Spatiotemporal Characteristics of the COVID-19 Epidemic in the United States

Spatiotemporal Ecologic Analysis of COVID-19 Vaccination Coverage and Outcomes, Oklahoma, USA, February 2020-December 2021

Data on COVID-19 cases, deaths, hospitalizations, and vaccinations in Oklahoma, USA, have not been systematically described. The relationship between vaccination and COVID-19-related outcomes over time has not been investigated. We graphically described data collected during February 2020-December 2021 and conducted spatiotemporal modeling of monthly increases in COVID-19 cumulative death and hospitalization rates, adjusting for cumulative case rate, to explore the relationship. A 1 percentage point increase (absolute change) in the cumulative vaccination rate was associated with a 6.3% (95% CI 1.4%-10.9%) relative decrease in death outcome during April-June 2021, and a 1.9% (95% CI 1.1%-2.6%) relative decrease in death outcome and 1.1% (95% CI 0.5%-1.7%) relative decrease in hospitalization outcome during July-December 2021; the effect on hospitalizations was driven largely by data from urban counties. Our findings from Oklahoma suggest that increasing cumulative vaccination rates might reduce the increase in cumulative death and hospitalization rates from COVID-19.

Drivers and collaborative governance of public health emergency response in the context of digital city

Introduction

With the frequent occurrence of public health events, the government inevitably makes many mistakes in emergency management. In modern emergency management, it is particularly important to promote the diversification of emergency management subjects and improve the government's emergency management ability.

Methods

In order to make up for the deficiency of government's participation in public health emergency management, this paper analyzes the driving factors and driving effects of enterprises' participation in public health emergency response under the background of digital city. A fully explained structural model is used to analyze the relationship between the different drivers. In addition, the spatial and temporal distribution characteristics of public health events were analyzed through spatial auto-correlation. On this basis, the government cooperative governance strategy is discussed.

Results and discussion

The results show that in the context of digital cities, there are 14 driving factors for enterprises to participate in public health emergency response. The most important factors are the company's own development needs, relative technical advantages and so on. The driving efficiency is mainly concentrated in three aspects: psychology, resources and structure. Public health events have periodicity in time distribution and regional differences in spatial distribution. The significance of this study is to help the government improve the emergency management ability from different angles.

The transmission network and spatial-temporal distributions of COVID-19: A case study in Lanzhou, China

Public emergencies exert substantial adverse effects on the socioeconomic development of cities. Investigating the transmission characteristics of COVID-19 can lead to evidence-based strategies for future pandemic intervention and prevention. Drawing upon primary COVID-19 data collected at both the street level and from individuals with confirmed cases in Lanzhou, China, our study examined the spatial-temporal distribution of the pandemic at a detailed level. First, we constructed transmission networks based on social relationships and spatial behavior to elucidate the actual natural transmission chain of COVID-19. We then analyze key information regarding pandemic spread, such as superspreaders, superspreading places, and peak hours. Furthermore, we constructed a space-time path model to deduce the spatial transmission trajectory of the pandemic while validating it with real activity trajectory data from confirmed cases. Finally, we investigate the impacts of pandemic prevention and control policies. The progression of the pandemic exhibits distinct stages and spatial clustering characteristics. People with complex social relationships and daily life trajectories and places with high pedestrian flow and commercial activity venues are prone to becoming superspreaders and superspreading places. The transmission path of the pandemic showed a pattern of short-distance and adjacent transmission, with most areas not affected. Early-stage control measures effectively disrupt transmission hotspots and impede the spatiotemporal trajectory of pandemic propagation, thereby enhancing the efficacy of prevention and control efforts. These findings elucidate the characteristics and transmission processes underlying pandemics, facilitating targeted and adaptable policy formulation to shape sustainable and resilient cities.

Conditioning factors in the spreading of Covid-19 - Does geography matter?

There is evidence in literature that the spread of COVID-19 can be influenced by various geographic factors, including territorial features, climate, population density, socioeconomic conditions, and mobility. The objective of the paper is to provide an updated literature review on geographical studies analysing the factors which influenced COVID-19 spreading. This literature review took into account not only the geographical aspects but also the COVID-19-related outcomes (infections and deaths) allowing to discern the potential influencing role of the geographic factors per type of outcome. A total of 112 scientific articles were selected, reviewed and categorized according to subject area, aim, country/region of study, considered geographic and COVID-19 variables, spatial and temporal units of analysis, methodologies, and main findings. Our literature review showed that territorial features may have played a role in determining the uneven geography of COVID-19; for instance, a certain agreement was found regarding the direct relationship between urbanization degree and COVID-19 infections. For what concerns climatic factors, temperature was the variable that correlated the best with COVID-19 infections. Together with climatic factors, socio-demographic ones were extensively taken into account. Most of the analysed studies agreed that population density and human mobility had a significant and direct relationship with COVID-19 infections and deaths. The analysis of the different approaches used to investigate the role of geographic factors in the spreading of the COVID-19 pandemic revealed that the significance/representativeness of the outputs is influenced by the scale considered due to the great spatial variability of geographic aspects. In fact, a more robust and significant association between geographic factors and COVID-19 was found by studies conducted at subnational or local scale rather than at country scale.

An integrated chance constraints approach for optimal vaccination strategies under uncertainty for COVID-19

Despite concerted efforts by health authorities worldwide to contain COVID-19, the SARS-CoV-2 virus has continued to spread and mutate into new variants with uncertain transmission characteristics. Therefore, there is a need for new data-driven models for determining optimal vaccination strategies that adapt to the new variants with their uncertain transmission characteristics. Motivated by this challenge, we derive an integrated chance constraints stochastic programming (ICC-SP) approach for finding vaccination strategies for epidemics that incorporates population demographics for any region of the world, uncertain disease transmission and vaccine efficacy. An optimal vaccination strategy specifies the proportion of individuals in a given household-type to vaccinate to bring the reproduction number to below one. The ICC-SP approach provides a quantitative method that allows to bound the expected excess of the reproduction number above one by an acceptable amount according to the decision-maker's level of risk. This new methodology involves a multi-community household based epidemiology model that uses census demographics data, vaccination status, age-related heterogeneity in disease susceptibility and infectivity, virus variants, and vaccine efficacy. The new methodology was tested on real data for seven neighboring counties in the United States state of Texas. The results are promising and show, among other findings, that vaccination strategies for controlling an outbreak should prioritize vaccinating certain household sizes as well as age groups with relatively high combined susceptibility and infectivity.

Spatio-temporal clustering analysis using generalized lasso with an application to reveal the spread of Covid-19 cases in Japan

This study addressed the issue of determining multiple potential clusters with regularization approaches for the purpose of spatio-temporal clustering. The generalized lasso framework has flexibility to incorporate adjacencies between objects in the penalty matrix and to detect multiple clusters. A generalized lasso model with two L 1 penalties is proposed, which can be separated into two generalized lasso models: trend filtering of temporal effect and fused lasso of spatial effect for each time point. To select the tuning parameters, the approximate leave-one-out cross-validation (ALOCV) and generalized cross-validation (GCV) are considered. A simulation study is conducted to evaluate the proposed method compared to other approaches in different problems and structures of multiple clusters. The generalized lasso with ALOCV and GCV provided smaller MSE in estimating the temporal and spatial effect compared to unpenalized method, ridge, lasso, and generalized ridge. In temporal effects detection, the generalized lasso with ALOCV and GCV provided relatively smaller and more stable MSE than other methods, for different structure of true risk values. In spatial effects detection, the generalized lasso with ALOCV provided higher index of edges detection accuracy. The simulation also suggested using a common tuning parameter over all time points in spatial clustering. Finally, the proposed method was applied to the weekly Covid-19 data in Japan form March 21, 2020, to September 11, 2021, along with the interpretation of dynamic behavior of multiple clusters.

Spatiotemporal analysis of the morbidity of global Omicron from November 2021 to February 2022

The Omicron variant was first reported to the World Health Organization (WHO) from South Africa on November 24, 2021; this variant is spreading rapidly worldwide. No study has conducted a spatiotemporal analysis of the morbidity of Omicron infection at the country level; hence, to explore the spatial transmission of the Omicron variant among the 220 countries worldwide, we aimed to the analyze its spatial autocorrelation and to conduct a multiple linear regression to investigate the underlying factors associated with the pandemic. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the local indicators of spatial association (LISA) were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. This study was an ecological study. Data on the number of confirmed cases were extracted from the WHO website. The spatiotemporal characteristic was described in a thematic map. The Global Moran Index (Moran's I) was used to detect the spatial autocorrelation, while the LISA were used to analyze the local spatial correlation characteristics. The joinpoint regression model was used to explore the change in the trend of the Omicron incidence over time. The association between the morbidity of Omicron and influencing factors were analyzed using multiple linear regression. The value of Moran's I was positive (Moran's I = 0.061, Z-score = 3.772, p = 0.007), indicating a spatial correlation of the morbidity of Omicron at the country level. From November 26, 2021 to February 26, 2022; the morbidity showed obvious spatial clustering. Hotspot clustering was observed mostly in Europe (locations in High-High category: 24). Coldspot clustering was observed mostly in Africa and Asia (locations in Low-Low category: 32). The result of joinpoint regression showed an increasing trend from December 21, 2021 to January 26, 2022. Results of the multiple linear regression analysis demonstrated that the morbidity of Omicron was strongly positively correlated with income support (coefficient = 1.905, 95% confidence interval [CI]: 1.354-2.456, p < 0.001) and strongly negatively correlated with close public transport (coefficient = -1.591, 95% CI: -2.461 to -0.721, p = 0.001). Omicron outbreaks exhibited spatial clustering at the country level worldwide; the countries with higher disease morbidity could impact the other countries that are surrounded by and close to it. The locations with High-High clustering category, which referred to the countries with higher disease morbidity, were mainly observed in Europe, and its adjoining country also showed high spatial clustering. The morbidity of Omicron increased from December 21, 2021 to January 26, 2022. The higher morbidity of Omicron was associated with the economic and policy interventions implemented; hence, to deal with the epidemic, the prevention and control measures should be strengthened in all aspects.

Evaluation of Vulnerability Factors for Developing Stress and Depression due to COVID-19 Spread and its Associated Lockdown

Background

COVID-19 is a pandemic that has been widespread throughout the world. The disease and the measures employed to contain its spread have a detrimental effect on the mental health of individuals. Countries across the world have applied variable combinations of quarantine and social distancing measures to contain the spread of COVID-19. This project aims at identifying the susceptible groups for the development of depression and stress due to COVID-19-associated containment measures. This evaluation will help in prioritizing efforts to ameliorate the detrimental effects of COVID-19 on psychological health.

Methods

A cross-sectional study was conducted through an online survey that included questions on the demographics and COVID-19 experience. The prevalence of depressive symptoms was evaluated using the PHQ-9 survey, whereas stress levels were detected using the perceived stress scale (PSS). Data regarding demographics as well as exposure to COVID-19, working at home and the financial impact of the pandemic were collected.

Results

Data were collected from 1541 participants from the MENA region. Depressive symptoms were detected in 54.2% of the participants, and the average stress score was 18.4±0.8. Adjusting for demographics and other variables, younger participants were more likely to report depressive symptoms and higher stress scores. Additionally, younger age, female gender, the coexistence of depressive symptoms, negative effects on monthly income, and ability to do work were found to be independent predictors of higher stress scores.

Conclusion

Young individuals are more likely to develop depression symptoms and stress. Thus, there is a need for prompt measures to alleviate COVID-19-associated effects on this group.

Impact of pre-pandemic travel mobility patterns on the spatial diffusion of COVID-19 in South Korea

Introduction

Physical mobility is critical for the spread of infectious diseases in humans. However, few studies have conducted empirical investigations on the impact of pre-pandemic travel mobility patterns on the diffusion of coronavirus disease 2019 (COVID-19). Therefore, this study examines its impact at the city-county level on the diffusion by the wave period during the two-year pandemic in South Korea.

Methods

This study first employs factor analysis by using the travel origin-destination data by travel mode at the county level as of 2019 to derive pre-pandemic travel mobility patterns. Next, the study identifies how they had affected the diffusion of COVID-19 over time by employing the negative binomial regression models on confirmed COVID-19 cases for each wave, including the entire pandemic period.

Results

The study derived five pre-pandemic mobility patterns: 1) rail-oriented mobility, 2) intra-county bus-oriented mobility, 3) road-oriented mobility, 4) high-speed rail-oriented mobility, and 5) inter-county bus-oriented mobility. Among them, the biggest risk to the diffusion of COVID-19 was the rail-oriented mobility before the pandemic if controlling such measures as accessibility, physical environment, and demographic and socioeconomic indicators. In addition, the order of the magnitudes for the impact of pre-pandemic travel mobility factors on its spatial diffusion had not changed during experiencing the three different wave periods during the two-year pandemic in South Korea.

Conclusions

The study concludes that the rail-oriented travel mobility pattern before the pandemic could pose the greatest threat factor to the spatial spread of COVID-19 at any scale and time. Policymakers should develop strategies to prevent the spatial spread of COVID-19 by reducing human mobility for daily living in areas with strong rail mobility patterns formed before the pandemic.

Effects of Meteorological Factors and Air Pollutants on COVID-19 Transmission under the Action of Control Measures

At present, COVID-19 is still spreading, and its transmission patterns and the main factors that affect transmission behavior still need to be thoroughly explored. To this end, this study collected the cumulative confirmed cases of COVID-19 in China by 8 April 2020. Firstly, the spatial characteristics of the COVID-19 transmission were investigated by the spatial autocorrelation method. Then, the factors affecting the COVID-19 incidence rates were analyzed by the generalized linear mixed effect model (GLMMs) and geographically weighted regression model (GWR). Finally, the geological detector (GeoDetector) was introduced to explore the influence of interactive effects between factors on the COVID-19 incidence rates. The results showed that: (1) COVID-19 had obvious spatial aggregation. (2) The control measures had the largest impact on the COVID-19 incidence rates, which can explain the difference of 34.2% in the COVID-19 incidence rates, while meteorological factors and pollutant factors can only explain the difference of 1% in the COVID-19 incidence rates. It explains that some of the literature overestimates the impact of meteorological factors on the spread of the epidemic. (3) The influence of meteorological factors was stronger than that of air pollution factors, and the interactive effects between factors were stronger than their individual effects. The interaction between relative humidity and NO2 was stronger. The results of this study will provide a reference for further prevention and control of COVID-19.

COVID-19 vaccination policies under uncertain transmission characteristics using stochastic programming

We develop a new stochastic programming methodology for determining optimal vaccination policies for a multi-community heterogeneous population. An optimal policy provides the minimum number of vaccinations required to drive post-vaccination reproduction number to below one at a desired reliability level. To generate a vaccination policy, the new method considers the uncertainty in COVID-19 related parameters such as efficacy of vaccines, age-related variation in susceptibility and infectivity to SARS-CoV-2, distribution of household composition in a community, and variation in human interactions. We report on a computational study of the new methodology on a set of neighboring U.S. counties to generate vaccination policies based on vaccine availability. The results show that to control outbreaks at least a certain percentage of the population should be vaccinated in each community based on pre-determined reliability levels. The study also reveals the vaccine sharing capability of the proposed approach among counties under limited vaccine availability. This work contributes a decision-making tool to aid public health agencies worldwide in the allocation of limited vaccines under uncertainty towards controlling epidemics through vaccinations.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities

Regional public attention has been critical during the COVID-19 pandemic, impacting the effectiveness of sub-national non-pharmaceutical interventions. While studies have focused on public attention at the national level, sub-national public attention has not been well investigated. Understanding sub-national public attention can aid local governments in designing regional scientific guidelines, especially in large countries with substantial spatiotemporal disparities in the spread of infections. Here, we evaluated the online public attention to the COVID-19 pandemic using internet search data and developed a regional public risk perception index (PRPI) that depicts heterogeneous associations between local pandemic risk and public attention across 366 Chinese cities. We used the Bayesian Spatiotemporally Varying Coefficients (STVC) model, a full-map local regression for estimating spatiotemporal heterogeneous relationships of variables, and improved it to the Bayesian Spatiotemporally Interacting Varying Coefficients (STIVC) model to incorporate space-time interaction non-stationarity at spatial or temporal stratified scales. COVID-19 daily cases (median contribution 82.6%) was the most critical factor affecting public attention, followed by urban socioeconomic conditions (16.7%) and daily population mobility (0.7%). After adjusting national and provincial impacts, city-level influence factors accounted for 89.4% and 58.6% in spatiotemporal variations of public attention. Spatiotemporal disparities were substantial among cities and provinces, suggesting that observing national-level public dynamics alone was insufficient. Multi-period PRPI maps revealed clusters and outlier cities with potential public panic and low health literacy. Bayesian STVC series models are systematically proposed and provide a multi-level spatiotemporal heterogeneous analytical framework for understanding collective human responses to major public health emergencies and disasters.

An analysis of the dynamic spatial spread of COVID-19 across South Korea

The first case of coronavirus disease 2019 (COVID-19) in South Korea was confirmed on January 20, 2020, approximately three weeks after the report of the first COVID-19 case in Wuhan, China. By September 15, 2021, the number of cases in South Korea had increased to 277,989. Thus, it is important to better understand geographical transmission and design effective local-level pandemic plans across the country over the long term. We conducted a spatiotemporal analysis of weekly COVID-19 cases in South Korea from February 1, 2020, to May 30, 2021, in each administrative region. For the spatial domain, we first covered the entire country and then focused on metropolitan areas, including Seoul, Gyeonggi-do, and Incheon. Moran's I and spatial scan statistics were used for spatial analysis. The temporal variation and dynamics of COVID-19 cases were investigated with various statistical visualization methods. We found time-varying clusters of COVID-19 in South Korea using a range of statistical methods. In the early stage, the spatial hotspots were focused in Daegu and Gyeongsangbuk-do. Then, metropolitan areas were detected as hotspots in December 2020. In our study, we conducted a time-varying spatial analysis of COVID-19 across the entirety of South Korea over a long-term period and found a powerful approach to demonstrating the current dynamics of spatial clustering and understanding the dynamic effects of policies on COVID-19 across South Korea. Additionally, the proposed spatiotemporal methods are very useful for understanding the spatial dynamics of COVID-19 in South Korea.

What Is the Impact of Early and Subsequent Epidemic Characteristics on the Pre-delta COVID-19 Epidemic Size in the United States?

It is still uncertain how the epidemic characteristics of COVID-19 in its early phase and subsequent waves contributed to the pre-delta epidemic size in the United States. We identified the early and subsequent characteristics of the COVID-19 epidemic and the correlation between these characteristics and the pre-delta epidemic size. Most (96.1% (49/51)) of the states entered a fast-growing phase before the accumulative number of cases reached (30). The days required for the number of confirmed cases to increase from 30 to 100 was 5.6 (5.1−6.1) days. As of 31 March 2021, all 51 states experienced at least 2 waves of COVID-19 outbreaks, 23.5% (12/51) experienced 3 waves, and 15.7% (8/51) experienced 4 waves, the epidemic size of COVID-19 was 19,275−3,669,048 cases across the states. The pre-delta epidemic size was significantly correlated with the duration from 30 to 100 cases (p = 0.003, r = −0.405), the growth rate of the fast-growing phase (p = 0.012, r = 0.351), and the peak cases in the subsequent waves (K1 (p < 0.001, r = 0.794), K2 (p < 0.001, r = 0.595), K3 (p < 0.001, r = 0.977), and K4 (p = 0.002, r = 0.905)). We observed that both early and subsequent epidemic characteristics contribute to the pre-delta epidemic size of COVID-19. This identification is important to the prediction of the emerging viral infectious diseases in the primary stage.

SARS-CoV-2 transmission potential and rural-urban disease burden disparities across Alabama, Louisiana, and Mississippi, March 2020 - May 2021

PURPOSE

To quantify and compare SARS-CoV-2 transmission potential across Alabama, Louisiana, and Mississippi and selected counties.

METHODS

To determine the time-varying reproduction number R_t of SARS-CoV-2, we applied the R package EpiEstim to the time series of daily incidence of confirmed cases (mid-March 2020 - May 17, 2021) shifted backward by 9 days. Median R_t percentage change when policies changed was determined. Linear regression was performed between log₁₀-transformed cumulative incidence and log₁₀-transformed population size at four time points.

RESULTS

Stay-at-home orders, face mask mandates, and vaccinations were associated with the most significant reductions in SARS-CoV-2 transmission in the three southern states. R_t across the three states decreased significantly by ≥20% following stay-at-home orders. We observed varying degrees of reductions in R_t across states following other policies. Rural Alabama counties experienced higher per capita cumulative cases relative to urban ones as of June 17 and October 17, 2020. Meanwhile, Louisiana and Mississippi saw the disproportionate impact of SARS-CoV-2 in rural counties compared to urban ones throughout the study period.

CONCLUSION

State and county policies had an impact on local pandemic trajectories. The rural-urban disparities in case burden call for evidence-based approaches in tailoring health promotion interventions and vaccination campaigns to rural residents.

The Assessment of COVID-19 Vulnerability Risk for Crisis Management

The subject of this article is to determine COVID-19 vulnerability risk and its change over time in association with the state health care system, turnover, and transport to support the crisis management decision-making process. The aim was to determine the COVID-19 Vulnerability Index (CVI) based on the selected criteria. The risk assessment was carried out with methodology that includes the application of multicriteria analysis and spatiotemporal aspect of available data. Particularly the Spatial Multicriteria Analysis (SMCA) compliant with the Analytical Hierarchy Process (AHP), which incorporated selected population and environmental criteria were used to analyse the ongoing pandemic situation. The influence of combining several factors in the pandemic situation analysis was illustrated. Furthermore, the static and dynamic factors to COVID-19 vulnerability risk were determined to prevent and control the spread of COVID-19 at the early stage of the pandemic situation. As a result, areas with a certain level of risk in different periods of time were determined. Furthermore, the number of people exposed to COVID-19 vulnerability risk in time was presented. These results can support the decision-making process by showing the area where preventive actions should be considered.

The demographic and geographic impact of the COVID pandemic in Bulgaria and Eastern Europe in 2020

The COVID-19 pandemic followed a unique trajectory in Eastern Europe compared to other heavily affected regions, with most countries there only experiencing a major surge of cases and deaths towards the end of 2020 after a relatively uneventful first half of the year. However, the consequences of that surge have not received as much attention as the situation in Western countries. Bulgaria, even though it has been one of the most heavily affected countries, has been one of those neglected cases. We use mortality and mobility data from Eurostat, official governmental and other sources to examine the development and impact of the COVID-19 pandemic in Bulgaria and other European countries. We find a very high level of excess mortality in Eastern European countries measured by several metrics including excess mortality rate (EMR), P-scores, potential years of life lost (PYLL) and its age standardised version (ASYR), and working years of life lost (WYLL). By the last three metrics Eastern Europe emerges as the hardest hit region by the pandemic in Europe in 2020. With a record EMR at ~0.27% and a strikingly large and mostly unique to it mortality rate in the working age (15-64 years) demographics, Bulgaria emerges as one of the most affected countries in Eastern Europe. The high excess mortality in Bulgaria correlates with insufficient intensity of testing, with delayed imposition of "lockdown" measures, and with high prevalence of cardiovascular diseases. We also find major geographic and demographic disparities within the country, with considerably lower mortality observed in major cities relative to more remote areas (likely due to disparities in the availability of medical resources). Analysis of the course of the epidemic revealed that individual mobility measures were predictive of the eventual decline in cases and deaths. However, while mobility declined as a result of the imposition of a lockdown, it already trended downwards before such measures were introduced, which resulted in a reduction of deaths independent of the effect of restrictions. Large excess mortality and high numbers of potential years of life lost are observed as a result of the COVID pandemic in Bulgaria, as well as in several other countries in Eastern Europe. Significant delays in the imposition of stringent mobility-reducing measures combined with a lack of medical resources likely caused a substantial loss of life, including in the working age population.

Comparing the dynamics of COVID-19 infection and mortality in the United States, India, and Brazil

This paper compares and contrasts the spread and impact of COVID-19 in the three countries most heavily impacted by the pandemic: the United States (US), India and Brazil. All three of these countries have a federal structure, in which the individual states have largely determined the response to the pandemic. Thus, we perform an extensive analysis of the individual states of these three countries to determine patterns of similarity within each. First, we analyse structural similarity and anomalies in the trajectories of cases and deaths as multivariate time series. Next, we study the lengths of the different waves of the virus outbreaks across the three countries and their states. Finally, we investigate suitable time offsets between cases and deaths as a function of the distinct outbreak waves. In all these analyses, we consistently reveal more characteristically distinct behaviour between US and Indian states, while Brazilian states exhibit less structure in their wave behaviour and changing progression between cases and deaths.

Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data

A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.

Evaluating COVID-19 control measures in mass gathering events with vaccine inequalities

With the increasing global adoption of COVID-19 vaccines, limitations on mass gathering events have started to gradually loosen. However, the large vaccine inequality recorded among different countries is an important aspect that policymakers must address when implementing control measures for such events. In this paper, we propose a model for the assessment of different control measures with the consideration of vaccine inequality in the population. Two control measures are considered: selecting participants based on vaccine efficacy and restricting the event capacity. We build the model using agent-based modeling to capture the spatiotemporal crowd dynamics and utilize a genetic algorithm to assess the control strategies. This assessment is based on factors that are important for policymakers such as disease prevalence, vaccine diversity, and event capacity. A quantitative evaluation of vaccine diversity using the Simpson's Diversity Index is also provided. The Hajj ritual is used as a case study. We show that strategies that prioritized lowering the prevalence resulted in low event capacity but facilitated vaccine diversity. Moreover, strategies that prioritized diversity resulted in high infection rates. However, increasing the prioritization of participants with high vaccine efficacy significantly decreased the disease prevalence. Strategies that prioritized ritual capacity did not show clear trends.

Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling

The coronavirus disease of 2019 (COVID-19) pandemic is currently a global challenge, with 210 countries, including Indonesia, seeking to minimize its spread. Therefore, this study aims to determine the spatiotemporal spread pattern of this virus in Surabaya using various data on confirmed cases from 28 April to 26 October 2021. It also aims to determine the relationship between pollutant parameters, such as carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃), as well as the government’s high social restrictions policy in Java-Bali. Several methods, such as the weighted mean center, directional distribution, Getis–Ord Gi*, Moran’s I, and geographically weighted regression, were used to identify the spatial spread pattern of the virus. The weighted mean center indicated that the epicenter location of the outbreak moved randomly. The directional distribution demonstrated a decrease of 21 km² at the end of the study phase, which proved that its spread has significantly reduced in Surabaya. Meanwhile, the Getis–Ord Gi* results demonstrated that the eastern and southern parts of the study region were highly infected. Moran’s I demonstrate that COVID-19 cases clustered during the spike. The geographically weighted regression model indicated a number of influence zones in the northeast, northwest, and a few in the southwest parts at the peak of R² 0.55. The relationship between COVID-19 cases and air pollution parameters proved that people living at the outbreak’s center have low pollution levels due to lockdown. Furthermore, the lockdown policy reduced CO, NO₂, SO₂, and O₃. In addition, increase in air pollutants; namely, NO₂, CO, SO₂ and O₃, was recorded after 7 weeks of lockdown implementation (started from 18 August).

Estimating a continuously varying offset between multivariate time series with application to COVID-19 in the United States

This paper introduces new methods to track the offset between two multivariate time series on a continuous basis. We then apply this framework to COVID-19 counts on a state-by-state basis in the United States to determine the progression from cases to deaths as a function of time. Across multiple approaches, we reveal an "up-down-up" pattern in the estimated offset between reported cases and deaths as the pandemic progresses. This analysis could be used to predict imminent increased load on a healthcare system and aid the allocation of additional resources in advance.

Epidemic Risk Assessment by a Novel Communication Station Based Method

The COVID-19 pandemic has caused serious consequences in the last few months and trying to control it has been the most important objective. With effective prevention and control methods, the epidemic has been gradually under control in some countries and it is essential to ensure safe work resumption in the future. Although some approaches are proposed to measure people's healthy conditions, such as filling health information forms or evaluating people's travel records, they cannot provide a fine-grained assessment of the epidemic risk. In this paper, we propose a novel epidemic risk assessment method based on the granular data collected by the communication stations. We first compute the epidemic risk of these stations in different intervals by combining the number of infected persons and the way they pass through the station. Then, we calculate the personnel risk in different intervals according to the station trajectory of the queried person. This method could assess people's epidemic risk accurately and efficiently. We also conduct extensive simulations and the results verify the effectiveness of the proposed method.

Discovering spatiotemporal patterns of COVID-19 pandemic in South Korea

A novel severe acute respiratory syndrome coronavirus 2 emerged in December 2019, and it took only a few months for WHO to declare COVID-19 as a pandemic in March 2020. It is very challenging to discover complex spatial-temporal transmission mechanisms. However, it is crucial to capture essential features of regional-temporal patterns of COVID-19 to implement prompt and effective prevention or mitigation interventions. In this work, we develop a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics. The compatible window is a selected representative subdomain of time series data, in which compatibility between spatial and temporal resolutions is established so that DMD can provide meaningful data analysis. A total of four compatible windows have been selected from COVID-19 time-series data from January 20, 2020, to May 10, 2021, in South Korea. The spatiotemporal patterns of these four windows are then analyzed. Several hot and cold spots were identified, their spatial-temporal relationships, and some hidden regional patterns were discovered. Our analysis reveals that the first wave was contained in the Daegu and Gyeongbuk areas, but it spread rapidly to the whole of South Korea after the second wave. Later on, the spatial distribution is seen to become more homogeneous after the third wave. Our analysis also identifies that some patterns are not related to regional relevance. These findings have then been analyzed and associated with the inter-regional and local characteristics of South Korea. Thus, the present study is expected to provide public health officials helpful insights for future regional-temporal specific mitigation plans.

A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020

COVID-19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID-19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID-19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID-19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID-19 transmission and the associated risk factors.

Dynamics of Covid-19 mortality and social determinants of health: a spatiotemporal analysis of exceedance probabilities

PURPOSE

To determine the association of social factors with Covid-19 mortality and identify high-risk clusters.

METHODS

Data on Covid-19 deaths across 3,108 contiguous U.S. counties from the Johns Hopkins University and social determinants of health (SDoH) data from the County Health Ranking and the Bureau of Labor Statistics were fitted to Bayesian semi-parametric spatiotemporal Negative Binomial models, and 95% credible intervals (CrI) of incidence rate ratios (IRR) were used to assess the associations. Exceedance probabilities were used for detecting clusters.

RESULTS

As of October 31, 2020, the median mortality rate was 40.05 per 100, 000. The monthly urban mortality rates increased with unemployment (IRRadjusted:1.41, 95% CrI: 1.24, 1.60), percent Black population (IRRadjusted:1.05, 95% CrI: 1.04, 1.07), and residential segregation (IRRadjusted:1.03, 95% CrI: 1.02, 1.04). The rural monthly mortality rates increased with percent female population (IRRadjusted: 1.17, 95% CrI: 1.11, 1.24) and percent Black population (IRRadjusted:1.07 95% CrI:1.06, 1.08). Higher college education rates were associated with decreased mortality rates in rural and urban counties. The dynamics of exceedance probabilities detected the shifts of high-risk clusters from the Northeast to Southern and Midwestern counties.

CONCLUSIONS

Spatiotemporal analyses enabled the inclusion of unobserved latent risk factors and aid in scientifically grounded decision-making at a granular level.

Associations Between Governor Political Affiliation and COVID-19 Cases, Deaths, and Testing in the U.S

INTRODUCTION

The response to the COVID-19 pandemic became increasingly politicized in the U.S., and the political affiliation of state leaders may contribute to policies affecting the spread of the disease. This study examines the differences in COVID-19 infection, death, and testing by governor party affiliation across the 50 U.S. states and the District of Columbia.

METHODS

A longitudinal analysis was conducted in December 2020 examining COVID-19 incidence, death, testing, and test positivity rates from March 15, 2020 through December 15, 2020. A Bayesian negative binomial model was fit to estimate the daily risk ratios and posterior intervals comparing rates by gubernatorial party affiliation. The analyses adjusted for state population density, rurality, Census region, age, race, ethnicity, poverty, number of physicians, obesity, cardiovascular disease, asthma, smoking, and presidential voting in 2020.

RESULTS

From March 2020 to early June 2020, Republican-led states had lower COVID-19 incidence rates than Democratic-led states. On June 3, 2020, the association reversed, and Republican-led states had a higher incidence (risk ratio=1.10, 95% posterior interval=1.01, 1.18). This trend persisted through early December 2020. For death rates, Republican-led states had lower rates early in the pandemic but higher rates from July 4, 2020 (risk ratio=1.18, 95% posterior interval=1.02, 1.31) through mid-December 2020. Republican-led states had higher test positivity rates starting on May 30, 2020 (risk ratio=1.70, 95% posterior interval=1.66, 1.73) and lower testing rates by September 30, 2020 (risk ratio=0.95, 95% posterior interval=0.90, 0.98).

CONCLUSIONS

Gubernatorial party affiliation may drive policy decisions that impact COVID-19 infections and deaths across the U.S. Future policy decisions should be guided by public health considerations rather than by political ideology.

A Cross-Sectional Comparison of Physical Activity during COVID-19 in a Sample of Rural and Non-Rural Participants in the US

Physical activity (PA) pre-COVID-19 was lower in rural areas compared to non-rural areas. The purpose of this study was to determine COVID-19's impact on PA in rural and non-rural residents. A cross-sectional study consisting of a convenience sample of 278 participants (50% rural, 50% non-rural) from 25 states completed an online survey describing their PA behaviors and perceptions during COVID-19. The global physical activity questionnaire was used to determine PA in various domains and summed to determine if the participant met the PA guidelines. Rural participants had a significantly higher body mass index, lower income, and a lower educational attainment. Conversely, non-rural participants reported more barriers to PA. There was no difference in the perception of COVID-19's impact on PA, specifically; however, rural participants were significantly less likely to meet cardiorespiratory PA recommendations compared to non-rural participants. Conclusions: This study demonstrates the continued disparity in PA between rural and non-rural residents, despite the supposition of COVID-19 being less impactful in rural areas due to sparse populations. Efforts should be pursued to close the PA gap between rural and non-rural residents.

Spatiotemporal characteristics and factor analysis of SARS-CoV-2 infections among healthcare workers in Wuhan, China

BACKGROUND

Studying the spatiotemporal distribution of SARS-CoV-2 infections among healthcare workers (HCWs) can aid in protecting them from exposure.

AIM

To describe the spatiotemporal distributions of SARS-CoV-2 infections among HCWs in Wuhan, China.

METHODS

In this study, an open-source dataset of HCW diagnoses was provided. A geographical detector technique was then used to investigate the impacts of hospital level, type, distance from the infection source, and other external indicators of HCW infections.

FINDINGS

The number of daily HCW infections over time in Wuhan followed a log-normal distribution, with its mean observed on January 23rd, 2020, and a standard deviation of 10.8 days. The implementation of high-impact measures, such as the lockdown of the city, may have increased the probability of HCW infections in the short term, especially for those in the outer ring of Wuhan. The infection of HCWs in Wuhan exhibited clear spatial heterogeneity. The number of HCW infections was higher in the central city and lower in the outer city.

CONCLUSION

HCW infections displayed significant spatial autocorrelation and dependence. Factor analysis revealed that hospital level and type had an even greater impact on HCW infections; third-class and general hospitals closer to infection sources were correlated with especially high risks of infection.

Modeling of Various Spatial Patterns of SARS-CoV-2: The Case of Germany

Among numerous publications about the SARS-CoV-2, many articles present research from the geographic point of view. The cartographic research method used in this area of science can be successfully applied to analyze the spatiotemporal characteristics of the pandemic using limited data and can be useful for a quick and preliminary assessment of the spread of infections. In this paper, research on the spatial differentiation of the structure and homogeneity of the system in which SARS-CoV-2 occurs, as well as spatial concentration of people infected was undertaken. The phenomena were investigated in a period of two infection waves in Germany: in spring and autumn 2020. We applied the potential model, entropy, centrographic method, and Lorenz curve in spatial analysis. The potentials model made it possible to distinguish core regions with a high level of the growth of new infections, along with areas of their impact, and regions with a low level of generation of new infections. The entropy showed the spatial distribution of differentiation of the studied system and the change of these characteristics between spring and autumn. The concentration method allowed for spatial and numerical demonstration of the concentration of infected population in a given area. We wanted to show that it is possible to draw meaningful conclusions about the pandemic characteristics using only basic data about infections, along with proper cartographic methods. The results can be used to designate the zones of the greatest threats, and thus, the areas where the most intense actions should be taken.

Stay-at-home policy is a case of exception fallacy: an internet-based ecological study

A recent mathematical model has suggested that staying at home did not play a dominant role in reducing COVID-19 transmission. The second wave of cases in Europe, in regions that were considered as COVID-19 controlled, may raise some concerns. Our objective was to assess the association between staying at home (%) and the reduction/increase in the number of deaths due to COVID-19 in several regions in the world. In this ecological study, data from www.google.com/covid19/mobility/ , ourworldindata.org and covid.saude.gov.br were combined. Countries with > 100 deaths and with a Healthcare Access and Quality Index of ≥ 67 were included. Data were preprocessed and analyzed using the difference between number of deaths/million between 2 regions and the difference between the percentage of staying at home. The analysis was performed using linear regression with special attention to residual analysis. After preprocessing the data, 87 regions around the world were included, yielding 3741 pairwise comparisons for linear regression analysis. Only 63 (1.6%) comparisons were significant. With our results, we were not able to explain if COVID-19 mortality is reduced by staying at home in ~ 98% of the comparisons after epidemiological weeks 9 to 34.

A multicenter analysis of the clinical microbiology and antimicrobial usage in hospitalized patients in the US with or without COVID-19

BACKGROUND

Past respiratory viral epidemics suggest that bacterial infections impact clinical outcomes. There is minimal information on potential co-pathogens in patients with coronavirus disease-2019 (COVID-19) in the US. We analyzed pathogens, antimicrobial use, and healthcare utilization in hospitalized US patients with and without severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2).

METHODS

This multicenter retrospective study included patients with > 1 day of inpatient admission and discharge/death between March 1 and May 31, 2020 at 241 US acute care hospitals in the BD Insights Research Database. We assessed microbiological testing data, antimicrobial utilization in admitted patients with ≥24 h of antimicrobial therapy, and length of stay (LOS).

RESULTS

A total of 141,621 patients were tested for SARS-CoV-2 (17,003 [12.0%] positive) and 449,339 patients were not tested. Most (> 90%) patients tested for SARS-CoV-2 had additional microbiologic testing performed compared with 41.9% of SARS-CoV-2-untested patients. Non-SARS-CoV-2 pathogen rates were 20.9% for SARS-CoV-2-positive patients compared with 21.3 and 27.9% for SARS-CoV-2-negative and -untested patients, respectively. Gram-negative bacteria were the most common pathogens (45.5, 44.1, and 43.5% for SARS-CoV-2-positive, -negative, and -untested patients). SARS-CoV-2-positive patients had higher rates of hospital-onset (versus admission-onset) non-SARS-CoV-2 pathogens compared with SARS-CoV-2-negative or -untested patients (42.4, 22.2, and 19.5%, respectively), more antimicrobial usage (68.0, 45.2, and 25.1% of patients), and longer hospital LOS (mean [standard deviation (SD)] of 8.6 [11.4], 5.1 [8.9], and 4.2 [8.0] days) and intensive care unit (ICU) LOS (mean [SD] of 7.8 [8.5], 3.6 [6.2], and 3.6 [5.9] days). For all groups, the presence of a non-SARS-CoV-2 pathogen was associated with increased hospital LOS (mean [SD] days for patients with versus without a non-SARS-CoV-2 pathogen: 13.7 [15.7] vs 7.3 [9.6] days for SARS-CoV-2-positive patients, 8.2 [11.5] vs 4.3 [7.9] days for SARS-CoV-2-negative patients, and 7.1 [11.0] vs 3.9 [7.4] days for SARS-CoV-2-untested patients).

CONCLUSIONS

Despite similar rates of non-SARS-CoV-2 pathogens in SARS-CoV-2-positive, -negative, and -untested patients, SARS-CoV-2 was associated with higher rates of hospital-onset infections, greater antimicrobial usage, and extended hospital and ICU LOS. This finding highlights the heavy burden of the COVID-19 pandemic on healthcare systems and suggests possible opportunities for diagnostic and antimicrobial stewardship.

Associations between governor political affiliation and COVID-19 cases, deaths, and testing in the United States

INTRODUCTION

The response to the COVID-19 pandemic became increasingly politicized in the United States (US) and political affiliation of state leaders may contribute to policies affecting the spread of the disease. This study examined differences in COVID-19 infection, death, and testing by governor party affiliation across 50 US states and the District of Columbia.

METHODS

A longitudinal analysis was conducted in December 2020 examining COVID-19 incidence, death, testing, and test positivity rates from March 15 through December 15, 2020. A Bayesian negative binomial model was fit to estimate daily risk ratios (RRs) and posterior intervals (PIs) comparing rates by gubernatorial party affiliation. The analyses adjusted for state population density, rurality, census region, age, race, ethnicity, poverty, number of physicians, obesity, cardiovascular disease, asthma, smoking, and presidential voting in 2020.

RESULTS

From March to early June, Republican-led states had lower COVID-19 incidence rates compared to Democratic-led states. On June 3, the association reversed, and Republican-led states had higher incidence (RR=1.10, 95% PI=1.01, 1.18). This trend persisted through early December. For death rates, Republican-led states had lower rates early in the pandemic, but higher rates from July 4 (RR=1.18, 95% PI=1.02, 1.31) through mid-December. Republican-led states had higher test positivity rates starting on May 30 (RR=1.70, 95% PI=1.66, 1.73) and lower testing rates by September 30 (RR=0.95, 95% PI=0.90, 0.98).

CONCLUSION

Gubernatorial party affiliation may drive policy decisions that impact COVID-19 infections and deaths across the US. Future policy decisions should be guided by public health considerations rather than political ideology.

Spatiotemporal Differences of COVID-19 Infection Among Healthcare Workers and Patients in China From January to March 2020

Studying the spatiotemporal differences in coronavirus disease (COVID-19) between social groups such as healthcare workers (HCWs) and patients can aid in formulating epidemic containment policies. Most previous studies of the spatiotemporal characteristics of COVID-19 were conducted in a single group and did not explore the differences between groups. To fill this research gap, this study assessed the spatiotemporal characteristics and differences among patients and HCWs infection in Wuhan, Hubei (excluding Wuhan), and China (excluding Hubei). The temporal difference was greater in Wuhan than in the rest of Hubei, and was greater in Hubei (excluding Wuhan) than in the rest of China. The incidence was high in healthcare workers in the early stages of the epidemic. Therefore, it is important to strengthen the protective measures for healthcare workers in the early stage of the epidemic. The spatial difference was less in Wuhan than in the rest of Hubei, and less in Hubei (excluding Wuhan) than in the rest of China. The spatial distribution of healthcare worker infections can be used to infer the spatial distribution of the epidemic in the early stage and to formulate control measures accordingly.

Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh

The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world including Bangladesh. We conducted this study to assess how COVID-19 cases clustered across districts in Bangladesh and whether the pattern and duration of clusters changed following the country's containment strategy using Geographic information system (GIS) software. We calculated the epidemiological measures including incidence, case fatality rate (CFR) and spatiotemporal pattern of COVID-19. We used inverse distance weighting (IDW), Geographically weighted regression (GWR), Moran's I and Getis-Ord Gi* statistics for prediction, spatial autocorrelation and hotspot identification. We used retrospective space-time scan statistic to analyse clusters of COVID-19 cases. COVID-19 has a CFR of 1.4%. Over 50% of cases were reported among young adults (21-40 years age). The incidence varies from 0.03 - 0.95 at the end of March to 15.59-308.62 per 100,000, at the end of July. Global Moran's Index indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran's I analysis stated a distinct High-High (HH) clustering of COVID-19 cases among Dhaka, Gazipur and Narayanganj districts. Twelve statistically significant high rated clusters were identified by space-time scan statistics using a discrete Poisson model. IDW predicted the cases at the undetermined area, and GWR showed a strong relationship between population density and case frequency, which was further established with Moran's I (0.734; p ≤ 0.01). Dhaka and its surrounding six districts were identified as the significant hotspot whereas Chattogram was an extended infected area, indicating the gradual spread of the virus to peripheral districts. This study provides novel insights into the geostatistical analysis of COVID-19 clusters and hotspots that might assist the policy planner to predict the spatiotemporal transmission dynamics and formulate imperative control strategies of SARS-CoV-2 in Bangladesh. The geospatial modeling tools can be used to prevent and control future epidemics and pandemics.

The Spatio-Temporal Characteristics and Influencing Factors of Covid-19 Spread in Shenzhen, China-An Analysis Based on 417 Cases

The global pandemic of COVID-19 has made it the focus of current attention. At present, the law of COVID-19 spread in cities is not clear. Cities have long been difficult areas for epidemic prevention and control because of the high population density, high mobility of people, and high frequency of contacts. This paper analyzed case information for 417 patients with COVID-19 in Shenzhen, China. The nearest neighbor index method, kernel density method, and the standard deviation ellipse method were used to analyze the spatio-temporal characteristics of the COVID-19 spread in Shenzhen. The factors influencing that spread were then explored using the multiple linear regression method. The results show that: (1) The development of COVID-19 epidemic situation in Shenzhen occurred in three stages. The patients showed significant hysteresis from the onset of symptoms to hospitalization and then to diagnosis. Prior to 27 January, there was a relatively long time interval between the onset of symptoms and hospitalization for COVID-19; the interval decreased thereafter. (2) The epidemic site (the place where the patient stays during the onset of the disease) showed an agglomeration in space. The degree of agglomeration constantly increased across the three time nodes of 31 January, 14 February, and 22 February. The epidemic sites formed a "core area" in terms of spatial distribution and spread along the "northwest-southeast" direction of the city. (3) Economic and social factors significantly impacted the spread of COVID-19, while environmental factors have not played a significant role.

Systematic review of the prevalence of current smoking among hospitalized COVID-19 patients in China: could nicotine be a therapeutic option?

The effects of smoking on Corona Virus Disease 2019 (COVID-19) are currently unknown. The purpose of this study was to systematically examine the prevalence of current smoking among hospitalized patients with COVID-19 in China, considering the high-population smoking prevalence in China (26.6%). A systematic review of the literature (PubMed) was performed on April 1. Thirteen studies examining the clinical characteristics of hospitalized COVID-19 patients in China and presenting data on the smoking status were found. The pooled prevalence of current smoking from all studies was calculated by random-effect meta-analysis. To address the possibility that some smokers had quit shortly before hospitalization and were classified as former smokers on admission to the hospital, we performed a secondary analysis in which all former smokers were classified as current smokers. A total of 5960 patients were included in the studies identified. The current smoking prevalence ranged from 1.4% (95% CI 0.0-3.4%) to 12.6% (95% CI 10.6-14.6%). An unusually low prevalence of current smoking was observed from the pooled analysis (6.5%, 95% CI 4.9-8.2%) as compared to population smoking prevalence in China. The secondary analysis, classifying former smokers as current smokers, found a pooled estimate of 7.3% (95% CI 5.7-8.9%). In conclusion, an unexpectedly low prevalence of current smoking was observed among patients with COVID-19 in China, which was approximately 1/4th the population smoking prevalence. Although the generalized advice to quit smoking as a measure to reduce health risk remains valid, the findings, together with the well-established immunomodulatory effects of nicotine, suggest that pharmaceutical nicotine should be considered as a potential treatment option in COVID-19.