Impact of intervention on the spread of COVID-19 in India: A model based study.

The outbreak of coronavirus disease 2019 (COVID-19), caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already created emergency situations in almost every country of the world. The disease spreads all over the world within a very short period of time after its first identification in Wuhan, China in December, 2019. In India, the outbreak, starts on 2nd March, 2020 and after that the cases are increasing exponentially. Very high population density, the unavailability of specific medicines or vaccines, insufficient evidences regarding the transmission mechanism of the disease also make it more difficult to fight against the disease properly in India. Mathematical models have been used to predict the disease dynamics and also to assess the efficiency of the intervention strategies in reducing the disease burden. In this work, we propose a mathematical model to describe the disease transmission mechanism between the individuals. Our proposed model is fitted to the daily new reported cases in India during the period 2nd March, 2020 to 12th November, 2020. We estimate the basic reproduction number, effective reproduction number and epidemic doubling time from the incidence data for the above-mentioned period. We further assess the effect of implementing preventive measures in reducing the new cases. Our model projects the daily new COVID-19 cases in India during 13th November, 2020 to 25th February, 2021 for a range of intervention strength. We also investigate that higher intervention effort is required to control the disease outbreak within a shorter period of time in India. Moreover, our analysis reveals that the strength of the intervention should be increased over the time to eradicate the disease effectively.

Optimal test-kit-based intervention strategy of epidemic spreading in heterogeneous complex networks.

We propose a deterministic compartmental model of infectious disease that considers the test kits as an important ingredient for the suppression and mitigation of epidemics. A rigorous simulation (with an analytical argument) is provided to reveal the effective reduction of the final outbreak size and the peak of infection as a function of basic reproduction number in a single patch. Furthermore, to study the impact of long and short-distance human migration among the patches, we consider heterogeneous networks where the linear diffusive connectivity is determined by the network link structure. We numerically confirm that implementation of test kits in a fraction of nodes (patches) having larger degrees or betweenness centralities can reduce the peak of infection (as well as the final outbreak size) significantly. A next-generation matrix-based analytical treatment is provided to find out the critical transmission probability in the entire network for the onset of epidemics. Finally, the optimal intervention strategy is validated in two real networks: the global airport network and the transportation network of Kolkata, India.

Disease control through removal of population using Z-control approach.

Present study considers the situation where the removal of population is adopted as a prevention measure for isolating the susceptible population from an infected region to reduce the disease prevalence. To investigate the scenario, a dynamic error based method, Z-type control is applied in an SI type disease model with the aim of achieving a predetermined disease prevalence. The controlled system is designed by introducing a new compartment (the population in an infection-free region) in the uncontrolled system to capture the removal of susceptible population from the infected region to an infection free region. By performing numerical simulations, our study shows that using Z-control mechanism, the removal of susceptible to an infection free region can effectively achieve a predetermined disease prevalence. The removal rates required for achieving a specific reduction in infected population for different levels of disease endemicity are quantified. Furthermore, the global sensitivity analysis (PRCC) is also performed to have a more clear insights on the correlations of the control parameter with the model parameters.

Impact of adult mosquito control on dengue prevalence in a multi-patch setting: A case study in Kolkata (2014-2015).

Dengue is one of the deadliest mosquito-borne disease prevalent mainly in tropical and sub-tropical regions. Controlling the spread of this disease becomes a major concern to the public health authority. World Health Organization (WHO) adopted several mosquito control strategies to reduce the disease prevalence. In this work, a general multi-patch non-autonomous dengue model is formulated to capture the temporal and spatial transmission mechanism of the disease and the effectiveness of different adult mosquito control strategies in reducing dengue prevalence is evaluated. During the period (2014-2015) the dengue situation of Kolkata which is one of the most dengue affected city in India is considered in our study. Depending on geographical location, Kolkata is divided into five regions and our model is fitted to the monthly dengue cases of these five regions during the above-mentioned period. By considering control specific characteristics (e.g. efficacy, environment persistence) of the mosquito control strategies, we study the efficiency of three adult mosquito controls and their combined effect in reducing dengue prevalence. From our study, it is observed that control with higher environment persistence performs better in comparison to the controls having low environment persistence. It is also observed that, connectedness between the regions play a key role in the effectiveness of the control strategies.

Quantifying optimal resource allocation strategies for controlling epidemics.

Frequent emergence of communicable diseases is a major concern worldwide. Lack of sufficient resources to mitigate the disease burden makes the situation even more challenging for lower-income countries. Hence, strategy development for disease eradication and optimal management of the social and economic burden has garnered a lot of attention in recent years. In this context, we quantify the optimal fraction of resources that can be allocated to two major intervention measures, namely reduction of disease transmission and improvement of healthcare infrastructure. Our results demonstrate that the effectiveness of each of the interventions has a significant impact on the optimal resource allocation in both long-term disease dynamics and outbreak scenarios. The optimal allocation strategy for long-term dynamics exhibits non-monotonic behaviour with respect to the effectiveness of interventions, which differs from the more intuitive strategy recommended in the case of outbreaks. Further, our results indicate that the relationship between investment in interventions and the corresponding increase in patient recovery rate or decrease in disease transmission rate plays a decisive role in determining optimal strategies. Intervention programmes with decreasing returns promote the necessity for resource sharing. Our study provides fundamental insights into determining the best response strategy when controlling epidemics in resource-constrained situations.

Personnel Scheduling during the COVID-19 Pandemic: A Probabilistic Graph-Based Approach.

Effective personnel scheduling is crucial for organizations to match workload demands. However, staff scheduling is sometimes affected by unexpected events, such as the COVID-19 pandemic, that disrupt regular operations. Limiting the number of on-site staff in the workplace together with regular testing is an effective strategy to minimize the spread of infectious diseases like COVID-19 because they spread mostly through close contact with people. Therefore, choosing the best scheduling and testing plan that satisfies the goals of the organization and prevents the virus's spread is essential during disease outbreaks. In this paper, we formulate these challenges in the framework of two Mixed Integer Non-linear Programming (MINLP) models. The first model aims to derive optimal staff occupancy and testing strategies to minimize the risk of infection among employees, while the second is aimed only at optimal staff occupancy under a random testing strategy. To solve the problems expressed in the models, we propose a canonical genetic algorithm as well as two commercial solvers. Using both real and synthetic contact networks of employees, our results show that following the recommended occupancy and testing strategy reduces the risk of infection 25-60% under different scenarios. The minimum risk of infection can be achieved when the employees follow a planned testing strategy. Further, vaccination status and interaction rate of employees are important factors in developing scheduling strategies that minimize the risk of infection.

On the optimal presence strategies for workplace during pandemics: A COVID-19 inspired probabilistic model.

During pandemics like COVID-19, both the quality and quantity of services offered by businesses and organizations have been severely impacted. They often have applied a hybrid home office setup to overcome this problem, although in some situations, working from home lowers employee productivity. So, increasing the rate of presence in the office is frequently desired from the manager's standpoint. On the other hand, as the virus spreads through interpersonal contact, the risk of infection increases when workplace occupancy rises. Motivated by this trade-off, in this paper, we model this problem as a bi-objective optimization problem and propose a practical approach to find the trade-off solutions. We present a new probabilistic framework to compute the expected number of infected employees for a setting of the influential parameters, such as the incidence level in the neighborhood of the company, transmission rate of the virus, number of employees, rate of vaccination, testing frequency, and rate of contacts among the employees. The results show a wide range of trade-offs between the expected number of infections and productivity, for example, from 1 to 6 weekly infections in 100 employees and a productivity level of 65% to 85%. This depends on the configuration of influential parameters and the occupancy level. We implement the model and the algorithm and perform several experiments with different settings of the parameters. Moreover, we developed an online application based on the result in this paper which can be used as a recommender for the optimal rate of occupancy in companies/workplaces.

A Web-Based COVID-19 Tool for Testing Residents in Retirement Homes: Development Study.

BACKGROUND: Long-term care facilities have been widely affected by the COVID-19 pandemic. Empirical evidence demonstrated that older people are the most impacted and are at higher risk of mortality after being infected. Regularly testing care facility residents is a practical approach to detecting infections proactively. In many cases, the care staff must perform the tests on the residents while also providing essential care, which in turn causes imbalances in their working time. Once an outbreak occurs, suppressing the spread of the virus in retirement homes (RHs) is challenging because the residents are in contact with each other, and isolation measures cannot be widely enforced. Regular testing strategies, on the other hand, have been shown to effectively prevent outbreaks in RHs. However, high-frequency testing may consume substantial staff working time, which results in a trade-off between the time invested in testing and the time spent providing essential care to residents. OBJECTIVE: We developed a web application (Retirement Home Testing Optimizer) to assist RH managers in identifying effective testing schedules for residents. The outcome of the app, called the "testing strategy," is based on dividing facility residents into groups and then testing no more than 1 group per day. METHODS: We created the web application by incorporating influential factors such as the number of residents and staff, the average rate of contacts, the amount of time spent to test, and constraints on the test interval and size of groups. We developed mixed integer nonlinear programming models for balancing staff workload in long-term care facilities while minimizing the expected detection time of a probable infection inside the facility. Additionally, by leveraging symmetries in the problem, we proposed a fast and efficient local search method to find the optimal solution. RESULTS: Considering the number of residents and staff and other practical constraints of the facilities, the proposed application computes the optimal trade-off testing strategy and suggests the corresponding grouping and testing schedule for residents. The current version of the application is deployed on the server of the Where2Test project and is accessible on their website. The application is open source, and all contents are offered in English and German. We provide comprehensive instructions and guidelines for easy use and understanding of the application's functionalities. The application was launched in July 2022, and it is currently being tested in RHs in Saxony, Germany. CONCLUSIONS: Recommended testing strategies by our application are tailored to each RH and the goals set by the managers. We advise the users of the application that the proposed model and approach focus on the expected scenarios, that is, the expected risk of infection, and they do not guarantee the avoidance of worst-case scenarios.

Role of assortativity in predicting burst synchronization using echo state network.

In this study, we use a reservoir computing based echo state network (ESN) to predict the collective burst synchronization of neurons. Specifically, we investigate the ability of ESN in predicting the burst synchronization of an ensemble of Rulkov neurons placed on a scale-free network. We have shown that a limited number of nodal dynamics used as input in the machine can capture the real trend of burst synchronization in this network. Further, we investigate the proper selection of nodal inputs of degree-degree (positive and negative) correlated networks. We show that for a disassortative network, selection of different input nodes based on degree has no significant role in the machine's prediction. However, in the case of assortative network, training the machine with the information (i.e., time series) of low degree nodes gives better results in predicting the burst synchronization. The results are found to be consistent with the investigation carried out with a continuous time Hindmarsh-Rose neuron model. Furthermore, the role of hyperparameters like spectral radius and leaking parameter of ESN on the prediction process has been examined. Finally, we explain the underlying mechanism responsible for observing these differences in the prediction in a degree correlated network.

Reservoir computing on epidemic spreading: A case study on COVID-19 cases.

A reservoir computing based echo state network (ESN) is used here for the purpose of predicting the spread of a disease. The current infection trends of a disease in some targeted locations are efficiently captured by the ESN when it is fed with the infection data for other locations. The performance of the ESN is first tested with synthetic data generated by numerical simulations of independent uncoupled patches, each governed by the classical susceptible-infected-recovery model for a choice of distributed infection parameters. From a large pool of synthetic data, the ESN predicts the current trend of infection in 5% patches by exploiting the uncorrelated infection trend of 95% patches. The prediction remains consistent for most of the patches for approximately 4 to 5 weeks. The machine's performance is further tested with real data on the current COVID-19 pandemic collected for different countries. We show that our proposed scheme is able to predict the trend of the disease for up to 3 weeks for some targeted locations. An important point is that no detailed information on the epidemiological rate parameters is needed; the success of the machine rather depends on the history of the disease progress represented by the time-evolving data sets of a large number of locations. Finally, we apply a modified version of our proposed scheme for the purpose of future forecasting.