Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States.

This article examines three spatiotemporal methods used for analyzing of infectious diseases, with a focus on COVID-19 in the United States. The methods considered include inverse distance weighting (IDW) interpolation, retrospective spatiotemporal scan statistics and Bayesian spatiotemporal models. The study covers a 12-month period from May 2020 to April 2021, including monthly data from 49 states or regions in the United States. The results show that the spread of COVID-19 pandemic increased rapidly to a high value in winter of 2020, followed by a brief decline that later reverted into another increase. Spatially, the COVID-19 epidemic in the United States exhibited a multi-centre, rapid spread character, with clustering areas represented by states such as New York, North Dakota, Texas and California. By demonstrating the applicability and limitations of different analytical tools in investigating the spatiotemporal dynamics of disease outbreaks, this study contributes to the broader field of epidemiology and helps improve strategies for responding to future major public health events.

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.

A multivariate spatio-temporal model for the incidence of imported COVID-19 cases and COVID-19 deaths in Cuba.

To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.

Spatio-temporal relative risks and priority areas for visceral leishmaniasis control in Brazil, between 2001 and 2020.

Visceral leishmaniasis (VL) is a pressing public health problem in Brazil. The proper implementation of disease control programs in priority areas is a challenge for healthcare managers. The present study aimed to analyze the spatio-temporal distribution and identify high risk areas of VL occurrence in the Brazilian territory. We analyzed data regarding new cases with confirmed diagnosis of VL in Brazilian municipalities, from 2001 to 2020, extracted from the Brazilian Information System for Notifiable Diseases. The Local Index of Spatial Autocorrelation (LISA) was used to identify contiguous areas with high incidence rates in different periods of the temporal series. Clusters of high spatio-temporal relative risks were identified using the scan statistics. The accumulated incidence rate in the analyzed period was 33.53 cases per 100,000 inhabitants. The number of municipalities that reported cases showed an upward trend from 2001 onward, although there was a decrease in 2019 and 2020. According to LISA, the number of municipalities considered a priority increased in Brazil and in most states. Priority municipalities were predominantly concentrated in the states of Tocantins, Maranhão, Piauí, and Mato Grosso do Sul, in addition to more specific areas of Pará, Ceará, Piauí, Alagoas, Pernambuco, Bahia, São Paulo, Minas Gerais, and Roraima. The spatio-temporal clusters of high-risk areas varied throughout the time series and were relatively higher in the North and Northeast regions. Recent high-risk areas were found in Roraima and municipalities in northeastern states. VL expanded territorially in Brazil in the 21st century. However, there is still a considerable spatial concentration of cases. The areas identified in the present study should be prioritized for disease control actions.

Novel Graph Topology Learning for Spatio-Temporal Analysis of COVID-19 Spread.

This article presents a new graph-learning technique to accurately infer the graph structure of COVID-19 data, helping to reveal the correlation of pandemic dynamics among different countries and identify influential countries for pandemic response analysis. The new technique estimates the graph Laplacian of the COVID-19 data by first deriving analytically its precise eigenvectors, also known as graph Fourier transform (GFT) basis. Given the eigenvectors, the eigenvalues of the graph Laplacian are readily estimated using convex optimization. With the graph Laplacian, we analyze the confirmed cases of different COVID-19 variants among European countries based on centrality measures and identify a different set of the most influential and representative countries from the current techniques. The accuracy of the new method is validated by repurposing part of COVID-19 data to be the test data and gauging the capability of the method to recover missing test data, showing 33.3% better in root mean squared error (RMSE) and 11.11% better in correlation of determination than existing techniques. The set of identified influential countries by the method is anticipated to be meaningful and contribute to the study of COVID-19 spread.

Modeling the spread of COVID-19 in spatio-temporal context.

This study aims to use data provided by the Virginia Department of Public Health to illustrate the changes in trends of the total cases in COVID-19 since they were first recorded in the state. Each of the 93 counties in the state has its COVID-19 dashboard to help inform decision makers and the public of spatial and temporal counts of total cases. Our analysis shows the differences in the relative spread between the counties and compares the evolution in time using Bayesian conditional autoregressive framework. The models are built under the Markov Chain Monte Carlo method and Moran spatial correlations. In addition, Moran's time series modeling techniques were applied to understand the incidence rates. The findings discussed may serve as a template for other studies of similar nature.

[SARS-coV-2 spatio - temporal analysis in Cali, Colombia]. / Análisis espacio-temporal del SARS-COV-2 en Cali, Colombia.

OBJECTIVE: To describe the spatio-temporal distribution of the COVID-19 in the city of Cali during the first month of the epidemic. METHODS: An exploratory analysis of spatial data was carried out, consisting of a kernel density analysis and the presence of spatial patterns was verified by the K-Ripley function. RESULTS: The spatial distribution of the cases tends to initially concentrate in the north and south of the city, with a changing dynamic towards the east and west. CONCLUSIONS: The identified spatial pattern may be influenced by the isolation measures taken at the local and national level, but the effect of the low access of the general population to diagnostic tests, delays and restraints to know the results cannot be ruled out and even possible biases due to difficulties in the technique of taking the sample or its conservation.

Spatio-Temporal Distribution Characteristics and Drivers of PM2.5 Pollution in Henan Province, Central China, before and during the COVID-19 Epidemic.

PM2.5 is the main cause of haze pollution, and studying its spatio-temporal distribution and driving factors can provide a scientific basis for prevention and control policies. Therefore, this study uses air quality monitoring information and socioeconomic data before and during the COVID-19 outbreak in 18 prefecture-level cities in Henan Province from 2017 to 2020, using spatial autocorrelation analysis, ArcGIS mapping, and the spatial autocorrelation analysis. ArcGIS mapping and the Durbin model were used to reveal the characteristics of PM2.5 pollution in Henan Province in terms of spatial and temporal distribution characteristics and analyze its causes. The results show that: (1) The annual average PM2.5 concentration in Henan Province fluctuates, but decreases from 2017 to 2020, and is higher in the north and lower in the south. (2) The PM2.5 concentrations in Henan Province in 2017-2020 are positively autocorrelated spatially, with an obvious spatial spillover effect. Areas characterized by a high concentration saw an increase between 2017 and 2019, and a decrease in 2020; values in low-concentration areas remained stable, and the spatial range showed a decreasing trend. (3) The coefficients of socio-economic factors that increased the PM2.5 concentration were construction output value > industrial electricity consumption > energy intensity; those with negative effects were: environmental regulation > green space coverage ratio > population density. Lastly, PM2.5 concentrations were negatively correlated with precipitation and temperature, and positively correlated with humidity. Traffic and production restrictions during the COVID-19 epidemic also improved air quality.

Spatio-Temporal Bayesian Models for Malaria Risk Using Survey and Health Facility Routine Data in Rwanda.

INTRODUCTION: Malaria is a life-threatening disease ocuring mainly in developing countries. Almost half of the world's population was at risk of malaria in 2020. Children under five years age are among the population groups at considerably higher risk of contracting malaria and developing severe disease. Most countries use Demographic and Health Survey (DHS) data for health programs and evaluation. However, malaria elimination strategies require a real-time, locally-tailored response based on malaria risk estimates at the lowest administrative levels. In this paper, we propose a two-step modeling framework using survey and routine data to improve estimates of malaria risk incidence in small areas and enable quantifying malaria trends. METHODS: To improve estimates, we suggest an alternative approach to modeling malaria relative risk by combining information from survey and routine data through Bayesian spatio-temporal models. We model malaria risk using two steps: (1) fitting a binomial model to the survey data, and (2) extracting fitted values and using them in the Poison model as nonlinear effects in the routine data. We modeled malaria relative risk among under-five-year old children in Rwanda. RESULTS: The estimation of malaria prevalence among children who are under five years old using Rwanda demographic and health survey data for the years 2019-2020 alone showed a higher prevalence in the southwest, central, and northeast of Rwanda than the rest of the country. Combining with routine health facility data, we detected clusters that were undetected based on the survey data alone. The proposed approach enabled spatial and temporal trend effect estimation of relative risk in local/small areas in Rwanda. CONCLUSIONS: The findings of this analysis suggest that using DHS combined with routine health services data for active malaria surveillance may provide provide more precise estimates of the malaria burden, which can be used toward malaria elimination targets. We compared findings from geostatistical modeling of malaria prevalence among under-five-year old children using DHS 2019-2020 and findings from malaria relative risk spatio-temporal modeling using both DHS survey 2019-2020 and health facility routine data. The strength of routinely collected data at small scales and high-quality data from the survey contributed to a better understanding of the malaria relative risk at the subnational level in Rwanda.

A spatiotemporal decay model of human mobility when facing large-scale crises.

A common feature of large-scale extreme events, such as pandemics, wildfires, and major storms is that, despite their differences in etiology and duration, they significantly change routine human movement patterns. Such changes, which can be major or minor in size and duration and which differ across contexts, affect both the consequences of the events and the ability of governments to mount effective responses. Based on naturally tracked, anonymized mobility behavior from over 90 million people in the United States, we document these mobility differences in space and over time in six large-scale crises, including wildfires, major tropical storms, winter freeze and pandemics. We introduce a model that effectively captures the high-dimensional heterogeneity in human mobility changes following large-scale extreme events. Across five different metrics and regardless of spatial resolution, the changes in human mobility behavior exhibit a consistent hyperbolic decline, a pattern we characterize as "spatiotemporal decay." When applied to the case of COVID-19, our model also uncovers significant disparities in mobility changes-individuals from wealthy areas not only reduce their mobility at higher rates at the start of the pandemic but also maintain the change longer. Residents from lower-income regions show a faster and greater hyperbolic decay, which we suggest may help account for different COVID-19 rates. Our model represents a powerful tool to understand and forecast mobility patterns post emergency, and thus to help produce more effective responses.

Lagged meteorological impacts on COVID-19 incidence among high-risk counties in the United States-a spatiotemporal analysis.

BACKGROUND: The associations between meteorological factors and coronavirus disease 2019 (COVID-19) have been discussed globally; however, because of short study periods, the lack of considering lagged effects, and different study areas, results from the literature were diverse and even contradictory. OBJECTIVE: The primary purpose of this study is to conduct more reliable research to evaluate the lagged meteorological impacts on COVID-19 incidence by considering a relatively long study period and diversified high-risk areas in the United States. METHODS: This study adopted the distributed lagged nonlinear model with a spatial function to analyze COVID-19 incidence predicted by multiple meteorological measures from March to October of 2020 across 203 high-risk counties in the United States. The estimated spatial function was further smoothed within the entire continental United States by the biharmonic spline interpolation. RESULTS: Our findings suggest that the maximum temperature, minimum relative humidity, and precipitation were the best meteorological predictors. Most significantly positive associations were found from 3 to 11 lagged days in lower levels of each selected meteorological factor. In particular, a significantly positive association appeared in minimum relative humidity higher than 88.36% at 5-day lag. The spatial analysis also shows excessive risks in the north-central United States. SIGNIFICANCE: The research findings can contribute to the implementation of early warning surveillance of COVID-19 by using weather forecasting for up to two weeks in high-risk counties.

Spatiotemporal evolution of COVID-19 in Portugal's Mainland with self-organizing maps.

BACKGROUND: Self-Organizing Maps (SOM) are an unsupervised learning clustering and dimensionality reduction algorithm capable of mapping an initial complex high-dimensional data set into a low-dimensional domain, such as a two-dimensional grid of neurons. In the reduced space, the original complex patterns and their interactions can be better visualized, interpreted and understood. METHODS: We use SOM to simultaneously couple the spatial and temporal domains of the COVID-19 evolution in the 278 municipalities of mainland Portugal during the first year of the pandemic. Temporal 14-days cumulative incidence time series along with socio-economic and demographic indicators per municipality were analyzed with SOM to identify regions of the country with similar behavior and infer the possible common origins of the incidence evolution. RESULTS: The results show how neighbor municipalities tend to share a similar behavior of the disease, revealing the strong spatiotemporal relationship of the COVID-19 spreading beyond the administrative borders of each municipality. Additionally, we demonstrate how local socio-economic and demographic characteristics evolved as determinants of COVID-19 transmission, during the 1st wave school density per municipality was more relevant, where during 2nd wave jobs in the secondary sector and the deprivation score were more relevant. CONCLUSIONS: The results show that SOM can be an effective tool to analysing the spatiotemporal behavior of COVID-19 and synthetize the history of the disease in mainland Portugal during the period in analysis. While SOM have been applied to diverse scientific fields, the application of SOM to study the spatiotemporal evolution of COVID-19 is still limited. This work illustrates how SOM can be used to describe the spatiotemporal behavior of epidemic events. While the example shown herein uses 14-days cumulative incidence curves, the same analysis can be performed using other relevant data such as mortality data, vaccination rates or even infection rates of other disease of infectious nature.

A Spatiotemporal Solution to Control COVID-19 Transmission at the Community Scale for Returning to Normalcy: COVID-19 Symptom Onset Risk Spatiotemporal Analysis.

BACKGROUND: Following the recent COVID-19 pandemic, returning to normalcy has become the primary goal of global cities. The key for returning to normalcy is to avoid affecting social and economic activities while supporting precise epidemic control. Estimation models for the spatiotemporal spread of the epidemic at the refined scale of cities that support precise epidemic control are limited. For most of 2021, Hong Kong has remained at the top of the "global normalcy index" because of its effective responses. The urban-community-scale spatiotemporal onset risk prediction model of COVID-19 symptom has been used to assist in the precise epidemic control of Hong Kong. OBJECTIVE: Based on the spatiotemporal prediction models of COVID-19 symptom onset risk, the aim of this study was to develop a spatiotemporal solution to assist in precise prevention and control for returning to normalcy. METHODS: Over the years 2020 and 2021, a spatiotemporal solution was proposed and applied to support the epidemic control in Hong Kong. An enhanced urban-community-scale geographic model was proposed to predict the risk of COVID-19 symptom onset by quantifying the impact of the transmission of SARS-CoV-2 variants, vaccination, and the imported case risk. The generated prediction results could be then applied to establish the onset risk predictions over the following days, the identification of high-onset-risk communities, the effectiveness analysis of response measures implemented, and the effectiveness simulation of upcoming response measures. The applications could be integrated into a web-based platform to assist the antiepidemic work. RESULTS: Daily predicted onset risk in 291 tertiary planning units (TPUs) of Hong Kong from January 18, 2020, to April 22, 2021, was obtained from the enhanced prediction model. The prediction accuracy in the following 7 days was over 80%. The prediction results were used to effectively assist the epidemic control of Hong Kong in the following application examples: identified communities within high-onset-risk always only accounted for 2%-25% in multiple epidemiological scenarios; effective COVID-19 response measures, such as prohibiting public gatherings of more than 4 people were found to reduce the onset risk by 16%-46%; through the effect simulation of the new compulsory testing measure, the onset risk was found to be reduced by more than 80% in 42 (14.43%) TPUs and by more than 60% in 96 (32.99%) TPUs. CONCLUSIONS: In summary, this solution can support sustainable and targeted pandemic responses for returning to normalcy. Faced with the situation that may coexist with SARS-CoV-2, this study can not only assist global cities in responding to the future epidemics effectively but also help to restore social and economic activities and people's normal lives.

Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis.

Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

Spatiotemporal Analysis of Black Carbon Sources: Case of Santiago, Chile, under SARS-CoV-2 Lockdowns.

BACKGROUND: The SARS-CoV-2 pandemic has temporarily decreased black carbon emissions worldwide. The use of multi-wavelength aethalometers provides a quantitative apportionment of black carbon (BC) from fossil fuels (BCff) and wood-burning sources (BCwb). However, this apportionment is aggregated: local and non-local BC sources are lumped together in the aethalometer results. METHODS: We propose a spatiotemporal analysis of BC results along with meteorological data, using a fuzzy clustering approach, to resolve local and non-local BC contributions. We apply this methodology to BC measurements taken at an urban site in Santiago, Chile, from March through December 2020, including lockdown periods of different intensities. RESULTS: BCff accounts for 85% of total BC; there was up to an 80% reduction in total BC during the most restrictive lockdowns (April-June); the reduction was 40-50% in periods with less restrictive lockdowns. The new methodology can apportion BCff and BCwb into local and non-local contributions; local traffic (wood burning) sources account for 66% (86%) of BCff (BCwb). CONCLUSIONS: The intensive lockdowns brought down ambient BC across the city. The proposed fuzzy clustering methodology can resolve local and non-local contributions to BC in urban zones.

Impact of COVID-19 pandemic on catheter ablation in China: A spatiotemporal analysis.

Background: The COVID-19 pandemic has significantly impacted routine cardiovascular health assessments and services. We aim to depict the temporal trend of catheter ablation (CA) and provide experience in dealing with the negative impact of the COVID-19 pandemic. Methods: Data on CA between January 2019, and December 2021, were extracted from the National Center for Cardiovascular Quality Improvement platform. CA alterations from 2019 to 2021 were assessed with a generalized estimation equation. Results: A total of 347,924 patients undergoing CA were included in the final analysis. The CA decreased remarkably from 122,839 in 2019 to 100,019 (-18.58%, 95% CI: -33.40% to -3.75%, p = 0.02) in 2020, and increased slightly to 125,006 (1.81%, 95% CI: -7.01% to 3.38%, p = 0.49) in 2021. The CA experienced the maximal reduction in February 2020 (-88.78%) corresponding with the peak of monthly new COVID-19 cases and decreased by 54.32% (95%CI: -71.27% to -37.37%, p < 0.001) during the 3-month lockdown and increased firstly in June 2020 relative to 2019. Since then, the CA in 2020 remained unchanged relative to 2019 (-0.06%, 95% CI: -7.01% to 3.38%, p = 0.98). Notably, the recovery of CA in 2021 to pre-COVID-19 levels was mainly driven by the growth of CA in secondary hospitals. Although there is a slight increase (2167) in CA in 2021 relative to 2019, both the absolute number and proportion of CA in the top 50 hospitals nationwide [53,887 (43.09%) vs. 63,811 (51.95%), p < 0.001] and top three hospitals in each province [66,152 (52.73%) vs. 72,392 (59.28%), p < 0.001] still declined significantly. Conclusions: The CA experienced a substantial decline during the early phase of the COVID-19 pandemic, and then gradually returned to pre-COVID-19 levels. Notably, the growth of CA in secondary hospitals plays an important role in the overall resumption, which implies that systematic guidance of secondary hospitals with CA experience may aid in mitigating the negative impact of the COVID-19 pandemic.

Spatio-temporal analysis of tuberculosis incidence in North Aceh District, Indonesia 2019-2021.

Tuberculosis (TB) infection continues to present as a leading cause of morbidity and mortality in North Aceh District, Aceh Province, Indonesia. Local TB spatial risk factors have been investigated but space-time clusters of TB in the district have not yet been the subject of study. To that end, research was undertaken to detect clusters of TB incidence during 2019-2021 in this district. First, the office of each of the 27 sub-districts wasgeocoded by collecting data of their geographical coordinates. Then, a retrospective space-time scan statistics analysis based on population data and annual TB incidence was performed using SaTScan TM v9.4.4. The Poisson model was used to identify the areas at high risk of TB and the clusters found were ranked by their likelihood ratio (LLR), with the significance level set at 0.05.There were 2,266 TB cases reported in North Aceh District and the annualized average incidence was 122.91 per 100,000 population. The SaTScan analysis identified that there were three most like clusters and ten secondary clusters, while Morans'Ishowed that there was spatial autocorrelation of TB in the district. The sub-district of GeureudongPase was consistently the location of most likely clusters. The indicators showed that there were significant differences between TB data before the COVID-19 pandemic and those found during the study period. These findings may assist health authorities to improve the TB preventive strategies and develop public health interventions, with special reference to the areas where the clusters were found.

A spatiotemporal analysis of the impact of the COVID-19 outbreak on noise pollution in Tehran, Iran.

Noise pollution is one of the non-natural hazards in cities. Long-term exposure to this kind of pollution has severe destructive effects on human health, including mental illness, stress, anxiety, hormonal disorders, hypertension and therefore also cardiovascular disease. One of the primary sources of noise pollution in cities is transportation. The COVID-19 outbreak caused a significant change in the pattern of transportation in cities of Iran. In this article, we studied the spatial and temporal patterns of noise pollution levels in Tehran before and after the outbreak of this disease. An overall analysis from one year before until one year after the outbreak, which showed that noise pollution in residential areas of Tehran had increased by 7% over this period. In contrast, it had diminished by about 2% in the same period in the city centre and around Tehran's Grand Bazaar. Apart from these changes, we observed no specific pattern in other city areas. However, a monthly data analysis based on the t-test, the results show that the early months of the virus outbreak were associated with a significant pollution reduction. However, this reduction in noise pollution was not sustained; instead a gradual increase in pollution occurred over the following months. In the months towards the end of the period analysed, noise pollution increased to a level even higher than before the outbreak. This increase can be attributed to the gradual reopening of businesses or people ignoring the prevailing conditions.

Modeling the spatio­temporal spread of COVID­19 cases, recoveries and deaths and effects of partial and full vaccination coverage in Canada.

The purposes of our study are to map high-risk areas in Canada as well as quantifying the effects of vaccination intervention and socio-demographic factors on the transmission rates of infection, recovery, and death related to COVID-19. The data of this research included weekly number of COVID­19 cases, recovered, and dead individuals from 2020 through 2021 in Canada at health region and provincial levels. These data were associated with cumulative rates of partial and full vaccination and socio-demographic factors. We applied the spatio-temporal Susceptible-Exposed-Infected-Removed (SEIR), and Susceptible-Exposed-Infected-Removed-Vaccinated (SEIRV) models. The results indicated the partial vaccination rate has a greater effect compared with full vaccination rate on decreasing the rate of infectious cases (risk ratio (RR) = 0.18; 95%CrI: 0.16-0.2; RR = 0.60; 95%CrI: 0.55-0.65, respectively) and increasing the rate of recovered cases (RR = 1.39; 95%CrI: 1.28-1.51; RR = 1.21; 95%CrI: 1.23-1.29, respectively). However, for mortality risk reduction, only increasing full vaccination rate was significantly associated (RR = 0.09; 95%CrI: 0.05-0.14). In addition, our results showed that regions with higher rates of elderly and aboriginal individuals, higher population density, and lower socioeconomic status (SES) contribute more to the risk of infection transmission. Rates of elderly and aboriginal individuals and SES of regions were significantly associated with recovery rate. However, elderly individuals rate of regions was only a significant predictor of mortality risk. Based on the results, protection against mild and severe COVID-19 infection after the primary vaccination series decreased.

Temporal and Spatial Cluster Analysis of 2019 Novel Coronavirus Pneumonia in Chongqing, 2020.1∼2020.2.

In order to explore the spatial and temporal distribution characteristics of COVID-19 in Chongqing from January 22 to February 25, 2010, and provide a series of suggestions for scientific prevention and control of epidemic situation, we will mainly analyze the epidemic situation data of Chongqing Municipal Health Committee members and improve the descriptive analysis. Regional distribution and spatiotemporal scans were analyzed for COVID-19 outbreaks using ArcGIS10.2 and SaTScan9. 5 software. After the analysis, a total of 576 novel coronavirus pneumonia patients were confirmed in Chongqing. The incidence trend increased rapidly from January 22 to January 31, then decreased gradually, and there were no new cases until February 25. The purely spatial scanning results were consistent with spatiotemporal scanning, and a first-level accumulation area was detected by spatiotemporal scanning in the east and northeast of Chongqing from January 22 to February 10. From January 22 to February 25, 2020,COVID-19 occurred in the eastern and northeast regions of Chongqing. It is recommended to strengthen the detection of cluster areas to prevent another outbreak of COVID-19 risk.