Selection of forecasting models for infectious diseases in large warships.

As important combat platforms, large warships have the characteristics of compact internal space and dense personnel. Once infectious diseases occur, they are very easy to spread. Therefore, it is very important to select suitable forecasting models for infectious diseases in this environment. This paper introduces 4 classic dynamics models of infectious diseases, summarizes various kinds of compartmental models and their key characteristics, and discusses several common practical simulation requirements, helping relevant health personnel to cope with the challenges in health and epidemic prevention such as the prevention and control of coronavirus disease 2019.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Selection of forecasting models for infectious diseases in large warships

As important combat platforms, large warships have the characteristics of compact internal space and dense personnel. Once infectious diseases occur, they are very easy to spread. Therefore, it is very important to select suitable forecasting models for infectious diseases in this environment. This paper introduces 4 classic dynamics models of infectious diseases, summarizes various kinds of compartmental models and their key characteristics, and discusses several common practical simulation requirements, helping relevant health personnel to cope with the challenges in health and epidemic prevention such as the prevention and control of coronavirus disease 2019.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Effect of Hybrid Immunity, School Reopening, and the Omicron Variant on Trajectory of Covid-19 Epidemic in India: A Modelling Study

Intro: The concurrent reopening of schools, increasing levels of hybrid immunity, and the emergence of the Omicron variant have affected the trajectory of the pandemic in India. We address related questions using the model Indian state of Andhra Pradesh (pop: 53 million). Method(s): A compartmental model which describes the disease progression of COVID-19 with two dose vaccination is employed to understand the effect of vaccination, immunity due to infection and VOC Omicron. This is an age- stratified as well as a contact-structured model. The introduction of the Omicron variant is modelled. We studied disease dynamics in a background of seropositivity gained from an earlier wave of infection as well as an ongoing vaccination program, together called "hybrid immunity". We demonstrate the effect of school reopening as well as of the Omicron (BA.2) variant on cases across different age-groups. Finding(s): Reopening schools increases cases in children as compared to adults, although most such cases are asymptomatic or mild. The height of this peak reduced as the background infection-induced seropositivity was increased from 20% to 40%. At reported values of seropositivity of 64%, no discernable peak was seen. We find that in the absence of vaccination, even at such high levels of seroprevalence, the emergence of the Omicron variant would have resulted in a large rise in cases across all age bands. Discussion(s): In India, the decreasing prevalence of immunologically naive individuals of all ages helped reduce the number of cases reported once schools were reopened. In addition, hybrid immunity, together with the lower intrinsic severity of disease associated with the Omicron variant, contributed to low reported COVID-19 hospitalizations and deaths. Conclusion(s): Mathematical modelling provides a powerful way of addressing central questions regarding the trajectory of the pandemic in India, clarifying the role of hybrid immunity.Copyright © 2023

Quantifying Population Immunity to Covid-19 in Washington State and Oregon

Background: Since early 2020, the novel SARS-CoV-2 virus has spread rapidly throughout the globe. Subsequently many individuals have developed some form of immunity due to either a prior infection, one or more vaccinations, or a combination of the two. Using local epidemic data and mathematical modeling, we enumerate the various immune populations in Washington State and Oregon and quantify the level of protection against infection and hospitalization. Method(s): We developed a compartmental model of ordinary differential equations, which stratifies the population by age (0-17 years, 18-49 years, 50-64 years, and 65+ years), region, type of immunity (naive, infectionderived, vaccine-derived, booster-derived, hybrid immunity, etc), and recency of immune conferring event (recent and waned). To track the number of individuals in each category we combine 1) literature-based estimates of susceptibility to infection and severe disease by age, immune status, and variant, 2) calibration to the number of severe infections (hospitalizations and deaths) and number of vaccinations and 3) validation with serological surveys of the population. Result(s): We estimate that by mid-April 2022 more than 95% of the populations of both Washington and Oregon had some immunity against COVID-19 infection and hospitalization. Younger age groups tended to have much higher rates of natural or hybrid immunity with 96% of 0-17-year-olds and 83% of 18-49-year-olds protected due to past infections. Overall, the population-level immunity against the Omicron variant reduced risk of infection by 59% (95% Credible Interval 54% - 62%) and risk of hospitalization by 79% (95% CI 77% - 81%) in Washington and 62% (95% CI 57% -66%) and 83% (95% CI 82% - 85%), respectively, in Oregon. There was similar population-level protection against Delta at the start of the Omicron wave in early December 2021, which reduced risk of infection by 60% (95% CI 56% - 63%) and risk of hospitalization 79% (95% CI 78% - 80%) in Washington and 66% (95%CI 63% - 70%) and 82% (81% - 83%), respectively, in Oregon. Conclusion(s): Very large waves of new infections throughout 2021 and early 2022, in addition to high levels of vaccination and boosting among the older age groups in Washington and Oregon have greatly reduced population susceptibility to currently circulating strains. However even very high population immunity has allowed for emergence of novel variants that escape existing immunity, highlighting the need for continued develop of new variantspecific boosters.

Population pharmacokinetics of dexamethasone in critically ill COVID-19 patients: inflammation might play a role

Introduction: One of the common causes of COVID-19 related death is acute respiratory distress syndrome (C-ARDS). Dexamethasone is the cornerstone in the therapy of C-ARDS and reduces mortality probably by suppressing inflammatory levels in ICU patients. Its anti-inflammatory effects may be concentration-related. However, no pharmacokinetic studies of dexamethasone have been conducted in ICU patients. Therefore, we designed a population pharmacokinetic study to gain a deeper understanding of the pharmacokinetics of dexamethasone in critically ill patients in order to identify relevant covariates that can be used to personalize dosing regimens and improve clinical outcomes. Method(s): This was a retrospective pilot study at the ICU of the Erasmus Medical Center. Blood samples were collected in adults at the ICU with COVID who were treated with fixed dose intravenous dexamethasone (6 mg/day). The data were analyzed using Nonlinear Mixed Effects Modelling (NONMEM) software for population pharmacokinetic analysis and clinically relevant covariates were selected and evaluated. Result(s): A total of 51 dexamethasone samples were measured in 18 patients. A two-compartment model with first-order kinetics best fitted the data. The mean population estimates for drug clearance and inter-compartment clearance were 2.85 L/h (IIV 62.9%) and 2.11 L/h, respectively, and central and peripheral volumes of distribution were 15.4 L and 12.3 L, respectively. The covariate analysis showed a significant correlation between dexamethasone clearance and CRP. Dexamethasone clearance decreased significantly with increasing CRP in the range of 0-50 mg/L and a correlation was observed with CRP up to 100 mg/L. Conclusion(s): The dexamethasone PK parameters of ICU COVID patients were quite different from those come from healthy populations. Inflammation might play an important role in dexamethasone clearance and the dosing should be more individualized in order to achieve best therapeutic effect in ICU patients.

Effects of Public Health Interventions against Covid-19 in France

Background: Non-pharmaceutical interventions (NPIs) and vaccines have been used by many countries to manage the dynamics of the COVID-19 pandemic. Despite numerous studies, considerable uncertainty remains about the effects of these public health interventions due to data quality issues and methodological challenges to estimating effects. However, producing accurate and precise estimates of the effects of these interventions is of utmost importance for the preparedness of any new epidemic. Method(s): We developed a population-based mechanistic compartmental model that includes the effect of NPIs on SARS-CoV-2 transmission and the effect of vaccination on the transmission and the rate of hospitalization. Our statistical approach estimated all parameters in one step, thus accurately propagating uncertainty, and representing spatial heterogeneity. We fitted the model to all available epidemiological data (hospital admissions and occupancy, cases, and deaths) from March 2020 to October 2021 in France. Hence, we estimated the time-varying transmission rate, and derived the effect of NPIs through an integrated regression model. We simulated counterfactual scenarios of the interplay of NPIs and vaccine availability and rollout with the same model. Result(s): We found that the first lockdown reduced transmission by 84% (95% CI [83-85]) and was more effective than the second and third lockdowns (reduction of 75% [72-77] and 9% [6-13], respectively). A 6pm curfew was more effective than an 8 pm curfew (transmission reduction of 69% [67-70] vs. 50% [48-53]). School closures had a smaller effect on transmission (15% [12-19]). By the end of the study period, the protection conferred by vaccines against hospitalization and against infection, considering viral variants and population vaccine coverage, ranged between 69-92% and 29-40%, respectively. In a scenario without vaccines, we predicted 209% (95% PI [34-520]) more deaths and 346% [101-798] more hospitalizations throughout the study period. Conversely, if an effective vaccine had been available after 100 days, 65% [36-80] deaths and 72% [45-84] hospitalizations could have been averted. Conclusion(s): Our results provide reliable effect and uncertainty estimates of each NPI and demonstrate that NPIs and vaccination synergistically reduced COVID-19 transmission, hospitalization, and deaths. This emphasizes the importance of stringent NPIs and a high vaccination rate to prevent further epidemic resurgences and control other emerging respiratory infectious diseases.

Sotrovimab Pharmacokinetics and Covariate Effects in Hematopoietic Stem Cell Transplant Recipients

Hematopoietic cell transplant (HCT) recipients are at increased risk of morbidity and mortality from COVID-19. They may have lower SARS-CoV-2-directed antibody levels due to protein loss from the gastrointestinal (GI) tract as a result of preparative regimen-related toxicity and graft-vs.-host disease (GVHD). In fact, previous studies suggested that GI GVHD or diarrhea from other etiologies were associated with a reduction in the half-life of monoclonal antibodies (mAbs). Hence, understanding the pharmacokinetic (PK) profile of mAbs targeting SARS-CoV-2 in this vulnerable population is critical for dose-selection and predicting the duration of protection against COVID-19. This analysis aims to use a population pharmacokinetics (popPK) approach to evaluate the PK of sotrovimab and the effect of covariates in HCT recipients. In a Phase I trial (COVIDMAB), all participants received 500 mg sotrovimab IV prophylactically within one week prior to starting transplant conditioning. Sotrovimab serum concentrations were determined weekly for up to 12 weeks in autologous (n=5) and allogeneic (n=15) HCT recipients (129 observations). Sotrovimb PK and the effect of covariates were analyzed using popPK modeling in NONMEM (version 7.4). GVHD and diarrhea severity data were collected weekly via survey and included as time-dependent covariates during the covariate screening process. The final PK model with covariates was validated using simulation-based validation and goodness of fit plots. PK data were compared to non-transplant patients from 1891 patients with COVID-19 in COMET-ICE, COMET-PEAK, BLAZE-4, and COMET-TAIL and 38 healthy individuals enrolled in GlaxoSmithKline Pharma Study 217653. A two-compartment model best described sotrovimab PK in HCT recipients. In comparison to non-transplant patients, sotrovimab clearance (CL) was 14.0% higher in HCT recipients. Weight was a significant covariate on sotrovimab CL and (Figure Presented) volume of distribution in the central compartment (V2). With every 10 kg increase in body weight, sotrovimab CL and V2 were estimated to increase by 9.5% and 5.5%, respectively. Diarrhea severity was also a significant covariate on sotrovimab CL. HCT recipients with grade 3 diarrhea showed an increase in CL by 1.5-fold compared to those without diarrhea. Based on popPK analyses, sotrovimab CL was higher in HCT recipients compared to non-transplant patients. Higher bodyweight as well as diarrhea resulted in increased sotrovimab CL. There were only 3 patients with GI GVHD, and larger studies are needed to determine whether diarrhea due to GI GVHD or conditioning toxicity was responsible for the observed increase in sotrovimab CL. Further validation of these findings in a larger number of HCT recipients is also warranted to help optimize mAb dosing for COVID-19 prophylaxis and determine whether presence of large-volume diarrhea may require intensified dosing strategiesCopyright © 2023 American Society for Transplantation and Cellular Therapy

Coupled disease-vaccination behavior dynamic analysis and its application in COVID-19 pandemic.

Predicting the evolutionary dynamics of the COVID-19 pandemic is a complex challenge. The complexity increases when the vaccination process dynamic is also considered. In addition, when applying a voluntary vaccination policy, the simultaneous behavioral evolution of individuals who decide whether and when to vaccinate must be included. In this paper, a coupled disease-vaccination behavior dynamic model is introduced to study the coevolution of individual vaccination strategies and infection spreading. We study disease transmission by a mean-field compartment model and introduce a non-linear infection rate that takes into account the simultaneity of interactions. Besides, the evolutionary game theory is used to investigate the contemporary evolution of vaccination strategies. Our findings suggest that sharing information with the entire population about the negative and positive consequences of infection and vaccination is beneficial as it boosts behaviors that can reduce the final epidemic size. Finally, we validate our transmission mechanism on real data from the COVID-19 pandemic in France.

Analysis of the potential impact of effectiveness and availability of mRNA influenza vaccine on hospitalization and mortality

Background. Influenza viruses constantly change because of antigenic drift. Due to the time currently needed to develop and distribute flu shots, vaccines are often illmatched to circulating influenza strains. One silver lining of the COVID-19 pandemic was the acceleration of mRNA technology, which could significantly reduce the timeline between strain choice and deployment, potentially increasing vaccine efficacy. Significant private and public investments would be required to accommodate accelerated vaccine development and approval. Hence, it is important to understand the potential impact of mRNA technology on influenza hospitalizations and mortality. Methods. We developed a compartmental model stratified by age group to evaluate the potential effect of increased vaccine effectiveness (defined as a two-level measure of protection against infection and hospitalization) on influenza hospitalizations and mortality in the United States. We assume that mRNA technology can only shorten the time from strain choice to distribution but not distribution and administration. Thus, later decisions on vaccine composition would increase effectiveness but reduce availability. To assess this tradeoff, we evaluated two scenarios where strain choice was delayed until summer resulting in a more effective vaccine: (1) available to all age groups in the fall, or (2) available by August but only for adults 65 years and older. Results. Assuming current vaccine coverage rates, if not available until October, the vaccine would need a minimum of 80% effectiveness against infection to see a decrease in hospitalizations and deaths (Figures 1A and 1B). When delayed until November, even a 100% effective vaccine could not reduce hospitalizations or deaths (Figures 1C and 1D). For the elderly, a 50% effective vaccine against infection (Figures 1E and 1F) or a vaccine 40% effective against infection and 60% against hospitalization available in late summer was similar to an 80% effective vaccine available in October for all ages. Age-stratified weekly number of influenza-associated hospitalization per 100,000 population and total number of deaths in the United States for an mRNA vaccine that would be available in either October (A and B), November (C and D), or by late summer but only for the 65+ age group (E and F). The Baseline represents the 10-year average weekly hospitalization rate and mortality during the Flu Season (October to May). Conclusion. As the majority of influenza-associated hospitalizations and deaths are in adults 65 years and older, a combination policy targeting higher vaccine effectiveness for this age group in the short term would be the most efficacious. (Figure Presented).

Comparing COVID-19 Case Prediction Between ARIMA Model and Compartment Model - China, December 2019-April 2020.

Introduction: To compare the performance between the compartment model and the autoregressive integrated moving average (ARIMA) model that were applied to the prediction of new infections during the coronavirus disease 2019 (COVID-19) epidemic. Methods: The compartment model and the ARIMA model were established based on the daily cases of new infection reported in China from December 2, 2019 to April 8, 2020. The goodness of fit of the two models was compared using the coefficient of determination (R2). Results: The compartment model predicts that the number of new cases without a cordon sanitaire, i.e., a restriction of mobility to prevent spread of disease, will increase exponentially over 10 days starting from January 23, 2020, while the ARIMA model shows a linear increase. The calculated R2 values of the two models without cordon sanitaire were 0.990 and 0.981. The prediction results of the ARIMA model after February 2, 2020 have a large deviation. The R2 values of complete transmission process fit of the epidemic for the 2 models were 0.964 and 0.933, respectively. Discussion: The two models fit well at different stages of the epidemic. The predictions of compartment model were more in line with highly contagious transmission characteristics of COVID-19. The accuracy of recent historical data had a large impact on the predictions of the ARIMA model as compared to those of the compartment model.

Exo-SIR: an epidemiological model to analyze the impact of exogenous spread of infection.

Epidemics like Covid-19 and Ebola have impacted people's lives significantly. The impact of mobility of people across the countries or states in the spread of epidemics has been significant. The spread of disease due to factors local to the population under consideration is termed the endogenous spread. The spread due to external factors like migration, mobility, etc., is called the exogenous spread. In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few variants of the model. The novelty in our model is that it captures both the exogenous and endogenous spread of the virus. First, we present an analytical study. Second, we simulate the Exo-SIR model with and without assuming contact network for the population. Third, we implement the Exo-SIR model on real datasets regarding Covid-19 and Ebola. We found that endogenous infection is influenced by exogenous infection. Furthermore, we found that the Exo-SIR model predicts the peak time better than the SIR model. Hence, the Exo-SIR model would be helpful for governments to plan policy interventions at the time of a pandemic.

Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model.

In this work, we propose a new mathematical modeling of the spread of COVID-19 infection in an arbitrary population, by modifying the SIQRD model as m-SIQRD model, while taking into consideration the eight governmental interventions such as cancellation of events, closure of public places etc., as well as the influence of the asymptomatic cases on the states of the model. We introduce robustness and improved accuracy in predictions of these models by utilizing a novel deep learning scheme. This scheme comprises of attention based architecture, alongside with Generative Adversarial Network (GAN) based data augmentation, for robust estimation of time varying parameters of m-SIQRD model. In this regard, we also utilized a novel feature extraction methodology by employing noise removal operation by Spline interpolation and Savitzky-Golay filter, followed by Principal Component Analysis (PCA). These parameters are later directed towards two main tasks: forecasting of states to the next 15 days, and estimation of best policy encodings to control the infected and deceased number within the framework of data driven synergetic control theory. We validated the superiority of the forecasting performance of the proposed scheme over countries of South Korea and Germany and compared this performance with 7 benchmark forecasting models. We also showed the potential of this scheme to determine best policy encodings in South Korea for 15 day forecast horizon.

Modeling pandemics and vaccine and equity issues

We present some recent activity in Ontario on the mathematical modeling of COVID-19 and the development of optimal strategies for vaccine distribution that take into account equity issues.

Modeling the Transmission Dynamics of COVID-19 Among Five High Burden African Countries.

Background: Today, coronavirus disease-19 has left a permanent dark mark on the history of human beings. The ongoing global pandemic outbreak of COVID-19 has spread to 58 African countries, with over 6.07 million confirmed cases and over 151,412 deaths. The five high burden African countries are South Africa, Morocco, Tunisia, Ethiopia, and Libya, with case fatality rates (CFR) of nearly 0.15%, 0.042%, 0.22%, 0.006%, and 0.086%, respectively. This is why the research aims to adequately understand the transmission dynamics of the virus and its variants in five high-burden African countries. Methods: Our study is a deterministic model, where the population is partitioned into five components on the epidemiological state of the individuals. We presented a year-structured susceptible, infected, mild severs, critical severe, and recover (SIMCR) compartmental model of COVID-19 disease transmission with incidence rate during the pandemic period. Results: The number of susceptible individuals increased by 30,711,930 in South Africa, 5,919,837 in Morocco, 3,485,020 in Tunisia, 7,833,642 in Ethiopia, and 2,145,404 in Libya in the next 3 decades with compare to the unvaccinated population and the number of infected individuals decreased by 30,479,271 in South Africa, 19,809,751 in Morocco, 3,456,406 in Tunisia, 7,761,993 in Ethiopia, and 2,125,038 in Libya. Conclusion: SIMCR model is used to describe the transmission of COVID-19 among five high-burden African countries. For the next 30 years, we will have around 86 million infected individuals and millions of death only in those five African countries. To reduce those problems, vaccination is the best and most effective mechanism. So vaccinating half of the populations in those countries helps to control and reduce the transmission rate of COVID-19 in Africa for the next 30 years. This leads to preventing 17,212,405 people from becoming infected and millions of deaths being reduced in those five high-burden African countries.

Fitting the reproduction number from UK coronavirus case data and why it is close to 1.

We present a method for rapid calculation of coronavirus growth rates and [Formula: see text]-numbers tailored to publicly available UK data. We assume that the case data comprise a smooth, underlying trend which is differentiable, plus systematic errors and a non-differentiable noise term, and use bespoke data processing to remove systematic errors and noise. The approach is designed to prioritize up-to-date estimates. Our method is validated against published consensus [Formula: see text]-numbers from the UK government and is shown to produce comparable results two weeks earlier. The case-driven approach is combined with weight-shift-scale methods to monitor trends in the epidemic and for medium-term predictions. Using case-fatality ratios, we create a narrative for trends in the UK epidemic: increased infectiousness of the B1.117 (Alpha) variant, and the effectiveness of vaccination in reducing severity of infection. For longer-term future scenarios, we base future [Formula: see text] on insight from localized spread models, which show [Formula: see text] going asymptotically to 1 after a transient, regardless of how large the [Formula: see text] transient is. This accords with short-lived peaks observed in case data. These cannot be explained by a well-mixed model and are suggestive of spread on a localized network. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Assessing Vaccination Prioritization Strategies for COVID-19 in South Africa Based on Age-Specific Compartment Model.

The vaccines are considered to be important for the prevention and control of coronavirus disease 2019 (COVID-19). However, considering the limited vaccine supply within an extended period of time in many countries where COVID-19 vaccine booster shot are taken and new vaccines are developed to suppress the mutation of virus, designing an effective vaccination strategy is extremely important to reduce the number of deaths and infections. Then, the simulations were implemented to study the relative reduction in morbidity and mortality of vaccine allocation strategies by using the proposed model and actual South Africa's epidemiological data. Our results indicated that in light of South Africa's demographics, vaccinating older age groups (>60 years) largely reduced the cumulative deaths and the "0-20 first" strategy was the most effective way to reduce confirmed cases. In addition, "21-30 first" and "31-40 first" strategies have also had a positive effect. Partial vaccination resulted in lower numbers of infections and deaths under different control measures compared with full vaccination in low-income countries. In addition, we analyzed the sensitivity of daily testing volume and infection rate, which are critical to optimize vaccine allocation. However, comprehensive reduction in infections was mainly affected by the vaccine proportion of the target age group. An increase in the proportion of vaccines given priority to "0-20" groups always had a favorable effect, and the prioritizing vaccine allocation among the "60+" age group with 60% of the total amount of vaccine consistently resulted in the greatest reduction in deaths. Meanwhile, we observed a significant distinction in the effect of COVID-19 vaccine allocation policies under varying priority strategies on relative reductions in the effective reproduction number. Our results could help evaluate to control measures performance and the improvement of vaccine allocation strategy for COVID-19 epidemic.

Use of Monte Carlo simulation to prevent pharmacokinetics impairment of amiodarone under VV ECMO

Introduction: Acute Respiratory Distress Syndrome (ARDS) became a leading cause of ICU admission since the COVID-19 outbreak. Refractory ARDS can benefit from Veno-Venous Extra Corporeal Membrane Oxygenation (VV ECMO). Amiodarone is used for treating cardiac arrhythmias and shockable cardiac arrest during cardiopulmonary resuscitation (CPR). Data about amiodarone under VV ECMO are still lacking. In a previous work led on a model of ARDS in pigs ongoing CPR, we showed a pharmacokinetics impairment of amiodarone under VV ECMO. We aimed to establish a PK modelling of amiodarone concentrations. Material and methods: Nonlinear mixed effects modelling approach was used to analyse plasma concentrations. Impact of VV ECMO on amiodarone pharmacokinetic profile were investigated. Using our final model, different dosing schemes for amiodarone (10 000 Monte Carlo simulations) were simulated in animals on ECMO VV. Results: A two-compartment model with first-order absorption and elimination was able to accurately describe amiodarone plasma concentrations. Interindividual variability was retained for clearance and central volume of distribution. Amiodarone PK parameters were influenced by the ECMO covariable. All parameters were well estimated. Goodness of fit plots comforted the accuracy of the model. Predicted-corrected visual predictive check of the final model was satisfactory. Simulated amiodarone exposure showed that amiodarone 600 mg bolus is required under VV ECMO to achieve similar AUC observed in the control group. Discussion/Conclusion: In our model of ARDS in pigs with cardiac arrest and benefiting from CPR and VV ECMO, a two-compartment model with first-order absorption and elimination was able to accurately describe amiodarone plasma concentrations. VV ECMO significantly modified both central distribution volume and amiodarone clearance. From Monte-Carlo simulation, we showed that a 2-fold increase of amiodarone doses should be considered to reach efficient drug exposure under VV ECMO.

Artificial Intelligence and Internet of Things (AI-IoT) Technologies in Response to COVID-19 Pandemic: A Systematic Review

The origin of the COVID-19 pandemic has given overture to redirection, as well as innovation to many digital technologies. Even after the progression of vaccination efforts across the globe, total eradication of this pandemic is still a distant future due to the evolution of new variants. To proactively deal with the pandemic, the health care service providers and the caretaker organizations require new technologies, alongside improvements in existing related technologies, Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning in terms of infrastructure, efficiency, privacy, and security. This paper provides an overview of current theoretical and application prospects of IoT, AI, cloud computing, edge computing, deep learning techniques, blockchain technologies, social networks, robots, machines, privacy, and security techniques. In consideration of these prospects in intersection with the COVID-19 pandemic, we reviewed the technologies within the broad umbrella of AI-IoT technologies in the most concise classification scheme. In this review, we illustrated that AI-IoT technological applications and innovations have most impacted the field of healthcare. The essential AI-IoT technologies found for healthcare were fog computing in IoT, deep learning, and blockchain. Furthermore, we highlighted several aspects of these technologies and their future impact with a novel methodology of using techniques from image processing, machine learning, and differential system modeling.

The minimal COVID-19 vaccination coverage and efficacy to compensate for a potential increase of transmission contacts, and increased transmission probability of the emerging strains.

BACKGROUND: Mass immunization is a potentially effective approach to finally control the local outbreak and global spread of the COVID-19 pandemic. However, it can also lead to undesirable outcomes if mass vaccination results in increased transmission of effective contacts and relaxation of other public health interventions due to the perceived immunity from the vaccine. METHODS: We designed a mathematical model of COVID-19 transmission dynamics that takes into consideration the epidemiological status, public health intervention status (quarantined/isolated), immunity status of the population, and strain variations. Comparing the control reproduction numbers and the final epidemic sizes (attack rate) in the cases with and without vaccination, we quantified some key factors determining when vaccination in the population is beneficial for preventing and controlling future outbreaks. RESULTS: Our analyses predicted that there is a critical (minimal) vaccine efficacy rate (or a critical quarantine rate) below which the control reproduction number with vaccination is higher than that without vaccination, and the final attack rate in the population is also higher with the vaccination. We also predicted the worst case scenario occurs when a high vaccine coverage rate is achieved for a vaccine with a lower efficacy rate and when the vaccines increase the transmission efficient contacts. CONCLUSIONS: The analyses show that an immunization program with a vaccine efficacy rate below the predicted critical values will not be as effective as simply investing in the contact tracing/quarantine/isolation implementation. We reached similar conclusions by considering the final epidemic size (or attack rates). This research then highlights the importance of monitoring the impact on transmissibility and vaccine efficacy of emerging strains.

Assessment on the anti-epidemic measures: Mathematical model establishment for COVID-19 epidemic and application of recurrent neural network

How to evaluate the effectiveness of epidemic prevention measures scientifically and reasonably has become the most urgent task for governments around the world. The rapid spread of the coronavirus has brought great challenges to the global community. In this paper, the effectiveness of the following three epidemic prevention measures, including makeshift hospitals, closed cities, and wearing masks, is evaluated by establishing relevant mathematical models and applying the circulating neural network. For the scenes of closed cities and makeshift hospitals, the improved model of SEIR eight cabins is established in this paper, and the parameters are updated in real-time by Long Short-Term Memory. The following results are obtained: the closed city measures greatly reduce the probability of the transfer of susceptible persons to latent ones, and the epidemic is effectively controlled. Regarding the issue of wearing masks, this paper established the MUEIR model and solved with a particle swarm optimization algorithm. It is concluded that the number of infected people decreased by 42% compared with the natural situation, indicating the effectiveness of wearing masks. In conclusion, the effectiveness of the above three epidemic prevention measures is scientifically evaluated, and artificial intelligence technology is combined to achieve intelligent dynamic prediction of the epidemic development trend. © 2022 IEEE.