COVID-19 Transmission Patterns Research Articles

Probabilistic modeling of COVID-19 events: Exploring new alpha generated family for enhanced analysis capabilities

This study explores a new family of distributions to enhance the analysis capabilities of COVID-19 events, providing valuable insights for informed decision-making and effective public health management. Accurate understanding and prediction of COVID-19 transmission patterns and their impacts are crucial in reducing the mortality rate, especially in proactive control and mitigation efforts. The proposed approach focuses on a best-fit probabilistic model that incorporates a comprehensive assessment of various statistical measures. Additionally, seven well-recognized classical point estimation methods are employed to identify the most suitable approach for assisting epidemiologists in their analysis. The study analyzes COVID-19 data from multiple countries, including the Netherlands, Mexico, the United Kingdom, China, Canada, Saudi Arabia, and Italy, considering different aspects such as mortality rates and the number of deaths. By evaluating the performance of the new alpha generated family of distributions in modeling COVID-19 events. This research contributes to the advancement of our understanding of the disease's probabilistic nature. The findings have practical implications, guiding the development of public health policies, resource allocation strategies, and intervention plans, ultimately facilitating more effective control and mitigation of COVID-19 outbreaks.

Dynamic Patterns and Modeling of Early COVID-19 Transmission by Dynamic Mode Decomposition.

Understanding the transmission patterns and dynamics of COVID-19 is critical to effective monitoring, intervention, and control for future pandemics. The aim of this study was to investigate the spatial and temporal characteristics of COVID-19 transmission during the early stage of the outbreak in the US, with the goal of informing future responses to similar outbreaks. We used dynamic mode decomposition (DMD) and national data on COVID-19 cases (April 6, 2020-October 9, 2020) to model the spread of COVID-19 in the US as a dynamic system. DMD can decompose the complex evolution of disease cases into linear combinations of simple spatial patterns or structures (modes) with time-dependent mode amplitudes (coefficients). The modes reveal the hidden dynamic behaviors of the data. We identified geographic patterns of COVID-19 spread and quantified time-dependent changes in COVID-19 cases during the study period. The magnitude analysis from the dominant mode in DMD showed that California, Louisiana, Kansas, Georgia, and Texas had higher numbers of COVID-19 cases than other areas during the study period. States such as Arizona, Florida, Georgia, Massachusetts, New York, and Texas showed simultaneous increases in the number of COVID-19 cases, consistent with data from the Centers for Disease Control and Prevention. Results from DMD analysis indicate that certain areas in the US shared similar trends and similar spatiotemporal transmission patterns of COVID-19. These results provide valuable insights into the spread of COVID-19 and can inform policy makers and public health authorities in designing and implementing mitigation interventions.

An analysis of the spatio-temporal behavior of COVID-19 patients using activity trajectory data

During the global pandemic, COVID-19 patients' activity trajectories and actions emerge as revelatory conduits elucidating their spatiotemporal behavior and transmission dynamics. This study analyzes COVID-19 patients' behavior in Nanjing and Yangzhou, China, by using patient activity trajectory data in conjunction with complex network theory. The main findings are as follows: (1) The evaluation of the activity network structure of patients revealed that “residential areas” and “vegetable markets” had the highest betweenness centrality, indicating that these are the primary nodes of COVID-19 transmission. (2) The power-law distribution of the degree distribution of nodes for different facility types revealed that residential areas, vegetable markets, and shopping malls had the most scale-free characteristics, indicating that a large number of patients visited these three facility types at a few access points. (3) Community detection showed that patient visitation sites in Nanjing and Yangzhou were divided into five or six communities, with the largest community containing the outbreak origin and several residential areas surrounding it. (4) Patients had fewer activities across administrative regions but more activities across the life circle when the pandemic broke out in the suburbs, and more activities across administrative regions but fewer activities across the life circle when the pandemic broke out in the central city. Based on these findings, this paper makes recommendations for future pandemic preparedness in an effort to achieve effective pandemic control and reduce the damage caused by pandemics. Overall, this study provides insights into understanding the transmission patterns of COVID-19 and may inform future pandemic control strategies.

TransCode: Uncovering COVID-19 transmission patterns via deep learning

BackgroundThe heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning.MethodsWe introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors. First, in Hong Kong, China, we construct the mobility trajectories of confirmed cases using their visiting records. Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution. Integrating the spatial and temporal information, we represent the TransCode via spatiotemporal transmission networks. Further, we propose a deep transfer learning model to adapt the TransCode of Hong Kong, China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises: New York City, San Francisco, Toronto, London, Berlin, and Tokyo, where fine-scale data are limited. All the data used in this study are publicly available.ResultsThe TransCode of Hong Kong, China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns (e.g., the imported and exported transmission intensities) at the district and constituency levels during different COVID-19 outbreaks waves. By adapting the TransCode of Hong Kong, China to other data-limited densely populated metropolises, the proposed method outperforms other representative methods by more than 10% in terms of the prediction accuracy of the disease dynamics (i.e., the trend of case numbers), and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.ConclusionsThe fine-scale transmission patterns due to the metapopulation level mobility (e.g., travel across different districts) and contact behaviors (e.g., gathering in social-economic centers) are one of the main contributors to the rapid spread of the virus. Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.Graphical

Mathematical Modeling of COVID-19 with Periodic Transmission: The Case of South Africa

The data on SARS-CoV-2 (COVID-19) in South Africa show seasonal transmission patterns to date, with the peaks having occurred in winter and summer since the outbreaks began. The transmission dynamics have mainly been driven by variations in environmental factors and virus evolution, and the two are at the center of driving the different waves of the disease. It is thus important to understand the role of seasonality in the transmission dynamics of COVID-19. In this paper, a compartmental model with a time-dependent transmission rate is formulated and the stabilities of the steady states analyzed. We note that if R 0 < 1 , the disease-free equilibrium is globally asymptotically stable, and the disease completely dies out; and when R 0 > 1 , the system admits a positive periodic solution, and the disease is uniformly or periodically persistent. The model is fitted to data on new cases in South Africa for the first four waves. The model results indicate the need to consider seasonality in the transmission dynamics of COVID-19 and its importance in modeling fluctuations in the data for new cases. The potential impact of seasonality in the transmission patterns of COVID-19 and the public health implications is discussed.

Seroprevalence of SARS-CoV-2 immunoglobulin G antibody during COVID-19 pandemic in Fayoum District, Egypt: a community-based pilot survey.

controlling the worldwide pandemic, coronavirus disease (COVID-19), could be impossible due to the hesitancy about the available vaccines and the difficulty to implement strict restrictions. Little information is available about herd immunity in the highly vulnerable region of North East Africa, Egypt. The objective of this study was to assess the seroprevalence of SARS-CoV-2 during the pandemic in one of the highly vulnerable populations in Egypt, the Fayoum district of Fayoum Governorate. Additionally, to assess the predictive value of symptoms and other associated risk factors towards a positive COVID-19 test. in this cross-sectional community-based pilot study, immunoglobulin G (IgG) antibodies that are specific for the SARS-CoV-2 spike (S1-RBD) protein were tested during the period from February 2021 to July 2021. out of 155 participants, 60.6% were SARS-CoV-2 seropositive. Out of symptomatic and asymptomatic individuals, 76.5% and 56.2% were seropositive, respectively. Surprisingly, only one individual had received the COVID-19 vaccine. Previous history of COVID-19; such as symptoms and gender are statistically significant predictors of high seroconversion independent of age, comorbidities, and level of education. this study which disclosed unexpectedly high SARS-CoV-2 seroconversion among the Egyptians, might provide a clear insight into COVID-19 transmission patterns and state of immunity. Further study with a larger sample size on a large scale is required to represent the whole local population.

Uncovering Construction Site–Specific Transmission Patterns of COVID-19: A Spatiotemporal Connectivity Analysis in Hong Kong

Uncovering Construction Site–Specific Transmission Patterns of COVID-19: A Spatiotemporal Connectivity Analysis in Hong Kong

Reactive–diffusion epidemic model on human mobility networks: Analysis and applications to COVID-19 in China

The complex dynamics of human mobility, combined with sporadic cases of local outbreaks, make assessing the impact of large-scale social distancing on COVID-19 propagation in China a challenge. In this paper, with the travel big dataset supported by Baidu migration platform, we develop a reactive–diffusion epidemic model on human mobility networks to characterize the spatio-temporal propagation of COVID-19, and a novel time-dependent function is incorporated into the model to describe the effects of human intervention. By applying the system control theory, we discuss both constant and time-varying threshold behavior of proposed model. In the context of population mobility-mediated epidemics in China, we explore the transmission patterns of COVID-19 in city clusters. The results suggest that human intervention significantly inhibits the high correlation between population mobility and infection cases. Furthermore, by simulating different population flow scenarios, we reveal spatial diffusion phenomenon of cases from cities with high infection density to cities with low infection density. Finally, our model exhibits acceptable prediction performance using actual case data. The localized analytical results verify the ability of the PDE model to correctly describe the epidemic propagation and provide new insights for controlling the spread of COVID-19.

Exploring COVID-19 transmission patterns and key factors during epidemics caused by three major strains in Asia

The COVID-19 epidemic has lasted for more than two years since the outbreak in late 2019. An urgent and challenging question is how to systematically evaluate epidemic developments in different countries, during different periods, and to determine which measures that could be implemented are key for successful epidemic prevention. In this study, SBD distance-based K-shape clustering and hierarchical clustering methods were used to analyse epidemics in Asian countries. For the hierarchical clustering, epidemic time series were divided into three periods (epidemics induced by the Original/Alpha, Delta and Omicron variants separately). Standard deviations, the Hurst index, mortality rates, peak value of confirmed cases per capita, average growth rates, and the control efficiency of each period were used to characterize the epidemics. In addition, the total numbers of cases in the different countries were analysed by correlation and regression in relation to 15 variables that could have impacts on COVID-19. Finally, some suggestions on prevention and control measures for each category of country are given. We found that the total numbers of cases per million of a population, total deaths per million and mortality rates were highly correlated with the proportion of people aged over 65 years, the prevalence of multiple diseases, and the national GDP. We also found significant associations between case numbers and vaccination rates, health expenditures, and stringency of control measures. Vaccinations have played a positive role in COVID-19, with a gradual decline in mortality rates in later periods, and are still playing protective roles against the Delta and Omicron strains. The stringency of control measures taken by a government is not an indicator of the appropriateness of a country's response to the outbreak, and a higher index does not necessarily mean more effective measures; a combination of factors such as national vaccination rates, the country's economic foundation and the availability of medical equipment is also needed. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”.

Addressing External Shock in Urban Agglomeration: Implications From the Transmission Pattern of COVID-19 in the Beijing-Tianjin-Hebei Area

Properly addressing external shocks in urban agglomeration is critical to sustaining the complex regional system. The COVID-19 pandemic has been widely acknowledged as an unintended external shock, but the temporal and spatial transmission patterns are largely ignored. This study analyzed the temporal and spatial transmission patterns of COVID-19 at the macro, meso, and micro levels, and proposes a conceptual model for regional comprehensive risk calculation, taking the Beijing-Tianjin-Hebei (BTH) area as the focus region. Our results showed that 1) at the temporal scale, the epidemic in the BTH area experienced stages of rapid increase, gradual decrease, and stabilization, and the first wave of the epidemic was under control from 23 February 2020; 2) at the spatial scale, confirmed cases were largely distributed at the terminal of the migration network, with closely interconnected cities in the BTH area, including Beijing, Tianjin, Tangshan, and Langfang, holding the highest comprehensive epidemic risk, thus requiring special attention for epidemic prevention and control. Finally, a “two-wheels” conceptual framework was built to discuss implications for future policies for addressing external shocks. Our proposed framework consists of an isolation wheel, which involves information sharing from the holistic perspective, and a circulation wheel, which emphasizes stakeholder involvement from the individual perspective. The findings of this study provide a knowledge basis for epidemic prevention and control as well as useful implications for addressing external shocks in the future.

Covidseeker: A Geospatial Temporal Surveillance Tool.

Introduction: Geospatial temporal data derived from smartphones traditionally used for purposes of navigation may offer valuable information for public health surveillance and locational hot spotting. Our objective was to develop a web-based application, called Covidseeker, that captures continuous fine-grained geospatial temporal data from smartphones and leverages these data to study transmission patterns of COVID-19. Methods: This report describes the development of Covidseeker and the process by which it utilizes geospatial temporal data from smartphones and processes it into a usable format to study geospatial temporal patterns of COVID-19. We provide an overview of the design process, the principles, the software architecture, and the dashboard of the Covidseeker application and consider key challenges and strategic uses of capturing geospatial temporal data and the potential for future applications in outbreak surveillance. Results: A resource such as Covidseeker can support situational awareness by providing information about the location and timing of transmission of diseases such as COVID-19. Geospatial temporal data housed in smartphones hold tremendous potential to capture more depth about where and when transmission occurs and the patterns of human mobility that lead to increases in risk of COVID-19. Conclusion: An enormous and highly rich source of geospatial temporal information about human mobility can be used to provide highly localized discrete information that is difficult to capture by traditional sources. The architecture of Covidseeker can be applied to help track COVID-19 and should be integrated with traditional disease surveillance practices.

VSHR: A Mathematical Model for the Prediction of Second-Wave COVID-19 Epidemics in Malaysia.

Since December 2019, a novel coronavirus (COVID-19) has spread all over the world, causing unpredictable economic losses and public fear. Although vaccines against this virus have been developed and administered for months, many countries still suffer from secondary COVID-19 infections, including the United Kingdom, France, and Malaysia. Observations of COVID-19 infections in the United Kingdom and France and their governance measures showed a certain number of similarities. A further investigation of these countries' COVID-19 transmission patterns suggested that when a turning point appeared, the values of their stringency indices per population density (PSI) were nearly proportional to their absolute infection rate (AIR). To justify our assumptions, we developed a mathematical model named VSHR to predict the COVID-19 turning point for Malaysia. VSHR was first trained on 30-day infection records prior to the United Kingdom, Germany, France, and Belgium's known turning points. It was then transferred to Malaysian COVID-19 data to predict this nation's turning point. Given the estimated AIR parameter values in 5 days, we were now able to locate the turning point's appearance on June 2nd, 2021. VSHR offered two improvements: (1) gathered countries into groups based on their SI patterns and (2) generated a model to identify the turning point for a target country within 5 days with 90% CI. Our research on COVID-19's turning point for a country is beneficial for governments and clinical systems against future COVID-19 infections.

Measures to prevent nosocomial transmissions of COVID-19 based on interpersonal contact data.

With the global spreading of Coronavirus disease (COVID-19), many primary care medical workers have been infected, particularly in the early stages of this pandemic. Although extensive studies have explored the COVID-19 transmission patterns and (non-) pharmaceutical intervention to protect the general public, limited research has analysed the measures to prevent nosocomial transmission based upon detailed interpersonal contacts between medical staff and patients. This paper aims to develop and evaluate proactive prevention measures to contain the nosocomial transmission of COVID-19. The specific objectives are (1) to understand the virus transmission via interpersonal contacts among medical staff and patients; (2) to define proactive measures to reduce the risk of infection of medical staff and (3) evaluate the effectiveness of these measures to control the COVID-19 epidemic in hospitals. We observed the operation of a typical primary hospital in China to understand the interpersonal contacts among medical staff and patients. We defined effective distance as the indicator for risk of transmission. Then three proactive measures were proposed based upon the observations, including a medical staff rotation system, the establishment of a separate fever clinic and medical staff working alone. Finally, the impacts of these measures are evaluated with a modified Susceptible-Exposure-Infected-Removed model accommodating the situation of hospitals and asymptomatic and latent infection of COVID-19. The case study was conducted with the hospital observed in December 2019 and February 2020. The implementation of the medical staff rotation system has the most significant impact on containing the epidemic. The establishment of a separate fever clinic and medical staff working alone also benefits from inhibiting the epidemic outbreak. The simulation finds that if effective prevention and control measures are not taken in time, it will lead to a surge of infection cases in all asymptomatic probabilities and incubation periods.

A Fractional Ordered COVID-19 Model Incorporating Comorbidity and Vaccination

The primary goal of this research is to investigate COVID-19 transmission patterns in West Bengal, India in 2021; the first Coronavirus illness (COVID-19) in West Bengal was revealed on 17 March 2020. We employed the modified Susceptible-Asymptomatic-Vaccinated-Comorbidity-Infectious-Recovered (SAVICR) compartmental model as part of fractional orders because of the uncertainty created by the limited Coronavirus (COVID-19) information. In this article, two sub-compartments (Normal Infected and Infected with Co-morbidity) has been considered with vaccinated class, which is relevant in the present situation. We have studied the dynamical analysis of the system and also studied sensitivity of the parameters for West Bengal framework. We have also considered an optimal control problem taking social distancing (non-pharmaceutical treatments) as a control parameter along with vaccination.

Complexities in Case Definition of SARS-CoV-2 Reinfection: Clinical Evidence and Implications in COVID-19 Surveillance and Diagnosis.

Reinfection cases have been reported in some countries with clinical symptoms ranging from mild to severe. In addition to clinical diagnosis, virus genome sequence from the first and second infection has to be confirmed to either belong to separate clades or had significant mutations for the confirmation of SARS-CoV-2 reinfection. While phylogenetic analysis with paired specimens offers the strongest evidence for reinfection, there remains concerns on the definition of SARS-CoV-2 reinfection, for reasons including accessibility to paired-samples and technical challenges in phylogenetic analysis. In light of the emergence of new SARS-CoV-2 variants that are associated with increased transmissibility and immune-escape further understanding of COVID-19 protective immunity, real-time surveillance directed at identifying COVID-19 transmission patterns, transmissibility of emerging variants and clinical implications of reinfection would be important in addressing the challenges in definition of COVID-19 reinfection and understanding the true disease burden.

Breakpoint modelling of temporal associations between non-pharmaceutical interventions and symptomatic COVID-19 incidence in the Republic of Ireland.

To constrain propagation and mitigate the burden of COVID-19, most countries initiated and continue to implement several non-pharmaceutical interventions (NPIs), including national and regional lockdowns. In the Republic of Ireland, the first national lockdown was decreed on 23rd of March 2020, followed by a succession of restriction increases and decreases (phases) over the following year. To date, the effects of these interventions remain unclear, and particularly within differing population subsets. The current study sought to assess the impact of individual NPI phases on COVID-19 transmission patterns within delineated population subgroups in the Republic of Ireland. Confirmed, anonymised COVID-19 cases occurring between the 29th of February 2020 and 30th November 2020 (n = 72,654) were obtained. Segmented modelling via breakpoint regression with multiple turning points was employed to identify structural breaks across sub-populations, including primary/secondary infections, age deciles, urban/commuter/rural areas, patients with underlying health conditions, and socio-demographic profiles. These were subsequently compared with initiation dates of eight overarching NPI phases. Five distinct breakpoints were identified. The first breakpoint, associated with a decrease in the daily COVID-19 incidence, was reported within 14 days of the first set of restrictions in mid-March 2020 for most population sub-groups. Results suggest that moderately strict NPIs were more effective than the strictest Phase 5 (National Lockdown). Divergences were observed across population sub-groups; lagged response times were observed among populations >80 years, residents of rural/ commuter regions, and cases associated with a below-median deprivation score. Study findings suggest that many NPIs have been successful in decreasing COVID-19 incidence rates, however the strictest Phase 5 NPI was not. Moreover, NPIs were not equally successful across all sub-populations, with differing response times noted. Future strategies and interventions may need to be increasingly bespoke, based on sub-population profiles and required responses.

Trends in Incidence and Transmission Patterns of COVID-19 in Valencia, Spain

Limited information on the transmission and dynamics of SARS-CoV-2 at the city scale is available. To describe the local spread of SARS-CoV-2 in Valencia, Spain. This single-center epidemiological cohort study of patients with SARS-CoV-2 was performed at University General Hospital in Valencia (population in the hospital catchment area, 364 000), a tertiary hospital. The study included all consecutive patients with COVID-19 isolated at home from the start of the COVID-19 pandemic on February 19 until August 31, 2020. Cases of SARS-CoV-2 infection confirmed by the presence of IgM antibodies or a positive polymerase chain reaction test result on a nasopharyngeal swab were included. Cases in which patients with negative laboratory results met diagnostic and clinical criteria were also included. The primary outcome was the characterization of dissemination patterns and connections among the 20 neighborhoods of Valencia during the outbreak. To recreate the transmission network, the inbound and outbound connections were studied for each region, and the relative risk of infection was estimated. In total, 2646 patients were included in the analysis. The mean (SD) age was 45.3 (22.5) years; 1203 (46%) were male and 1442 (54%) were female (data were missing for 1); and the overall mortality was 3.7%. The incidence of SARS-CoV-2 cases was higher in neighborhoods with higher household income (β2 [for mean income per household] = 0.197; 95% CI, 0.057-0.351) and greater population density (β1 [inhabitants per km2] = 0.228; 95% CI, 0.085-0.387). Correlations with meteorological variables were not statistically significant. Neighborhood 3, where the hospital and testing facility were located, had the most outbound connections (14). A large residential complex close to the city (neighborhood 20) had the fewest connections (0 outbound and 2 inbound). Five geographically unconnected neighborhoods were of strategic importance in disrupting the transmission network. This study of local dissemination of SARS-COV-2 revealed nonevident transmission patterns between geographically unconnected areas. The results suggest that tailor-made containment measures could reduce transmission and that hospitals, including testing facilities, play a crucial role in disease transmission. Consequently, the local dynamics of SARS-CoV-2 spread might inform the strategic lockdown of specific neighborhoods to stop the contagion and avoid a citywide lockdown.

Uncovering transmission patterns of COVID-19 outbreaks: A region-wide comprehensive retrospective study in Hong Kong.

BackgroundGiven the dynamism and heterogeneity of COVID-19 transmission patterns, determining the most effective yet timely strategies for specific regions remains a severe challenge for public health decision-makers.MethodsIn this work, we proposed a spatiotemporal connectivity analysis method for discovering transmission patterns across geographic locations and age-groups throughout different COVID-19 outbreak phases. First, we constructed the transmission networks of the confirmed cases during different phases by considering the spatiotemporal connectivity of any two cases. Then, for each case and those cases immediately pointed from it, we characterized the corresponding cross-district/population transmission pattern by counting their district-to-district and age-to-age occurrences. By summating the cross-district/population transmission patterns of all cases during a given period, we obtained the aggregated cross-district and cross-population transmission patterns.FindingsWe conducted a region-wide comprehensive retrospective study in Hong Kong based on the complete data report of COVID-19 cases, covering all 18 districts between January 23, 2020, and January 8, 2021 (https://data.gov.hk/en-data/dataset/hk-dh-chpsebcddr-novel-infectious-agent). The spatiotemporal connectivity analysis clearly unveiled the quantitative differences among various outbreak waves in their transmission scales, durations, and patterns. Moreover, for the statistically similar waves, their cross-district/population transmission patterns could be quite different (e.g., the cross-district transmission of the fourth wave was more diverse than that of the third wave, while the transmission over age-groups of the fourth wave was more concentrated than that of the third wave). At an overall level, super-spreader individuals (highly connected cases in the transmission networks) were usually concentrated in only a few districts (2 out of 18 in our study) or age-groups (3 out of 11 in our study).InterpretationWith the discovered cross-district or cross-population transmission patterns, all of the waves of COVID-19 outbreaks in Hong Kong can be systematically scrutinized. Among all districts, quite a few (e.g., the Yau Tsim Mong district) were instrumental in spreading the virus throughout the pandemic. Aside from being exceptionally densely populated, these districts were also social-economic centers. With a variety of situated public venues, such as restaurants and singing/dancing clubs, these districts played host to all kinds of social gathering events, thereby providing opportunities for widespread and rapid transmission of the virus. Thus, these districts should be given the highest priority when deploying district-specific social distancing or intervention strategies, such as lockdown and stringent mandatory coronavirus testing for identifying and obstructing the chain of transmission. We also observed that most of the reported cases and the highly connected cases were middle-aged and elderly people (40- to 69-year-olds). People in these age-groups were active in various public places and social activities, and thus had high chances of being infected by or infecting others.FundingGeneral research fund of the Hong Kong research grants council.

Data-driven analysis of the simulations of the spread of COVID-19 under different interventions of China

ABSTRACT Since February 2020, COVID-19 has spread rapidly to more than 200 countries in the world. During the pandemic, local governments in China have implemented different interventions to efficiently control the spread of the epidemic. Characterizing transmission of COVID-19 under some typical interventions is essential to help countries develop appropriate interventions. Based on the pre-symptomatic transmission patterns of COVID-19, we established a novel compartmental model: Susceptible-Infectious-Confirmed-Removed (SICR) model, which allowed the effective reproduction number to change over time, thus the effects of policies could be reasonably estimated. Using the epidemic data of Wuhan, Wenzhou, and Shenzhen, we migrated the corresponding estimated policy modes to South Korea, Italy, and the United States and simulated the potential outcomes for these countries when they adopted similar policy strategies to China. We found that the mild interventions implemented in Shenzhen were effective in controlling the epidemic in the early stage, while more stringent policies which were implemented in Wuhan and Wenzhou were necessary if the epidemic became severe and needed to be controlled in a short time.

A fractional order mathematical model for COVID-19 dynamics with quarantine, isolation, and environmental viral load

COVID-19 or coronavirus is a newly emerged infectious disease that started in Wuhan, China, in December 2019 and spread worldwide very quickly. Although the recovery rate is greater than the death rate, the COVID-19 infection is becoming very harmful for the human community and causing financial loses to their economy. No proper vaccine for this infection has been introduced in the market in order to treat the infected people. Various approaches have been implemented recently to study the dynamics of this novel infection. Mathematical models are one of the effective tools in this regard to understand the transmission patterns of COVID-19. In the present paper, we formulate a fractional epidemic model in the Caputo sense with the consideration of quarantine, isolation, and environmental impacts to examine the dynamics of the COVID-19 outbreak. The fractional models are quite useful for understanding better the disease epidemics as well as capture the memory and nonlocality effects. First, we construct the model in ordinary differential equations and further consider the Caputo operator to formulate its fractional derivative. We present some of the necessary mathematical analysis for the fractional model. Furthermore, the model is fitted to the reported cases in Pakistan, one of the epicenters of COVID-19 in Asia. The estimated value of the important threshold parameter of the model, known as the basic reproduction number, is evaluated theoretically and numerically. Based on the real fitted parameters, we obtained mathcal{R}_{0} approx 1.50. Finally, an efficient numerical scheme of Adams–Moulton type is used in order to simulate the fractional model. The impact of some of the key model parameters on the disease dynamics and its elimination are shown graphically for various values of noninteger order of the Caputo derivative. We conclude that the use of fractional epidemic model provides a better understanding and biologically more insights about the disease dynamics.