Nonlinear dynamics modeling and epidemic forecast of COVID-19

To study the spreading trend and risk of COVID-19, according to the characteristics of COVID-19, this paper proposes a new transmission dynamic model named SLIR(susceptible-low-risk-infected-recovered), based on the classic SIR model by considering government control and personal protection measures. The equilibria, stability and bifurcation of the model are analyzed to reveal the propagation mechanism of COVID-19. In order to improve the prediction accuracy of the model, the least square method is employed to estimate the model parameters based on the real data of COVID-19 in the United States. Finally, the model is used to predict and analyze COVID-19 in the United States. The simulation results show that compared with the traditional SIR model, this model can better predict the spreading trend of COVID-19 in the United States, and the actual official data has further verified its effectiveness. The proposed model can effectively simulate the spreading of COVID-19 and help governments choose appropriate prevention and control measures. Copyright ©2023 Control and Decision.

Progress and Perspective of Transmission Dynamics Models in Prevention and Control of Infectious Diseases

The current respiratory infectious disease has expanded over the world, posing a serious threat to people's physical and mental health, as well as their lives. Science and technology immediately united to fight against such deadly infectious disease in the past 100 years. Mathematical models have proved invaluable to understand and help control infectious disease epidemics. By simplifying real world phenomena, these models describe, analyze, and predict disease transmission patterns, producing tractable solutions in the face of quickly changing situations. In this Chapter, we firstly summarized the history and development of the mathematical models in infectious diseases. Afterwards, the specific transmission dynamics models with different model structures used in fitting and forecasting the situation of the current respiratory infectious disease were introduced, aiming different analytical objectives including but not limited to parameter estimation, trend prediction and early warning, prevention and control measures effectiveness evaluation, and transmission uncertainty exploration. Summary in values of transmission dynamics models is followed to illustrate their contribution in understanding and combating infectious disease outbreaks. Despite their utility, however, mathematical models are facing several important challenges which, if ignored, would result in biased estimation of the crucial epidemiological parameters, bad fitting of the data, or misinterpretation of the results. In conclusion, mathematical modeling should be one of the most valuable tools to reflect such huge uncertainties or, on the other hand, warn of the worst situation. An appreciation of models' shortcomings not only clarifies why they cannot do but helps anticipate what they can. © People's Medical Publishing House, PR of China 2022.

Spatial Epidemiology of COVID-19 Pandemic: Disease Risk, Prognosis, and Complications

Why do some populations display a higher attack and mortality rate from the current coronavirus disease 2019 (COVID-19) pandemic than others? Are there geographic, environmental, behavioral, genetic, and comorbidity differences that influence spatial dynamics of COVID-19 transmission and outcomes? Where are the regional and country-level hotspots, and what drives those hotspots? These are some of the questions the current chapter strives to answer. The dynamics of transmission and consequences of COVID-19 are not homogeneous but instead have a geographical and spatial clustering. Population-level genetic, vaccination rates, health care disparities, SARS-CoV-2 variants, and meteorological factors are all underlying determinants of the disease dynamics globally, regionally, nationally, and locally. Disease surveillance frameworks to control, mitigate, and prevent the SARS- CoV-2 infections, particularly in low- and middle-income countries, are critical. Lastly, we highlight the spatial differences in the consequences of the pandemic focusing on behavioral and post-acute sequelae of SARS-CoV-2 infection. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

A Multi-SCALE Community Network-Based SEIQR Model to Evaluate the Dynamic NPIs of COVID-19.

Regarding the problem of epidemic outbreak prevention and control, infectious disease dynamics models cannot support urban managers in reducing urban-scale healthcare costs through community-scale control measures, as they usually have difficulty meeting the requirements for simulation at different scales. In this paper, we propose combining contact networks at different spatial scales to study the COVID-19 outbreak in Shanghai from March to July 2022, calculate the initial Rt through the number of cases at the beginning of the outbreak, and evaluate the effectiveness of dynamic non-pharmaceutical interventions (NPIs) adopted at different time periods in Shanghai using our proposed approach. In particular, our proposed contact network is a three-layer multi-scale network that is used to distinguish social interactions occurring in areas of different sizes, as well as to distinguish between intensive and non-intensive population contacts. This susceptible-exposure-infection-quarantine-recovery (SEIQR) epidemic model constructed based on a multi-scale network can more effectively assess the feasibility of small-scale control measures, such as assessing community quarantine measures and mobility restrictions at different moments and phases of an epidemic. Our experimental results show that this model can meet the simulation needs at different scales, and our further discussion and analysis show that the spread of the epidemic in Shanghai from March to July 2022 can be successfully controlled by implementing a strict long-term dynamic NPI strategy.

SARS-CoV-2 Next Generation Sequencing (NGS) data from clinical isolates from the East Texas Region of the United States.

The SARS-CoV-2 virus has evolved throughout the pandemic and is likely to continue evolving into new variants. Some of these variants may affect functional properties, including infectivity, interactions with host immunity, and disease severity. And compromised vaccine efficacy is an emerging concern with every new viral variant. Next-generation sequencing (NGS) has emerged as the tool of choice for discovering new variants and understanding the transmission dynamics of SARS-CoV-2. Deciphering the SARS-CoV-2 genome has enabled epidemiological survivance and forecast of altered etiologically. Clinical presentations of the infection are influenced by comorbidities such as age, immune status, diabetes, and the infecting variant. Thus, clinical management and vaccine efficacy may differ for new variants. For example, some monoclonal antibody treatments are variant-specific, and some vaccines are less efficacious against the omicron and delta variants of SARS-CoV-2. Consequently, determining the local outbreaks and monitoring SARS-CoV-2 Variants of Concern (VOC) is one of the primary strategies for the pandemic's containment. Although next-generation sequencing (NGS) is a gold standard for genomic surveillance and variant discovery, the assays are not approved for variant diagnosis for clinical decision-making. Advanta Genetics, Texas, USA, optimized Illumina COVID-seq protocol to reduce cost without compromising accuracy and validated the Illumina COVID-Seq assay as a Laboratory Developed Test (LDT) according to the guidelines prescribed by the College of American Pathologists (CAP) and Clinical Laboratory Improvement Amendments (CLIA). The whole genome of the virus was sequenced in (n = 161) samples from the East Texas region using the Illumina MiniSeq® instrument and analyzed by using Illumina baseSpace (https://basespace.illumina.com) bioinformatics pipeline. Briefly, the library was prepared by using Illumina COVIDSeq research use only (RUO) kit, and the individual libraries were normalized using the DNA concentration measured by Qubit Flex Fluorometer, and the pooled libraries were sequenced on Illumina MiniSeq® Instrument. Illumina baseSpace application was used for sequencing QC, FASTQ generation, genome assembly, and identification of SARS-CoV-2 variants. This whole genome shotgun project (n = 161) has been deposited at GISAID.

The Pandemic Puzzle-Reviewing the Existing Pieces, Searching for the Missing Ones

The research carried out on socioeconomic implication models of (re)emerging infectious diseases triggering pandemics has shown us that these largely depended on infection transmission, conditioned by the type of pathogen and the human host. Also, these depended on certain external factors, such as the phenomenon of globalization, pollution, fragile health systems, modification of human behaviors, expansion of human habitat near the outbreaks, favorable vectors involved in the transmission and development of new pandemics and last but not least of wars or civil revolts. The present research attempts to provide some responses to the following questions: 1. What have been the most recent and important emerging infectious disease pandemics and what were the risk factors? 2. What was the socioeconomic impact generated by these pandemics and what important lessons did we learn/identify? 3. What measures and/or directions must be implemented/addressed to prevent/possibly stop a future wave of infections or a new pandemic? The answers to these questions are substantiated by different indicators (transmission potential and pathogen severity) through which we focused to offer some suggestions/directions regarding the way in which these pandemics could be anticipated or prevent, indicators that otherwise are already used by public authorities in the development and exploration of intervention strategies. However, through the elaboration and staged presentation of how these pandemics acted as well as the socioeconomic implications and human reactions, this research could be useful in leading to the development of new, effective ways to prevent the transmission of (re)emerging infectious diseases.

Numerical Investigation of Malaria Disease Dynamics in Fuzzy Environment

The application of fuzzy theory is vital in all scientific disciplines. The construction of mathematical models with fuzziness is little studied in the literature. With this in mind and for a better understanding of the disease, an SEIR model of malaria transmission with fuzziness is examined in this study by extending a classical model of malaria transmission. The parameters beta and delta, being function of the malaria virus load, are considered fuzzy numbers. Three steady states and the reproduction number of the model are analyzed in fuzzy senses. A numerical technique is developed in a fuzzy environment to solve the studied model, which retains essential properties such as positivity and dynamic consistency. Moreover, numerical simulations are carried out to illustrate the analytical results of the developed technique. Unlike most of the classical methods in the literature, the proposed approach converges unconditionally and can be considered a reliable tool for studying malaria disease dynamics.

Quantifying Mycobacterium tuberculosis transmission dynamics across global settings: a systematic analysis.

The degree to which individual heterogeneity in the production of secondary cases ("superspreading") affects tuberculosis (TB) transmission has not been systematically studied. We searched for population-based or surveillance studies in which whole genome sequencing was used to estimate TB transmission and the size distributions of putative TB transmission clusters were enumerated. We fit cluster size distribution data to a negative binomial branching process model to jointly infer the transmission parameters $R$ (the reproductive number) and dispersion parameter, $k$, which quantifies the propensity of superspreading in a population (generally, lower values of $k$ ($<1.0$) suggest increased heterogeneity). Of 4,796 citations identified in our initial search, nine studies met inclusion criteria ($n=5$ all TB; $n=4$ drug resistant TB) from eight global settings. Estimated $R$ values (range: 0.10, 0.73) were below 1.0, consistent with declining epidemics in the included settings; estimated $k$ values were well below 1.0 (range: 0.02, 0.48), indicating the presence of substantial individual-level heterogeneity in transmission across all settings. We estimated that a minority of cases (range 2-31%) drive the majority (80%) of ongoing transmission at the population level. Identifying sources of heterogeneity and accounting for them in TB control may have a considerable impact on mitigating TB transmission.

TransCode: Uncovering COVID-19 transmission patterns via deep learning.

BACKGROUND: The 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. METHODS: We 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. RESULTS: The 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. CONCLUSIONS: The 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.

Coughs, colds and "freshers' flu" survey in the University of Cambridge, 2007-2008.

Universities provide many opportunities for the spread of infectious respiratory illnesses. Students are brought together into close proximity from all across the world and interact with one another in their accommodation, through lectures and small group teaching and in social settings. The COVID-19 global pandemic has highlighted the need for sufficient data to help determine which of these factors are important for infectious disease transmission in universities and hence control university morbidity as well as community spillover. We describe the data from a previously unpublished self-reported university survey of coughs, colds and influenza-like symptoms collected in Cambridge, UK, during winter 2007-2008. The online survey collected information on symptoms and socio-demographic, academic and lifestyle factors. There were 1076 responses, 97% from University of Cambridge students (5.7% of the total university student population), 3% from staff and <1% from other participants, reporting onset of symptoms between September 2007 and March 2008. Undergraduates are seen to report symptoms earlier in the term than postgraduates; differences in reported date of symptoms are also seen between subjects and accommodation types, although these descriptive results could be confounded by survey biases. Despite the historical and exploratory nature of the study, this is one of few recent detailed datasets of influenza-like infection in a university context and is especially valuable to share now to improve understanding of potential transmission dynamics in universities during the current COVID-19 pandemic.

Coupling the Within-Host Process and Between-Host Transmission of COVID-19 Suggests Vaccination and School Closures are Critical.

Most models of COVID-19 are implemented at a single micro or macro scale, ignoring the interplay between immune response, viral dynamics, individual infectiousness and epidemiological contact networks. Here we develop a data-driven model linking the within-host viral dynamics to the between-host transmission dynamics on a multilayer contact network to investigate the potential factors driving transmission dynamics and to inform how school closures and antiviral treatment can influence the epidemic. Using multi-source data, we initially determine the viral dynamics and estimate the relationship between viral load and infectiousness. Then, we embed the viral dynamics model into a four-layer contact network and formulate an agent-based model to simulate between-host transmission. The results illustrate that the heterogeneity of immune response between children and adults and between vaccinated and unvaccinated infections can produce different transmission patterns. We find that school closures play a significant effect on mitigating the pandemic as more adults get vaccinated and the virus mutates. If enough infected individuals are diagnosed by testing before symptom onset and then treated quickly, the transmission can be effectively curbed. Our multiscale model reveals the critical role played by younger individuals and antiviral treatment with testing in controlling the epidemic.

Transmission Dynamics and Epidemiological Characteristics of the SARS-CoV-2 Delta Variant - Hunan Province, China, 2021.

What is already known about this topic?: Little is known about the epidemiology, natural history, and transmission patterns of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant. Monitoring the evolution of viral fitness of SARS-CoV-2 in the host population is key for preparedness and response planning. What is added by this report?: We analyzed a successfully contained local outbreak of Delta that took place in Hunan, China, and provided estimates of time-to-key event periods, infectiousness over time, and risk factors for SARS-CoV-2 infection and transmission for a still poorly understood variant. What are the implications for public health practice?: Our findings simultaneously shed light on both the characteristics of the Delta variant, by identifying key age groups, risk factors, and transmission pathways, and planning a future response effort against SARS-CoV-2.

Multiple pathways of SARS-CoV-2 nosocomial transmission uncovered by integrated genomic and epidemiological analyses during the second wave of the COVID-19 pandemic in the UK.

Introduction: Throughout the global COVID-19 pandemic, nosocomial transmission has represented a major concern for healthcare settings and has accounted for many infections diagnosed within hospitals. As restrictions ease and novel variants continue to spread, it is important to uncover the specific pathways by which nosocomial outbreaks occur to understand the most suitable transmission control strategies for the future. Methods: In this investigation, SARS-CoV-2 genome sequences obtained from 694 healthcare workers and 1,181 patients were analyzed at a large acute NHS hospital in the UK between September 2020 and May 2021. These viral genomic data were combined with epidemiological data to uncover transmission routes within the hospital. We also investigated the effects of the introduction of the highly transmissible variant of concern (VOC), Alpha, over this period, as well as the effects of the national vaccination program on SARS-CoV-2 infection in the hospital. Results: Our results show that infections of all variants within the hospital increased as community prevalence of Alpha increased, resulting in several outbreaks and super-spreader events. Nosocomial infections were enriched amongst older and more vulnerable patients more likely to be in hospital for longer periods but had no impact on disease severity. Infections appeared to be transmitted most regularly from patient to patient and from patients to HCWs. In contrast, infections from HCWs to patients appeared rare, highlighting the benefits of PPE in infection control. The introduction of the vaccine at this time also reduced infections amongst HCWs by over four-times. Discussion: These analyses have highlighted the importance of control measures such as regular testing, rapid lateral flow testing alongside polymerase chain reaction (PCR) testing, isolation of positive patients in the emergency department (where possible), and physical distancing of patient beds on hospital wards to minimize nosocomial transmission of infectious diseases such as COVID-19.

GEOGRAPHICAL SEVIRD COVID-19 MODEL WITH TRAVEL RESTRICTIONS

Susceptible-Infected-Recovered (SIR) models have been widely used to study the spread of Covid-19. These models have been improved to include other states (e.g., exposed, deceased) as well as geographical level transmission dynamics. In this paper, we present an extension to an existing SEVIRD (Susceptible - Exposed - Vaccinated - Infected - Recovered - Death) model to include the effect of air and maritime travel as well as travel restrictions. We use the model to simulate the spread of Covid-19 through 13 different countries. The case study shown illustrates how the model can be used for rapid prototyping at a geographical level and adapted to include changing policies. © 2022 Society for Modeling & Simulation International (SCS)

Optimization of the Illumina COVIDSeq™ protocol for decentralized, cost-effective genomic surveillance.

A decentralized surveillance system to identify local outbreaks and monitor SARS-CoV-2 Variants of Concern is one of the primary strategies for the pandemic's containment. Although next-generation sequencing (NGS) is a gold standard for genomic surveillance and variant discovery, the technology is still cost-prohibitive for decentralized sequencing, particularly in small independent labs with limited resources. We have optimized the Illumina COVIDSeq™ protocol for the Illumina MiniSeq instrument to reduce cost without compromising accuracy. We slashed the library preparation cost by half by using 50% of recommended reagents at each step and normalizing the libraries before pooling to achieve uniform coverage. Reagent-only cost (∼$43.27/sample) for SARS-CoV-2 variant analysis with this normalized input protocol on MiniSeq instruments is comparable to what is achieved on high throughput instruments such as NextSeq and NovaSeq. Using this modified protocol, we tested 153 clinical samples, and 90% of genomic coverage was achieved for 142/153 samples analyzed in this study. The lineage was correctly assigned to all samples (152/153) except for one. This modified protocol can help laboratories with constrained resources to contribute in decentralized COVID-19 surveillance in the post-vaccination era.

Decrease in overdispersed secondary transmission of COVID-19 over time in Japan.

Coronavirus disease 2019 (COVID-19) has been described as having an overdispersed offspring distribution, i.e. high variation in the number of secondary transmissions of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) per single primary COVID-19 case. Accordingly, countermeasures focused on high-risk settings and contact tracing could efficiently reduce secondary transmissions. However, as variants of concern with elevated transmissibility continue to emerge, controlling COVID-19 with such focused approaches has become difficult. It is vital to quantify temporal variations in the offspring distribution dispersibility. Here, we investigated offspring distributions for periods when the ancestral variant was still dominant (summer, 2020; wave 2) and when Alpha variant (B.1.1.7) was prevailing (spring, 2021; wave 4). The dispersion parameter (k) was estimated by analysing contact tracing data and fitting a negative binomial distribution to empirically observed offspring distributions from Nagano, Japan. The offspring distribution was less dispersed in wave 4 (k = 0.32; 95% confidence interval (CI) 0.24-0.43) than in wave 2 (k = 0.21 (95% CI 0.13-0.36)). A high proportion of household transmission was observed in wave 4, although the proportion of secondary transmissions generating more than five secondary cases did not vary over time. With this decreased variation, the effectiveness of risk group-focused interventions may be diminished.

Risk Assessment of Importation and Local Transmission of COVID-19 in South Korea: Statistical Modeling Approach.

BACKGROUND: Despite recent achievements in vaccines, antiviral drugs, and medical infrastructure, the emergence of COVID-19 has posed a serious threat to humans worldwide. Most countries are well connected on a global scale, making it nearly impossible to implement perfect and prompt mitigation strategies for infectious disease outbreaks. In particular, due to the explosive growth of international travel, the complex network of human mobility enabled the rapid spread of COVID-19 globally. OBJECTIVE: South Korea was one of the earliest countries to be affected by COVID-19. In the absence of vaccines and treatments, South Korea has implemented and maintained stringent interventions, such as large-scale epidemiological investigations, rapid diagnosis, social distancing, and prompt clinical classification of severely ill patients with appropriate medical measures. In particular, South Korea has implemented effective airport screenings and quarantine measures. In this study, we aimed to assess the country-specific importation risk of COVID-19 and investigate its impact on the local transmission of COVID-19. METHODS: The country-specific importation risk of COVID-19 in South Korea was assessed. We investigated the relationships between country-specific imported cases, passenger numbers, and the severity of country-specific COVID-19 prevalence from January to October 2020. We assessed the country-specific risk by incorporating country-specific information. A renewal mathematical model was employed, considering both imported and local cases of COVID-19 in South Korea. Furthermore, we estimated the basic and effective reproduction numbers. RESULTS: The risk of importation from China was highest between January and February 2020, while that from North America (the United States and Canada) was high from April to October 2020. The R0 was estimated at 1.87 (95% CI 1.47-2.34), using the rate of α=0.07 for secondary transmission caused by imported cases. The Rt was estimated in South Korea and in both Seoul and Gyeonggi. CONCLUSIONS: A statistical model accounting for imported and locally transmitted cases was employed to estimate R0 and Rt. Our results indicated that the prompt implementation of airport screening measures (contact tracing with case isolation and quarantine) successfully reduced local transmission caused by imported cases despite passengers arriving from high-risk countries throughout the year. Moreover, various mitigation interventions, including social distancing and travel restrictions within South Korea, have been effectively implemented to reduce the spread of local cases in South Korea.

Time-frequency volatility transmission among energy commodities and financial markets during the COVID-19 pandemic: A Novel TVP-VAR frequency connectedness approach.

This paper investigates the dynamic volatility spillover among energy commodities and financial markets in pre-and mid-COVID-19 periods by utilizing a novel TVP-VAR frequency connectedness approach and the QMLE-based realized volatility data. Our findings indicate that the volatility spillover is mainly driven by long-term components and prominently time-varying with a remarkable but short-lived surge during the COVID-19 outbreak. We further spot that WTI and NGS are prevailingly transmitting and being exposed to the system volatility simultaneously, especially during the global pandemic, suggesting the energy commodity market becoming more integrated with, more influential and meanwhile vulnerable to global financial markets.

Data-Driven Modeling and Analysis for COVID-19 Pandemic Hospital Beds Planning

The COVID-19 pandemic presents unprecedented challenges for the US healthcare system, and the critical care settings are heavily impacted by the pressures of caring for COVID-19 patients. However, hospital pandemic preparedness has been hampered by a lack of disease specific planning guidelines. In this paper, we proposed a holistic modeling and analysis approach, with a system dynamics model to predict COVID-19 cases and a discrete-event simulation to evaluate hospital bed utilization, to support the hospital planning decisions. Our model was trained using the public data from the JHU Coronavirus Resource Center and was validated using historical patient census data from the University of Florida Health Jacksonville, Jacksonville, FL and public data from the Florida Department of Health (FDOH). Various experiments were conducted to investigate different control measures and the variants of the virus and their impact on the disease transmission, and subsequently, the hospital planning needs. Our proposed approach can be tailored to a given hospital setting of interest and is also generalizable to other hospitals to tackle the pandemic planning challenge. Note to Practitioners-We proposed a holistic modeling and analysis approach to support hospital preparedness and resource planning during the COVID-19 pandemic. To capture the highly dynamic pandemic environment, we developed a numerical method to estimate R-0, the effective basic reproductive rate, and used the most recent estimated data series of daily R-0 to project the change in R-0 in a short-term forecast window. The prediction of the daily confirmed cases in that forecast window were then obtained based on recursively solving the system dynamics model, and was validated to be very close to the real confirmed cases from the public record. This data-driven approach allows us to gain a systematic understanding of the common trends across different states and regions, and to evaluate the effect of the control measures like the stay-at-home order and the impact of the virus variants on the disease transmission behavior. Furthermore, the dynamic prediction allows us to evaluate the hospital resource needs during different stages of the pandemic. The insights obtained through this effort shed light on the impact of interventions (e.g., vaccines and control measures) on the hospital preparedness to support appropriate hospital resource allocation.