Population density and basic reproductive number of COVID-19 across United States counties

The Impact of Wireless Emergency Alerts on a Floating Population in Seoul, South Korea: Panel Data Analysis

BACKGROUND

Wireless emergency alerts (WEAs), which deliver disaster information directly to individuals' mobile phones, have been widely used to provide information related to COVID-19 and to encourage compliance with social distancing guidelines during the COVID-19 pandemic. The floating population refers to the number of people temporarily staying in a specific area, and this demographic data can be a useful indicator to understand the level of social distancing people are complying with during the COVID-19 pandemic.

OBJECTIVE

This study aimed to empirically analyze the impact of WEAs on the floating population where WEAs were transmitted in the early stages of the COVID-19 pandemic. As most WEA messages focus on compliance with the government's social distancing guidelines, one of the goals of transmitting WEAs during the COVID-19 pandemic is to control the floating population at an appropriate level.

METHODS

We investigated the empirical impact of WEAs on the floating population across 25 districts in Seoul by estimating a panel regression model at the district-hour level with a series of fixed effects. The main independent variables were the number of instant WEAs, the daily cumulative number of WEAs, the total cumulative number of WEAs, and information extracted from WEAs by natural language processing at the district-hour level. The data set provided a highly informative empirical setting as WEAs were sent by different local governments with various identifiable district-hour-level data.

RESULTS

The estimates of the impact of WEAs on the floating population were significantly negative (-0.013, P=.02 to -0.014, P=.01) across all specifications, implying that an additional WEA issuance reduced the floating population by 1.3% (=100(1-e-0.013)) to 1.4% (=100(1-e-0.014)). Although the coefficients of DCN (the daily cumulative number of WEAs) were also negative (-0.0034, P=.34 to -0.0052, P=.15) across all models, they were not significant. The impact of WEAs on the floating population doubled (-0.025, P=.02 to -0.033, P=.005) when the first 82 days of observations were used as subsamples to reduce the possibility of people blocking WEAs.

CONCLUSIONS

Our results suggest that issuing WEAs and distributing information related to COVID-19 to a specific district was associated with a decrease in the floating population of that district. Furthermore, among the various types of information in the WEAs, location information was the only significant type of information that was related to a decrease in the floating population. This study makes important contributions. First, this study measured the impact of WEAs in a highly informative empirical setting. Second, this study adds to the existing literature on the mechanisms by which WEAs can affect public response. Lastly, this study has important implications for making optimal WEAs and suggests that location information should be included.

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.

Social Capital and Willingness to Comply With Anti-Pandemic Government Intervention

This study examines the relationship between individual-level social capital and compliance attitudes toward health protective measures in the context of COVID-19. We drew on secondary population-based data fielded during the pandemic's initial phase (April - June of 2020). The analytic sample consists of 9124 older American adults (ages 55 and over) across 18 U.S. States and Metropolitan Statistical Areas. We estimated mixed-effects models with random intercepts and slopes. People who are better socially connected are more willing to comply with anti-pandemic government intervention. This relationship is stronger among those who are more psychologically distressed. Its magnitude also increases in more densely populated areas and places with higher numbers of coronavirus infection. Older Americans' anti-coronavirus compliance attitudes is significantly driven by preexisting interpersonal connectedness and civic engagement. The role of social capital is also contingent on the existing levels of risk factor (threat and vulnerability).

Differential contagiousness of respiratory disease across the United States

The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, R0. This number depends on both pathogen and population properties. On the basis of compartmental models that reproduce Coronavirus Disease 2019 (COVID-19) surveillance data, we used Bayesian inference and the next-generation matrix approach to estimate region-specific R0 values for 280 of 384 metropolitan statistical areas (MSAs) in the United States (US), which account for 95% of the US population living in urban areas and 82% of the total population. We focused on MSA populations after finding that these populations were more uniformly impacted by COVID-19 than state populations. Our maximum a posteriori (MAP) estimates for R0 range from 1.9 to 7.7 and quantify the relative susceptibilities of regional populations to spread of respiratory diseases. ONE-SENTENCE SUMMARY: Initial contagiousness of Coronavirus Disease 2019 varied over a 4-fold range across urban areas of the United States.

Infection-Induced SARS-CoV-2 Seroprevalence among Blood Donors, Japan, 2022

A nationwide survey of SARS-CoV-2 antinucleocapsid seroprevalence among blood donors in Japan revealed that, as of November 2022, infection-induced seroprevalence of the population was 28.6% (95% CI 27.6%-29.6%). Seroprevalence studies might complement routine surveillance and ongoing monitoring efforts to provide a more complete real-time picture of COVID-19 burden.

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.

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.

Trajectories of COVID-19: A longitudinal analysis of many nations and subnational regions

The COVID-19 pandemic is the first to be rapidly and sequentially measured by nation-wide PCR community testing for the presence of the viral RNA at a global scale. We take advantage of the novel "natural experiment" where diverse nations and major subnational regions implemented various policies including social distancing and vaccination at different times with different levels of stringency and adherence. Initially, case numbers expand exponentially with doubling times of ~1-2 weeks. In the nations where interventions were not implemented or perhaps lees effectual, case numbers increased exponentially but then stabilized around 102-to-103 new infections (per km2 built-up area per day). Dynamics under effective interventions were perturbed and infections decayed to low levels. They rebounded concomitantly with the lifting of social distancing policies or pharmaceutical efficacy decline, converging on a stable equilibrium setpoint. Here we deploy a mathematical model which captures this V-shape behavior, incorporating a direct measure of intervention efficacy. Importantly, it allows the derivation of a maximal estimate for the basic reproductive number Ro (mean 1.6-1.8). We were able to test this approach by comparing the approximated "herd immunity" to the vaccination coverage observed that corresponded to rapid declines in community infections during 2021. The estimates reported here agree with the observed phenomena. Moreover, the decay (0.4-0.5) and rebound rates (0.2-0.3) were similar throughout the pandemic and among all the nations and regions studied. Finally, a longitudinal analysis comparing multiple national and regional results provides insights on the underlying epidemiology of SARS-CoV-2 and intervention efficacy, as well as evidence for the existence of an endemic steady state of COVID-19.

Novel Approach for Identification of Basic and Effective Reproduction Numbers Illustrated with COVID-19

This paper presents a novel numerical technique for the identification of effective and basic reproduction numbers, Re and R0, for long-term epidemics, using an inverse problem approach. The method is based on the direct integration of the SIR (Susceptible-Infectious-Removed) system of ordinary differential equations and the least-squares method. Simulations were conducted using official COVID-19 data for the United States and Canada, and for the states of Georgia, Texas, and Louisiana, for a period of two years and ten months. The results demonstrate the applicability of the method in simulating the dynamics of the epidemic and reveal an interesting relationship between the number of currently infectious individuals and the effective reproduction number, which is a useful tool for predicting the epidemic dynamics. For all conducted experiments, the results show that the local maximum (and minimum) values of the time-dependent effective reproduction number occur approximately three weeks before the local maximum (and minimum) values of the number of currently infectious individuals. This work provides a novel and efficient approach for the identification of time-dependent epidemics parameters.

Understanding the challenges to COVID-19 vaccines and treatment options, herd immunity and probability of reinfection

Unlike pandemics in the past, the outbreak of coronavirus disease 2019 (COVID-19), which rapidly spread worldwide, was met with a different approach to control and measures implemented across affected countries. The lack of understanding of the fundamental nature of the outbreak continues to make COVID-19 challenging to manage for both healthcare practitioners and the scientific community. Challenges to vaccine development and evaluation, current therapeutic options, convalescent plasma therapy, herd immunity, and the emergence of reinfection and new variants remain the major obstacles to combating COVID-19. This review discusses these challenges in the management of COVID-19 at length and highlights the mechanisms needed to provide better understanding of this pandemic.

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.

A spatiotemporal analysis of the social determinants of health for COVID-19

This research aims to uncover how the association between social determinants of health and COVID-19 cases and fatality rate have changed across time and space. To begin to understand these associations and show the benefits of analysing temporal and spatial variations in COVID-19, we utilized Geographically Weighted Regression (GWR). The results emphasize the advantages for using GWR in data with a spatial component, while showing the changing spatiotemporal magnitude of association between a given social determinant and cases or fatalities. While previous research has demonstrated the merits of GWR for spatial epidemiology, our study fills a gap in the literature, by examining a suite of variables across time to reveal how the pandemic unfolded across the US at a county-level spatial scale. The results speak to the importance of understanding the local effects that a social determinant may have on populations at the county level. From a public health perspective, these results can be used for an understanding of the disproportionate disease burden felt by different populations, while upholding and building upon trends observed in epidemiological literature.

How oral intake, mobility, and activity measures can inform discharge recommendations: A retrospective analysis of hospitalized inmate and non-inmate COVID-19 patients

BACKGROUND

Patients who were incarcerated were disproportionately impacted by COVID-19 compared to the general public. Further, the impact of multidisciplinary rehabilitation assessments and interventions on the outcomes of patients admitted to the hospital with COVID-19 is limited.

OBJECTIVE

To compare functional outcomes of oral intake, mobility, and activity between inmates and non-inmates who were diagnosed with COVID-19 and examine the relationships among these functional measures and discharge destination.

METHODS

A retrospective analysis was performed on patients admitted to the hospital for COVID-19 at a large academic medical center. Scores on functional measures including Functional Oral Intake Scale (FOIS) and Activity Measure for Post-Acute Care (AM-PAC) were collected and compared between inmates and non-inmates. Binary logistic regression models were used to evaluate the odds of 1) whether patients were discharged to the same place they were admitted and 2) patients being discharged with a total oral diet with no restrictions. Independent variables were considered significant if the 95% CIs of the odds ratios (ORs) did not include 1.0.

RESULTS

A total of 83 patients (inmates, n=38; non-inmates, n=45) were included in the final analysis. There were no differences between inmates and non-inmates on initial (P=.39) and final FOIS scores (P=.35) or on initial, final, or change scores (P>.05) on the AM-PAC. When examining separate regression models using AM-PAC mobility or AM-PAC activity scores as independent variables, greater age upon admission decreased the odds (OR=0.922; 95%CI=0.875 to 0.972 and OR=0.918; 95%CI=0.871 to 0.968) of patients being discharged with a total oral diet with no restrictions. The following factors increased the odds of patients being discharged to the same place they were admitted from: being an inmate (OR=5.285; 95%CI= 1.334 to 20.931) and (OR=6.083; 95%CI=1.548 to 23.912); "Other" race (OR=7.596; 95%CI=1.203 to 47.968) and (OR=8.515; 95%CI=1.311 to 55.291); and female sex (OR=4.671; 95%CI=1.086 to 20.092) and (OR=4.977; 95%CI=1.146 to 21.615).

CONCLUSIONS

Results from this study provide an opportunity to learn how functional measures may be used to better understand discharge outcomes in both inmate and non-inmate patients admitted to the hospital with COVID-19 during the initial period of the pandemic.

CLINICALTRIAL

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.

Extensive SARS-CoV-2 testing reveals BA.1/BA.2 asymptomatic rates and underreporting in school children

Background

Case underreporting during the coronavirus disease 2019 (COVID-19) pandemic has been a major challenge to the planning and evaluation of public health responses. School children were often considered a less vulnerable population and underreporting rates may have been particularly high. In January 2022, the Canadian province of Newfoundland and Labrador (NL) was experiencing an Omicron variant outbreak (BA.1/BA.2 subvariants) and public health officials recommended that all returning students complete two rapid antigen tests (RATs) to be performed three days apart.

Methods

To estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we asked parents and guardians to report the results of the RATs completed by K-12 students (approximately 59,000 students) using an online survey.

Results

When comparing the survey responses with the number of cases and tests reported by the NL testing system, we found that one out of every 4.3 (95% CI, 3.1-5.3) positive households were captured by provincial case count, with 5.1% positivity estimated from the RAT results and 1.2% positivity reported by the provincial testing system. Of positive test results, 62.9% (95% CI, 44.3-83.0) were reported for elementary school students, and the remaining 37.1% (95% CI, 22.7-52.9) were reported for junior high and high school students. Asymptomatic infections were 59.8% of the positive cases. Given the low survey participation rate (3.5%), our results may suffer from sample selection biases and should be interpreted with caution.

Conclusion

The underreporting ratio is consistent with ratios calculated from serology data and provides insights into infection prevalence and asymptomatic infections in school children; a currently understudied population.

Assessing COVID-19 pandemic policies and behaviours and their economic and educational trade-offs across US states from Jan 1, 2020, to July 31, 2022: an observational analysis

BACKGROUND

The USA struggled in responding to the COVID-19 pandemic, but not all states struggled equally. Identifying the factors associated with cross-state variation in infection and mortality rates could help to improve responses to this and future pandemics. We sought to answer five key policy-relevant questions regarding the following: 1) what roles social, economic, and racial inequities had in interstate variation in COVID-19 outcomes; 2) whether states with greater health-care and public health capacity had better outcomes; 3) how politics influenced the results; 4) whether states that imposed more policy mandates and sustained them longer had better outcomes; and 5) whether there were trade-offs between a state having fewer cumulative SARS-CoV-2 infections and total COVID-19 deaths and its economic and educational outcomes.

METHODS

Data disaggregated by US state were extracted from public databases, including COVID-19 infection and mortality estimates from the Institute for Health Metrics and Evaluation's (IHME) COVID-19 database; Bureau of Economic Analysis data on state gross domestic product (GDP); Federal Reserve economic data on employment rates; National Center for Education Statistics data on student standardised test scores; and US Census Bureau data on race and ethnicity by state. We standardised infection rates for population density and death rates for age and the prevalence of major comorbidities to facilitate comparison of states' successes in mitigating the effects of COVID-19. We regressed these health outcomes on prepandemic state characteristics (such as educational attainment and health spending per capita), policies adopted by states during the pandemic (such as mask mandates and business closures), and population-level behavioural responses (such as vaccine coverage and mobility). We explored potential mechanisms connecting state-level factors to individual-level behaviours using linear regression. We quantified reductions in state GDP, employment, and student test scores during the pandemic to identify policy and behavioural responses associated with these outcomes and to assess trade-offs between these outcomes and COVID-19 outcomes. Significance was defined as p<0·05.

FINDINGS

Standardised cumulative COVID-19 death rates for the period from Jan 1, 2020, to July 31, 2022 varied across the USA (national rate 372 deaths per 100 000 population [95% uncertainty interval [UI] 364-379]), with the lowest standardised rates in Hawaii (147 deaths per 100 000 [127-196]) and New Hampshire (215 per 100 000 [183-271]) and the highest in Arizona (581 per 100 000 [509-672]) and Washington, DC (526 per 100 000 [425-631]). A lower poverty rate, higher mean number of years of education, and a greater proportion of people expressing interpersonal trust were statistically associated with lower infection and death rates, and states where larger percentages of the population identify as Black (non-Hispanic) or Hispanic were associated with higher cumulative death rates. Access to quality health care (measured by the IHME's Healthcare Access and Quality Index) was associated with fewer total COVID-19 deaths and SARS-CoV-2 infections, but higher public health spending and more public health personnel per capita were not, at the state level. The political affiliation of the state governor was not associated with lower SARS-CoV-2 infection or COVID-19 death rates, but worse COVID-19 outcomes were associated with the proportion of a state's voters who voted for the 2020 Republican presidential candidate. State governments' uses of protective mandates were associated with lower infection rates, as were mask use, lower mobility, and higher vaccination rate, while vaccination rates were associated with lower death rates. State GDP and student reading test scores were not associated with state COVD-19 policy responses, infection rates, or death rates. Employment, however, had a statistically significant relationship with restaurant closures and greater infections and deaths: on average, 1574 (95% UI 884-7107) additional infections per 10 000 population were associated in states with a one percentage point increase in employment rate. Several policy mandates and protective behaviours were associated with lower fourth-grade mathematics test scores, but our study results did not find a link to state-level estimates of school closures.

INTERPRETATION

COVID-19 magnified the polarisation and persistent social, economic, and racial inequities that already existed across US society, but the next pandemic threat need not do the same. US states that mitigated those structural inequalities, deployed science-based interventions such as vaccination and targeted vaccine mandates, and promoted their adoption across society were able to match the best-performing nations in minimising COVID-19 death rates. These findings could contribute to the design and targeting of clinical and policy interventions to facilitate better health outcomes in future crises.

FUNDING

Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.

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.

TransCode: Uncovering COVID-19 transmission patterns via deep learning

BACKGROUND

The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning.

METHODS

We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors. First, in Hong Kong, China, we construct the mobility trajectories of confirmed cases using their visiting records. Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution. Integrating the spatial and temporal information, we represent the TransCode via spatiotemporal transmission networks. Further, we propose a deep transfer learning model to adapt the TransCode of Hong Kong, China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises: New York City, San Francisco, Toronto, London, Berlin, and Tokyo, where fine-scale data are limited. All the data used in this study are publicly available.

RESULTS

The TransCode of Hong Kong, China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns (e.g., the imported and exported transmission intensities) at the district and constituency levels during different COVID-19 outbreaks waves. By adapting the TransCode of Hong Kong, China to other data-limited densely populated metropolises, the proposed method outperforms other representative methods by more than 10% in terms of the prediction accuracy of the disease dynamics (i.e., the trend of case numbers), and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.

CONCLUSIONS

The fine-scale transmission patterns due to the metapopulation level mobility (e.g., travel across different districts) and contact behaviors (e.g., gathering in social-economic centers) are one of the main contributors to the rapid spread of the virus. Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.

Social divisions and risk perception drive divergent epidemics and large later waves

During infectious disease outbreaks, individuals may adopt protective measures like vaccination and physical distancing in response to awareness of disease burden. Prior work showed how feedbacks between epidemic intensity and awareness-based behaviour shape disease dynamics. These models often overlook social divisions, where population subgroups may be disproportionately impacted by a disease and more responsive to the effects of disease within their group. We develop a compartmental model of disease transmission and awareness-based protective behaviour in a population split into two groups to explore the impacts of awareness separation (relatively greater in- vs. out-group awareness of epidemic severity) and mixing separation (relatively greater in- vs. out-group contact rates). Using simulations, we show that groups that are more separated in awareness have smaller differences in mortality. Fatigue (i.e. abandonment of protective measures over time) can drive additional infection waves that can even exceed the size of the initial wave, particularly if uniform awareness drives early protection in one group, leaving that group largely susceptible to future infection. Counterintuitively, vaccine or infection-acquired immunity that is more protective against transmission and mortality may indirectly lead to more infections by reducing perceived risk of infection and therefore vaccine uptake. Awareness-based protective behaviour, including awareness separation, can fundamentally alter disease dynamics. Social media summary: Depending on group division, behaviour based on perceived risk can change epidemic dynamics & produce large later waves.

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.

Early Introduction and Community Transmission of SARS-CoV-2 Omicron Variant, New York, New York, USA

The Omicron variant of SARS-CoV-2 has become dominant in most countries and has raised significant global health concerns. As a global commerce center, New York, New York, USA, constantly faces the risk for multiple variant introductions of SARS-CoV-2. To elucidate the introduction and transmission of the Omicron variant in the city of New York, we created a comprehensive genomic and epidemiologic analysis of 392 Omicron virus specimens collected during November 25-December 11, 2021. We found evidence of 4 independent introductions of Omicron subclades, including the Omicron subclade BA.1.1 with defining substitution of R346K in the spike protein. The continuous genetic divergence within each Omicron subclade revealed their local community transmission and co-circulation in New York, including both household and workplace transmissions supported by epidemiologic evidence. Our study highlights the urgent need for enhanced genomic surveillance and effective response planning for better prevention and management of emerging SARS-CoV-2 variants.

Geographic disparities in COVID-19 testing and outcomes in Florida

BACKGROUND

Understanding geographic disparities in Coronavirus Disease 2019 (COVID-19) testing and outcomes at the local level during the early stages of the pandemic can guide policies, inform allocation of control and prevention resources, and provide valuable baseline data to evaluate the effectiveness of interventions for mitigating health, economic and social impacts. Therefore, the objective of this study was to identify geographic disparities in COVID-19 testing, incidence, hospitalizations, and deaths during the first five months of the pandemic in Florida.  METHODS: Florida county-level COVID-19 data for the time period March-July 2020 were used to compute various COVID-19 metrics including testing rates, positivity rates, incidence risks, percent of hospitalized cases, hospitalization risks, case-fatality rates, and mortality risks. High or low risk clusters were identified using either Kulldorff's circular spatial scan statistics or Tango's flexible spatial scan statistics and their locations were visually displayed using QGIS.

RESULTS

Visual examination of spatial patterns showed high estimates of all COVID-19 metrics for Southern Florida. Similar to the spatial patterns, high-risk clusters for testing and positivity rates and all COVID-19 outcomes (i.e. hospitalizations and deaths) were concentrated in Southern Florida. The distributions of these metrics in the other parts of Florida were more heterogeneous. For instance, testing rates for parts of Northwest Florida were well below the state median (11,697 tests/100,000 persons) but they were above the state median for North Central Florida. The incidence risks for Northwest Florida were equal to or above the state median incidence risk (878 cases/100,000 persons), but the converse was true for parts of North Central Florida. Consequently, a cluster of high testing rates was identified in North Central Florida, while a cluster of low testing rate and 1-3 clusters of high incidence risks, percent of hospitalized cases, hospitalization risks, and case fatality rates were identified in Northwest Florida. Central Florida had low-rate clusters of testing and positivity rates but it had a high-risk cluster of percent of hospitalized cases.

CONCLUSIONS

Substantial disparities in the spatial distribution of COVID-19 outcomes and testing and positivity rates exist in Florida, with Southern Florida counties generally having higher testing and positivity rates and more severe outcomes (i.e. hospitalizations and deaths) compared to Northern Florida. These findings provide valuable baseline data that is useful for assessing the effectiveness of preventive interventions, such as vaccinations, in various geographic locations in the state. Future studies will need to assess changes in spatial patterns over time at lower geographical scales and determinants of any identified patterns.

Has the COVID-19 pandemic converged across countries?

The outbreak of COVID-19 has induced economic and financial disruptions to global economies, consistent with those experienced during previous episodes of economic or financial crises. This study offers a critical perspective into the spread of the virus by investigating the convergence patterns of COVID-19 across 155 countries from March 2020 to August 2021. The club clustering algorithm is used to verify the convergence patterns of infection and death rates in these countries. The findings show that full panel convergence cannot be achieved indicating the presence of sub-convergent clusters. Cluster formation for death rates includes the Americas, Africa, the Middle East, and Asia, among others. To understand the factors driving these results, we analyse the determinants of the convergence process of COVID-19. The probability of belonging to a cluster with higher death intensity increases with being above the age of 65, poverty, and for female smokers while handwashing shows beneficial effect on case intensity.

Measuring efficiency of the global fight against the COVID-19 pandemic

Objectives

The ongoing COVID-19 pandemic has led to an unprecedented loss of life and a severe economic downturn across the globe. Countries have adopted various social distancing and vaccination policies to reduce the spread of the disease and lessen the impact on healthcare systems. The world should work together to confront the disaster and challenge of COVID-19.

Methods

This study uses stochastic frontier analysis to measure the efficiency and influencing factors of the global response to COVID-19 epidemics and to provide follow-up strategies and reference guidelines.

Results

The results of this study show that (1) the average efficiency of the global response to COVID-19 is not good, with significant space for improvement of up to 60%; (2) adequate medical supplies and equipment can reduce mortality; (3) the initial implementation of social distancing policies and wearing masks can effectively reduce the infection rate; and (4) as infection rates and vaccination rates increase so that most people have basic immunity to COVID-19, the epidemic will gradually be reduced.

Conclusions

As the world becomes more aware of the COVID-19 disease, humans will gradually return to normal social interaction and lifestyles. The results of this study are expected to provide a reference for the future direction of the global fight against epidemics and the improvement of public health policies.

A global spatial analysis of factors associated with case and mortality rates for coronavirus disease 2019 during the first year of the pandemic

BACKGROUND

A increasing number of studies have revealed associations between country-level determinants and coronavirus disease 2019 (COVID-19) outcomes. This ecological study was conducted to analyze country-level parameters related to COVID-19 infections and deaths during the first year of the pandemic.

METHODS

The examined predictors comprised demographics, economic factors, disease prevalence and healthcare system status, and the relevant data were obtained from public databases. The index dates were set to 15 July 2020 (Time 1) and 15 December 2020 (Time 2). The adjusted spatial autoregression models used a first-order queen contiguity spatial weight for the main analysis and a second-order queen contiguity spatial weight for a sensitivity analysis to examine the predictors associated with COVID-19 case and mortality rates.

RESULTS

Obesity was significantly and positively associated with COVID-19 case and mortality rates in both the main and sensitivity analyses. The sensitivity analysis revealed that a country's gross domestic product, population density, life expectancy and proportion of the population older than 65 y are positively associated with COVID-19 case and mortality rates.

CONCLUSIONS

With the increasing global prevalence of obesity, the relationship between obesity and COVID-19 disease at the country level must be clarified and continually monitored.

Using a real-world network to model the trade-off between stay-at-home restriction, vaccination, social distancing and working hours on COVID-19 dynamics

Background

Human behaviour, economic activity, vaccination, and social distancing are inseparably entangled in epidemic management. This study aims to investigate the effects of various parameters such as stay-at-home restrictions, work hours, vaccination, and social distance on the containment of pandemics such as COVID-19.

Methods

To achieve this, we have developed an agent based model based on a time-dynamic graph with stochastic transmission events. The graph is constructed from a real-world social network. The edges of graph have been categorized into three categories: home, workplaces, and social environment. The conditions needed to mitigate the spread of wild-type COVID-19 and the delta variant have been analyzed. Our purposeful agent based model has carefully executed tens of thousands of individual-based simulations. We propose simple relationships for the trade-offs between effective reproduction number (R _e), transmission rate, working hours, vaccination, and stay-at-home restrictions.

Results

We have found that the effect of a 13.6% increase in vaccination for wild-type (WT) COVID-19 is equivalent to reducing four hours of work or a one-day stay-at-home restriction. For the delta, 20.2% vaccination has the same effect. Also, since we can keep track of household and non-household infections, we observed that the change in household transmission rate does not significantly alter the R _e. Household infections are not limited by transmission rate due to the high frequency of connections. For the specifications of COVID-19, the R _e depends on the non-household transmissions rate.

Conclusions

Our findings highlight that decreasing working hours is the least effective among the non-pharmaceutical interventions. Our results suggest that policymakers decrease work-related activities as a last resort and should probably not do so when the effects are minimal, as shown. Furthermore, the enforcement of stay-at-home restrictions is moderately effective and can be used in conjunction with other measures if absolutely necessary.

Preventative practices and effects of the COVID-19 pandemic on caregivers of children with pediatric pulmonary hypertension

BACKGROUND

Pulmonary hypertension (PH) is a serious and life-threatening disease characterized by elevated mean arterial pressure and pulmonary vascular resistance. COVID-19 may exacerbate PH, as evidenced by higher mortality rates among those with PH. The objective of this study was to understand the unique burdens that the COVID-19 pandemic has placed upon families of children living with PH.

METHODS

Participants were recruited online through the "Families of children with pulmonary hypertension" Facebook group and asked to complete a survey about their experiences during the COVID-19 pandemic.

RESULTS

A total of 139 parents/caregivers of children living with PH completed the online survey. Almost all (85.6%) of parents/caregivers had received the COVID-19 vaccine, though only 59.7% reported a willingness to vaccinate their child with PH against COVID-19. Over 75% of parents/caregivers felt that they practiced preventative measures (e.g., wearing a facemask, social distancing, and avoiding gatherings) more than those in the community where they live. They also reported several hardships related to caring for their child with PH during the pandemic such as financial duress, loss of work, and affording treatment costs.

CONCLUSIONS

These findings indicate that parents/caregivers of children at higher risk for COVID-19 complications may be more willing to act on clinical recommendations themselves as proxy for protecting those at high risk. The economic, emotional and social impacts of COVID-19 are significantly greater for high-risk individuals.

Do the determinants of COVID-19 transmission differ by epidemic wave? Evidence from U.S. counties

This paper uses data from the United States to examine determinants of the spread of COVID-19 during three different epidemic waves. We address how sociodemographic and economic attributes, industry composition, density, crowding in housing, and COVID-19-related variables are associated with the transmission of COVID-19. After controlling for spatial autocorrelation, our findings indicate that the percentage of people in poverty, number of restaurants, and percentage of workers teleworking were associated with the COVID-19 incidence rate during all three waves. Our results also show that dense areas were more vulnerable to the transmission of COVID-19 after the first epidemic wave. Regarding the density of supermarkets, our study elaborates the negative aspects of wholesale retail stores, which likely provide a vulnerable place for virus transmission. Our results suggest that sociodemographic and economic attributes were the determinants of the early phase of the pandemic, while density showed positive association with the transmission during subsequent waves. We provide implications for regions serving as gateway cities with high density and number of population. To add, we further provide evidence that non-pharmaceutical interventions in the early stage may mitigate the virus transmission.

A comparison of COVID-19 outbreaks across US Combined Statistical Areas using new methods for estimating R0 and social distancing behaviour

We investigated the initial outbreak rates and subsequent social distancing behaviour over the initial phase of the COVID-19 pandemic across 29 Combined Statistical Areas (CSAs) of the United States. We used the Numerus Model Builder Data and Simulation Analysis (NMB-DASA) web application to fit the exponential phase of a SCLAIV+D (Susceptible, Contact, Latent, Asymptomatic infectious, symptomatic Infectious, Vaccinated, Dead) disease classes model to outbreaks, thereby allowing us to obtain an estimate of the basic reproductive number R₀ for each CSA. Values of R₀ ranged from 1.9 to 9.4, with a mean and standard deviation of 4.5±1.8. Fixing the parameters from the exponential fit, we again used NMB-DASA to estimate a set of social distancing behaviour parameters to compute an epidemic flattening index c_flatten. Finally, we applied hierarchical clustering methods using this index to divide CSA outbreaks into two clusters: those presenting a social distancing response that was either weaker or stronger. We found c_flatten to be more influential in the clustering process than R₀. Thus, our results suggest that the behavioural response after a short initial exponential growth phase is likely to be more determinative of the rise of an epidemic than R₀ itself.

Outcomes of a Community Engagement and Information Gathering Program to Support Telephone-Based COVID-19 Contact Tracing: Descriptive Analysis

BACKGROUND

Contact tracing is an important public health tool for curbing the spread of infectious diseases. Effective and efficient contact tracing involves the rapid identification of individuals with infection and their exposed contacts and ensuring their isolation or quarantine, respectively. Manual contact tracing via telephone call and digital proximity app technology have been key strategies in mitigating the spread of COVID-19. However, many people are not reached for COVID-19 contact tracing due to missing telephone numbers or nonresponse to telephone calls. The New York City COVID-19 Trace program augmented the efforts of telephone-based contact tracers with information gatherers (IGs) to search and obtain telephone numbers or residential addresses, and community engagement specialists (CESs) made home visits to individuals that were not contacted via telephone calls.

OBJECTIVE

The aim of this study was to assess the contribution of information gathering and home visits to the yields of COVID-19 contact tracing in New York City.

METHODS

IGs looked for phone numbers or addresses when records were missing phone numbers to locate case-patients or contacts. CESs made home visits to case-patients and contacts with no phone numbers or those who were not reached by telephone-based tracers. Contact tracing management software was used to triage and queue assignments for the telephone-based tracers, IGs, and CESs. We measured the outcomes of contact tracing-related tasks performed by the IGs and CESs from July 2020 to June 2021.

RESULTS

Of 659,484 cases and 861,566 contact records in the Trace system, 28% (185,485) of cases and 35% (303,550) of contacts were referred to IGs. IGs obtained new phone numbers for 33% (61,804) of case-patients and 11% (31,951) of contacts; 50% (31,019) of the case-patients and 46% (14,604) of the contacts with new phone numbers completed interviews; 25% (167,815) of case-patients and 8% (72,437) of contacts were referred to CESs. CESs attempted 80% (132,781) of case and 69% (49,846) of contact investigations, of which 47% (62,733) and 50% (25,015) respectively, completed interviews. An additional 12,192 contacts were identified following IG investigations and 13,507 following CES interventions.

CONCLUSIONS

Gathering new or missing locating information and making home visits increased the number of case-patients and contacts interviewed for contact tracing and resulted in additional contacts. When possible, contact tracing programs should add information gathering and home visiting strategies to increase COVID-19 contact tracing coverage and yields as well as promote equity in the delivery of this public health intervention.

Comorbidities, sociodemographic factors, and determinants of health on COVID-19 fatalities in the United States

Previous studies have evaluated comorbidities and sociodemographic factors individually or by type but not comprehensively. This study aims to analyze the influence of a wide variety of factors in a single study to better understand the big picture of their effects on case-fatalities. This cross-sectional study used county-level comorbidities, social determinants of health such as income and race, measures of preventive healthcare, age, education level, average household size, population density, and political voting patterns were all evaluated on a national and regional basis. Analysis was performed through Generalized Additive Models and adjusted by the COVID-19 Community Vulnerability Index (CCVI). Effect estimates of COVID-19 fatality rates for risk factors such as comorbidities, sociodemographic factors and determinant of health. Factors associated with reducing COVID-19 fatality rates were mostly sociodemographic factors such as age, education and income, and preventive health measures. Obesity, minimal leisurely activity, binge drinking, and higher rates of individuals taking high blood pressure medication were associated with increased case fatality rate in a county. Political leaning influenced case case-fatality rates. Regional trends showed contrasting effects where larger household size was protective in the Midwest, yet harmful in Northeast. Notably, higher rates of respiratory comorbidities such as asthma and chronic obstructive pulmonary disease (COPD) diagnosis were associated with reduced case-fatality rates in the Northeast. Increased rates of chronic kidney disease (CKD) within counties were often the strongest predictor of increased case-fatality rates for several regions. Our findings highlight the importance of considering the full context when evaluating contributing factors to case-fatality rates. The spectrum of factors identified in this study must be analyzed in the context of one another and not in isolation.

Modeling COVID-19 Transmission Dynamics: A Bibliometric Review

A good amount of research has evolved just in three years in COVID-19 transmission, mortality, vaccination, and some socioeconomic studies. A few bibliometric reviews have already been performed in the literature, especially on the broad theme of COVID-19, without any particular area such as transmission, mortality, or vaccination. This paper fills this gap by conducting a bibliometric review on COVID-19 transmission as the first of its kind. The main aim of this study is to conduct a bibliometric review of the literature in the area of COVID-19 transmission dynamics. We have conducted bibliometric analysis using descriptive and network analysis methods to review the literature in this area using RStudio, Openrefine, VOSviewer, and Tableau. We reviewed 1103 articles published in 2020-2022. The result identified the top authors, top disciplines, research patterns, and hotspots and gave us clear directions for classifying research topics in this area. New research areas are rapidly emerging in this area, which needs constant observation by researchers to combat this global epidemic.

Spatiotemporal Bayesian estimation of the number of under-reported COVID-19 cases in Victoria Australia

Having an estimate of the number of under-reported cases is crucial in determining the true burden of a disease. In the COVID-19 pandemic, there is a great need to quantify the true disease burden by capturing the true incidence rate to establish appropriate measures and strategies to combat the disease. This study investigates the under-reporting of COVID-19 cases in Victoria, Australia, during the third wave of the pandemic as a result of variation in geographic area and time. It is aimed to determine potential under-reported areas and generate the true picture of the disease in terms of the number of cases. A two-tiered Bayesian hierarchical model approach is employed to estimate the true incidence and detection rates through Bayesian model averaging. The proposed model goes beyond testing inequality across areas by looking into other covariates such as weather, vaccination rates, and access to vaccination and testing centres, including interactions and variations between space and time. This model aims for parsimony yet allows a broader range of scope to capture the underlying dynamic of the reported COVID-19 cases. Moreover, it is a data-driven, flexible, and generalisable model to a global context such as cross-country estimation and across time points under strict pandemic conditions.

Comparison of the Effective Reproduction Number (Rt) Estimation Methods of COVID-19 Using Simulation Data Based on Available Data from Iran, USA, UK, India, and Brazil

BACKGROUND

Accurate determination of the effective reproduction number (Rt) is a very important strategy in the epidemiology of contagious diseases, including coronavirus disease 2019 (COVID-19). This study compares different methods of estimating the Rt of susceptible population to identify the most accurate method for estimating Rt.

STUDY DESIGN

A secondary study.

METHODS

The value of Rt was estimated using attack rate (AR), exponential growth (EG), maximum likelihood (ML), time-dependent (TD), and sequential Bayesian (SB) methods, for Iran, the United States, the United Kingdom, India, and Brazil from June to October 2021. In order to accurately compare these methods, a simulation study was designed using forty scenarios.

RESULTS

The lowest mean square error (MSE) was observed for TD and ML methods, with 15 and 12 cases, respectively. Therefore, considering the estimated values of Rt based on the TD method, it was found that Rt values in the United Kingdom (1.33; 95% CI: 1.14-1.52) and the United States (1.25; 95% CI: 1.12-1.38) substantially have been more than those in other countries, such as Iran (1.07; 95% CI: 0.95-1.19), India (0.99; 95% CI: 0.89-1.08), and Brazil (0.98; 95% CI: 0.84-1.14) from June to October 2021.

CONCLUSION

The important result of this study is that TD and ML methods lead to a more accurate estimation of Rt of population than other methods. Therefore, in order to monitor and determine the epidemic situation and have a more accurate prediction of the incidence rate, as well as control COVID-19 and similar diseases, the use of these two methods is suggested to more accurately estimate Rt.

Genomic profile of SARS-CoV-2 Omicron variant and its correlation with disease severity in Rajasthan

Background

Omicron, a new variant of Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2), was first detected in November 2021. This was believed to be highly transmissible and was reported to evade immunity. As a result, an urgent need was felt to screen all positive samples so as to rapidly identify Omicron cases and isolate them to prevent the spread of infection. Genomic surveillance of SARS-CoV-2 was planned to correlate disease severity with the genomic profile.

Methods

All the SARS-CoV-2 positive cases detected in the state of Rajasthan were sent to our Lab. Samples received from 24 November 2021 to 4 January 2022 were selected for Next-Generation Sequencing (NGS). Processing was done as per protocol on the Ion Torrent S5 System for 1,210 samples and bioinformatics analysis was done.

Results

Among the 1,210 samples tested, 762 (62.9%) were Delta/Delta-like and other lineages, 291 (24%) were Omicron, and 157 (12.9%) were invalid or repeat samples. Within a month, the proportion of Delta and other variants was reversed, 6% Omicron became 81%, and Delta and other variants became 19%, initially all Omicron cases were seen in international travelers and their contacts but soon community transmission was seen. The majority of patients with Omicron were asymptomatic (56.7%) or had mild disease (33%), 9.2% had moderate symptoms, and two (0.7%) had severe disease requiring hospitalization, of which one (0.3%) died and the rest were (99.7%) recovered. History of vaccination was seen in 81.1%, of the previous infection in 43.2% of cases. Among the Omicron cases, BA.1 (62.8%) was the predominant lineage followed by BA.2 (23.7%) and B.1.529 (13.4%), rising trends were seen initially for BA.1 and later for BA.2 also. Although 8.9% of patients with Delta lineage during that period were hospitalized, 7.2% required oxygen, and 0.9% died. To conclude, the community spread of Omicron occurred in a short time and became the predominant circulating variant; BA.1 was the predominant lineage detected. Most of the cases with Omicron were asymptomatic or had mild disease, and the mortality rate was very low as compared to Delta and other lineages.

Adaptive SIR model with vaccination: simultaneous identification of rates and functions illustrated with COVID-19

An Adaptive Susceptible-Infected-Removed-Vaccinated (A-SIRV) epidemic model with time-dependent transmission and removal rates is constructed for investigating the dynamics of an epidemic disease such as the COVID-19 pandemic. Real data of COVID-19 spread is used for the simultaneous identification of the unknown time-dependent rates and functions participating in the A-SIRV system. The inverse problem is formulated and solved numerically using the Method of Variational Imbedding, which reduces the inverse problem to a problem for minimizing a properly constructed functional for obtaining the sought values. To illustrate and validate the proposed solution approach, the present study used available public data for several countries with diverse population and vaccination dynamics—the World, Israel, The United States of America, and Japan.

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

Connected in health: Place-to-place commuting networks and COVID-19 spillovers

Biweekly county COVID-19 data were linked with Longitudinal Employer-Household Dynamics data to analyze population risk exposures enabled by pre-pandemic, country-wide commuter networks. Results from fixed-effects, spatial, and computational statistical approaches showed that commuting network exposure to COVID-19 predicted an area's COVID-19 cases and deaths, indicating spillovers. Commuting spillovers between counties were independent from geographic contiguity, pandemic-time mobility, or social media ties. Results suggest that commuting connections form enduring social linkages with effects on health that can withstand mobility disruptions. Findings contribute to a growing relational view of health and place, with implications for neighborhood effects research and place-based policies.

Voting, contagion and the trade-off between public health and political rights: Quasi-experimental evidence from the Italian 2020 polls

Natural disasters raise challenging trade-offs between public health safety and inalienable rights like the active involvement in political choices through voting. We exploit a quasi-experimental setting provided by multiple ballots across regions and municipalities during the Italian 2020 elections to estimate the effect of voters' turnout on the spread of COVID-19. By employing an event-study design with a two-stage Control Function strategy, we find that post-poll new COVID infections increased by an average of 1.1% for each additional percentage point of turnout. Based on these estimates and real political events, we also show through a simulation that in-person voting during a high-infection regime may have a large impact on public health outcomes, more than doubling new infections, deaths and hospitalizations. These findings suggest that policy-makers' responses to natural disasters should be flexible and contingent to the emergency severity, in order to minimize social costs for citizens.

A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties

A mature and hybrid machine-learning model is verified by mature empirical analysis to measure county-level COVID-19 vulnerability and track the impact of the imposition of pandemic control policies in the U.S. A total of 30 county-level social, economic, and medical variables and a timeline of the imposed policies constitutes a COVID-19 database. A hybrid feature-selection model composed of four machine-learning algorithms is developed to emphasize the regional impact of community features on the case fatality rate (CFR). A COVID-19 vulnerability index (COVULin) is proposed to measure the county's vulnerability, the effects of model's parameters on mortality, and the efficiency of control policies. The results showed that the dense counties in which minority groups represent more than 45% of the population and those with poverty rates greater than 24% were the most vulnerable counties during the first and the last pandemic peaks, respectively. Highly-correlated CFR and COVULin scores indicated a close agreement between the model outcomes and COVID-19 impacts. Counties with higher poverty and uninsured rates were the most resistant to government intervention. It is anticipated that the proposed model can play an essential role in identifying vulnerable communities and help reduce damages during long-term alike disasters.

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.

Comparison of effectiveness of enhanced infection countermeasures in different scenarios, using a dynamic-spread-function model

When formulating countermeasures to epidemics such as those generated by COVID-19, estimates of the benefits of a given intervention for a specific population are highly beneficial to policy makers. A recently introduced tool, known as the "dynamic-spread" SIR model, can perform population-specific risk assessment. Behavior is quantified by the dynamic-spread function, which includes the mechanisms of droplet reduction using facemasks and transmission control due to social distancing. The spread function is calibrated using infection data from a previous wave of the infection, or other data felt to accurately represent the population behaviors. The model then computes the rate of spread of the infection for different hypothesized interventions, over the time window for the calibration data. The dynamic-spread model was used to assess the benefit of three enhanced intervention strategies - increased mask filtration efficiency, higher mask compliance, and elevated social distancing - in four COVID-19 scenarios occurring in 2020: the first wave (i.e. until the first peak in numbers of new infections) in New York City; the first wave in New York State; the spread aboard the Diamond Princess Cruise Liner; and the peak occurring after re-opening in Harris County, Texas. Differences in the efficacy of the same intervention in the different scenarios were estimated. As an example, when the average outward filtration efficiency for facemasks worn in New York City was increased from an assumed baseline of 67% to a hypothesized 90%, the calculated peak number of new infections per day decreased by 40%. For the same baseline and hypothesized filtration efficiencies aboard the Diamond Princess Cruise liner, the calculated peak number of new infections per day decreased by about 15%. An important factor contributing to the difference between the two scenarios is the lower mask compliance (derivable from the spread function) aboard the Diamond Princess.

The Town-Level Prevalence of Chronic Lung Conditions and Death From COVID-19 Among Older Adults in Connecticut and Rhode Island

Introduction

As of November 2021, older adults (aged ≥65 y) accounted for 81% of all deaths from COVID-19 in the US. Chronic lung diseases increase the risk for severe COVID-19 illness and death. The aim of this research was to examine the association between town-level rates of asthma and chronic obstructive pulmonary disease (COPD) and deaths from COVID-19 in 208 towns in Connecticut and Rhode Island.

Methods

We conducted a multistep analysis to examine the association between town-level chronic lung conditions and death from COVID-19. Pairwise correlations were estimated and bivariate maps were created to assess the relationship between COVID-19 deaths per 100,000 people and 1) asthma prevalence and 2) COPD prevalence among adults aged 65 years or older. We used multiple linear regression models to examine whether chronic lung conditions and other town-level factors were associated with COVID-19 death rates in Connecticut and Rhode Island.

Results

Initial bivariate correlation and mapping analyses suggested positive correlations between asthma and COPD prevalence and COVID-19 death rates. However, after controlling for town-level factors associated with chronic lung conditions and COVID-19 death rates, multiple linear regression models did not support an association, but town-level factors (African American race and Hispanic ethnicity, age ≥65 y, and low educational attainment) were significant predictors of COVID-19 death rates.

Conclusion

We found significant associations between town-level factors and COVID-19, adding to the current understanding of the impact of social determinants of health on outcomes.

Tracking the molecular evolution and transmission patterns of SARS-CoV-2 lineage B.1.466.2 in Indonesia based on genomic surveillance data

Background

As a new epi-center of COVID-19 in Asia and a densely populated developing country, Indonesia is facing unprecedented challenges in public health. SARS-CoV-2 lineage B.1.466.2 was reported to be an indigenous dominant strain in Indonesia (once second only to the Delta variant). However, it remains unclear how this variant evolved and spread within such an archipelagic nation.

Methods

For statistical description, the spatiotemporal distributions of the B.1.466.2 variant were plotted using the publicly accessible metadata in GISAID. A total of 1302 complete genome sequences of Indonesian B.1.466.2 strains with high coverage were downloaded from the GISAID’s EpiCoV database on 28 August 2021. To determine the molecular evolutionary characteristics, we performed a time-scaled phylogenetic analysis using the maximum likelihood algorithm and called the single nucleotide variants taking the Wuhan-Hu-1 sequence as reference. To investigate the spatiotemporal transmission patterns, we estimated two dynamic parameters (effective population size and effective reproduction number) and reconstructed the phylogeography among different islands.

Results

As of the end of August 2021, nearly 85% of the global SARS-CoV-2 lineage B.1.466.2 sequences (including the first one) were obtained from Indonesia. This variant was estimated to account for over 50% of Indonesia’s daily infections during the period of March–May 2021. The time-scaled phylogeny suggested that SARS-CoV-2 lineage B.1.466.2 circulating in Indonesia might have originated from Java Island in mid-June 2020 and had evolved into two disproportional and distinct sub-lineages. High-frequency non-synonymous mutations were mostly found in the spike and NSP3; the S-D614G/N439K/P681R co-mutations were identified in its larger sub-lineage. The demographic history was inferred to have experienced four phases, with an exponential growth from October 2020 to February 2021. The effective reproduction number was estimated to have reached its peak (11.18) in late December 2020 and dropped to be less than one after early May 2021. The relevant phylogeography showed that Java and Sumatra might successively act as epi-centers and form a stable transmission loop. Additionally, several long-distance transmission links across seas were revealed.

Conclusions

SARS-CoV-2 variants circulating in the tropical archipelago may follow unique patterns of evolution and transmission. Continuous, extensive and targeted genomic surveillance is essential.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12985-022-01830-1.

Inter-Country COVID-19 Contagiousness Variation in Eight African Countries

The estimates of contiguousness parameters of an epidemic have been used for health-related policy and control measures such as non-pharmaceutical control interventions (NPIs). The estimates have varied by demographics, epidemic phase, and geographical region. Our aim was to estimate four contagiousness parameters: basic reproduction number (R₀), contact rate, removal rate, and infectious period of coronavirus disease 2019 (COVID-19) among eight African countries, namely Angola, Botswana, Egypt, Ethiopia, Malawi, Nigeria, South Africa, and Tunisia using Susceptible, Infectious, or Recovered (SIR) epidemic models for the period 1 January 2020 to 31 December 2021. For reference, we also estimated these parameters for three of COVID-19's most severely affected countries: Brazil, India, and the USA. The basic reproduction number, contact and remove rates, and infectious period ranged from 1.11 to 1.59, 0.53 to 1.0, 0.39 to 0.81; and 1.23 to 2.59 for the eight African countries. For the USA, Brazil, and India these were 1.94, 0.66, 0.34, and 2.94; 1.62, 0.62, 0.38, and 2.62, and 1.55, 0.61, 0.39, and 2.55, respectively. The average COVID-19 related case fatality rate for 8 African countries in this study was estimated to be 2.86%. Contact and removal rates among an affected African population were positively and significantly associated with COVID-19 related deaths (p-value < 0.003). The larger than one estimates of the basic reproductive number in the studies of African countries indicate that COVID-19 was still being transmitted exponentially by the 31 December 2021, though at different rates. The spread was even higher for the three countries with substantial COVID-19 outbreaks. The lower removal rates in the USA, Brazil, and India could be indicative of lower death rates (a proxy for good health systems). Our findings of variation in the estimate of COVID-19 contagiousness parameters imply that countries in the region may implement differential COVID-19 containment measures.

Genetic diversity of SARS-CoV-2 infections in Ghana from 2020-2021

The COVID-19 pandemic is one of the fastest evolving pandemics in recent history. As such, the SARS-CoV-2 viral evolution needs to be continuously tracked. This study sequenced 1123 SARS-CoV-2 genomes from patient isolates (121 from arriving travellers and 1002 from communities) to track the molecular evolution and spatio-temporal dynamics of the SARS-CoV-2 variants in Ghana. The data show that initial local transmission was dominated by B.1.1 lineage, but the second wave was overwhelmingly driven by the Alpha variant. Subsequently, an unheralded variant under monitoring, B.1.1.318, dominated transmission from April to June 2021 before being displaced by Delta variants, which were introduced into community transmission in May 2021. Mutational analysis indicated that variants that took hold in Ghana harboured transmission enhancing and immune escape spike substitutions. The observed rapid viral evolution demonstrates the potential for emergence of novel variants with greater mutational fitness as observed in other parts of the world.

Language and the cultural markers of COVID-19

Despite its universal nature, the impact of COVID-19 has not been geographically homogeneous. While certain countries and regions have been severely affected, registering record infection rates and excess deaths, others experienced only milder outbreaks. We investigate to what extent human factors, in particular cultural origins reflected in different attitudes and behavioural norms, can explain different degrees of exposure to the virus. Motivated by the linguistic relativity hypothesis, we take language as a proxy for cultural origins and exploit the exogenous variation in the language spoken around the border that divides the French- and German-speaking parts of Switzerland to estimate the impact of culture on exposure to COVID-19. The results obtained using a spatial regression discontinuity design reveal, that within 50- and 25- kilometres bandwidth from the language border, the average COVID-19 exposure levels for individuals in French speaking municipalities was higher. In particular, we find that German speaking municipalities were associated with a reduction of around 40% - 50% in the odds of COVID-19 exposure compared to the French speaking municipalities.

Reconsidering the role of place in health and welfare services: lessons from the COVID-19 pandemic in the United States and Canada

Places-the meaningful locations of daily life-have been central to the wellbeing of humans since they first formed social groups, providing a stable base for individuals, families, and communities. In the United States and Canada, as elsewhere, place also plays a foundational role in the provision of critical social and health services and resources. Yet the globally destabilizing events of the COVID-19 pandemic have dramatically challenged the concept, experience, and meaning of place. Place-centered public health measures such as lockdowns and stay-at-home orders have disrupted and transformed homes, neighborhoods, workplaces, and schools. These measures stressed families and communities, particularly among marginalized groups, and made the delivery of vital resources and services more difficult. At the same time, the pandemic has stimulated a range of creative and resilient responses. Building from an overview of these effects and drawing conceptually on theories of people-place relationships, this paper argues for critical attention to reconsidering and re-envisioning prevailing assumptions about place-centric policies, services, and practices. Such reappraisal is vital to ensuring that, going forward, scholars, policymakers, and practitioners can effectively design and deliver services capable of maintaining social connections, safety, and wellbeing in contexts of uncertainty, inequality, and flux.