Relationship between population density and COVID-19 incidence and mortality estimates: A county-level analysis

Exploring demographic, healthcare, and socio-economic factors as predictors of COVID-19 incidence rate: A spatial regression analysis

This study investigated the relationship between demographic, healthcare, and socio-economic factors, and COVID-19 incidence rate per 100,000 population in Thailand at the province level between January 2020 and March 2022, using a five-phase approach by spatial analysis. OLS models were initially used with significant variables: household, hospital, and industry density, nighttime light index (NTLI). Spatial dependency led to spatial error (SEM) and spatial lag models (SLM), performing better with similar significant variables being applied. SEM explains 58, 65 and, 70 percent in Wave 1, 4 and 5 of COVID-19 variation. SLM explains 25 and 76 percent in Wave 2 and 3 of incidence rate. Positive associations were found between incidence and household density, hospital/medical establishments with beds, Nighttime Light Index (NTLI), and negative with population, hospital, and industry density. Wave 5 showed significant changes with negative for household, hospital, and industry density, urban population; positive for hospital/medical establishments with beds, internet access, NTLI. The study showed that significant predictors of COVID-19 incidence rate vary across waves. Population, household and hospital density, urbanization, access to medical facilities, industrialization, internet access, and NTLI all play a role. The study suggests SEM and SLM models are more appropriate, providing useful information for policymakers and health officials in managing pandemic in Thailand.

Use of Population Weighted Density Index for Coronavirus Spread in the United States

Background: Understanding how population density affected the transmission of COVID-19 is vitally important, since crowded cities were the epicenters for the disease. Since human contact was the main cause of the spread, population-weighted densities have been shown to be a better measure than conventional densities, since the variation in density across subareas matters more than the density in the total area. Objectives: This study investigates the impact of population-weighted density and other demographics on the rate of COVID-19 spread in the United States. Methods: The study considers population-weighted density and many other demographics. The population-weighted density index is the weighted average of density across the tracts, where tracts are weighted by population. Multivariate analysis has been used to determine the elasticity of the spread. Results: Using U.S. county-level data, we calculated the elasticity of COVID-19 spread with respect to population-weighted density to be 0.085 after controlling for other factors. In addition to the density, the proportion of people over 65 years of age, the number of total healthcare workers, and average temperature in each county positively contributed to the case numbers, while education level and income per capita had a negative effect. Discussion: For the spread, understanding the population characteristics and dynamics is as important as understanding the infectious disease itself. This will help policy makers to utilize and reallocate the resources more effectively. If the spread is successfully contained early, there will be less stress placed upon the healthcare system, resulting in better healthcare access for those who are sick. Conclusions: Our analysis suggests that population-weighted density can be a useful tool to control and manage outbreaks, especially within the early stage of the spread. We presented the early dynamics of the spread and recommended a policy measure on how to transfer healthcare workers from low-spread-risk areas to high-spread-risk areas to utilize resources better.

Tracking the genetic diversity of SARS-CoV-2 variants in Nicaragua throughout the COVID-19 Pandemic

The global circulation of SARS-CoV-2 has been extensively documented, yet the dynamics within Central America, particularly Nicaragua, remain underexplored. This study characterizes the genomic diversity of SARS-CoV-2 in Nicaragua from March 2020 through December 2022, utilizing 1064 genomes obtained via next-generation sequencing. These sequences were selected nationwide and analyzed for variant classification, lineage predominance, and phylogenetic diversity. We employed both Illumina and Oxford Nanopore Technologies for all sequencing procedures. Results indicated a temporal and spatial shift in dominant lineages, initially from B.1 and A.2 in early 2020 to various Omicron subvariants towards the study's end. Significant lineage shifts correlated with changes in COVID-19 positivity rates, underscoring the epidemiological impact of variant dissemination. The comparative analysis with regional data underscored the low diversity of circulating lineages in Nicaragua and their delayed introduction compared to other countries in the Central American region. The study also linked specific viral mutations with hospitalization rates, emphasizing the clinical relevance of genomic surveillance. This research advances the understanding of SARS-CoV-2 evolution in Nicaragua and provide valuable information regarding its genetic diversity for public health officials in Central America. We highlight the critical role of ongoing genomic surveillance in identifying emergent lineages and informing public health strategies.

Bayesian hierarchical spatiotemporal models for prediction of (under)reporting rates and cases: COVID-19 infection among the older people in the United States during the 2020-2022 pandemic

The gap between the reported and actual COVID-19 infection cases has been an issue of concern. Here, we present Bayesian hierarchical spatiotemporal disease mapping models for state-level predictions of COVID-19 infection risks and (under)reporting rates among people aged 65 and above during the first two years of the pandemic in the United States. With prior elicitation based on recent prevalence studies, the study suggests that the median state-level reporting rate of COVID-19 infection was 90% (interquartile range: [78%, 96%]). Our study uncovers spatiotemporal variations and dynamics in state-level infection risks and (under)reporting rates, suggesting time-varying associations between higher population density, higher percentage of minorities, and higher percentage of vaccination and increased risks of COVID-19 infection, as well as an association between more easily accessible tests and higher reporting rates. With sensitivity analyses, we highlight the impact and importance of incorporating covariates information and objective prior references for evaluating the issue of underreporting.

The complex link between socioeconomic deprivation and COVID-19. Evidence from small areas of Catalonia

This ecological study assesses the association between the incidence rate of COVID-19 confirmed cases and socioeconomic deprivation in the Catalan small areas for the first six waves of the pandemic. The association is estimated using Poisson regressions and, in contrast to previous studies, considering that the relationship is not linear but rather depends on the degree of deprivation. The results show that the association between deprivation and incidence varied between waves, not only in intensity but also in its sign. Although it was insignificant in the first, third and fourth waves, the association was positive and significant in the second, becoming significantly negative in the fifth and sixth waves. Interestingly, the evidence suggests that the link between both magnitudes was not homogeneous throughout the distribution of deprivation, the pattern also varying between waves. The results are discussed in view of the role of non-pharmacological interventions and vaccination, as well as potential biases (for example that associated with differences between population groups in the propensity to be tested in each wave).

Risk perceptions and behaviors concerning rural tourism and economic-political drivers of COVID-19 policy in 2020

When COVID-19 was first introduced to the United States, state and local governments enacted a variety of policies intended to mitigate the virulence of the epidemic. At the time, the most effective measures to prevent the spread of COVID-19 included stay-at-home orders, closing of nonessential businesses, and mask mandates. Although it was well known that regions with high population density and cold climates were at the highest risk for disease spread, rural counties that are economically reliant on tourism were incentivized to enact fewer precautions against COVID-19. The uncertainty of the COVID-19 pandemic, the multiple policies to reduce transmission, and the changes in outdoor recreation behavior had a significant impact on rural tourism destinations and management of protected spaces. We utilize fine-scale incidence and demographic data to study the relationship between local economic and political concerns, COVID-19 mitigation measures, and the subsequent severity of outbreaks throughout the continental United States. We also present results from an online survey that measured travel behavior, health risk perceptions, knowledge and experience with COVID-19, and evaluation of destination attributes by 407 out-of-state visitors who traveled to Maine from 2020 to 2021. We synthesize this research to present a narrative on how perceptions of COVID-19 risk and public perceptions of rural tourism put certain communities at greater risk of illness throughout 2020. This research could inform future rural destination management and public health policies to help reduce negative socioeconomic, health and environmental impacts of pandemic-derived changes in travel and outdoor recreation behavior.

Vaccine effectiveness of COVID-19 and rebound in the real world

We intend to evaluate the relationship between the rates of global SARS-CoV-2 vaccination and the number of COVID-19 confirmed cases, as well as the mortality rate after the declaration of a pandemic. Of the data from 191 countries at the time of data retraction, we selected 111 countries that have SARS-CoV-2 vaccination reports. We stratified countries into high-income and non-high-income countries (HIC and non-HIC) based on World Bank income-group. We used a fixed-effects model (FEM) and performed a longitudinal analysis. The number of confirmed cases decreased as the vaccination rates increased in both non-HICs (B =  - 0.027, T =  - 2.0) and HICs (B =  - 0.207, T =  - 17.5). The number of deaths decreased as the vaccination rates increased in both non-HICs (B =  - 0.151, T =  - 2.3) and HICs (B =  - 0.230, T =  - 40.9). For full vaccination, this measure had a negative association with daily confirmed cases and daily deaths in both non-HICs and HICs. In non-HICs, daily cases and daily deaths decreased as the first vaccination and full vaccination coverages increased. However in HICs, daily cases and daily deaths decreased as the first vaccination and full vaccination coverages increased in the early phase, but after a certain period, they tended to increase again. We observed a significant association between the increase in vaccination coverage in the real world and reduced daily confirmed cases and deaths. However, as the confirmed cases and deaths have rebounded in HICs, our findings indicate that COVID-19 is not completely prevented through vaccine distribution.

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.

COVID-19 infodemic and health-related quality of life in patients with chronic respiratory diseases: A multicentre, observational study

Background

The explosion of information, misinformation and disinformation (the "infodemic") related to the coronavirus disease 2019 (COVID-19) pandemic on digital and social media is reported to affect mental health and quality of life. However, reports assessing the COVID-19 infodemic on health-related quality of life (HRQL) in patients with chronic diseases are scarce. In this study, we investigated the associations between the infodemic and HRQL in uninfected individuals with pre-existing chronic respiratory diseases (CRDs) such as asthma, chronic obstructive pulmonary disease (COPD) and other CRDs.

Methods

We conducted a multi-national, cross-sectional, observational study in Canada, India, New Zealand and the United Kingdom where we distributed a set of digitised questionnaires among 1018 participants with chronic respiratory diseases who were not infected with the SARS-CoV-2 virus at least three months prior to the study. We collected information about the infodemic such as news watching or social media use more than usual during the pandemic. HRQL was assessed using the short form of the chronic respiratory questionnaire (SF-CRQ). Demographic information, comorbidities, compliance, mental health, behavioural function, and social support were also recorded. We analysed the direct and indirect relationships between infodemic and HRQL using structural equation models (SEM).

Results

Of all participants, 54% were females and had a mean (standard deviation (SD)) age of 53 (17) years. We found that higher infodemic was associated with worse emotional function (regression coefficient β = -0.08; 95% confidence interval (CI) = -0.14 to -0.01), which means a one SD change of the higher infodemic latent variable was associated with a 0.08 SD change of emotional function level. The association between higher infodemic and worse emotional function was mediated by worse mental health and behavioural functions but is marginally ameliorated by improved social support. In stratification analysis, we found significant disease and country-wise variations in the associations between infodemic and SF-CRQ domain scores.

Conclusions

These results provide new evidence that the COVID-19 infodemic significantly influences the HRQL in patients with CRDs through a complex interplay between mental health, behavioural function, and social support. This new dimension of research also opens avenues for further research on infodemic-related health effects in other chronic diseases.

Determinants of differences in RT-PCR testing rates among Southeast Asian countries during the first six months of the COVID-19 pandemic

A positive correlation has been demonstrated between gross domestic product (GDP) per capita and COVID-19 tests per 1000 people. Although frequently used as an indicator of economic performance, GDP per capita does not directly reflect income distribution inequalities and imposed health costs. In this longitudinal ecological study, we aimed to determine if, besides GDP per capita, indicators relating to governance, public health measures enforcement, and health and research investment explain differences in RT-PCR testing rates among countries in Southeast Asia (SEA) during the first six months of the COVID-19 pandemic. Using open-access COVID-19 panel data, we estimated the effect of various indicators (GDP per capita, health expenditure per capita, number of researchers per one million population, corruption perceptions index, stringency index, regional authority index) on daily COVID-19 testing by performing fixed-effects negative binomial regression. After accounting for all indicators, the number of daily confirmed COVID-19 cases, and population density, the model provided a 2019 GDP per capita coefficient of 0.0046330 (95% CI: 0.0040171, 0.0052488; p <0.001), indicating that a rise in 2019 GDP per capita by 100 international dollars is associated with a 46.33% increase in the number of daily tests performed. Additionally, all indicators were significantly associated with the daily number of RT-PCR testing on multivariable analysis. In conclusion, we identified different country-level indicators significantly associated with differences in COVID-19 testing rates among SEA countries. Due to the study's ecological design, we caution on applying our results to the individual level given potential for systematic differences between the included countries. Additional investigation is likewise needed to understand how government expenditure on healthcare may have impacted COVID-19 testing capacity during the initial stages of the pandemic.

Gender differences in estimated excess mortality during the COVID-19 pandemic in Thailand

BACKGROUND

There is a limited body of research specifically examining gender inequality in excess mortality and its variations across age groups and geographical locations during the COVID-19 pandemic. This study aims to fill this gap by analyzing the patterns of gender inequality in excess all-cause mortality in Thailand during the COVID-19 pandemic.

METHODS

Data pertaining to all-cause deaths and population between January 1, 2010, and December 31, 2021, were obtained from Thailand's Bureau of Registration Administration. A seasonal autoregressive integrated moving average (SARIMA) technique was used to estimate excess mortality during the pandemic between January 2020 to December 2021. Gender differential excess mortality was measured as the difference in age-standardized mortality rates between men and women.

RESULTS

Our SARIMA-based estimate of all-cause mortality in Thailand during the COVID-19 pandemic amounted to 1,032,921 deaths, with COVID-19-related fatalities surpassing official figures by 1.64 times. The analysis revealed fluctuating patterns of excess and deficit in all-cause mortality rates across different phases of the pandemic, as well as among various age groups and regions. In 2020, the most pronounced gender disparity in excess all-cause mortality emerged in April, with 4.28 additional female deaths per 100,000, whereas in 2021, the peak gender gap transpired in August, with 7.52 more male deaths per 100,000. Individuals in the 80 + age group exhibited the largest gender gap for most of the observed period. Gender differences in excess mortality were uniform across regions and over the period observed. Bangkok showed the highest gender disparity during the peak of the fourth wave, with 24.18 more male deaths per 100,000.

CONCLUSION

The findings indicate an overall presence of gender inequality in excess mortality during the COVID-19 pandemic in Thailand, observed across age groups and regions. These findings highlight the need for further attention to be paid to gender disparities in mortality and call for targeted interventions to address these disparities.

Social media trust predicts lower COVID-19 vaccination rates and higher excess mortality over 2 years

Trust plays a crucial role in implementing public health interventions against the COVID-19 pandemic. We examined the prospective associations of interpersonal, institutional, and media trust with vaccination rates and excess mortality over time in two multinational studies. In study 1, we investigated the country-level relationships between interpersonal trust, vaccination rates, and excess mortality across 54 countries. Interpersonal trust at the country level was calculated by aggregating data of 80,317 participants from the World Values Survey in 2017-20. Data on vaccination rates and excess mortality were obtained from the World Health Organization. Our findings indicated that higher levels of interpersonal trust were linked to higher vaccination rates and lower excess mortality rates in both 2020 and 2021. In study 2, we collected data from 18,171 adults in 35 countries/societies, stratified by age, gender, and region of residence. At the country/society level, interpersonal trust and trust in local healthcare facilities, local healthcare services, and healthcare professionals were associated with higher vaccination rates and lower excess mortality, whereas social media trust was associated with lower vaccination rates and higher excess mortality across three time points over 2 years. Our findings are robust when controlling for country-level covariates of the government stringency index, population density, and medical resources (i.e. critical care beds) in both studies.

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.

Cross-Risk Between Tuberculosis and COVID-19 in East Java Province, Indonesia: An Analysis of Tuberculosis and COVID-19 Surveillance Registry Period 2020-2022

INTRODUCTION

Tuberculosis (TB) and COVID-19 are highly transmissible diseases and pose a serious risk to public health. Unfortunately, information on cross-risk between the two diseases was still sparse. Our main objective was to estimate the excess risk among TB patients in getting COVID-19 infection and vice versa.

METHODS

The study design was a series of analyses of existing data from TB and COVID-19 registries in East Java Province, Indonesia. The study period was from January 2020 to June 2022. Case-by-case data for this study were obtained from the registration systems for TB and COVID-19 in separate databases. In comparing risk across different groups, adjusting for differences in risk factors that influence the outcome was essential. We overcame this problem by employing a standardized morbidity ratio.

RESULTS

Among 92,424 newly diagnosed TB patients, 1,326 were subsequently infected with COVID-19 during the study period, compared with 1,679 expected. The standardized morbidity ratio (95% confidence interval) was 72.61% (60.19%, 85.03%). Among 635,946 newly diagnosed COVID-19-infected patients, 987 subsequently got active TB during the study period against 1,679 expected. The standardized morbidity ratio (95% confidence interval) was 55.33% (49.24%, 61.42%).

CONCLUSION

There was no evidence of excess risk in either direction, the excess risk among TB patients in getting COVID-19 infection and vice versa.

Development of a COVID-19 Vulnerability Index (CVI) for the Counties and Residents of New Jersey, USA

The coronavirus disease 2019, or COVID-19, has impacted countless aspects of everyday life since it was declared a global pandemic by the World Health Organization in March of 2020. From societal to economic impacts, COVID-19 and its variants will leave a lasting impact on our society and the world. During the height of the pandemic, it became increasingly evident that indices, such as the Center for Disease Control's (CDC) Social Vulnerability Index (SVI), were instrumental in predicting vulnerabilities within a community. The CDC's SVI provides important estimates on which communities will be more susceptible to 'hazard events' by compiling a variety of data from the U.S. Census and the American Community Survey. The CDC's SVI does not directly consider the susceptibility of a community to a global pandemic, such as the COVID-19 pandemic, due to the four themes and 15 factors that contribute to the index. Thus, the objective of this research is to develop a COVID-19 Vulnerability Index, or CVI, to evaluate a community's susceptibility to future pandemics. With 15 factors considered for CDC's SVI, 26 other factors were also considered for the development of the CVI that covered themes such as socioeconomic status, environmental factors, healthcare capacity, epidemiological factors, and disability. All factors were equally weighted to calculate the CVI based on New Jersey. The CVI was validated by comparing index results to real-world COVID-19 data from New Jersey's 21 counties and CDC's SVI. The results present a stronger positive linear relationship between the CVI and the New Jersey COVID-19 mortality/population and infection/population than there is with the SVI. The results of this study indicate that Essex County has the highest CVI, and Hunterdon County has the lowest CVI. This is due to factors such as disparity in wealth, population density, minority status, and housing conditions, as well as other factors that were used to compose the CVI. The implications of this research will provide a critical tool for decision makers to utilize in allocating resources should another global pandemic occur. This CVI, developed through this research, can be used at the county, state, and global levels to help measure the vulnerability to future pandemics.

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.

Dynamics of SARS-CoV-2 seroprevalence during the first year of the COVID-19 pandemic in the Northeast region of Brazil

In this household-based seroepidemiological survey, we analyzed the dynamics of SARS-CoV-2 seroprevalence during the first year of the COVID-19 pandemic in Sergipe State, Northeast Brazil, the poorest region of the country. A total of 16,547 individuals were tested using a rapid IgM-IgG antibody test and fluorescence immunoassay (FIA). Seroprevalence rates were presented according to age, sex, and geographic region. A comparative analysis was performed between the results obtained in July 2020 (peak of the first wave), August - November 2020 (end of the first wave), and February - March 2021 (beginning of the second wave). Seroprevalence rates in the three phases were estimated at 9.3% (95% CI 8.5-10.1), 12.0% (95% CI 11.2-12.9) and 15.4% (95% CI 14.5-16.4). At the end of the first wave, there was a rise in seroprevalence in the countryside (p < 0.001). At the beginning of the second wave, we found an increase in seroprevalence among women (p < 0.001), adults aged 20 to 59 years (p < 0.001), and the elderly (p < 0.001). In this phase, we found an increase in estimates both in metropolitan areas and in the countryside (p < 0.001). This study showed an increase in SARS-CoV-2 seroprevalence over the first year of the pandemic, with approximately one in six people having anti-SARS-CoV-2 antibodies at the beginning of the second wave of COVID-19. Furthermore, our results suggest a rapid spread of COVID-19 from metropolitan areas to the countryside during the first months of the pandemic.

Estimation of the size of the COVID-19 pandemic using the epidemiological wavelength model: results from OECD countries

OBJECTIVES

This study aimed to simplify the previously developed epidemiological wavelength model and to expand the scope of the model with additional variables to estimate the magnitude of the COVID-19 pandemic. The applicability of the extended wavelength model was tested in Organisation for Economic Cooperation and Development (OECD) member countries.

STUDY DESIGN

The epidemiological wavelengths of OECD member countries for the years 2020, 2021 and 2022 were estimated comparatively, considering the cumulative number of COVID-19 cases.

METHODS

The size of the COVID-19 pandemic was estimated using the wavelength model. The scope of the wavelength model was expanded to include additional variables. The extended estimation model was improved by adding population density and human development index variables, in addition to the number of COVID-19 cases and number of days since the first case reported from the previous estimation model.

RESULTS

According to the findings obtained from the wavelength model, the country with the highest epidemiological wavelength for the years 2020, 2021 and 2022 was the United States (We = 29.96, We = 28.63 and We = 28.86, respectively), and the country with the lowest wavelength was Australia (We = 10.50, We = 13.14 and We = 18.44, respectively). The average wavelength score of OECD member countries was highest in 2022 (We = 24.32) and lowest in 2020 (We = 22.84). The differences in the periodic wavelengths of OECD countries were analysed with the dependent t-test for paired samples in two periods, 2020-2021 and 2021-2022. There was a statistically significant difference between wavelengths in the 2020-2021 and 2021-2022 groups (t(36) = -3.670; P < 0.001).

CONCLUSIONS

Decision-makers can use the extended wavelength model to easily follow the progress of the epidemic and to make quicker and more reliable decisions.

Privacy by Projection: Federated Population Density Estimation by Projecting on Random Features

We consider the problem of population density estimation based on location data crowdsourced from mobile devices, using kernel density estimation (KDE). In a conventional, centralized setting, KDE requires mobile users to upload their location data to a server, thus raising privacy concerns. Here, we propose a Federated KDE framework for estimating the user population density, which not only keeps location data on the devices but also provides probabilistic privacy guarantees against a malicious server that tries to infer users' location. Our approach Federated random Fourier feature (RFF) KDE leverages a random feature representation of the KDE solution, in which each user's information is irreversibly projected onto a small number of spatially delocalized basis functions, making precise localization impossible while still allowing population density estimation. We evaluate our method on both synthetic and real-world datasets, and we show that it achieves a better utility (estimation performance)-vs-privacy (distance between inferred and true locations) tradeoff, compared to state-of-the-art baselines (e.g., GeoInd). We also vary the number of basis functions per user, to further improve the privacy-utility trade-off, and we provide analytical bounds on localization as a function of areal unit size and kernel bandwidth.

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.

Detection of outlier prefectures on the mortality due to COVID-19 in Japan

Japan has suffered from COVID-19 with significant mortality, but its prefectural differences are not well investigated. Since the mortality due to COVID-19 was likely to be associated with the number of infected cases, the population density, and the proportion of the elderly population, we tried to detect the outlier prefectures by multiple linear regression analyses. With the use of the Hampel identifier, we found that Hokkaido and Hyogo were the outlier prefectures with higher mortality after adjusting the variables above. Further studies should delineate the causes of these differences.

Assessing the Fear Factor of Coronavirus Disease 2019 (COVID-19) in Korea Using the COVID-19 Phobia Scale: A Cross-Sectional Study

BACKGROUND

A study on coronavirus disease 2019 (COVID-19) phobia among students revealed that fear of contracting COVID-19 was associated with commuting to school and spending time with others at school. Therefore, it is the need-of-the-hour for the Korean government to identify factors affecting COVID-19 phobia among university students and to consider these factors while framing the policy direction for the process of returning to normalcy in university education. Consequently, we aimed to identify the current state of COVID-19 phobia among Korean undergraduate and graduate students and the factors affecting COVID-19 phobia.

METHODS

This cross-sectional survey was conducted to identify the factors affecting COVID-19 phobia among Korean undergraduate and graduate students. The survey collected 460 responses from April 5 to April 16, 2022. The questionnaire was developed based on the COVID-19 Phobia Scale (C19P-S). Multiple linear regression was performed on the C19P-S scores using five models with the following dependent variables: Model 1, total C19P-S score; Model 2, psychological subscale score; Model 3, psychosomatic subscale score; Model 4, social subscale score; and Model 5, economic subscale score. The fit of these five models was established, and a P-value of less than 0.05 (F test) was considered statistically significant.

RESULTS

An analysis of the factors affecting the total C19P-S score led to the following findings: women significantly outscored men (difference: 4.826 points, P = 0.003); the group that favored the government's COVID-19 mitigation policy scored significantly lower than those who did not favor it (difference: 3.161 points, P = 0.037); the group that avoided crowded places scored significantly higher than the group that did not avoid crowded places (difference: 7.200 points, P < 0.001); and those living with family/friends scored significantly higher than those in other living situations (difference: 4.606 points, P = 0.021). Those in favor of the COVID-19 mitigation policy had significantly lower psychological fear than those who were against it (difference: -1.686 points, P = 0.004). Psychological fear was also significantly higher for those who avoided crowded places compared to those who did not difference: 2.641 points, P < 0.001). Fear was significantly higher in people cohabitating than those living alone (difference: 1.543 points, P = 0.043).

CONCLUSION

The Korean government, in their pursuit of a policy that eases COVID-19-related restrictions, will also have to spare no efforts in providing correct information to prevent the escalation of COVID-19 phobia among people with a high fear of contracting the disease. This should be done through trustworthy information sources, such as the media, public agencies, and COVID-19 professionals.

Stochastic optimization for vaccine and testing kit allocation for the COVID-19 pandemic

We present a formal mathematical modeling framework for a multi-agent sequential decision problem during an epidemic. The problem is formulated as a collaboration between a vaccination agent and learning agent to allocate stockpiles of vaccines and tests to a set of zones under various types of uncertainty. The model is able to capture passive information processes and maintain beliefs over the uncertain state of the world. We designed a parameterized direct lookahead approximation which is robust and scalable under different scenarios, resource scarcity, and beliefs about the environment. We design a test allocation policy designed to capture the value of information and demonstrate that it outperforms other learning policies when there is an extreme shortage of resources (information is scarce). We simulate the model with two scenarios including a resource allocation problem to each state in the United States and another for the nursing homes in Nevada. The US example demonstrates the scalability of the model and the nursing home example demonstrates the robustness under extreme resource shortages.

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.

Excess Mortality during the COVID-19 Pandemic in Cities of Chile: Magnitude, Inequalities, and Urban Determinants

We estimated excess mortality in Chilean cities during the COVID-19 pandemic and its association with city-level factors. We used mortality, and social and built environment data from the SALURBAL study for 21 Chilean cities, composed of 81 municipalities or "comunas", grouped in 4 macroregions. We estimated excess mortality by comparing deaths from January 2020 up to June 2021 vs 2016-2019, using a generalized additive model. We estimated a total of 21,699 (95%CI 21,693 to 21,704) excess deaths across the 21 cities. Overall relative excess mortality was highest in the Metropolitan (Santiago) and the North regions (28.9% and 22.2%, respectively), followed by the South and Center regions (17.6% and 14.1%). At the city-level, the highest relative excess mortality was found in the Northern cities of Calama and Iquique (around 40%). Cities with higher residential overcrowding had higher excess mortality. In Santiago, capital of Chile, municipalities with higher educational attainment had lower relative excess mortality. These results provide insight into the heterogeneous impact of COVID-19 in Chile, which has served as a magnifier of preexisting urban health inequalities, exhibiting different impacts between and within cities. Delving into these findings could help prioritize strategies addressed to prevent deaths in more vulnerable communities.

Spatial heterogeneity affects predictions from early-curve fitting of pandemic outbreaks: a case study using population data from Denmark

The modelling of pandemics has become a critical aspect in modern society. Even though artificial intelligence can help the forecast, the implementation of ordinary differential equations which estimate the time development in the number of susceptible, (exposed), infected and recovered (SIR/SEIR) individuals is still important in order to understand the stage of the pandemic. These models are based on simplified assumptions which constitute approximations, but to what extent this are erroneous is not understood since many factors can affect the development. In this paper, we introduce an agent-based model including spatial clustering and heterogeneities in connectivity and infection strength. Based on Danish population data, we estimate how this impacts the early prediction of a pandemic and compare this to the long-term development. Our results show that early phase SEIR model predictions overestimate the peak number of infected and the equilibrium level by at least a factor of two. These results are robust to variations of parameters influencing connection distances and independent of the distribution of infection rates.

Mediation effect of diversity and availability of high transit service on transit oriented development and spread of COVID-19

The impact of the novel coronavirus disease (COVID-19) continues unabated. Still, it seems that apart from contact and respiratory transmission, the design and development pattern of an area does echoes to be a contributing factor in virus spreadability. The present study considers land use and transportation system parameters under TOD mode of 16 BRT station provinces in Bhopal, India, and COVID-19 cases data were collected from April 2020 to August 2020. Further, the Pearson correlation and mediational analysis were employed to determine the relationship between TODness and COVID-19 spread cases. The bootstrapping method was used to evaluate the mediation effect and describe why and under what conditions they are related. The study shows that TODness and COVID-19 spread cases are positively correlated. The results show a considerable correlation at (p < 0.05) is 0.405 of the dispersed along with TODness of an area in the analysed 16 BRT station areas. In particular, dispersed demonstrated a high-level correlation of 0.681 with TOD areas, whereas a moderate correlation of 0.322 with non-TOD areas was mediated by diversity and the number of available transit service indicators. Diversity and availability of high-quality transit services effectively spread the virus, whereas population density and public transport mediation effects are insignificant. Outcomes from this study may help government authorities and policymakers devise a strategy and adopt preventive measures in subsequent waves of the pandemic.

Graphical abstract

Regional Association between Mean Air Temperature and Case Numbers of Multiple SARS-CoV-2 Lineages throughout the Pandemic

The association between mean air temperature and new SARS-CoV-2 case numbers throughout the ongoing coronavirus disease 2019 (COVID-19) pandemic was investigated to identify whether diverse SARS-CoV-2 lineages may exhibit diverse environmental behaviors. The number of new COVID-19 daily cases in the province of Verona was obtained from the Veneto Regional Healthcare Service, whilst the mean daily air temperature during the same period was retrieved from the Regional Agency for Ambient Prevention and Protection of Veneto. A significant inverse correlation was found between new COVID-19 daily cases and mean air temperature in Verona up to Omicron BA.1/BA.2 predominance (correlation coefficients between −0.79 and −0.41). The correlation then became positive when the Omicron BA.4/BA.5 lineages were prevalent (r = 0.32). When the median value (and interquartile range; IQR) of new COVID-19 daily cases recorded during the warmer period of the year in Verona (June–July) was compared across the three years of the pandemic, a gradual increase could be seen over time, from 1 (IQR, 0–2) in 2020, to 22 (IQR, 11–113) in 2021, up to 890 (IQR, 343–1345) in 2022. These results suggest that measures for preventing SARS-CoV-2 infection should not be completely abandoned during the warmer periods of the year.

Assessing the impact of long-term exposure to nine outdoor air pollutants on COVID-19 spatial spread and related mortality in 107 Italian provinces

This paper investigates the air quality in 107 Italian provinces in the period 2014-2019 and the association between exposure to nine outdoor air pollutants and the COVID-19 spread and related mortality in the same areas. The methods used were negative binomial (NB) regression, ordinary least squares (OLS) model, and spatial autoregressive (SAR) model. The results showed that (i) common air pollutants-nitrogen dioxide (NO₂), ozone (O₃), and particulate matter (PM_2.5 and PM₁₀)-were highly and positively correlated with large firms, energy and gas consumption, public transports, and livestock sector; (ii) long-term exposure to NO₂, PM_2.5, PM₁₀, benzene, benzo[a]pyrene (BaP), and cadmium (Cd) was positively and significantly correlated with the spread of COVID-19; and (iii) long-term exposure to NO_2, O₃, PM_2.5, PM₁₀, and arsenic (As) was positively and significantly correlated with COVID-19 related mortality. Specifically, particulate matter and Cd showed the most adverse effect on COVID-19 prevalence; while particulate matter and As showed the largest dangerous impact on excess mortality rate. The results were confirmed even after controlling for eighteen covariates and spatial effects. This outcome seems of interest because benzene, BaP, and heavy metals (As and Cd) have not been considered at all in recent literature. It also suggests the need for a national strategy to drive down air pollutant concentrations to cope better with potential future pandemics.

Multifaceted Assessment of Wastewater-Based Epidemiology for SARS-CoV-2 in Selected Urban Communities in Davao City, Philippines: A Pilot Study

Over 60 countries have integrated wastewater-based epidemiology (WBE) in their COVID-19 surveillance programs, focusing on wastewater treatment plants (WWTP). In this paper, we piloted the assessment of SARS-CoV-2 WBE as a complementary public health surveillance method in susceptible communities in a highly urbanized city without WWTP in the Philippines by exploring the extraction and detection methods, evaluating the contribution of physico-chemical-anthropogenic factors, and attempting whole-genome sequencing (WGS). Weekly wastewater samples were collected from sewer pipes or creeks in six communities with moderate-to-high risk of COVID-19 transmission, as categorized by the City Government of Davao from November to December 2020. Physico-chemical properties of the wastewater and anthropogenic conditions of the sites were noted. Samples were concentrated using a PEG-NaCl precipitation method and analyzed by RT-PCR to detect the SARS-CoV-2 N, RdRP, and E genes. A subset of nine samples were subjected to WGS using the Minion sequencing platform. SARS-CoV-2 RNA was detected in twenty-two samples (91.7%) regardless of the presence of new cases. Cycle threshold values correlated with RNA concentration and attack rate. The lack of a sewershed map in the sampled areas highlights the need to integrate this in the WBE planning. A combined analysis of wastewater physico-chemical parameters such as flow rate, surface water temperature, salinity, dissolved oxygen, and total dissolved solids provided insights on the ideal sampling location, time, and method for WBE, and their impact on RNA recovery. The contribution of fecal matter in the wastewater may also be assessed through the coliform count and in the context of anthropogenic conditions in the area. Finally, our attempt on WGS detected single-nucleotide polymorphisms (SNPs) in wastewater which included clinically reported and newly identified mutations in the Philippines. This exploratory report provides a contextualized framework for applying WBE surveillance in low-sanitation areas.

Temporal Geospatial Analysis of COVID-19 Pre-Infection Determinants of Risk in South Carolina

Disparities and their geospatial patterns exist in morbidity and mortality of COVID-19 patients. When it comes to the infection rate, there is a dearth of research with respect to the disparity structure, its geospatial characteristics, and the pre-infection determinants of risk (PIDRs). This work aimed to assess the temporal-geospatial associations between PIDRs and COVID-19 infection at the county level in South Carolina. We used the spatial error model (SEM), spatial lag model (SLM), and conditional autoregressive model (CAR) as global models and the geographically weighted regression model (GWR) as a local model. The data were retrieved from multiple sources including USAFacts, U.S. Census Bureau, and the Population Estimates Program. The percentage of males and the unemployed population were positively associated with geodistributions of COVID-19 infection (p values < 0.05) in global models throughout the time. The percentage of the white population and the obesity rate showed divergent spatial correlations at different times of the pandemic. GWR models fit better than global models, suggesting nonstationary correlations between a region and its neighbors. Characterized by temporal-geospatial patterns, disparities in COVID-19 infection rate and their PIDRs are different from the mortality and morbidity of COVID-19 patients. Our findings suggest the importance of prioritizing different populations and developing tailored interventions at different times of the pandemic.

Counties with Lower Insurance Coverage and Housing Problems Are Associated with Both Slower Vaccine Rollout and Higher COVID-19 Incidence

Equitable vaccination distribution is a priority for outcompeting the transmission of COVID-19. Here, the impact of demographic, socioeconomic, and environmental factors on county-level vaccination rates and COVID-19 incidence changes is assessed. In particular, using data from 3142 US counties with over 328 million individuals, correlations were computed between cumulative vaccination rate and change in COVID-19 incidence from 1 December 2020 to 6 June 2021, with 44 different demographic, environmental, and socioeconomic factors. This correlation analysis was also performed using multivariate linear regression to adjust for age as a potential confounding variable. These correlation analyses demonstrated that counties with high levels of uninsured individuals have significantly lower COVID-19 vaccination rates (Spearman correlation: −0.460, p-value: <0.001). In addition, severe housing problems and high housing costs were strongly correlated with increased COVID-19 incidence (Spearman correlations: 0.335, 0.314, p-values: <0.001, <0.001). This study shows that socioeconomic factors are strongly correlated to both COVID-19 vaccination rates and incidence rates, underscoring the need to improve COVID-19 vaccination campaigns in marginalized communities.