COVID-19 Incidence Data Research Articles

Analysis of the incidence of influenza before and after the COVID-19 pandemic in South Korea.

Influenza outbreaks of varying size occur every year, but during the COVID-19 pandemic, many countries experienced influenza at lower levels. However, following the relaxation of COVID-19 prevention measures in 2022, the incidence of influenza began to increase gradually. Thus, this study compared the occurrence of influenza from week 36 of 2017, before the COVID-19 outbreak, until 2023. The analysis was conducted using influenza-like illness occurrence data available from the Korea Disease Control and Prevention Agency infectious disease website. Additionally, to examine the changes in COVID-19 and influenza occurrence during the pandemic, COVID-19 incidence data from 20 January 2020 to 31 August 2023 were obtained from the KDCA Coronavirus Disease 19 homepage. During the COVID-19 pandemic, which corresponds to the 2020/2021 and 2021/2022 influenza seasons, there was no seasonal influenza epidemic, and the incidence rates were below the usual outbreak levels. However, in the 2022/2023 season, when the spread of COVID-19 had eased, a seasonal pattern similar to that observed before the COVID-19 pandemic was noted. Furthermore, correlation analysis between the rates of influenza-like illness and COVID-19 incidence showed no significant correlation during the entire period. However, a significant correlation emerged in 2023 (r = 0.393, p <0.05). These results suggest that influenza was suppressed during the COVID-19 pandemic but returned to typical seasonal patterns after the COVID-19 prevention policies were eased. The positive correlation observed between the incidences of COVID-19 and influenza in 2023 indicates that COVID-19, no longer a novel pandemic-causing infectious disease, may have transitioned to an endemic pattern similar to seasonal influenza.

An Efficient Approach to Nowcasting the Time-varying Reproduction Number.

Estimating the instantaneous reproduction number ( ) in near real time is crucial for monitoring and responding to epidemic outbreaks on a daily basis. However, such estimates often suffer from bias due to reporting delays inherent in surveillance systems. We propose a fast and flexible Bayesian methodology to overcome this challenge by estimating while taking into account reporting delays. Furthermore, the method naturally takes into account the uncertainty associated with the nowcasting of cases to get a valid uncertainty estimation of the nowcasted reproduction number. We evaluate the proposed methodology through a simulation study and apply it to COVID-19 incidence data in Belgium.

Generalized functional additive mixed models with (functional) compositional covariates for areal Covid-19 incidence curves

Abstract We extend the generalized functional additive mixed model to include compositional and functional compositional (density) covariates carrying relative information of a whole. Relying on the isometric isomorphism of the Bayes Hilbert space of probability densities with a sub-space of the L2, we include functional compositions as transformed functional covariates with constrained yet interpretable effect function. The extended model allows for the estimation of linear, non-linear, and time-varying effects of scalar and functional covariates, as well as (correlated) functional random effects, in addition to the compositional effects. We use the model to estimate the effect of the age, sex, and smoking (functional) composition of the population on regional Covid-19 incidence data for Spain, while accounting for climatological and socio-demographic covariate effects and spatial correlation.

Mathematical analysis and simulation of COVID-19 model with booster dose vaccination strategy in Bangladesh

The control of COVID-19 transmission remains a crucial issue despite the administration of multiple doses of vaccines, given the ongoing emergence of new variants. This study proposes a mathematical model of COVID-19 with multiple doses of vaccinations. We find disease-free equilibrium (DFE) and disease-endemic equilibrium (DEE) and perform their stability analysis to explore in which parameters space the disease will be fade-out or persists in the population. This study derives mathematical expressions for the basic reproductive numbers (R0) using the Next-Generation Matrix method. Using surface and contour plots, we brought to light the exciting consequences of disease transmission rate and other parameter values on the basic reproduction number (R0). It demonstrates that the disease-free equilibrium point is locally stable when R0 < 1, whereas the disease-endemic equilibrium point is locally stable for R0 > 1. We also perform bifurcation analysis to demonstrate in which conditions the disease will be stable or unstable when R0 = 1. Numerical simulations show that increasing first dose vaccination rate is more effective than second and booster doses vaccination rate for reducing the number of asymptomatic and symptomatic cases in Bangladesh. We use COVID-19 incidence data from WHO between January and December 2022 and estimate parameters using the least-squares technique. We perform the sensitivity index of symptomatic and asymptomatic cases to identify the most significant parameter for disease outbreaks and found that transmission rate had the highest impact on disease prevalence. This research provides insight to prevent and control of COVID-19 transmission with booster-dose vaccination in Bangladesh.

Effective Utilization of Data for Predicting COVID-19 Dynamics: An Exploration through Machine Learning Models

This study is centered around the COVID-19 pandemic which has posed a global health concern for over three years. It emphasizes the importance of effectively utilizing epidemic simulation models for informed decision-making concerning epidemic control. The challenge lies in appropriately choosing, adapting, and interpreting these models. The research constructs three statistical machine learning models to predict the spread of COVID-19 in specific regions and evaluates their performance using real COVID-19 incidence data. The paper presents short-term (3, 7, 14, 21, and 30 days) forecasts of COVID-19 morbidity and mortality for Germany, Japan, South Korea, and Ukraine. The precision of each model was scrutinized based on the type of input data used. Recommendations are provided on how various data sources can enhance the interpretation quality of machine learning models predicting infectious disease dynamics. The initial findings suggest the need for the comprehensive utilization of all available data, favoring cumulative data during holiday-rich periods and daily data otherwise. To minimize the absolute error, databases should be compiled using daily morbidity and mortality rates.

Epidemiological characteristics of reinfection of 2019-nCoV and influencing factors in Ningbo

Objective: To analyze the epidemiological characteristics of reinfection of 2019-nCoV and influencing factors, and provide evidence for effective prevention and control of COVID-19 epidemic. Methods: The incidence data of COVID-19 in Ningbo from January 1, 2020 to November 30, 2022 were collected from the infectious disease surveillance system of Chinese information system for disease control and prevention. The incidence of reinfection of 2019-nCoV was investigated by using questionnaire. logistic regression analysis was used to analyze the influences of gender, age, time interval from the first infection, history of underlying disease, 2019-nCoV vaccination dose and disease severity on the reinfection. Results: A total of 897 previous 2019-nCoV infection cases were investigated, of which 115 experienced the reinfection of 2019-nCoV, the reinfection rate was 12.82%. The interval between the two infections M(Q1, Q3) was 1 052 (504, 1 056) days. Univariate analysis showed that age, 2019-nCoV vaccination dose, history of underlying disease, type of 2019-nCoV variant causing the first infection, time interval from the first infection and severity of the first infection were associated with the reinfection rate (all P<0.05). Multivariate logistic regression analysis showed that the risk for reinfection in age group 30- years was higher than that in age group ≥60 years (OR=2.10, 95%CI: 1.11-3.97). No reinfection occurred in those with time interval from the first infection of <6 months, and the risk for reinfection was higher in those with the time interval of ≥12 months than in those with the time interval of 6- months (OR=6.68, 95%CI: 3.46-12.90). The risk for reinfection was higher in the common or mild cases than in the asymptomatic cases (OR=2.64, 95%CI: 1.18-5.88; OR=2.79, 95%CI: 1.27-6.11). Conclusion: The time interval from the first infection was an important influencing factor for the reinfection of 2019-nCoV, and the probability of the reinfection within 6 months was low.

Spatio-Temporal Patterns of the SARS-CoV-2 Epidemic in Germany.

Results from an explorative study revealing spatio-temporal patterns of the SARS-CoV-2/ COVID-19 epidemic in Germany are presented. We dispense with contestable model assumptions and show the intrinsic spatio-temporal patterns of the epidemic dynamics. The analysis is based on COVID-19 incidence data, which are age-stratified and spatially resolved at the county level, provided by the Federal Government's Public Health Institute of Germany (RKI) for public use. Although the 400 county-related incidence time series shows enormous heterogeneity, both with respect to temporal features as well as spatial distributions, the counties' incidence curves organise into well-distinguished clusters that coincide with East and West Germany. The analysis is based on dimensionality reduction, multidimensional scaling, network analysis, and diversity measures. Dynamical changes are captured by means of difference-in-difference methods, which are related to fold changes of the effective reproduction numbers. The age-related dynamical patterns suggest a considerably stronger impact of children, adolescents and seniors on the epidemic activity than previously expected. Besides these concrete interpretations, the work mainly aims at providing an atlas for spatio-temporal patterns of the epidemic, which serves as a basis to be further explored with the expertise of different disciplines, particularly sociology and policy makers. The study should also be understood as a methodological contribution to getting a handle on the unusual complexity of the COVID-19 pandemic.

Impact of cross border reverse migration in Delhi–UP region of India during COVID-19 lockdown

Abstract The declaration of a nationwide lockdown in India led to millions of migrant workers, particularly from Uttar Pradesh (UP) and Bihar, returning to their home states without proper transportation and social distancing from cities such as Delhi, Mumbai, and Hyderabad. This unforeseen migration and social mixing accelerated the transmission of diseases across the country. To analyze the impact of reverse migration on disease progression, we have developed a disease transmission model for the neighboring Indian states of Delhi and UP. The model’s essential mathematical properties, including positivity, boundedness, equilibrium points (EPs), and their linear stability, as well as computation of the basic reproduction number ( R 0 ) \left({R}_{0}) , are studied. The mathematical analysis reveals that the model with active reverse migration cannot reach a disease-free equilibrium, indicating that the failure of restrictive mobility intervention caused by reverse migration kept the disease propagation alive. Further, PRCC analysis highlights the need for effective home isolation, better disease detection techniques, and medical interventions to curb the spread. The study estimates a significantly shorter doubling time for exponential growth of the disease in both regions. In addition, the occurrence of synchronous patterns between epidemic trajectories of the Delhi and UP regions accentuates the severe implications of migrant plight on UP’s already fragile rural health infrastructure. By using COVID-19 incidence data, we quantify key epidemiological parameters, and our scenario analyses demonstrate how different lockdown plans might have impacted disease prevalence. Based on our observations, the transmission rate has the most significant impact on COVID-19 cases. This case study exemplifies the importance of carefully considering these issues before implementing lockdowns and social isolation throughout the country to combat future outbreaks.

A new RNN based machine learning model to forecast COVID-19 incidence, enhanced by the use of mobility data from the bike-sharing service in Madrid

As a respiratory virus, COVID-19 propagates based on human-to-human interactions with positive COVID-19 cases. The temporal evolution of new COVID-19 infections depends on the existing number of COVID-19 infections and the people's mobility. This article proposes a new model to predict upcoming COVID-19 incidence values that combines both current and near-past incidence values together with mobility data. The model is applied to the city of Madrid (Spain). The city is divided into districts. The weekly COVID-19 incidence data per district is used jointly with a mobility estimation based on the number of rides reported by the bike-sharing service in the city of Madrid (BiciMAD). The model employs a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) to detect temporal patterns for COVID-19 infections and mobility data, and combines the output of the LSTM layers into a dense layer that can learn the spatial patterns (the spread of the virus between districts). A baseline model that employs a similar RNN but only based on the COVID-19 confirmed cases with no mobility data is presented and used to estimate the model gain when adding mobility data. The results show that using the bike-sharing mobility estimation the proposed model increases the accuracy by 11.7% compared with the baseline model.

Distribution of Coronavirus Disease (Covid-19) in West Sumatra Province with Local Indicator of Spatial Association (LISA) Cluster Map

&lt;div&gt;&lt;table cellspacing="0" cellpadding="0" align="left"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td align="left" valign="top"&gt;&lt;p&gt;&lt;em&gt;The COVID-19 pandemic in West Sumatra Province indicates a greater number of cases and mortality. The spread of COVID-19 is related to the mobility of the population, so the potential for transmission between regions is difficult to control. This study aims to determine the local index used in evaluating the tendency for local spatial groupings and can show some form of spatial relationship. Spatial analysis were conducted on 2020 to analyze spatial distribution of Covid-19 in West Sumatera Province. Spatial relationship was assessed by Local Indicator of Spatial Association (LISA). Mapping can be done with the LISA cluster map. The data used is COVID-19 incidence data based on reports from district or city in West Sumatra Province in 4 June 2020. We used Open Geoda Software to analyze the spatial distribution. There is positive spatial autocorrelation and classification in hot spots, cold spots, and outliers in the spread of COVID-19 cases in West Sumatra Province. Hot spots were found in Padang City, Bukittinggi City, and Padang Panjang City. Cold spots also detected in several districts, that is &lt;/em&gt;&lt;em&gt;West Pasaman, Pasaman, Payakumbuh, Solok, Padang Pariaman, Pariaman, Sawahlunto, and Sijunjung. &lt;/em&gt;&lt;em&gt;The transmission of the COVID-19 case does not recognize regional boundaries, but the grouping of districts or cities based on regional vulnerability is important as part of local control efforts to allocate resources. Coordination and collaboration among local governments need to be strengthened in preventing transmission between regions and reducing the number of cases in vulnerable areas based on hot spots and cold spots from LISA cluster map. It’s necessary for intervention programs more focused and effectively.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords: COVID-19, Mapping, Spatial, Vulnerability&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;

Using Mobile Phone Data to Assess Socio-Economic Disparities in Unhealthy Food Reliance during the COVID-19 Pandemic.

Background: Although COVID-19 has disproportionately affected socio-economically vulnerable populations, research on its impact on socio-economic disparities in unhealthy food reliance remains scarce. Methods: This study uses mobile phone data to evaluate the impact of COVID-19 on socio-economic disparities in reliance on convenience stores and fast food. Reliance is defined in terms of the proportion of visits to convenience stores out of the total visits to both convenience and grocery stores, and the proportion of visits to fast food restaurants out of the total visits to both fast food and full-service restaurants. Visits to each type of food outlet at the county level were traced and aggregated using mobile phone data before being analyzed with socio-economic demographics and COVID-19 incidence data. Results: Our findings suggest that a new COVID-19 case per 1,000 population decreased a county's odds of relying on convenience stores by 3.41% and increased its odds of fast food reliance by 0.72%. As a county's COVID-19 incidence rate rises by an additional case per 1,000 population, the odds of relying on convenience stores increased by 0.01%, 0.02%, and 0.06% for each additional percentage of Hispanics, college-educated residents, and every additional year in median age, respectively. For fast food reliance, as a county's COVID-19 incidence rate increases by one case per 1,000 population, the odds decreased by 0.003% for every additional percentage of Hispanics but increased by 0.02% for every additional year in the county's median age. Conclusion: These results complement existing literature to promote equitable food environments.

A two-strain model of infectious disease spread with asymmetric temporary immunity periods and partial cross-immunity.

We introduce a two-strain model with asymmetric temporary immunity periods and partial cross-immunity. We derive explicit conditions for competitive exclusion and coexistence of the strains depending on the strain-specific basic reproduction numbers, temporary immunity periods, and degree of cross-immunity. The results of our bifurcation analysis suggest that, even when two strains share similar basic reproduction numbers and other epidemiological parameters, a disparity in temporary immunity periods and partial or complete cross-immunity can provide a significant competitive advantage. To analyze the dynamics, we introduce a quasi-steady state reduced model which assumes the original strain remains at its endemic steady state. We completely analyze the resulting reduced planar hybrid switching system using linear stability analysis, planar phase-plane analysis, and the Bendixson-Dulac criterion. We validate both the full and reduced models with COVID-19 incidence data, focusing on the Delta (B.1.617.2), Omicron (B.1.1.529), and Kraken (XBB.1.5) variants. These numerical studies suggest that, while early novel strains of COVID-19 had a tendency toward dramatic takeovers and extinction of ancestral strains, more recent strains have the capacity for co-existence.

Accounting for the Potential of Overdispersion in Estimation of the Time-varying Reproduction Number.

The time-varying reproduction number, Rt, is commonly used to monitor the transmissibility of an infectious disease during an epidemic, but standard methods for estimating Rt seldom account for the impact of overdispersion on transmission. We developed a negative binomial framework to estimate Rt and a time-varying dispersion parameter (kt). We applied the framework to COVID-19 incidence data in Hong Kong in 2020 and 2021. We conducted a simulation study to compare the performance of our model with the conventional Poisson-based approach. Our framework estimated an Rt peaking around 4 (95% credible interval = 3.13, 4.30), similar to that from the Poisson approach but with a better model fit. Our approach further estimated kt <0.5 at the start of both waves, indicating appreciable heterogeneity in transmission. We also found that kt decreased sharply to around 0.4 when a large cluster of infections occurred. Our proposed approach can contribute to the estimation of Rt and monitoring of the time-varying dispersion parameters to quantify the role of superspreading.

Fine-scale variation in the effect of national border on COVID-19 spread: A case study of the Saxon-Czech border region

The global extent and temporally asynchronous pattern of COVID-19 spread have repeatedly highlighted the role of international borders in the fight against the pandemic. Additionally, the deluge of high resolution, spatially referenced epidemiological data generated by the pandemic provides new opportunities to study disease transmission at heretofore inaccessible scales. Existing studies of cross-border infection fluxes, for both COVID-19 and other diseases, have largely focused on characterizing overall border effects. Here, we couple fine-scale incidence data with localized regression models to quantify spatial variation in the inhibitory effect of an international border. We take as a case study the border region between the German state of Saxony and the neighboring regions in northwestern Czechia, where municipality-level COVID-19 incidence data are available on both sides of the border. Consistent with past studies, we find an overall inhibitory effect of the border, but with a clear asymmetry, where the inhibitory effect is stronger from Saxony to Czechia than vice versa. Furthermore, we identify marked spatial variation along the border in the degree to which disease spread was inhibited. In particular, the area around Löbau in Saxony appears to have been a hotspot for cross-border disease transmission. The ability to identify infection flux hotspots along international borders may help to tailor monitoring programs and response measures to more effectively limit disease spread.

Space-Distributed Traffic-Enhanced LSTM-Based Machine Learning Model for COVID-19 Incidence Forecasting.

The COVID-19 virus continues to generate waves of infections around the world. With major areas in developing countries still lagging behind in vaccination campaigns, the risk of new variants that can cause re-infections worldwide makes the monitoring and forecasting of the evolution of the virus a high priority. Having accurate models able to forecast the incidence of the spread of the virus provides help to policymakers and health professionals in managing the scarce resources in an optimal way. In this paper, a new machine learning model is proposed to forecast the spread of the virus one-week ahead in a geographic area which combines mobility and COVID-19 incidence data. The area is divided into zones or districts according to the location of the COVID-19 measuring points. A traffic-driven mobility estimate among adjacent districts is proposed to capture the spatial spread of the virus. Traffic-driven mobility in adjacent districts will be used together with COVID-19 incidence data to feed a new deep learning LSTM-based model which will extract patterns from mobility-modulated COVID-19 incidence spatiotemporal data in order to optimize one-week ahead estimations. The model is trained and validated with open data available for the city of Madrid (Spain) for 3 different validation scenarios. A baseline model based on previous literature able to extract temporal patterns in COVID-19 incidence time series is also trained with the same dataset. The results show that the proposed model, based on the combination of traffic and COVID-19 incidence data, is able to outperform the baseline model in all the validation scenarios.

The impact of a power law-induced memory effect on the SARS-CoV-2 transmission

It is well established that COVID-19 incidence data follows some power law growth pattern. Therefore, it is natural to believe that the COVID-19 transmission process follows some power law. However, we found no existing model on COVID-19 with a power law effect only in the disease transmission process. Inevitably, it is not clear how this power law effect in disease transmission can influence multiple COVID-19 waves in a location. In this context, we developed a completely new COVID-19 model where a force of infection function in disease transmission follows some power law. Furthermore, different realistic epidemiological scenarios like imperfect social distancing among home-quarantined individuals, disease awareness, vaccination, treatment, and possible reinfection of the recovered population are also considered in the model. Applying some recent techniques, we showed that the proposed system converted to a COVID-19 model with fractional order disease transmission, where order of the fractional derivative (α) in the force of infection function represents the memory effect in disease transmission. We studied some mathematical properties of this newly formulated model and determined the basic reproduction number (R0). Furthermore, we estimated several epidemiological parameters of the newly developed fractional order model (including memory index α) by fitting the model to the daily reported COVID-19 cases from Russia, South Africa, UK, and USA, respectively, for the time period March 01, 2020, till December 01, 2021. Variance-based Sobol’s global sensitivity analysis technique is used to measure the effect of different important model parameters (including α) on the number of COVID-19 waves in a location (WC). Our findings suggest that α along with the average transmission rate of the undetected (symptomatic and asymptomatic) cases in the community (β1) are mainly influencing multiple COVID-19 waves in those four locations. Numerically, we identified the regions in the parameter space of α and β1 for which multiple COVID-19 waves are occurring in those four locations. Furthermore, our findings suggested that increasing memory effect in disease transmission (α→ 0) may decrease the possibility of multiple COVID-19 waves and as well as reduce the severity of disease transmission in those four locations. Based on all the results, we try to identify a few non-pharmaceutical control strategies that may reduce the risk of further SARS-CoV-2 waves in Russia, South Africa, UK, and USA, respectively.

Data-driven clustering of infectious disease incidence into age groups.

Understanding the patterns of infectious diseases spread in the population is an important element of mitigation and vaccination programs. A major and common characteristic of most infectious diseases is age-related heterogeneity in the transmission, which potentially can affect the dynamics of an epidemic as manifested by the pattern of disease incidence in different age groups. Currently there are no statistical criteria of how to partition the disease incidence data into clusters. We develop the first data-driven methodology for deciding on the best partition of incidence data into age-groups, in a well defined statistical sense. The method employs a top-down hierarchical partitioning algorithm, with a stopping criteria based on multiple hypotheses significance testing controlling the family wise error rate. The type one error and statistical power of the method are tested using simulations. The method is then applied to Covid-19 incidence data in Israel, in order to extract the significant age-group clusters in each wave of the epidemic.

Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada.

Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran's I spatial autocorrelation, and Local Moran's I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources. Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.

Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada

Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran’s I spatial autocorrelation, and Local Moran’s I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources. Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.

Transmission chains of local epidemic of COVID-19 caused by 2019-nCoV Delta variant in Zhenhai district, Ningbo

Objective: To investigate the local epidemic of COVID-19 caused by 2019-nCoV Delta variant in Zhenhai district of Ningbo, identify the transmission chain and provide reference for the prevention and control of COVID-19 epidemic. Methods: The incidence data of COVID-19 in Zhenhai from 6 to 18 December, 2021 were collected in field investigation. Field epidemiological investigation was conducted to understand the epidemiological characteristics of COVID-19 cases and analyze the transmission chains. Results: The first case might be infected with 2019-nCoV through direct or indirect exposure when passing through a medium-risk area, then a family cluster was caused, and the epidemic spread through close contacts of family members with others such as work, daily life, and moxibustion. The epidemic lasted for 14 days, and 74 confirmed COVID-19 cases were reported. The median incubation period was 4.0(3.0,5.8)d. All the cases were in a chain of transmission for more than 6 generations, and the intergenerational interval was 3.5(2.0,5.3)d. The gene sequencing result indicated that the pathogen was Delta AY.4 variant of 2019-nCoV. Both the epidemiological investigation and the gene sequencing results supported that the local COVID-19 epidemic in Zhenhai was associated with the COVID-19 epidemic in Shanghai. Conclusions: The transmission chain of this epidemic was clear. Delta AY.4 variant has obvious characteristic to cause case clusters in families, places with poor ventilation, and residential communities. It is suggested to strengthen the health management in key areas and key populations, and increase the frequency of nucleic acid testing.