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.

Probability of Infectious Disease in Humans during Epidemic

Popular SIR models and their modifications used to generate predictions about epidemics and, specifically, the COVID-19 pandemic, are inadequate. The aim of this study was to find the laws describing the probability of infection in a biological object. Using theoretical methods of research based on the probability theory, we constructed the laws describing the probability of infection in a human depending on the infective dose and considering the temporal characteristics of a given infection. The so-called generalized time-factor law, which factors in the time of onset and the duration of an infectious disease, was found to be the most general. Among its special cases are the law describing the probability of infection developing by some point in time t, depending on the infective dose, and the law that does not factor in the time of onset. The study produced a full list of quantitative characteristics of pathogen virulence. The laws described in the study help to solve practical tasks and should lie at the core of mathematical epidemiological modeling.Copyright © 2022 Obstetrics, Gynecology and Reproduction. 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.

Analysis and simulation of modified susceptible-infected-recovered model with vaccination for COVID-19 outbreak

In this paper, we develop and analyse a modified susceptible-infected-recovered (SIR) compartment model by integrating the vaccination factor as a model parameter to investigate the effect of vaccination parameter on the long-term outcomes of the COVID-19 pandemic. Mathematical analysis is used to determine the disease-free equilibrium, the endemic equilibrium, and the basic reproduction number of the developed model. The stability of the model is studied using the Routh-Hurwitz criterion, and numerical simulations are conducted to assess the impact of vaccination on the disease at different rates. The findings suggest that vaccination rate influences the transmission dynamics, and the vaccine can speed up the COVID-19 recovery and contain the outbreak. © 2023 Inderscience Enterprises Ltd.

Change time estimation uncertainty in nonlinear dynamical systems with applications to COVID‐19

The impact that each individual non‐pharmaceutical intervention (NPI) had on the spread rate of COVID‐19 is difficult to estimate, since several NPIs were implemented in rapid succession in most countries. In this article, we analyze the detectability of sudden changes in a parameter of nonlinear dynamical systems, which could be used to represent NPIs or mutations of the virus, in the presence of measurement noise. Specifically, by taking an agnostic approach, we provide necessary conditions for when the best possible unbiased estimator is able to isolate the effect of a sudden change in a model parameter, by using the Hammersley–Chapman–Robbins (HCR) lower bound. Several simplifications to the calculation of the HCR lower bound are given, which depend on the amplitude of the sudden change and the dynamics of the system. We further define the concept of the most informative sample based on the largest ℓ2 distance between two output trajectories, which is a good indicator of when the HCR lower bound converges. These results are thereafter used to analyze the susceptible‐infected‐removed model. For instance, we show that performing analysis using the number of recovered/deceased, as opposed to the cumulative number of infected, may be an inferior signal to use since sudden changes are fundamentally more difficult to estimate and seem to require more samples. Finally, these results are verified by simulations and applied to real data from the spread of COVID‐19 in France. [ FROM AUTHOR] Copyright of International Journal of Robust & Nonlinear Control is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

An Optimal Vaccine Allocation Model Considering Vaccine Hesitancy and Efficacy Rates Among Populations

Vaccines need to be urgently allocated in pandemics like the ongoing COVID-19 pandemic. In the literature, vaccines are optimally allocated using various mathematical models, including the extensively used Susceptible-Infected-Recovered epidemic model. However, these models do not account for the time duration concerning multi-dose vaccines, time duration from infection to recovery or death, the vaccine hesitancy (i.e., delay in acceptance or refusal of vaccination), and vaccine efficacy (i.e., the time-varying protection capability of the vaccine). To make the vaccine allocation model more applicable to reality, this paper presents an optimal model considering the above mentioned time duration concerning multi-dose vaccination, time duration from infection to recovery or death, hesitancy rates, efficacy levels, and also breakthrough rates - the rates at which individuals get infected after vaccination. This vaccine allocation model is constructed using a revised Susceptible-Infected-Recovered model. The concept of people∗week infections is introduced to measure the number of infected people within a certain time duration, and in this paper, the amount of people∗week infections is minimized by the proposed vaccine allocation model. Our case study of the New York State 2021 population of 19,840,000 shows that this optimal allocation method can avoid 0.05%2.75% people∗week infections than the baseline allocation method when 2 to 11 million vaccines are optimally allocated. In conclusion, the obtained optimal allocation method can effectively reduce people∗week infections and avoid vaccine waste when more vaccines are available. © 2013 IEEE.

The effect of medical innovation on the cost-effectiveness of Covid 19-related policies in the United States using a SIR model.

BACKGROUND: During 2020-21, the United States used a multifaceted approach to control SARS-CoV-2 (Covid-19) and reduce mortality and morbidity. This included non-medical interventions (NMIs), aggressive vaccine development and deployment, and research into more effective approaches to medically treat Covid-19. Each approach had both costs and benefits. The objective of this study was to calculate the Incremental Cost Effectiveness Ratio (ICER) for three major Covid-19 policies: NMIs, vaccine development and deployment (Vaccines), and therapeutics and care improvements within the hospital setting (HTCI). METHODS: To simulate the number of QALYs lost per scenario, we developed a multi-risk Susceptible-Infected-Recovered (SIR) model where infection and fatality rates vary between regions. We use a two equation SIR model. The first equation represents changes in the number of infections and is a function of the susceptible population, the infection rate and the recovery rate. The second equation shows the changes in the susceptible population as people recover. Key costs included loss of economic productivity, reduced future earnings due to educational closures, inpatient spending and the cost of vaccine development. Benefits included reductions in Covid-19 related deaths, which were offset in some models by additional cancer deaths due to care delays. RESULTS: The largest cost is the reduction in economic output associated with NMI ($1.7 trillion); the second most significant cost is the educational shutdowns, with estimated reduced lifetime earnings of $523B. The total estimated cost of vaccine development is $55B. HTCI had the lowest cost per QALY gained vs "do nothing" with a cost of $2,089 per QALY gained. Vaccines cost $34,777 per QALY gained in isolation, while NMIs alone were dominated by other options. HTCI alone dominated most alternatives, except the combination of HTCI and Vaccines ($58,528 per QALY gained) and HTCI, Vaccines and NMIs ($3.4 m per QALY gained). CONCLUSIONS: HTCI was the most cost effective and was well justified under any standard cost effectiveness threshold. The cost per QALY gained for vaccine development, either alone or in concert with other approaches, is well within the standard for cost effectiveness. NMIs reduced deaths and saved QALYs, but the cost per QALY gained is well outside the usual accepted limits.

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)

On the Parametrization of Epidemiologic Models-Lessons from Modelling COVID-19 Epidemic

Numerous prediction models of SARS-CoV-2 pandemic were proposed in the past. Unknown parameters of these models are often estimated based on observational data. However, lag in case-reporting, changing testing policy or incompleteness of data lead to biased estimates. Moreover, parametrization is time-dependent due to changing age-structures, emerging virus variants, non-pharmaceutical interventions, and vaccination programs. To cover these aspects, we propose a principled approach to parametrize a SIR-type epidemiologic model by embedding it as a hidden layer into an input-output non-linear dynamical system (IO-NLDS). Observable data are coupled to hidden states of the model by appropriate data models considering possible biases of the data. This includes data issues such as known delays or biases in reporting. We estimate model parameters including their time-dependence by a Bayesian knowledge synthesis process considering parameter ranges derived from external studies as prior information. We applied this approach on a specific SIR-type model and data of Germany and Saxony demonstrating good prediction performances. Our approach can estimate and compare the relative effectiveness of non-pharmaceutical interventions and provide scenarios of the future course of the epidemic under specified conditions. It can be translated to other data sets, i.e., other countries and other SIR-type models. Copyright © 2022, MDPI. All rights reserved.

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 SCS.

COMPARATIVE ANALYSIS OF EPIDEMIOLOGICAL, FORECASTING AND DEEP LEARNING MODELS FOR COVID19 SPREAD IN INDIA

Covid19 is affecting across many nations and most population of the world. As per WHO there are 270million confirmed with about 5.3 million fatalities as on December 15th, 2021. Many governments, organizations and local bodies have been applying various models in order to estimate the disease spread and appliede varied strategies to curb the spread. There are many models proposed by mathematicians and statisticians for the same. In the current work a comparison is done with mathematical disease spread models SIR, SIRD, classic time series forecasting modelARIMA, and artificial neural network models RNN, LSTM with Covid19 India data. The study investigates the effect of disease containment policies and vaccination drives for Covid19 data in the context of India using SIR Model. All the models are built for multiple time prediction windows starting from 5 days up to 45 days. The models are evaluated with MAE, MAPE and RMSE for multiple states and India level data. It is inferred that the prediction time of 5 days has best results for SIR model. The ARIMA model can predict withacceptable performance up to 30 days. RNN and LSTM models can predict for 5 days within acceptable performance. The best model that can predict longer durations and has good performance is ARIMA model. A detailed report on the model details and performance is the outcome of this study.

New SIR Model and Vaccine rate with application

This search will show new SIR model and vaccine rate for dynamic system this study offer the stability, which way given by this study has proposed to all equilibrium points of this model. We depended on Routh Hurwitz criteria and lyapunov function methods in our study New SIR model and vaccine are come from actual analysis of susceptible, infection and recovery data within corona virus (covid-19) in Iraq during 3 months (1 January to 31 March), The proposed model has formed substantially by estimating last square root curve fit of Runge kutta (Rk4) parameters. The endemic equilibrium points will be found and discuss the stability of the model at each endemic equilibrium point and the trajectories of S,I and R are plotted to show the convergence of equilibrium points of the model.

Optimizing the mitigation of epidemic spreading through targeted adoption of contact tracing apps

The ongoing COVID-19 pandemic is the first epidemic in human history in which digital contact tracing has been deployed at a global scale. Tracking and quarantining all the contacts of individuals who test positive for a virus can help slow down an epidemic, but the impact of contact tracing is severely limited by the generally low adoption of contact-tracing apps in the population. We derive here an analytical expression for the effectiveness of contact-tracing app installation strategies in a susceptible-infected-recovered (SIR) model on a given contact graph. We propose a decentralized heuristic to improve the effectiveness of contact tracing under fixed adoption rates, which targets a set of individuals to install contact-tracing apps and can be easily implemented. Simulations on a large number of real-world contact networks confirm that this heuristic represents a feasible alternative to the current state of the art. © 2022 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Managing an evolving pandemic: Cryptic circulation of the Delta variant during the Omicron rise.

SARS-CoV-2 continued circulation results in mutations and the emergence of various variants. Until now, whenever a new, dominant, variant appeared, it overpowered its predecessor after a short parallel period. The latest variant of concern, Omicron, is spreading swiftly around the world with record morbidity reports. Unlike the Delta variant, previously considered to be the main variant of concern in most countries, including Israel, the dynamics of the Omicron variant showed different characteristics. To enable quick assessment of the spread of this variant we developed an RT-qPCR primers-probe set for the direct detection of Omicron variant. Characterized as highly specific and sensitive, the new Omicron detection set was deployed on clinical and wastewater samples. In contrast to the expected dynamics whereupon the Delta variant diminishes as Omicron variant increases, representative results received from wastewater detection indicated a cryptic circulation of the Delta variant even with the increased levels of Omicron variant. Resulting wastewater data illustrated the very initial Delta-Omicron dynamics occurring in real time. Despite this, the future development and dynamics of the two variants side-by-side is still mainly unknown. Based on the initial results, a double susceptible-infected-recovered model was developed for the Delta and Omicron variants. According to the developed model, it can be expected that the Omicron levels will decrease until eliminated, while Delta variant will maintain its cryptic circulation. If this comes to pass, the mentioned cryptic circulation may result in the reemergence of a Delta morbidity wave or in the possible generation of a new threatening variant. In conclusion, the deployment of wastewater-based epidemiology is recommended as a convenient and representative tool for pandemic containment.

Change time estimation uncertainty in nonlinear dynamical systems with applications to COVID‐19

The impact that each individual non‐pharmaceutical intervention (NPI) had on the spread rate of COVID‐19 is difficult to estimate, since several NPIs were implemented in rapid succession in most countries. In this article, we analyze the detectability of sudden changes in a parameter of nonlinear dynamical systems, which could be used to represent NPIs or mutations of the virus, in the presence of measurement noise. Specifically, by taking an agnostic approach, we provide necessary conditions for when the best possible unbiased estimator is able to isolate the effect of a sudden change in a model parameter, by using the Hammersley–Chapman–Robbins (HCR) lower bound. Several simplifications to the calculation of the HCR lower bound are given, which depend on the amplitude of the sudden change and the dynamics of the system. We further define the concept of the most informative sample based on the largest ℓ2 distance between two output trajectories, which is a good indicator of when the HCR lower bound converges. These results are thereafter used to analyze the susceptible‐infected‐removed model. For instance, we show that performing analysis using the number of recovered/deceased, as opposed to the cumulative number of infected, may be an inferior signal to use since sudden changes are fundamentally more difficult to estimate and seem to require more samples. Finally, these results are verified by simulations and applied to real data from the spread of COVID‐19 in France. [ FROM AUTHOR] Copyright of International Journal of Robust & Nonlinear Control is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Impact of media coverage on a fractional-order SIR epidemic model

In this work, we formulated and analyzed a fractional-order epidemic model of infectious disease (such as SARS, 2019-nCoV and COVID-19) concerning media effect. The model is based on classical susceptible-infected-recovered (SIR) model. Basic properties regarding positivity, boundedness and non-negative solutions are discussed. Basic reproduction number (R0) of the system has been calculated using next-generation matrix method and it is seen that the disease-free equilibrium is locally as well as globally asymptotically stable if R0 < 1, otherwise unstable. The existence of endemic equilibrium point is established using the Lambert W function. The condition for global stability has been derived. Numerical simulation suggests that fractional order and media have a large effect on our system dynamics. When media impact is stronger enough, our fractional-order system stabilizes the oscillation. © 2022 World Scientific Publishing Company.

Modeling U.S. health agencies' message dissemination on twitter and users' exposure to vaccine-related misinformation using system dynamics

This research intends to answer: how do (i) generation frequency and (ii) retweeting count of health agencies' messages impact the exposure of the general users to vaccine-related misinformation on Twitter? We creatively employed a Susceptible-Infected-Recovered (SIR) System Dynamics paradigm to model interactions between message dissemination of 168 U.S. health agencies and proportions of users who are at different exposure statuses to misinformation, namely "Susceptible", "Infected", or "Recovered" status. The SIR model was built based on the vaccine-relevant tweets posted over November and December in 2020. Our preliminary outcomes suggest that augmenting the generation frequency of agencies' messages and increasing retweeting count can effectively moderate the exposure risk to vaccine-related misinformation. This model illustrates how health agencies may combat vaccine hesitancy through credible information dissemination on social media. It offers a novel approach for crisis informatics studies to model different information categories and the impacted population in the complex digital world. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

SIRVVD model-based verification of the effect of first and second doses of COVID-19/SARS-CoV-2 vaccination in Japan.

As of August 2021, COVID-19 is still spreading in Japan. Vaccination, one of the key measures to bring COVID-19 under control, began in February 2021. Previous studies have reported that COVID-19 vaccination reduces the number of infections and mortality rates. However, simulations of spreading infection have suggested that vaccination in Japan is insufficient. Therefore, we developed a susceptible-infected-recovered-vaccination1-vaccination2-death model to verify the effect of the first and second vaccination doses on reducing the number of infected individuals in Japan; this includes an infection simulation. The results confirm that appropriate vaccination measures will sufficiently reduce the number of infected individuals and reduce the mortality rate.

From SARS to COVID-19: The evolving role of China-ASEAN production network.

Two decades after the severe acute respiratory syndrome (SARS) outbreak, Asia is confronted with the coronavirus disease 2019 (COVID-19) that has significantly affected the region's economy. In a novel general equilibrium model, we introduce epidemiological dynamics to the production network between China and the Association of Southeast Asian Nations (ASEAN). In this model, agents' risk-averse behavior during pandemics leads to shifting demands across economic sectors. We calibrate the model with the Organisation for Economic Co-operation and Development (OECD) inter-country input-output tables for pre-SARS and pre-COVID-19 periods and control for disease dynamics across years. Findings show that in the absence of policy interventions, China's greater significance in global value chains is associated with substantial economic impacts, within China and the ASEAN region. Our sensitivity analyses further show that China's containment efforts reduce the spillover effects on ASEAN.

Transitioning Out of the Coronavirus Lockdown: A Framework for Evaluating Zone-Based Social Distancing.

In the face of elevated pandemic risk, canonical epidemiological models imply the need for extreme social distancing over a prolonged period. Alternatively, people could be organized into zones, with more interactions inside their zone than across zones. Zones can deliver significantly lower infection rates, with less social distancing, particularly if combined with simple quarantine rules and contact tracing. This paper provides a framework for understanding and evaluating the implications of zones, quarantines, and other complementary policies.