COVID-19 Model Based on Conformable Fractional Derivativeand Its Numerical Solution

After the outbreak of COVID-19, it is of great significance to find an appropriate dynamic model of COVID-19 epidemic in order to master its transmission law, predict its development trend, and provide corresponding prevention and control basis. In this paper, the SEIRV chamber model is adopted, and the dynamics model of infectious disease is established by combining the fractional derivative of Conformable. The fractional derivative differential equation of Conformable is discretized by numerical method and its numerical solution is obtained. In addition, numerical simulation was carried out on the confirmed data of Wuhan city from January 23, 2020 to February 11, 2020. At the same time, consider that the Wuhan municipal government revised the epidemic data on February 12, 2020, adding nearly 14,000 people. The order α value of SEIRV model is modified, and then the revised data is simulated. The simulation results are in good agreement with the published data. The results show that compared with the traditional integer order model, the fractional order model can simulate the modified data. This reflects the advantages of fractional infectious disease dynamics model, and can provide certain reference value for the prediction of COVID-19 model. © 2022 Editorial Borad of Complex Systems and Complexity Science. All rights reserved.

Community incidence patterns drive the risk of SARS-CoV-2 outbreaks and alter intervention impacts in a high-risk institutional setting.

Optimization of control measures for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in high-risk institutional settings (e.g., prisons, nursing homes, or military bases) depends on how transmission dynamics in the broader community influence outbreak risk locally. We calibrated an individual-based transmission model of a military training camp to the number of RT-PCR positive trainees throughout 2020 and 2021. The predicted number of infected new arrivals closely followed adjusted national incidence and increased early outbreak risk after accounting for vaccination coverage, masking compliance, and virus variants. Outbreak size was strongly correlated with the predicted number of off-base infections among staff during training camp. In addition, off-base infections reduced the impact of arrival screening and masking, while the number of infectious trainees upon arrival reduced the impact of vaccination and staff testing. Our results highlight the importance of outside incidence patterns for modulating risk and the optimal mixture of control measures in institutional settings.

International trade resilience with applied welfare economics: an analysis on personal protective equipment

A global crisis such as a pandemic causes a decrease in the global trade of medical supplies. One of the most significant issues healthcare workers and people face is the shortage of personal protective equipment (PPE) items. This study constructs the first international trade model to link infectious disease dynamics and global trade networks, considering the important relationship between government preparedness, domestic manufacturers, and consumers. We examine social welfare measures here in the presence of quantity controls and taxes on the global trade flows. An equilibrium coverage among countries is investigated that integrates net government revenue, purchasing cost, transportation cost, and the health cost caused by the shortage of PPE supply. We develop an optimisation model that balances domestic firms and the global trade network to satisfy the total demand for each traded PPE product. The proportional change in value-added on domestic production is also studied by considering the marginal manufacturing costs of a face mask. The results obtained from testing our model show that the average quantity coverage by the global trade networks among four countries decreased by up to 28 % using the proposed trade policy. Hence, a large amount of demand is met by relying on domestic production.

Feature-Modified SEIR Model for Pandemic Simulation and Evaluation of Intervention Approaches

SEIR (susceptible-exposed-infected-recovered) model has been widely used to study infectious disease dynamics. For instance, there have been many applications of SEIR analyzing the spread of COVID to provide suggestions on pandemic/epidemic interventions. Nonetheless, existing models simplify the population, regardless of different demographic features and activities related to the spread of the disease. This paper provides a comprehensive SEIR model to enhance the prediction quality and effectiveness of intervention strategies. The new SEIR model estimates the exposed population via a new approach involving health conditions (sensitivity to disease) and social activity level (contact rate). To validate our model, we compare the estimated infection cases via our model with actual confirmed cases from CDC and the classic SEIR model. We also consider various protocols and strategies to utilize our modified SEIR model on many simulations and evaluate their effectiveness. © 2022 IEEE.

The illusion of personal health decisions for infectious disease management: disease spread in social contact networks.

Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals' infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies.

Analysis and Prediction of COVID-19 Spread in Ernakulam District, Kerala

The world is witnessing a pandemic of SARS-CoV2 infection since the first quarter of the twenty-first century. Ever since the first case was reported in Wuhan city of China in December 2019, the virus has spread over 223 countries. Understanding and predicting the dynamics of COVID-19 spread through data analysis will empower our administrations with insights for better planning and response against the burden inflicted on our health care infrastructure and economy. The aim of the study was to analyze and predict COVID-19 spread in Ernakulam district of Kerala. Data was extracted from lab data management system (LDMS), a government portal to enter all the COVID-19 testing details. Using the EpiModel package of R-mathematical modeling of infectious disease dynamics, the predictive analysis for hospitalization rate, percentage of patients requiring oxygen and ICU admission, percentage of patients getting admitted, duration of hospital stay, case fatality rate, age group and gender-wise fatality rate, and hospitalization rate were computed. While calculating the above-said variables, the percentage of vaccinated population, breakthrough infections, and percentage of hospitalization among the vaccinated was also taken into consideration. The time trend of patients in ICU showed men outnumbered women. Positive cases were more among 20–30 years, while 61–70 years age group had more risk for ICU admission. An increase in CFR with advancing age and also a higher CFR among males were seen. Conclusions: Analyzing and predicting the trend of COVID-19 would help the governments to better utilize their limited healthcare resources and adopt timely measures to contain the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analysis and Prediction of COVID-19 Spread in Ernakulam District, Kerala

The world is witnessing a pandemic of SARS-CoV2 infection since the first quarter of the twenty-first century. Ever since the first case was reported in Wuhan city of China in December 2019, the virus has spread over 223 countries. Understanding and predicting the dynamics of COVID-19 spread through data analysis will empower our administrations with insights for better planning and response against the burden inflicted on our health care infrastructure and economy. The aim of the study was to analyze and predict COVID-19 spread in Ernakulam district of Kerala. Data was extracted from lab data management system (LDMS), a government portal to enter all the COVID-19 testing details. Using the EpiModel package of R-mathematical modeling of infectious disease dynamics, the predictive analysis for hospitalization rate, percentage of patients requiring oxygen and ICU admission, percentage of patients getting admitted, duration of hospital stay, case fatality rate, age group and gender-wise fatality rate, and hospitalization rate were computed. While calculating the above-said variables, the percentage of vaccinated population, breakthrough infections, and percentage of hospitalization among the vaccinated was also taken into consideration. The time trend of patients in ICU showed men outnumbered women. Positive cases were more among 20–30 years, while 61–70 years age group had more risk for ICU admission. An increase in CFR with advancing age and also a higher CFR among males were seen. Conclusions: Analyzing and predicting the trend of COVID-19 would help the governments to better utilize their limited healthcare resources and adopt timely measures to contain the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Detailed Study of a Fractal-Fractional Transmission Dynamical Model of Viral Infectious Disease with Vaccination

This article is devoted to investigate a mathematical model consisting on susceptible, exposed, infected, quarantined, vaccinated, and recovered compartments of COVID-19. The concerned model describes the transmission mechanism of the disease dynamics with therapeutic measures of vaccination of susceptible people along with the cure of the infected population. In the said study, we use the fractal-fractional order derivative to understand the dynamics of all compartments of the proposed model in more detail. Therefore, the first model is formulated. Then, two equilibrium points disease-free (DF) and endemic are computed. Furthermore, the basic threshold number is also derived. Some sufficient conditions for global asymptotical stability are also established. By using the next-generation matrix method, local stability analysis is developed. We also attempt the sensitivity analysis of the parameters of the proposed model. Finally, for the numerical simulations, the Adams-Bashforth method is used. Using some available data, the results are displayed graphically using various fractal-fractional orders to understand the mechanism of the dynamics. In addition, we compare our numerical simulation with real data in the case of reported infected cases. © 2022 Kamal Shah et al.

Nonlinear time-series forecasts for decision support: short-term demand for ICU beds in Santiago, Chile, during the 2021 COVID-19 pandemic.

In Chile, due to the explosive increase of new Coronavirus disease 2019 (COVID-19) cases during the first part of 2021, the ability of health services to accommodate new incoming cases was jeopardized. It has become necessary to be able to manage intensive care unit (ICU) capacity, and for this purpose, monitoring both the evolution of new cases and the demand for ICU beds has become urgent. This paper presents short-term forecast models for the number of new cases and the number of COVID-19 patients admitted to ICUs in the Metropolitan Region in Chile.

Tracing and testing multiple generations of contacts to COVID-19 cases: cost-benefit trade-offs.

Traditional contact tracing tests the direct contacts of those who test positive. But, by the time an infected individual is tested, the infection starting from the person may have infected a chain of individuals. Hence, why should the testing stop at direct contacts, and not test secondary, tertiary contacts or even contacts further down? One deterrent in testing long chains of individuals right away may be that it substantially increases the testing load, or does it? We investigate the costs and benefits of such multi-hop contact tracing for different number of hops. Considering diverse contact networks, we show that the cost-benefit trade-off can be characterized in terms of a single measurable attribute, the initial epidemic growth rate. Once this growth rate crosses a threshold, multi-hop contact tracing substantially reduces the outbreak size compared with traditional tracing. Multi-hop even incurs a lower cost compared with the traditional tracing for a large range of values of the growth rate. The cost-benefit trade-offs can be classified into three phases depending on the value of the growth rate. The need for choosing a larger number of hops becomes greater as the growth rate increases or the environment becomes less conducive toward containing the disease.

Analysis of COVID-19 Epidemic Disease Dynamics Using Deep Learning

COVID-19 has induced anxiety, depression, and fear among people around the world with its cases. During this period, people undergo mixed emotion. Social media is a tool that affected human life during this time in a dominant manner. Twitter is a trending social media platform. Analyzing sentiment of tweets related to COVID-19 can help to analyze the sentiments around the world. In this system, we have taken the dataset which contains tweets related to COVID-19 from IEEE dataport. SVM and LSTM models are built which classifies the tweets as positive, negative, and neutral accordingly. The performance of LSTM model is further analyzed by using hyperparameter tuning method. LSTM gave better results than SVM. It gave an accuracy of 94.58%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A brief note on a multistrain SIR model with complete cross-protection and nonlinear force of infection

We formulate and investigate a compartmental model for a multi-strain SIR disease with complete cross immunity and nonlinear force of infection. More specifically we consider a hyperbolic force of infection with saturation effects. Using a combination of analytical and computational methods, we find that under such a nonlinear force of infection in the longterm several strains can be established in a population. This is in contrast to a comparable model with SI mass action transmission, i.e., the more familiar linear force of infection, in which case a principle of competitive exclusion holds, such that only the strain with the highest basic reproduction number will be present in the longterm.

Dynamics of disease characteristics and clinical management of critically ill COVID-19 patients over the time course of the pandemic: an analysis of the prospective, international, multicentre RISC-19-ICU registry.

BACKGROUND: It remains elusive how the characteristics, the course of disease, the clinical management and the outcomes of critically ill COVID-19 patients admitted to intensive care units (ICU) worldwide have changed over the course of the pandemic. METHODS: Prospective, observational registry constituted by 90 ICUs across 22 countries worldwide including patients with a laboratory-confirmed, critical presentation of COVID-19 requiring advanced organ support. Hierarchical, generalized linear mixed-effect models accounting for hospital and country variability were employed to analyse the continuous evolution of the studied variables over the pandemic. RESULTS: Four thousand forty-one patients were included from March 2020 to September 2021. Over this period, the age of the admitted patients (62 [95% CI 60-63] years vs 64 [62-66] years, p < 0.001) and the severity of organ dysfunction at ICU admission decreased (Sequential Organ Failure Assessment 8.2 [7.6-9.0] vs 5.8 [5.3-6.4], p < 0.001) and increased, while more female patients (26 [23-29]% vs 41 [35-48]%, p < 0.001) were admitted. The time span between symptom onset and hospitalization as well as ICU admission became longer later in the pandemic (6.7 [6.2-7.2| days vs 9.7 [8.9-10.5] days, p < 0.001). The PaO2/FiO2 at admission was lower (132 [123-141] mmHg vs 101 [91-113] mmHg, p < 0.001) but showed faster improvements over the initial 5 days of ICU stay in late 2021 compared to early 2020 (34 [20-48] mmHg vs 70 [41-100] mmHg, p = 0.05). The number of patients treated with steroids and tocilizumab increased, while the use of therapeutic anticoagulation presented an inverse U-shaped behaviour over the course of the pandemic. The proportion of patients treated with high-flow oxygen (5 [4-7]% vs 20 [14-29], p < 0.001) and non-invasive mechanical ventilation (14 [11-18]% vs 24 [17-33]%, p < 0.001) throughout the pandemic increased concomitant to a decrease in invasive mechanical ventilation (82 [76-86]% vs 74 [64-82]%, p < 0.001). The ICU mortality (23 [19-26]% vs 17 [12-25]%, p < 0.001) and length of stay (14 [13-16] days vs 11 [10-13] days, p < 0.001) decreased over 19 months of the pandemic. CONCLUSION: Characteristics and disease course of critically ill COVID-19 patients have continuously evolved, concomitant to the clinical management, throughout the pandemic leading to a younger, less severely ill ICU population with distinctly different clinical, pulmonary and inflammatory presentations than at the onset of the pandemic.

The Microbiome as Part of the Contemporary View of Tuberculosis Disease.

The study of the microbiome has changed our overall perspective on health and disease. Although studies of the lung microbiome have lagged behind those on the gastrointestinal microbiome, there is now evidence that the lung microbiome is a rich, dynamic ecosystem. Tuberculosis is one of the oldest human diseases, it is primarily a respiratory infectious disease caused by strains from the Mycobacterium tuberculosis Complex. Even today, during the COVID-19 pandemic, it remains one of the principal causes of morbidity and mortality worldwide. Tuberculosis disease manifests itself as a dynamic spectrum that ranges from asymptomatic latent infection to life-threatening active disease. The review aims to provide an overview of the microbiome in the tuberculosis setting, both in patients' and animal models. We discuss the relevance of the microbiome and its dysbiosis, and how, probably through its interaction with the immune system, it is a significant factor in tuberculosis's susceptibility, establishment, and severity.

The impact of travelling on the COVID-19 infection cases in Germany.

BACKGROUND: COVID-19 continues to disrupt social lives and the economy of many countries and challenges their healthcare capacities. Looking back at the situation in Germany in 2020, the number of cases increased exponentially in early March. Social restrictions were imposed by closing e.g. schools, shops, cafés and restaurants, as well as borders for travellers. This reaped success as the infection rate descended significantly in early April. In mid July, however, the numbers started to rise again. Of particular reasons was that from mid June onwards, the travel ban has widely been cancelled or at least loosened. We aim to measure the impact of travellers on the overall infection dynamics for the case of (relatively) few infectives and no vaccinations available. We also want to analyse under which conditions political travelling measures are relevant, in particular in comparison to local measures. By travel restrictions in our model we mean all possible measures that equally reduce the possibility of infected returnees to further spread the disease in Germany, e.g. travel bans, lockdown, post-arrival tests and quarantines. METHODS: To analyse the impact of travellers, we present three variants of an susceptible-exposed-infected-recovered-deceased model to describe disease dynamics in Germany. Epidemiological parameters such as transmission rate, lethality, and detection rate of infected individuals are incorporated. We compare a model without inclusion of travellers and two models with a rate measuring the impact of travellers incorporating incidence data from the Johns Hopkins University. Parameter estimation was performed with the aid of the Monte-Carlo-based Metropolis algorithm. All models are compared in terms of validity and simplicity. Further, we perform sensitivity analyses of the model to observe on which of the model parameters show the largest influence the results. In particular, we compare local and international travelling measures and identify regions in which one of these shows larger relevance than the other. RESULTS: In the comparison of the three models, both models with the traveller impact rate yield significantly better results than the model without this rate. The model including a piecewise constant travel impact rate yields the best results in the sense of maximal likelihood and minimal Bayesian Information Criterion. We synthesize from model simulations and analyses that travellers had a strong impact on the overall infection cases in the considered time interval. By a comparison of the reproductive ratios of the models under traveller/no-traveller scenarios, we found that higher traveller numbers likely induce higher transmission rates and infection cases even in the further course, which is one possible explanation to the start of the second wave in Germany as of autumn 2020. The sensitivity analyses show that the travelling parameter, among others, shows a larger impact on the results. We also found that the relevance of travel measures depends on the value of the transmission parameter: In domains with a lower transmission parameter, caused either by the current variant or local measures, it is found that handling the travel parameters is more relevant than those with lower value of the transmission. CONCLUSIONS: We conclude that travellers is an important factor in controlling infection cases during pandemics. Depending on the current situation, travel restrictions can be part of a policy to reduce infection numbers, especially when case numbers and transmission rate are low. The results of the sensitivity analyses also show that travel measures are more effective when the local transmission is already reduced, so a combination of those two appears to be optimal. In any case, supervision of the influence of travellers should always be undertaken, as another pandemic or wave can happen in the upcoming years and vaccinations and basic hygiene rules alone might not be able to prevent further infection waves.

Fighting a Fire versus Waiting for the Wave: Useful and Not-So-Useful Analogies in Times of SARS-CoV-2

As SARS-CoV-2 has swept the planet, intermittent lockdowns have become a regular feature to control transmission. References to so-called recurring waves of infections remain pervasive among news headlines, political messaging, and public health sources. We explore the power of analogies to facilitate understanding of biological models and processes by reviewing strengths and limitations of analogies used throughout the COVID-19 pandemic. We consider how, when analogies fall short, their ability to persuade can mislead public perception, even if unintentionally. Although waves can convey patterns of disease outbreak, we suggest process-based analogies might be more effective communication tools, given that they can be easily mapped to underlying epidemiological concepts and extended to include complex dynamics. Although no single analogy perfectly captures disease dynamics, fire is particularly suitable for visualizing epidemiological models, underscoring the importance and reasoning behind control strategies and potentially conveying a sense of urgency that can galvanize individual and collective action.

Change in Disease Dynamics and Health Care Utilisation in Children during COVID 19 in a Tertiary Care Hospital of Eastern India

Introduction: The direct and indirect impact of SARS COVID 19 on the health of children was unprecedented. This study was conducted to compare the changing pattern of pediatric disease dynamics and the use of healthcare system before and after the SARS-CoV2 outbreak in a tertiary care hospital. Methodology: This retrospective, observational study was conducted by collecting data from medical records during COVID 19 pandemic from March 2020 till August 2020. This was compared with the data of 2019 during similar months. The impact of COVID 19 on use of paediatric health care service units like outpatient department, casualty, intensive care and immunization clinic were assessed. Results: There was a significant decline in routine OPD (68%) attendance during the COVID 19 period as compared to pre-COVID period. Paediatric ward admissions and PICU admissions were decreased by 55% and 42% respectively. We also observed a significant 43% decline in the number of children attending immunization clinic in the year 2020. Conclusion: The fear of COVID 19 pandemic and the measures taken to control the pandemic has affected the health seeking behaviour of patients. This evaluation of trends in healthcare use may help in planning the delivery of healthcare service delivery in future. @ The Journal retains the copyrights of this article.

Infectious Diseases Dynamic Transmissibility with Age Structure and Medical Resources

Tendency forecasting of infectious diseases, such as COVID-19, is urgently required to evaluate outbreak risk and control decisions. Although transmission models based on natural factors like virus propagation, temperature, and human modality are studied carefully, social factors cause high flexibility on dynamic propagation change under actual virus spreading conditions. We propose a time-variant relevance-based infected recovered extreme learning machine to generate a quantitative forecasting model with social factors. Also, embedded distance is used to measure the similarity and realize flexible forecasting based on social impactors. We investigated the age structure and the medical supply under the COVID-19 pandemic with nonidentical open-source data We found that embedded distance with the proposed model is highly consistent with projection accuracy, and the proposed method can achieve higher accuracy than existed methods. Based on the forecasting model, age distribution and medical supply make a difference in COVID-19 transmission. Areas with the middle proportion of the aged population face higher outbreaking risks, and sufficient medical supply control the infection speed efficiently within three weeks. This study provides an efficient projection of dynamic transmission under the social impact on infectious diseases pandemics. © 2021 IEEE

Estimating COVID-19 cases and deaths prevented by non-pharmaceutical interventions, and the impact of individual actions: A retrospective model-based analysis.

Simulation models from the early COVID-19 pandemic highlighted the urgency of applying non-pharmaceutical interventions (NPIs), but had limited empirical data. Here we use data from 2020-2021 to retrospectively model the impact of NPIs in Ontario, Canada. Our model represents age groups and census divisions in Ontario, and is parameterized with epidemiological, testing, demographic, travel, and mobility data. The model captures how individuals adopt NPIs in response to reported cases. We compare a scenario representing NPIs introduced within Ontario (closures of workplaces/schools, reopening of schools/workplaces with NPIs in place, individual-level NPI adherence) to counterfactual scenarios wherein alternative strategies (e.g. no closures, reliance on individual NPI adherence) are adopted to ascertain the extent to which NPIs reduced cases and deaths. Combined school/workplace closure and individual NPI adoption reduced the number of deaths in the best-case scenario for the case fatality rate (CFR) from 178548 [CI: 171845, 185298] to 3190 [CI: 3095, 3290] in the Spring 2020 wave. In the Fall 2020/Winter 2021 wave, the introduction of NPIs in workplaces/schools reduced the number of deaths from 20183 [CI: 19296, 21057] to 4102 [CI: 4075, 4131]. Deaths were several times higher in the worst-case CFR scenario. Each additional 9-16 (resp. 285-578) individuals who adopted NPIs in the first wave prevented one additional infection (resp., death). Our results show that the adoption of NPIs prevented a public health catastrophe. A less comprehensive approach, employing only closures or individual-level NPI adherence, would have resulted in a large number of cases and deaths.

Infusing culture in compartmental epidemic models

There has been a recent surge of interest in modeling and forecasting epidemic outbreaks. Human behavior plays a key role in disease transmission and prevention. In this paper, we propose a systematic approach to model the effects of changes in human behavior by infusing socio-cultural factors within compartmentalized epidemic models. In particular, we have identified risk perception beliefs as critical epidemic related socio-cultural factors. We evaluated our model using the 2009 H1N1 epidemic scenario in Mexico and a preliminary COVID-19 scenario in the US. Our results show that including cultural information from even a sparse, small subset of events provides a significant improvement in prediction accuracy and explanatory capabilities. © 2021 IEEE