socioeconomic factors that could significantly impact these outcomes.

Objectives

This study aimed to determine whether altitude, population density, and percentage of population in total poverty are associated with COVID-19 incidence per 1000 inhabitants and COVID-19 case-fatality rate in Peru, from 2020 to 2022.

Methods

This study utilized a multiple group ecological design and relied on secondary databases containing daily records of COVID-19 positive cases and deaths due to COVID-19. An epidemiological analysis was performed, subsequently processed using a random effects model.

Results

As of August 2022, Peru had recorded a total of 3,838,028 COVID-19 positive cases and 215,023 deaths due to COVID-19. Our analysis revealed a statistically significant negative association between altitude and COVID-19 incidence (aBETA: -0.004; Standard Error: 0.001; p < 0.05). Moreover, we observed a positive association between population density and incidence (aBETA: 0.006; Standard Error: 0.001; p < 0.05). However, we found no significant association between the percentage of population in total poverty and COVID-19 incidence.

Conclusion

Our study found that an increase in altitude was associated with a decrease in COVID-19 incidence, while an increase in population density was associated with an increase in COVID-19 incidence. High altitude, population density and percentage of population in total poverty does not change case-fatality rate due to COVID-19.

Advancing application of satellite remote sensing technologies for linking atmospheric and built environment to health

Background

The intricate interplay between human well-being and the surrounding environment underscores contemporary discourse. Within this paradigm, comprehensive environmental monitoring holds the key to unraveling the intricate connections linking population health to environmental exposures. The advent of satellite remote sensing monitoring (SRSM) has revolutionized traditional monitoring constraints, particularly limited spatial coverage and resolution. This innovation finds profound utility in quantifying land covers and air pollution data, casting new light on epidemiological and geographical investigations. This dynamic application reveals the intricate web connecting public health, environmental pollution, and the built environment.

Objective

This comprehensive review navigates the evolving trajectory of SRSM technology, casting light on its role in addressing environmental and geographic health issues. The discussion hones in on how SRSM has recently magnified our understanding of the relationship between air pollutant exposure and population health. Additionally, this discourse delves into public health challenges stemming from shifts in urban morphology.

Methods

Utilizing the strategic keywords "SRSM," "air pollutant health risk," and "built environment," an exhaustive search unfolded across prestigious databases including the China National Knowledge Network (CNKI), PubMed and Web of Science. The Citespace tool further unveiled interconnections among resultant articles and research trends.

Results

Synthesizing insights from a myriad of articles spanning 1988 to 2023, our findings unveil how SRMS bridges gaps in ground-based monitoring through continuous spatial observations, empowering global air quality surveillance. High-resolution SRSM advances data precision, capturing multiple built environment impact factors. Its application to epidemiological health exposure holds promise as a pioneering tool for contemporary health research.

Conclusion

This review underscores SRSM's pivotal role in enriching geographic health studies, particularly in atmospheric pollution domains. The study illuminates how SRSM overcomes spatial resolution and data loss hurdles, enriching environmental monitoring tools and datasets. The path forward envisions the integration of cutting-edge remote sensing technologies, novel explorations of urban-public health associations, and an enriched assessment of built environment characteristics on public well-being.

Strategizing global health governance: unpacking opportunities and challenges for least developed nations within the WHO pandemic treaty framework

Exploring the intricacies of the proposed WHO pandemic treaty, this paper underscores its potential benefits and challenges for Least Developed Nations (LDNs) in the global health landscape. While the treaty could elevate LDNs' access to vital resources, fortify health systems, and amplify their voice in global health governance, tangible challenges in safeguarding equitable access, protecting sovereignty, and ensuring compliance are illuminated. Concluding with targeted recommendations, the paper advocates for treaty revisions that assure resource access, safeguard LDNs' autonomy, and foster capacity-building. In essence, the paper emphasizes the imperative of genuinely empowering LDNs, crafting a pandemic treaty that establishes a more equitable, resilient, and inclusive global health future.

The impact of Covid-19 on demographic components in Spain, 2020-31: A scenario approach

While considerable attention has been paid to the impact of Covid-19 on mortality and fertility, few studies have attempted to evaluate the pandemic's effect on international migration. We analyse the impact of Covid-19 on births, deaths, and international migration in Spain during 2020, comparing observed data with estimated values assuming there had been no pandemic. We also assess the consequences of three post-pandemic scenarios on the size and structure of the population to 2031. Results show that in 2020, excess mortality equalled 16.2 per cent and births were 6.5 per cent lower than expected. Immigration was the most affected component, at 36.0 per cent lower than expected, while emigration was reduced by 23.8 per cent. If net migration values recover to pre-pandemic levels in 2022, the size and structure of the population in 2031 will be barely affected. Conversely, if levels do not recover until 2025, there will be important changes to Spain's age structure.

Impact of geographical, meteorological, demographic, and economic indicators on the trend of COVID-19: A global evidence from 202 affected countries

Research problem

Public health and the economy face immense problems because of pathogens in history globally. The outbreak of novel SARS-CoV-2 emerged in the form of coronavirus (COVID-19), which affected global health and the economy in almost all countries of the world.

Study design

The objective of this research is to examine the trend of COVID-19, deaths, and transmission rates in 202 affected countries. The virus-affected countries were grouped according to their continent, meteorological indicators, demography, and income. This is quantitative research in which we have applied the Poisson regression method to assess how temperature, precipitation, population density, and income level impact COVID-19 cases and fatalities. This has been done by using a semi-parametric and additive polynomial model.

Findings

The trend analysis depicts that COVID-19 cases per million were comparatively higher for two groups of countries i.e., (a) average temperature below 7.5 °C and (b) average temperature between 7.5 °C and 15 °C, up to the 729th day of the outbreak. However, COVID-19 cases per million were comparatively low in the countries having an average temperature between 22.5 °C and 30 °C. The day-wise trend was comparatively higher for the countries having average precipitation between (a) 1 mm and 750 mm and (b) 750 mm and 1500 mm up to the 729th day of the outbreak. The day-wise trend was comparatively higher for the countries having more than 1000 people per sq. km. Discussing the COVID-19 cases per million, the day-wise trend was higher for the HICs, followed by UMICs, LMICs, and LIC.

Conclusion

The study highlights the need for targeted interventions and responses based on the specific circumstances and factors affecting each country, including their geographical location, temperature, precipitation levels, population density, and per capita income.

Preparing Cities for Future Pandemics: Unraveling the Influence of Urban and Housing Variables on COVID-19 Incidence in Santiago de Chile

In this study, we analyzed how urban, housing, and socioeconomic variables are related to COVID-19 incidence. As such, we have analyzed these variables along with demographic, education, employment, and COVID-19 data from 32 communes in Santiago de Chile between March and August of 2020, before the release of the vaccines. The results of our Principal Component Analysis (PCA) confirmed that those communes with more economic, social, organizational, and infrastructural resources were overall less affected by COVID-19. As the dimensions affecting COVID-19 are based on structural variables, this study discusses to what extent our cities can be prepared for the next pandemic. Recommendations for local decision-makers in controlling illegal immigration and investing in housing and urban parks are drawn.

COVID-19 resilience index in European Union countries based on their risk and readiness scale

Addressing risks and pandemics at a country level is a complex task that requires transdisciplinary approaches. The paper aims to identify groups of the European Union countries characterized by a similar COVID-19 Resilience Index (CRI). Developed in the paper CRI index reflects the countries' COVID-19 risk and their readiness for a crisis situation, including a pandemic. Moreover, the study detects the factors that significantly differentiate the distinguished groups. According to our research, Bulgaria, Hungary, Malta, and Poland have the lowest COVID-19 Resilience Index score, with Croatia, Greece, Czechia, and Slovakia following close. At the same time, Ireland and Scandinavian countries occupy the top of the leader board, followed by Luxemburg. The Kruskal-Wallis test results indicate four COVID-19 risk indicators that significantly differentiate the countries in the first year of the COVID-19 pandemic. Among the significant factors are not only COVID-19-related factors, i.e., the changes in residential human mobility, the stringency of anti-COVID-19 policy, but also strictly environmental factors, namely pollution and material footprint. It indicates that the most critical global environmental issues might be crucial in the phase of a future pandemic. Moreover, we detect eight readiness factors that significantly differentiate the analysed country groups. Among the significant factors are the economic indicators such as GDP per capita and labour markets, the governance indicators such as Rule of Law, Access to Information, Implementation and Adaptability measures, and social indicators such as Tertiary Attainment and Research, Innovation, and Infrastructure.

COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?

INTRODUCTION

Maharashtra, India, remained a hotspot during the COVID-19 pandemic. After the initial complete lockdown, the state slowly relaxed restrictions. We aim to estimate the lockdown's impact on COVID-19 cases and associated healthcare costs.

METHODS

Using daily case data for 84 days (9 March-31 May 2020), we modeled the epidemic's trajectory and predicted new cases for different phases of lockdown. We fitted log-linear models to estimate the growth rate, basic (R₀), daily reproduction number (R_e), and case doubling time. Based on pre-restriction and Phase 1 R₀, we predicted new cases for the rest of the restriction phases, and we compared them with the actual number of cases during each phase. Furthermore, using the published and gray literature, we estimated the costs and savings of implementing these restrictions for the projected period, and we performed a sensitivity analysis.

RESULTS

The estimated median R₀ during the different phases was 1.14 (95% CI: 0.85, 1.45) for pre-lockdown, 1.67 (95% CI: 1.50, 1.82) for phase 1 (strict mobility restrictions), 1.24 (95% CI: 1.12, 1.35) for phase 2 (extension of phase 1 with no restrictions on agricultural and essential services), 1.12 (95% CI: 1.01, 1.23) for phase 3 (extension of phase 2 with mobility relaxations in areas with few infections), and 1.05 (95% CI: 0.99, 1.123) for phase 4 (implementation of localized lockdowns in high-case-load areas with fewer restrictions on other areas), respectively. The corresponding doubling time rate for cases (in days) was 17.78 (95% CI: 5.61, -15.19), 3.87 (95% CI: 3.15, 5.00), 10.37 (95% CI: 7.10, 19.30), 20.31 (95% CI: 10.70, 212.50), and 45.56 (95% CI: 20.50, -204.52). For the projected period, the cases could have reached 631,819 without the lockdown, as the actual reported number of cases was 64,975. From a healthcare perspective, the estimated total value of averted cases was INR 194.73 billion (USD 2.60 billion), resulting in net cost savings of 84.05%. The Incremental Cost-Effectiveness Ratio (ICER) per Quality Adjusted Life Year (QALY) for implementing the lockdown, rather than observing the natural course of the pandemic, was INR 33,812.15 (USD 450.83).

CONCLUSION

Maharashtra's early public health response delayed the pandemic and averted new cases and deaths during the first wave of the pandemic. However, we recommend that such restrictions be carefully used while considering the local socio-economic realities in countries like India.

The role of universal health coverage and global health security nexus and interplay on SARS-CoV-2 infection and case-fatality rates in Africa : a structural equation modeling approach

BACKGROUND

The Coronavirus Disease (COVID-19) caused by SARS-CoV-2 infections remains a significant health challenge worldwide. There is paucity of evidence on the influence of the universal health coverage (UHC) and global health security (GHS) nexus on SARS-CoV-2 infection risk and outcomes. This study aimed to investigate the effects of UHC and GHS nexus and interplay on SARS-CoV-2 infection rate and case-fatality rates (CFR) in Africa.

METHODS

The study employed descriptive methods to analyze the data drawn from multiple sources as well used structural equation modeling (SEM) with maximum likelihood estimation to model and assess the relationships between independent and dependent variables by performing path analysis.

RESULTS

In Africa, 100% and 18% of the effects of GHS on SARS-CoV-2 infection and RT-PCR CFR, respectively were direct. Increased SARS-CoV-2 CFR was associated with median age of the national population (β = -0.1244, [95% CI: -0.24, -0.01], P = 0.031 ); COVID-19 infection rate (β = -0.370, [95% CI: -0.66, -0.08], P = 0.012 ); and prevalence of obesity among adults aged 18 + years (β = 0.128, [95% CI: 0.06,0.20], P = 0.0001) were statistically significant. SARS-CoV-2 infection rates were strongly linked to median age of the national population (β = 0.118, [95% CI: 0.02,0.22 ], P = 0.024); population density per square kilometer, (β = -0.003, [95% CI: -0.0058, -0.00059], P = 0.016 ) and UHC for service coverage index (β = 0.089, [95% CI: 0.04,0.14, P = 0.001 ) in which their relationship was statistically significant.

CONCLUSIONS

The study shade a light that UHC for service coverage, and median age of the national population, population density have significant effect on COVID-19 infection rate while COVID-19 infection rate, median age of the national population and prevalence of obesity among adults aged 18 + years were associated with COVID-19 case-fatality rate. Both, UHC and GHS do not emerge to protect against COVID-19-related case fatality rate.

Investigating the Possible Reasons for the Low Reported Morbidity and Mortality of COVID-19 in African Countries: An Integrative Review

BACKGROUND

COVID-19 has impacted the world differentially with the highest mortality and morbidity rate burden in Europe and the USA and the lowest mortality and morbidity burden in Africa. This study aims to investigate the possible reasons why Africa recorded the lowest COVID-19 mortality and morbidity.

METHODS

The following search terms were used PubMed database: ["mortalit*" (tw) OR "morbidit*" (tw) AND "COVID-19" (tw) AND "Africa" (tw)]. Studies that discuss a factor for the low COVID-19 burden in Africa have a defined methodology, discuss its research question and mention its limitations are selected for review. Data from the final articles were extracted using a data collection tool.

RESULTS

Twenty-one studies were used in this integrative review. Results were grouped into 10 themes, which are younger African population, lower health capacity, weather, vaccines and drugs, effective pandemic response, lower population density and mobility, African socioeconomic status, lower prevalence of comorbidities, genetic difference and previous infection exposure. The low COVID-19 mortality and morbidity in Africa is largely a result of a combined effect of the younger African population and underreporting of COVID-19 cases.

CONCLUSIONS

There is a need to strengthen the health capacities of African countries. Moreover, African countries that have other health problem priorities may use a tailored approach to vaccinating the elderly. More definitive studies are needed to know the role of BCG vaccination, weather, genetic makeup and prior infection exposure in the differential impact of the COVID-19 pandemic.

Compact living or policy inaction? Effects of urban density and lockdown on the COVID-19 outbreak in the US

The coronavirus pandemic has reignited the debate over urban density. Popular media has been quick to blame density as a key contributor to rapid disease transmission, questioning whether compact cities are still a desirable planning goal. Past research on the density-pandemic connection have produced mixed results. This article offers a critical perspective on this debate by unpacking the effects of alternative measures of urban density, and examining the impacts of mandatory lockdowns and the stringency of other government restrictions on cumulative Covid-19 infection and mortality rates during the early phase of the pandemic in the US. Our results show a consistent positive effect of density on Covid-19 outcomes across urban areas during the first six months of the outbreak. However, we find modest variations in the density-pandemic relationship depending on how densities are measured. We also find relatively longer duration mandatory lockdowns to be associated with lower infection and mortality rates, and lockdown duration's effect to be relatively more pronounced in high-density urban areas. Moreover, we find that the timing of lockdown imposition and the stringency of the government's response additionally influence Covid-19 outcomes, and that the effects vary by urban density. We argue that the adverse impact of density on pandemics could be mitigated by adopting strict lockdowns and other stringent human mobility and interaction restriction policies in a spatially targeted manner. Our study helps to inform current and future government policies to contain the virus, and to make our cities more resilient against future shocks and threats.

Debates Paper: COVID-19 and urban informality: Exploring the implications of the pandemic for the politics of planning and inequality

The COVID-19 pandemic has highlighted a major contradiction in contemporary urban planning. This is the relationship between the entrepreneurial modes of urban politics that shape contemporary planning practice and the interrelated dynamics of economic precarity and informalisation of low-income communities that exacerbate contagion, and therefore enable pandemic spread. Through a review of literature on the urban dimensions of COVID-19, and on the historical relationship between pandemics and urban planning, we develop a framework for analysing the debates that are emerging around planning approaches to addressing contemporary pandemic risk in low-income, informalised communities. We argue that post-pandemic debates about urban planning responses are likely to take shape around three discourses that have framed approaches to addressing informalised communities under entrepreneurial urbanism - a revanchist approach based on territorial stigmatisation of spaces of the poor, an incrementalist approach premised on addressing the most immediate drivers of contagion, and a reformist approach that seeks to address the structural conditions that have produced economic precarity and shelter informality. We further argue that any effort to assess the political outfall of the COVID-19 pandemic in a given context needs to take an inter-scalar approach, analysing how debates over informality take shape at the urban and national scales.

A data-driven optimization model to response to COVID-19 pandemic: a case study

COVID-19 is a highly prevalent disease that has led to numerous predicaments for healthcare systems worldwide. Owing to the significant influx of patients and limited resources of health services, there have been several limitations associated with patients' hospitalization. These limitations can cause an increment in the COVID-19-related mortality due to the lack of appropriate medical services. They can also elevate the risk of infection in the rest of the population. The present study aims to investigate a two-phase approach to designing a supply chain network for hospitalizing patients in the existing and temporary hospitals, efficiently distributing medications and medical items needed by patients, and managing the waste created in hospitals. Since the number of future patients is uncertain, in the first phase, trained Artificial Neural Networks with historical data forecast the number of patients in future periods and generate scenarios. Through the use of the K-Means method, these scenarios are reduced. In the second phase, a multi-objective, multi-period, data-driven two-stage stochastic programming is developed using the acquired scenarios in the previous phase concerning the uncertainty and disruption in facilities. The objectives of the proposed model include maximizing the minimum allocation-to-demand ratio, minimizing the total risk of disease spread, and minimizing the total transportation time. Furthermore, a real case study is investigated in Tehran, the capital of Iran. The results showed that the areas with the highest population density and no facilities near them have been selected for the location of temporary facilities. Among temporary facilities, temporary hospitals can allocate up to 2.6% of the total demand, which puts pressure on the existing hospitals to be removed. Furthermore, the results indicated that the allocation-to-demand ratio can remain at an ideal level when disruptions occur by considering temporary facilities. Our analyses focus on: (1) Examining demand forecasting error and generated scenarios in the first phase, (2) exploring the impact of demand parameters on the allocation-to-demand ratio, total time and total risk, (3) investigating the strategy of utilizing temporary hospitals to address sudden changes in demand, (4) evaluating the effect of disruption to facilities on the supply chain network.

Relationships between COVID-19 and disaster risk in Costa Rican municipalities

The COVID-19 pandemic has had far-reaching impacts on every aspect of human life since the first confirmed case in December 2019. Costa Rica reported its first case of COVID-19 in March 2020, coinciding with a notable correlation between the occurrence of disaster events at the municipal scale over the past five decades. In Costa Rica, over 90% of disasters are hydrometeorological in nature, while geological disasters have caused significant economic and human losses throughout the country's history. To analyze the relationship between COVID-19 cases and disaster events in Costa Rica, two Generalized Linear Models (GLMs) were used to statistically evaluate the influence of socio-environmental parameters such as population density, social development index, road density, and non-forested areas. The results showed that population and road density are the most critical factors in explaining the spread of COVID-19, while population density and social development index can provide insights into disaster events at the municipal level in Costa Rica. This study provides valuable information for understanding municipal vulnerability and exposure to disasters in Costa Rica and can serve as a model for other countries to assess disaster risk.

SARS-CoV-2 case detection using community event-based surveillance system-February-September 2020: lessons learned from Senegal

The COVID-19 pandemic necessitated the rapid development and implementation of effective surveillance systems to detect and respond to the outbreak in Senegal. In this documentation, we describe the design and implementation of the Community Event-Based Surveillance (CEBS) system in Senegal to strengthen the existing Integrated Disease Surveillance and Response system. The CEBS system used a hotline and toll-free number to collect and triage COVID-19-related calls from the community. Data from the CEBS system were integrated with the national system for further investigation and laboratory testing. From February to September 2020, a total of 10 760 calls were received by the CEBS system, with 10 751 calls related to COVID-19. The majority of calls came from the Dakar region, which was the epicentre of the outbreak in Senegal. Of the COVID-19 calls, 50.2% were validated and referred to health districts for further investigation, and 25% of validated calls were laboratory-confirmed cases of SARS-CoV-2. The implementation of the CEBS system allowed for timely detection and response to potential COVID-19 cases, contributing to the overall surveillance efforts in the country. Lessons learned from this experience include the importance of decentralised CEBS, population sensitisation on hotlines and toll-free usage, and the potential role of Community Health Workers in triaging alerts that needs further analysis. This experience highlights the contribution of a CEBS system in Senegal and provides insights into the design and operation of such a system. The findings can inform other countries in strengthening their surveillance systems and response strategies.

Spatio-temporal multidisciplinary analysis of socio-environmental conditions to explore the COVID-19 early evolution in urban sites in South America

This study aimed to analyse how socio-environmental conditions affected the early evolution of COVID-19 in 14 urban sites in South America based on a spatio-temporal multidisciplinary approach. The daily incidence rate of new COVID-19 cases with symptoms as the dependent variable and meteorological-climatic data (mean, maximum, and minimum temperature, precipitation, and relative humidity) as the independent variables were analysed. The study period was from March to November of 2020. We inquired associations of these variables with COVID-19 data using Spearman's non-parametric correlation test, and a principal component analysis considering socio economic and demographic variables, new cases, and rates of COVID-19 new cases. Finally, an analysis using non-metric multidimensional scale ordering by the Bray-Curtis similarity matrix of meteorological data, socio economic and demographic variables, and COVID-19 was performed. Our findings revealed that the average, maximum, and minimum temperatures and relative humidity were significantly associated with rates of COVID-19 new cases in most of the sites, while precipitation was significantly associated only in four sites. Additionally, demographic variables such as the number of inhabitants, the percentage of the population aged 60 years and above, the masculinity index, and the GINI index showed a significant correlation with COVID-19 cases. Due to the rapid evolution of the COVID-19 pandemic, these findings provide strong evidence that biomedical, social, and physical sciences should join forces in truly multidisciplinary research that is critically needed in the current state of our region.

The connection between slums and COVID-19 cases in Jakarta, Indonesia: A case study of Kapuk Urban Village

COVID-19 has spread world-wide, and with multiple health, social, and economic ramifications. These present a formidable challenge for those belonging to vulnerable communities, such as those living in slums. There is now a growing literature urging attention to this challenge. However, few studies have examined the actual lived realities within these areas using direct, observational research, notwithstanding commentary elsewhere that such close attention is necessary to ensure effective action. This study took this approach in relation to a particular case-study, Kapuk Urban Village, in Jakarta, Indonesia. Drawing on an existing schema involving three spatial scales of slum areas (environs, settlement, and object), the research confirms how different built and socio-economic features can exacerbate vulnerability, and COVID-19 transmission. We also add to the body of knowledge by contributing a dimension of 'ground-level' research engagement. We conclude by discussing related ideas around ensuring community resilience and effective policy implementation, and recommend an "urban acupuncture" approach to encourage government regulations and actions better tailored to such communities.

Health system characteristics and COVID-19 performance in high-income countries

BACKGROUND

The COVID-19 pandemic has shaken everyday life causing morbidity and mortality across the globe. While each country has been hit by the pandemic, individual countries have had different infection and health trajectories. Of all welfare state institutions, healthcare has faced the most immense pressure due to the pandemic and hence, we take a comparative perspective to study COVID-19 related health system performance. We study the way in which health system characteristics were associated with COVID-19 excess mortality and case fatality rates before Omicron variant.

METHODS

This study analyses the health system performance during the pandemic in 43 OECD countries and selected non-member economies through three healthcare systems dimensions: (1) healthcare finance, (2) healthcare provision, (3) healthcare performance and health outcomes. Health system characteristics-related data is collected from the Global Health Observatory data repository, the COVID-19 related health outcome indicators from the Our World in Data statistics database, and the country characteristics from the World Bank Open Data and the OECD statistics databases.

RESULTS

We find that the COVID-19 excess mortality and case fatality rates were systematically associated with healthcare system financing and organizational structures, as well as performance regarding other health outcomes besides COVID-19 health outcomes.

CONCLUSION

Investments in public health systems in terms of overall financing, health workforce and facilities are instrumental in reducing COVID-19 related mortality. Countries aiming at improving their pandemic preparedness may develop health systems by strengthening their public health systems.

The COVID-19 pandemic's true death toll in Iran after two years: an interrupted time series analysis of weekly all-cause mortality data

INTRODUCTION

This study aimed to investigate overall and age group/region/sex-specific excess all-cause mortality from the inception of the COVID-19 pandemic in Iran until February 2022.

METHODS

Weekly all-cause mortality data were obtained for the period March 2015 until February 2022. We conducted interrupted time series analyses, using a generalized least-square regression model to estimate excess mortality after the COVID-19 pandemic. Using this approach, we estimated the expected post-pandemic death counts based on five years of pre-pandemic data and compared the results with observed mortality during the pandemic.

RESULTS

After the COVID-19 pandemic, we observed an immediate increase (1,934 deaths per week, p = 0.01) in weekly all-cause mortality. An estimated 240,390 excess deaths were observed in two years after the pandemic. Within the same period, 136,166 deaths were officially attributed to COVID-19. The excess mortality was greatest among males compared with females (326 versus 264 per 100k), with an increasing trend by age group. There is a clear increased excess mortality in the central and northwestern provinces.

CONCLUSION

We found that the full mortality burden during the outbreak has been much heavier than what is officially reported, with clear differences by sex, age group, and geographical region.

Modelling the spatiotemporal spread of COVID-19 outbreaks and prioritization of the risk areas in Toronto, Canada

Modelling the spatiotemporal spread of a highly transmissible disease is challenging. We developed a novel spatiotemporal spread model, and the neighbourhood-level data of COVID-19 in Toronto was fitted into the model to visualize the spread of the disease in the study area within two weeks of the onset of first outbreaks from index neighbourhood to its first-order neighbourhoods (called dispersed neighbourhoods). We also model the data to classify hotspots based on the overall incidence rate and persistence of the cases during the study period. The spatiotemporal spread model shows that the disease spread to 1-4 neighbourhoods bordering the index neighbourhood within two weeks. Some dispersed neighbourhoods became index neighbourhoods and further spread the disease to their nearby neighbourhoods. Most of the sources of infection in the dispersed neighbourhood were households and communities (49%), and after excluding the healthcare institutions (40%), it becomes 82%, suggesting the expansion of transmission was from close contacts. The classification of hotspots informs high-priority areas concentrated in the northwestern and northeastern parts of Toronto. The spatiotemporal spread model along with the hotspot classification approach, could be useful for a deeper understanding of spatiotemporal dynamics of infectious diseases and planning for an effective mitigation strategy where local-level spatially enabled data are available.

Towards building resilient cities to pandemics: A review of COVID-19 literature

With the global prevalence of COVID-19 disease, the concept of urban resilience against pandemics has drawn the attention of a wide range of researchers, urban planners, and policymakers. This study aims to identify the major dimensions and principles of urban resilience to pandemics through a systematic review focused on lessons learned from the COVID-19 pandemic and comparing different perspectives regarding resilient urban environments to such diseases. Based on the findings, the study proposes a conceptual framework and a series of principles of urban resilience to pandemics, consisting of four spatial levels: housing, neighborhoods, city, and the regional and national scales, and three dimensions of pandemic resilience: pandemic-related health requirements, environmental psychological principles, and general resilience principles. The findings show that resilient cities should be able to implement the pandemic-related health requirements, the psychological principles of the environment to reduce the stresses caused by the pandemic, and the general principles of resilience in the smart city context. This framework provides scholars and policymakers with a comprehensive understanding of resilience on different scales and assists them in making better-informed decisions.

The impact of built and socio-economic environment factors on Covid-19 transmission at the ZIP-code level in Florida

Most studies have explored the Covid-19 outbreak by mainly focusing on restrictive public policies, human health, and behaviors at the macro level. However, the impacts of built and socio-economic environments, accounting for spatial effects on the spread at the local levels, have not been thoroughly studied. In this study, the relationships between the spatial spread of the virus and various indicators of the built and socio-economic environments are investigated, using Florida ZIP-code data on accumulated cases before large-scale vaccination campaigns began in 2021. Spatial regression models are used to account for the spatial dependencies and interactions that are core factors in Covid-19 spread. This study reveals both the spillover dynamics of the coronavirus spread at the ZIP code level and the existence of spatial dependencies among the unobserved variables represented by the error term. In addition, the findings show a positive association between the expected number of Covid-19 cases and specific land uses, such as education facilities and retail densities. Finally, the study highlights critical socio-economic characteristics causing a substantial increase in Covid-19 spread. Such results could help policymakers, public health experts, and urban planners design strategies to mitigate the spread of future Covid-19-like diseases.

Modelling Impact of High-Rise, High-Density Built Environment on COVID-19 Risks: Empirical Results from a Case Study of Two Chinese Cities

Characteristics of the urban environment (e.g., building density and road network) can influence the spread and transmission of coronavirus disease 2019 (COVID-19) within cities, especially in high-density high-rise built environments. Therefore, it is necessary to identify the key attributes of high-density high-rise built environments to enhance modelling of the spread of COVID-19. To this end, case studies for testing attributes for modelling development were performed in two densely populated Chinese cities with high-rise, high-density built environments (Hong Kong and Shanghai).The investigated urban environmental features included 2D and 3D urban morphological indices (e.g., sky view factor, floor area ratio, frontal area density, height to width ratio, and building coverage ratio), socioeconomic and demographic attributes (e.g., population), and public service points-of-interest (e.g., bus stations and clinics). The modelling effects of 3D urban morphological features on the infection rate are notable in urban communities. As the spatial scale becomes larger, the modelling effect of 2D built environment factors (e.g., building coverage ratio) on the infection rate becomes more notable. The influence of several key factors (e.g., the building coverage ratio and population density) at different scales can be considered when modelling the infection risk in urban communities. The findings of this study clarify how attributes of built environments can be applied to predict the spread of infectious diseases. This knowledge can be used to develop effective planning strategies to prevent and control epidemics and ensure healthy cities.

Five waves of the COVID-19 pandemic and green-blue spaces in urban and rural areas in Poland

Several waves of COVID-19 caused by different SARS-CoV-2 variants have been recorded worldwide. During this period, many publications were released describing the influence of various factors, such as environmental, social and economic factors, on the spread of COVID-19. This paper presents the results of a detailed spatiotemporal analysis of the course of COVID-19 cases and deaths in five waves in Poland in relation to green‒blue spaces. The results, based on 380 counties, reveal that the negative correlation between the indicator of green‒blue space per inhabitant and the average daily number of COVID-19 cases and deaths was clearly visible during all waves. These relationships were described by a power equation (coefficient of determination ranging from 0.83 to 0.88) with a high level of significance. The second important discovery was the fact that the rates of COVID-19 cases and deaths were significantly higher in urban counties (low values of the green-blue space indicator in m²/people) than in rural areas. The developed models can be used in decision-making by local government authorities to organize anti-COVID-19 prevention measures, including local lockdowns, especially in urban areas.

Epidemic dynamics in census-calibrated modular contact network

Network-based models are apt for understanding epidemic dynamics due to their inherent ability to model the heterogeneity of interactions in the contemporary world of intense human connectivity. We propose a framework to create a wire-frame that mimics the social contact network of the population in a geography by lacing it with demographic information. The framework results in a modular network with small-world topology that accommodates density variations and emulates human interactions in family, social, and work spaces. When loaded with suitable economic, social, and urban data shaping patterns of human connectance, the network emerges as a potent decision-making instrument for urban planners, demographers, and social scientists. We employ synthetic networks to experiment in a controlled environment and study the impact of zoning, density variations, and population mobility on the epidemic variables using a variant of the SEIR model. Our results reveal that these demographic factors have a characteristic influence on social contact patterns, manifesting as distinct epidemic dynamics. Subsequently, we present a real-world COVID-19 case study for three Indian states by creating corresponding surrogate social contact networks using available census data. The case study validates that the demography-laced modular contact network reduces errors in the estimates of epidemic variables.

Survival analysis and mortality predictors of COVID-19 in a pediatric cohort in Mexico

Background

The new coronavirus SARS-CoV-2 pandemic has been relatively less lethal in children; however, poor prognosis and mortality has been associated with factors such as access to health services. Mexico remained on the list of the ten countries with the highest case fatality rate (CFR) in adults. It is of interest to know the behavior of COVID-19 in the pediatric population. The aim of this study was to identify clinical and sociodemographic variables associated with mortality due to COVID-19 in pediatric patients.

Objective

Using National open data and information from the Ministry of Health, Mexico, this cohort study aimed to identify clinical and sociodemographic variables associated with COVID-19 mortality in pediatric patients.

Method

A cohort study was designed based on National open data from the Ministry of Health, Mexico, for the period April 2020 to January 2022, and included patients under 18 years of age with confirmed SARS-CoV-2 infection. Variables analyzed were age, health services used, and comorbidities (obesity, diabetes, asthma, cardiovascular disease, immunosuppression, high blood pressure, and chronic kidney disease). Follow-up duration was 60 days, and primary outcomes were death, hospitalization, and requirement of intensive care. Statistical analysis included survival analysis, prediction models created using the Cox proportional hazards model, and Kaplan-Meier estimation curves.

Results

The cohort included 261,099 cases with a mean age of 11.2 ± 4 years, and of these, 11,569 (4.43%) were hospitalized and 1,028 (0.39%) died. Variables associated with risk of mortality were age under 12 months, the presence of comorbidities, health sector where they were treated, and first wave of infection.

Conclusion

Based on data in the National database, we show that the pediatric fatality rate due to SARS-CoV-2 is similar to that seen in other countries. Access to health services and distribution of mortality were heterogeneous. Vulnerable groups were patients younger than 12 months and those with comorbidities.

Epidemiological impact of COVID-19 in India: Country with second foremost positive cases in the world

The WHO tentatively called the new virus 2019 novel corona virus (COVID-19), which is become a effectively contagious disease in global health concern. India is the world's seventh-largest country, with twenty-eight (28) states and eight (8) union territories. In India, the highest COVID-19 case was recorded on 6th may 2021 which is 4,14,433 cases in the single day. On September 2020th, Maharashtra (11,45,840), Tamil Nadu (5,25,420) and Delhi (2,34,701) have the foremost COVID-19, and on May 2021st, Maharashtra (51,01,737), Tamil Nadu (13,80,259) and Delhi (13,23,567) have the foremost COVID-19 in India. Also, these states show the high death rate than other states. The positive COVID-19 cases are very drastically increased and death cases in these states due to the high population density. Hand hygiene, social distancing, and quarantine are some of the precautions that must be taken to prevent the virus from spreading in society. By increasing the detecting and testing capacity of the COVID-19 positive patients are also the way to enable the reduction of secondary cases with stricter quarantine rules.

Use of an E2SFCA method to assess healthcare resources in Jordan during COVID-19 pandemic

Healthcare spatial accessibility requires a better understanding and evaluation, especially during pandemic outbreaks like the recent COVID-19 pandemic. The main goal of this study is to measure and assess community-level spatial accessibility in Amman city to various COVID-19 related healthcare resources that could provide any urgent medical care for suspected or confirmed COVID-19 cases. To address this aim, the Enhanced 2-step floating catchment area (E2SFCA) method combined with several geospatial techniques were performed. The main E2SFCA results show the differences in the capacities and spatial accessibility of health facilities within Amman city, as well as how the variations are captured at different regions. The resulted spatial accessibility scores were presented in interactive Geo-spatial maps, analyzed, and compared for several health resources in public, private, and educational hospitals. The current research findings stated that although there are enough healthcare facilities to service almost the entire city, inappropriate health facility distribution, rather than a lack of resources, has resulted in coverage gaps in some areas. The center zones had been fully serviced, or perhaps over-served, by a large number of facilities. The other zones, on the contrary, were partially served or were even underserved by a certain number of resources.

Urban density and COVID-19: understanding the US experience

This paper revisits the debate around the link between population density and the severity of COVID-19 spread in the USA. We do so by conducting an empirical analysis based on graphical evidence, regression analysis and instrumental variable strategies borrowed from the agglomeration literature. Studying the period between the start of the epidemic and the beginning of the vaccination campaign at the end of 2020, we find that the cross-sectional relationship between density and COVID-19 deaths changed as the year evolved. Initially, denser counties experienced more COVID-19 deaths. Yet, by December, the relationship between COVID deaths and urban density was completely flat. This is consistent with evidence indicating density affected the timing of the outbreak-with denser locations more likely to have an early outbreak-yet had no influence on time-adjusted COVID-19 cases and deaths. Using data from Google, Facebook, the US Census and other sources, we investigate potential mechanisms behind these findings.

The correlation between attack rates and urban health indicators during the third wave of the COVID-19 outbreak in Turkey

This study aims to analyze the inter-provincial variation in the increase of attack rates in the third wave of the COVID-19 outbreak in Turkey and to determine their relationship with potential urban health indicators. In this ecological study, dependent variables were selected as the COVID-19 attack rates of provinces before the third wave and during the third peak and the attack rate increase ratio. Urban health indicators that can function as determinants of health were calculated for each province under five headings: demographic, health capacity, economic, environmental, and socio-cultural. The epidemiologic maps were produced to show the spatial distribution of COVID-19 attack rates pre- and during the third wave. The associations with urban indicators were conducted using bivariate analysis, including Pearson or Spearman correlation analysis. A multiple linear regression model was run with variables significantly associated with increased attack rates. The results of our study show significant regional variations in COVID-19 attack rates both at the beginning and during the third wave of the COVID-19 pandemic in Turkey. Among the provinces, the attack rate increase ratio has only shown significant correlations to education level and some economic indicators, such as income, employment, industrial activity measured by electric consumption, and economic activity in the manufacturing industry. The multivariate analysis determined that the indicator of economic activity in the manufacturing industry is related to the increase of the attack rate in the third wave. Our results show that the COVID-19 cases are higher in more developed cities with more manufacturing sector activity. It makes us think that it is mainly related to inequalities arising from access to health institutions and testing. It can be determined that the partly lockdown strategy, which excluded the industrial activity in the country, concluded the higher increase in the attack rates in highly industrialized provinces.

Characterizing COVID-19 waves in urban and rural districts of India

Understanding spatial determinants, i.e., social, infrastructural, and environmental features of a place, which shape infectious disease is critically important for public health. We present an exploration of the spatial determinants of reported COVID-19 incidence across India's 641 urban and rural districts, comparing two waves (2020-2021). Three key results emerge using three COVID-19 incidence metrics: cumulative incidence proportion (aggregate risk), cumulative temporal incidence rate, and severity ratio. First, in the same district, characteristics of COVID-19 incidences are similar across waves, with the second wave over four times more severe than the first. Second, after controlling for state-level effects, urbanization (urban population share), living standards, and population age emerge as positive determinants of both risk and rates across waves. Third, keeping all else constant, lower shares of workers working from home correlate with greater infection risk during the second wave. While much attention has focused on intra-urban disease spread, our findings suggest that understanding spatial determinants across human settlements is also important for managing current and future pandemics.

Epidemiological Context and Risk Factors Associated with the Evolution of the Coronavirus Disease (COVID-19): A Retrospective Cohort Study

Since its initial appearance in December 2019, COVID-19 has posed a serious challenge to healthcare authorities worldwide. The purpose of the current study was to identify the epidemiological context associated with the respiratory illness propagated by the spread of COVID-19 and outline various risk factors related to its evolution in the province of Debila (Southeastern Algeria). A retrospective analysis was carried out for a cohort of 612 COVID-19 patients admitted to hospitals between March 2020 and February 2022. The results were analyzed using descriptive statistics. Further, logistic regression analysis was employed to perform the odds ratio. In gendered comparison, males were found to have a higher rate of incidence and mortality compared to females. In terms of age, individuals with advanced ages of 60 years or over were typically correlated with higher rates of incidence and mortality in comparison toindividuals below this age. Furthermore, the current research indicated that peri-urban areas were less affected that the urban regions, which had relatively significant incidence and mortality rates. The summer season was marked with the highest incidence and mortality rate in comparison with other seasons. Patients who were hospitalized, were the age of 60 or over, or characterized by comorbidity, were mainly associated with death evolution (odds ratio [OR] = 8.695; p = 0.000), (OR = 6.192; p = 0.000), and (OR = 2.538; p = 0.000), respectively. The study identifies an important relationship between the sanitary status of patients, hospitalization, over-age categories, and the case severity of the COVID-19 patient.

Race-related differences in the economic, healthcare-access, and psychological impact of COVID-19: personal resources associated with resilience

Background

The impact of the coronavirus disease 2019 (COVID) is worse among those with fewer financial resources, in jobs not amenable to remote work, and in denser living conditions. People of color are more likely to be among these vulnerable groups. Although race itself is a social construction and not based on underlying genetic/biological differences, this study investigated race/ethnicity differences in the negative repercussions of COVID and in the benefits of psychological and social resources.

Methods

This cross-sectional, web-based study (n = 4817) was administered to a heterogeneous United States sample in Spring/Summer 2020. Information was gathered on the following COVID-specific variables: Infection Status, Coping with Lockdown, Social Support, Post-traumatic Growth, Interpersonal Conflict, Worry about Self, Financial Impact on Family, Lack of Money, Inadequate Access to Healthcare, and Housing Instability. Resilience was operationalized as the ability to maintain a sense of wellness in the face of the pandemic, using the DeltaQuest Wellness measure. Multivariate linear regression (adjusting for demographics) and propensity-matched cohort analysis (matched on demographics) evaluated the impact of COVID-specific variables on Wellness in separate models for Whites and Non-Whites.

Findings

Both sets of models retained the same COVID-specific variables and explained about half of the variance in wellness. Coping with Lockdown, Social Support, and Post-traumatic Growth were associated with higher levels of Wellness in both Whites and Non-Whites, while Interpersonal Conflict and Worry about Self were associated with lower levels of Wellness. While these associations are similar, Non-Whites reported worse levels of some positive resources (e.g., social support) and more challenging levels of negative stressors (e.g., interpersonal, worry, financial). Non-Whites also reported much higher levels of post-traumatic growth.

Conclusion

COVID was a source of worry and even conflict, but also unlocked people’s resources in use of health-enhancing behavioral strategies, social support, and renewed gratitude for sources of personal meaning and value. The similar relationships between Whites and Non-Whites on wellness and COVID-specific stressors across racial groups underscore that race is a social construction, not a biological fact. Focusing on a renewed appreciation for sources of personal meaning, and particularly faith, seemed to buffer much of the COVID-related stress for Non-Whites.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41687-022-00514-2.

Heterogeneity of the COVID-19 epidemic: what can we learn from it?

OBJECTIVES

The goal of this article is to evaluate and explain the heterogeneity of the Coronavirus disease 2019 (COVID-19) epidemic in Australia, to offer advice for stopping the current outbreak and preparing for a suitable response to epidemics in the future.

METHODS

We conducted a review to analyze the epidemic and explain its variable manifestation across states in Australia. Most COVID-19 cases and deaths were in the states of Victoria and New South Wales due to differences in the governance of the epidemic and public health responses (quarantine and contact tracing) among states.

RESULTS

Countries could learn from Australia's overall successful response not only through good governance, effective community participation, adequate public health, adequate health system capacity and multisectoral actions but also from the heterogeneity of the epidemic among states.

CONCLUSIONS

A successful response to epidemics in countries with a decentralized administration requires multilevel governance with alignment and harmonization of the response.