Community vulnerability and mobility: What matters most in spatio-temporal modeling of the COVID-19 pandemic?

Relationships between COVID-19 healthcare outcomes and county characteristics in the U.S. for Delta (B.1.617.2) and Omicron (B.1.1.529 and BA.1.1) variants

Background

COVID-19 is constantly evolving, and highly populated communities consist of many different characteristics that may contribute to COVID-19 health outcomes. Therefore, we aimed to (1) quantify the relationships between county characteristics and severe and non-severe county-level health outcomes related to COVID-19. We also aimed to (2) compare these relationships across time periods where the Delta (B.1.617.2) and Omicron (B.1.1.529 and BA.1.1) variants were dominant in the U.S.

Methods

We used multiple regression to measure the strength of relationships between healthcare outcomes and county characteristics in the 50 most populous U.S. counties.

Results

We found many different significant predictors including the proportion of a population vaccinated, median household income, population density, and the proportion of residents aged 65+, but mainly found that socioeconomic factors and the proportion of a population vaccinated play a large role in the dynamics of the spread and severity of COVID-19 in communities with high populations.

Discussion

The present study shines light on the associations between public health outcomes and county characteristics and how these relationships change throughout Delta and Omicron's dominance. It is important to understand factors underlying COVID-19 health outcomes to prepare for future health crises.

Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study

Background

Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia.

Methods

We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model.

Findings

When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale.

Interpretation

The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue.

Funding

Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).

The Effects of Time Window-Averaged Mobility on Effective Reproduction Number of COVID-19 Viral Variants in Urban Cities

During epidemics, the estimation of the effective reproduction number (ERN) associated with infectious disease is a challenging topic for policy development and medical resource management. The emergence of new viral variants is common in widespread pandemics including the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A simple approach is required toward an appropriate and timely policy decision for understanding the potential ERN of new variants is required for policy revision. We investigated time-averaged mobility at transit stations as a surrogate to correlate with the ERN using the data from three urban prefectures in Japan. The optimal time windows, i.e., latency and duration, for the mobility to relate with the ERN were investigated. The optimal latency and duration were 5-6 and 8 days, respectively (the Spearman's ρ was 0.109-0.512 in Tokyo, 0.365-0.607 in Osaka, and 0.317-0.631 in Aichi). The same linear correlation was confirmed in Singapore and London. The mobility-adjusted ERN of the Alpha variant was 15-30%, which was 20-40% higher than the original Wuhan strain in Osaka, Aichi, and London. Similarly, the mobility-adjusted ERN of the Delta variant was 20%-40% higher than that of the Wuhan strain in Osaka and Aichi. The proposed metric would be useful for the proper evaluation of the infectivity of different SARS-CoV-2 variants in terms of ERN as well as the design of the forecasting system.

COVID-19, stigma, and habituation: evidence from mobility data

BACKGROUND

The Japanese government has restricted people's going-out behavior by declaring a non-punitive state of emergency several times under COVID-19. This study aims to analyze how multiple policy interventions that impose non-legally binding restrictions on behavior associate with people's going-out.

THEORY

This study models the stigma model of self-restraint behavior under the pandemic with habituation effects. The theoretical result indicates that the state of emergency's self-restraint effects weaken with the number of times.

METHODS

The empirical analysis examines the impact of emergency declarations on going-out behavior using a prefecture-level daily panel dataset. The dataset includes Google's going-out behavior data, the Japanese government's policy interventions based on emergency declarations, and covariates that affect going-out behavior, such as weather and holidays.

RESULTS

First, for multiple emergency declarations from the beginning of the pandemic to 2021, the negative association between emergency declarations and mobility was confirmed in a model that did not distinguish the number of emergency declarations. Second, in the model that considers the number of declarations, the negative association was found to decrease with the number of declarations.

CONCLUSION

These empirical analyses are consistent with the results of theoretical analyses, which show that the negative association between people's going-out behavior and emergency declarations decreases in magnitude as the number of declarations increases.

Population flow based spatial-temporal eigenvector filtering modeling for exploring effects of health risk factors on COVID-19

The COVID-19 pandemic has had great impact on human health and social economy. Several studies examined spatial and temporal patterns of health risk factors associated with COVID-19, but population flow spillover effect has not been sufficiently considered. In this paper, a population flow-based spatial-temporal eigenvector filtering model (FLOW-ESTF) was developed to consider spatial-temporal patterns and population flow connectivity simultaneously. The proposed FLOW-ESTF method efficiently improved model prediction accuracy, which could help the government aware of the infection risk level and to make suitable control policies. The selected population flow spatial-temporal eigenvector contributed most to modeling and the visualization of corresponding eigenvector set helped to explore the underlying spatial-temporal patterns and pandemic transmission nodes. The model coefficients could reflect how health risk factors contribute the modeling of state-level COVID-19 weekly increased cases and how their influence changed through time, which could help people and government to better aware the potential health risks and to adjust control measures at different stage. The extracted population flow spatial-temporal eigenvector not only represents influence of population flow and its spillover effects but also represents some possible omitted health risk factors. This could provide an efficient path to solve the problem of spatial and temporal autocorrelation in COVID-19 modeling and an intuitive way to discover underlying spatial patterns, which will partially compensate for the problems of insufficient consideration of potential risk variables and missing data.

Spatial-temporal trends of COVID-19 infection and mortality in Sudan

Since its emergence, the coronavirus disease 2019 (COVID-19), is constantly affecting many parts of the globe and threatening millions of lives worldwide. Charting and aligning disease incidence to identify spatial clustering and patterns continue to be a substantial pathway to understanding disease epidemiology and is essential for implementing effective planning and prevention strategies. A national descriptive study was implemented to present the infection and mortality rates of the COVID-19 pandemic in all states of Sudan. Data were collected and summarized in monthly statistical reports of COVID-19 infection and mortality rates. The reports used were from May 2020 to March 2021. The highest COVID-19 incidence rate occurred in December 2020 with a total incidence of 4863 cases ranging from 0 cases in some of the states to 4164 cases in other states (mean = 270 ± 946, median = 21 cases). Followed by the incidence in May 2020 with a total of 4524 cases ranging from 4 to 3509 cases (mean = 251 ± 794, median = 31 cases). The western and southern states of the country had the lowest mortality rates. While, the middle states (Khartoum and El Gezira) had the highest mortalities. Northern and eastern states had lower mortalities than the middle states, yet, higher than the western states. A strong positive correlation between infection and mortality was found.

Thanksgiving and Christmas gatherings before the 2020-21 winter surge of COVID-19 in the United States

Objective

COVID-19 in the US disproportionately affected, and continues to affect, racial/ethnic minorities. Although risky social gatherings for Thanksgiving and Christmas in 2020 contributed substantially to the "winter surge" in cases and deaths, no research examines potential racial/ethnic differences in behaviors related to holiday gatherings.

Design

We used the Understanding America Survey (UAS) - Coronavirus Tracking, a nationally representative study of US adults, to examine associations between race/ethnicity and risky holiday gathering behavior (i.e., gathering with non-household members and with little to no social distancing or mask-wearing). We applied logistic regression models to examine racial/ethnic and socioeconomic differences in risky holiday gatherings while accounting for a person's pre-holiday perception of COVID-19 risk as well as related behaviors.

Results

Non-Hispanic Black adults showed a lower prevalence of attending a risky Thanksgiving gathering than did non-Hispanic White adults (15 % vs 43 %, p <.001). The magnitude of this racial/ethnic difference was also found for risky Christmas gatherings. Hispanic and "Other" race/ethnicity adults also appeared less likely than non-Hispanic whites to attend a risky holiday gathering. Higher-income households attended a risky holiday gathering more frequently, when compared with lower income households (p <.001). Logistic regression results, which controlled for other COVID-19 related behaviors, support these main findings.

Conclusions

Racial/ethnic minorities, and non-Hispanic Black adults in particular, appeared least likely to have engaged in risky holiday gatherings in late 2020. If replicated, our findings appear consistent with the notion that behavioral modification among racial/ethnic minorities may have reduced the intensity of the 2020/21 "winter surge" in COVID-19.

Key Influencing Factors and Optimization Strategy of Epidemic Resilience in Urban Communities-A Case Study of Nanjing, China

COVID-19 has posed a significantly severe impact on both people’s lives and the global economic development. Increasing the community epidemic resilience will considerably improve the national public health emergency response capacity from bottom to top. This study identifies the influencing factors of community epidemic resilience through systematic literature review under the 4R framework, then obtains the relationships of influencing factors through Interpretive structural model, and finally assesses the performance of epidemic resilience using PROMETHEE II method through empirical cases in Nanjing, China. The results show that: (1) Eight factors influencing the epidemic resilience of community are identified, and the economic level plays the root role; (2) Community epidemic resilience can be improved from robustness, rapidity, redundancy and resourcefulness aspects; (3) Through the empirical analysis, the epidemic resilience ranking of community can be displayed (Community D > T > S > F); (4) Additionally, the performance and sensitivity analysis of influencing factors in each community can be demonstrated. (5) Finally, four implications are proposed, namely, allocating public resources rationally, significantly increasing the economic level, ensuring the accuracy of information delivery and conducting disaster learning.

Exploring Health Impacts of Occupational Exposure to Carbon Monoxide in the Labour Community of Hattar Industrial Estate

This study was designed to assess the health impacts related to noninvasive carbon monoxide saturation (SPCO %) in the blood of respondents. For this purpose, 150 respondents from the labour community of Hattar Industrial Estate (testing site) and 100 respondents from Sultan Pur (control site) were selected. To achieve this objective, a Rad-57 Pulse CO-Oximeter was used for noninvasive carboxyhemoglobin measurement. Carbon monoxide saturation (SPCO%) in the blood of respondents from Hattar Industrial Estate, Haripur, Pakistan has been compared with the WHO’s standard concentration of SPCO% (5%). High saturation of carbon monoxide (carboxyhemoglobin SPCO) in the blood of respondents and disease association have been interpreted in graphs formed on the basis of statistical analysis in terms of frequencies, using statistical software (SPSS), based on demographic entries as well as exposure time of the employees in the processing, food and steel industries. The highest SPCO% measured was 17% in the steel industry and the lowest measured level was 4.2%. Frequencies and percentages of respiratory inflammation, dermatosis, asthma, breathing issues and eye inflammation among respondents were 29%, 35%, 16.7%, 23.5% and 9%, respectively. Prevalence of disease in three different groups of respondents (from three testing sites) was also analyzed on the basis of exposure time (hrs.) to carbon monoxide emissions. Prevalence of disease among the exposed and non-exposed groups was analyzed and showed comparatively lower disease prevalence in the group of respondents who were not exposed to high carbon monoxide emissions. The data of the current study was also subjected to statistical modelling to find the health risk of air pollutants (carbon monoxide) on population health by calculating attributable risk (AR) or attributable proportion (AP). Results indicated that attributable risk of carbon monoxide exposure for respiratory diseases, dermatosis and eye inflammation were 61.12%, 65.77% and 24.95% respectively. Findings of statistical modelling indicated that dermatosis and respiratory diseases were more prevalent in laborers of industrial units than those at control site.