Coupling Tracer Derived ACH with Risk Calculators to Calculate Risk of COVID-19 Infection

Risk calculators have been utilised to predict the risk of infection from SARS-CoV-2. Inputs include the dimensions of the indoor space, number of infected persons and activity, and inhalation rate of susceptible persons. The compartment model requires an estimate of the Air Changes per Hour (ACH) in the space, as the concentration is changing as a result of the dynamic balance between the generation and removal of exhaled quanta. ACH can be estimated using CO2, engineering drawings, or airflow measurements, but these estimates are often incorrect due to mechanical anomalies and mixing inefficiencies, or in the case of CO2, an absence of continuous occupancy for a sufficient amount of time. SF6 as a tracer gas to establish ACH has been used extensively for many decades to measure air exchange. This approach was utilised to assist a school in managing risk of infection in their facility during an exam period. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Modelling presymptomatic infectiousness in COVID-19

This paper considers SEPIR, an extension of the well-known SEIR continuous simulation compartment model. Both models can be fitted to real data as they include parameters that can be estimated from the data. SEPIR deploys an additional presymptomatic infectious compartment, not modelled in SEIR but known to exist in COVID-19. This stage can also be fitted to data. We focus on how to fit SEPIR to a first wave of COVID. Both SEIR and SEPIR and the existing SEIR models assume a homogeneous mixing population with parameters fixed. Moreover, neither includes dynamically varying control strategies deployed against the virus. If either model is to represent more than just a single wave of the epidemic, then the parameters of the model would have to be time dependent. In view of this, we also show how reproduction numbers can be calculated to investigate the long-term overall outcome of an epidemic. © 2023 The Operational Research Society.

Could Earlier Availability of Boosters and Pediatric Vaccines Have Reduced Impact of COVID-19?

The objective is to evaluate the impact of the earlier availability of COVID-19 vaccinations to children and boosters to adults in the face of the Delta and Omicron variants. We employed an agent-based stochastic network simulation model with a modified SEIR compartment model populated with demographic and census data for North Carolina. We found that earlier availability of childhood vaccines and earlier availability of adult boosters could have reduced the peak hospitalizations of the Delta wave by 10% and the Omicron wave by 42%, and could have reduced cumulative deaths by 9% by July 2022. When studied separately, we found that earlier childhood vaccinations reduce cumulative deaths by 2,611 more than earlier adult boosters. Therefore, the results of our simulation model suggest that the timing of childhood vaccination and booster efforts could have resulted in a reduced disease burden and that prioritizing childhood vaccinations would most effectively reduce disease spread. © 2022 IEEE.

Modeling infectious disease dynamics: Integrating contact tracing-based stochastic compartment and spatio-temporal risk models.

Major infectious diseases such as COVID-19 have a significant impact on population lives and put enormous pressure on healthcare systems globally. Strong interventions, such as lockdowns and social distancing measures, imposed to prevent these diseases from spreading, may also negatively impact society, leading to jobs losses, mental health problems, and increased inequalities, making crucial the prioritization of riskier areas when applying these protocols. The modeling of mobility data derived from contact-tracing data can be used to forecast infectious trajectories and help design strategies for prevention and control. In this work, we propose a new spatial-stochastic model that allows us to characterize the temporally varying spatial risk better than existing methods. We demonstrate the use of the proposed model by simulating an epidemic in the city of Valencia, Spain, and comparing it with a contact tracing-based stochastic compartment reference model. The results show that, by accounting for the spatial risk values in the model, the peak of infected individuals, as well as the overall number of infected cases, are reduced. Therefore, adding a spatial risk component into compartment models may give finer control over the epidemic dynamics, which might help the people in charge to make better decisions.

Derivation of Recommendations for Action for Sustainable Development in the Light of the COVID-19 Pandemic

With the outbreak of a pandemic due to the SARS-CoV-2 virus, the present paper gives an overview of the impacts that this unprecedented situation has had on the environment, the economy and society so far. It will be explained in the paper in what way modeling and simulation helps to understand and to forecast the spread of disease by focusing on compartment models and agent-based models. The presentation of the extent of the consequences in the form of a literature review and the following derivation of recommendations for action highlight the weaknesses of the current economic system. In this respect, the key finding of the research showed that society would be more resilient towards crises like COVID-19 with a regionally and long-term oriented economy that puts social equity and environmental protection first. Therefore, political leaders must rethink the way business is done and should use the crisis as an opportunity to unite the rebuilding of the economy with sustainable development. Models and simulations can assist in finding an appropriate action plan. © 2021 9th International Workshop on Simulation for Energy, Sustainable Development and Environment, SESDE 2021. All rights reserved.

Multiphase Compartment Models of Distribution of New Coronavirus Infections

A fundamentally new multiphase compartmental mathematical model for predicting the spread of several waves of coronavirus infection has been developed. Quality indicators in comparison with existing single-phase models are analyzed. The developed model will allow to model several waves of the process of spreading new coronavirus infections, to predict the process of loading the medical system, as well as the needs for staff, equipment and hospital beds during pandemics. © 2022 IEEE.

Impact of Vaccination and Testing in an Urban Campus Model for the SARS-CoV-2 Pandemic

The crisis induced by the Coronavirus pandemic severely impacted educational institutes. Even with vaccination efforts underway in 2021, it was not clear that sufficient confidence will be achieved for schools to reopen soon. This paper considers the impact of testing rates in addition to vaccination rates in order to reduce infections and hospitalizations and evaluates strategies that allow educational institute in urban settings to remain open. These strategies are also applicable to big campus style businesses and would help planning to keep the businesses open and help the economy. Our analysis is based on a graph model where nodes represent population groups and edges represent population exchanges due to commuting populations. The commuting population is associated with edges and is associated with one of the end nodes of the edge during part of the time period and with the other node during the remainder of the time period. The progression of the disease at each node is determined via compartment models, that include vaccination rates and testing to place infected people in quarantine along with consideration of asymptomatic and symptomatic populations. Applying this to a university population in Chicago with a substantial commuter population, chosen to be 80% of the school's population as an illustration, provides an analysis which specifies benefits of testing and vaccination strategies over a time period of 150 days. © 2022 Owner/Author.

Modeling and Predicting the COVID-19 Trajectory in India

The SARS-CoV-2 has a confirmed case count of about 11.3 million and a death count of about 158,000 in India as of March 13th, 2021. Despite the early social distancing and lockdown measures imposed by the government, these counts have continued to rise. Mathematical models prove extremely useful to predict the course of the pandemic and for the government to strategize accordingly. Over due course several models have emerged to predict the number of COVID-19 cases, but a thorough comparison among them is lacking. In this paper, we propose three novel Hybrid Models based on the compartment-based modeling over data from January 22nd, 2020 to December 3rd 2020 and then make comparisons among them and show through experiments that each performs a better fitting and prediction on the Johns Hopkins COVID-19 dataset pertaining to India than all other benchmark models discussed. Comparison of our proposed Hybrid models with the existing compartment models like SIR, SIRD and SEIRD demonstrates that our proposed Hybrid models not only overcome the performance inefficiencies related to the existing compartmental models but also achieve a better fitting on the Johns Hopkins COVID-19 dataset. © 2022 IEEE.

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.

DATA-DRIVEN REGULARIZATION OF INVERSE PROBLEM FOR SEIR-HCD MODEL OF COVID-19 PROPAGATION IN NOVOSIBIRSK REGION

The inverse problem for SEIR-HCD model of COVID-19 propagation in Novosibirsk region described by system of seven nonlinear ordinary differential equations (ODE) is numerical investigated. The inverse problem consists in identification of coefficients of ODE system (infection rate, portions of infected, hospitalized, mortality cases) and some initial conditions (initial number of asymptomatic and symptomatic infectious) by additional measurements about daily diagnosed, critical and mortality cases of COVID-19. Due to ill-posedness of inverse problem the regularization is applied based on usage of additional information about antibodies IgG to COVID-19 and detailed mortality statistics. The inverse problem is reduced to a minimization problem of misfit function. We apply data-driven approach based on combination of global (OPTUNA software) and gradient-type methods for solving the minimization problem. The numerical results show that adding new information and detailed statistics increased the forecasting scenario in 2 times.

Robust SM Observer for COVID data prediction and Parameters Estimation

The prediction and observation of the growth, and the peaks reproduction of the Coronavirus are of main importance. In this study, we revisit the old deterministic SIR model and show its ability to describe the disease spread;Then we use it to try some observers to avoid acquisition perturbations and measurements imperfections. After finite time converging observations an output re-injection is used for parameters estimation. Note that the data are not provided by sensors which are regular in their measurements, synchronised in time and robust versus noise © 2021 IEEE.

Integrating Human Mobility into the Epidemiological Models of COVID-19: Progress and Challenges

The spread of infectious diseases is usually a highly nonlinear space-time diffusion process. Epidemiological models can not only be used to predict the epidemic trend, but also be used to systematically and scientifically study the transmission mechanism of the complex processes under different hypothetical intervention scenarios, which provide crucial analytical and planning tools for public health studies and policy-making. Since host behavior is one of the critical driven factors for the dynamics of infectious diseases, it is important to effectively integrate human spatiotemporal behavior into the epidemiological models for human-hosted infectious diseases. Due to the rapid development of human mobility research and applications aided by big trajectory data, many of the epidemiological models for Coronavirus Disease 2019 (COVID-19) have already coupled human mobility. By incorporating real trajectory data such as mobile phone location data at an individual or aggregated level, researchers are working towards the direction of accurately depicting the real world, so as to improve the effectiveness of the model in guiding actual epidemic prevention and control. The epidemic trend prediction, Non-pharmaceutical Interventions (NPIs) evaluation, vaccination strategy design, and transmission driven factors have been studied by the epidemiological models coupled with human mobility, which provides scientific decision-making aid for controlling epidemic in different countries and regions. In order to systematically understand this important progress of epidemiological models, this study collected and summarized relevant literatures. First, the interactions between the COVID-19 epidemic and human mobility were analyzed, which demonstrated the necessity of integrating the complex spatiotemporal behavior, such as population-based or individual-based mobility, activity, and contact interaction, into the epidemiological models. Then, according to the modeling purpose and mechanism, the models integrated with human mobility were discussed by two types: short-term epidemic prediction models and process simulation models. Among them, based on the coupling methods of human mobility, short-term epidemic prediction models can further be divided into models coupled with first-order and second-order human mobility, while process simulation models can be divided into models coupled with population-based mobility and individual-based mobility. Finally, we concluded that epidemiological models integrating human mobility should be developed towards more complex human spatiotemporal behaviors with a fine spatial granularity. Besides, it is in urgent need to improve the model capability to better understand the disease spread processes over space and time, break through the bottleneck of the huge computational cost of fine-grained models, cooperate cutting-edge artificial intelligence approaches, and develop more universal and accessible modeling data sets and tools for general users. 2021, Science Press. All right reserved.

Contextual contact tracing based on stochastic compartment modeling and spatial risk assessment.

The current situation of COVID-19 highlights the paramount importance of infectious disease surveillance, which necessitates early monitoring for effective response. Policymakers are interested in data insights identifying high-risk areas as well as individuals to be quarantined, especially as the public gets back to their normal routine. We investigate both requirements by the implementation of disease outbreak modeling and exploring its induced dynamic spatial risk in form of risk assessment, along with its real-time integration back into the disease model. This paper implements a contact tracing-based stochastic compartment model as a baseline, to further modify the existing setup to include the spatial risk. This modification of each individual-level contact's intensity to be dependent on its spatial location has been termed as Contextual Contact Tracing. The results highlight that the inclusion of spatial context tends to send more individuals into quarantine which reduces the overall spread of infection. With a simulated example of an induced spatial high-risk, it is highlighted that the new spatio-SIR model can act as a tool to empower the analyst with a capability to explore disease dynamics from a spatial perspective. We conclude that the proposed spatio-SIR tool can be of great help for policymakers to know the consequences of their decision prior to their implementation.

A compartment modeling approach to reconstruct and analyze gender and age-grouped CoViD-19 Italian data for decision-making strategies.

BACKGROUND: Available national public data are often too incomplete and noisy to be used directly to interpret the evolution of epidemics over time, which is essential for making timely and appropriate decisions. The use of compartment models can be a worthwhile and attractive approach to address this problem. The present study proposes a model compartmentalized by sex and age groups that allows for more complete information on the evolution of the CoViD-19 pandemic in Italy. MATERIAL AND METHODS: Italian public data on CoViD-19 were pre-treated with a 7-day moving average filter to reduce noise. A time-varying susceptible-infected-recovered-deceased (SIRD) model distributed by age and sex groups was then proposed. Recovered and infected individuals distributed by groups were reconstructed through the SIRD model, which was also used to simulate and identify optimal scenarios of pandemic containment by vaccination. The simulation started from realistic initial conditions based on the SIRD model parameters, estimated from filtered and reconstructed Italian data, at different pandemic times and phases. The following three objective functions, accounting for total infections, total deaths, and total quality-adjusted life years (QALYs) lost, were minimized by optimizing the percentages of vaccinated individuals in five different age groups. RESULTS: The developed SIRD model clearly highlighted those pandemic phases in which younger people, who had more contacts and lower mortality, infected older people, characterized by a significantly higher mortality, especially in males. Optimizing vaccination strategies yielded different results depending on the cost function used. As expected, to reduce total deaths, the suggested strategy was to vaccinate the older age groups, whatever the baseline scenario. In contrast, for QALYs lost and total infections, the optimal vaccine solutions strongly depended on the initial pandemic conditions: during phases of high virus diffusion, the model suggested to vaccinate mainly younger groups with a higher contact rate. CONCLUSION: Because of the poor quality and insufficient availability of stratified public pandemic data, ad hoc information filtering and reconstruction procedures proved essential. The time-varying SIRD model, stratified by age and sex groups, provided insights and additional information on the dynamics of CoViD-19 infection in Italy, also supporting decision making for containment strategies such as vaccination.