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Lin J, Aprahamian H, Golovko G. A proactive/reactive mass screening approach with uncertain symptomatic cases. PLoS Comput Biol 2024; 20:e1012308. [PMID: 39141678 PMCID: PMC11346970 DOI: 10.1371/journal.pcbi.1012308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 08/26/2024] [Accepted: 07/09/2024] [Indexed: 08/16/2024] Open
Abstract
We study the problem of mass screening of heterogeneous populations under limited testing budget. Mass screening is an essential tool that arises in various settings, e.g., the COVID-19 pandemic. The objective of mass screening is to classify the entire population as positive or negative for a disease as efficiently and accurately as possible. Under limited budget, testing facilities need to allocate a portion of the budget to target sub-populations (i.e., proactive screening) while reserving the remaining budget to screen for symptomatic cases (i.e., reactive screening). This paper addresses this decision problem by taking advantage of accessible population-level risk information to identify the optimal set of sub-populations for proactive/reactive screening. The framework also incorporates two widely used testing schemes: Individual and Dorfman group testing. By leveraging the special structure of the resulting bilinear optimization problem, we identify key structural properties, which in turn enable us to develop efficient solution schemes. Furthermore, we extend the model to accommodate customized testing schemes across different sub-populations and introduce a highly efficient heuristic solution algorithm for the generalized model. We conduct a comprehensive case study on COVID-19 in the US, utilizing geographically-based data. Numerical results demonstrate a significant improvement of up to 52% in total misclassifications compared to conventional screening strategies. In addition, our case study offers valuable managerial insights regarding the allocation of proactive/reactive measures and budget across diverse geographic regions.
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Affiliation(s)
- Jiayi Lin
- Department of Industrial and Systems Engineering, Texas A&M University College Station, Texas, United States of America
| | - Hrayer Aprahamian
- Department of Industrial and Systems Engineering, Texas A&M University College Station, Texas, United States of America
| | - George Golovko
- Department of Pharmacology and Toxicology, The University of Texas Medical Branch Galveston, Texas, United States of America
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2
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Aprahamian H, Verter V, Zargoush M. Editorial: management science for pandemic prevention, preparedness, and response. Health Care Manag Sci 2024:10.1007/s10729-024-09674-7. [PMID: 38896296 DOI: 10.1007/s10729-024-09674-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 04/25/2024] [Indexed: 06/21/2024]
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3
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Lin J, Aprahamian H, Golovko G. An optimization framework for large-scale screening under limited testing capacity with application to COVID-19. Health Care Manag Sci 2024; 27:223-238. [PMID: 38656689 DOI: 10.1007/s10729-024-09671-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/27/2024] [Indexed: 04/26/2024]
Abstract
We consider the problem of targeted mass screening of heterogeneous populations under limited testing capacity. Mass screening is an essential tool that arises in various settings, e.g., ensuring a safe supply of blood, reducing prevalence of sexually transmitted diseases, and mitigating the spread of infectious disease outbreaks. The goal of mass screening is to classify whole population groups as positive or negative for an infectious disease as efficiently and accurately as possible. Under limited testing capacity, it is not possible to screen the entire population and hence administrators must reserve testing and target those among the population that are most in need or most susceptible. This paper addresses this decision problem by taking advantage of accessible population-level risk information to identify the optimal set of sub-populations to target for screening. We conduct a comprehensive analysis that considers the two most commonly adopted schemes: Individual testing and Dorfman group testing. For both schemes, we formulate an optimization model that aims to minimize the number of misclassifications under a testing capacity constraint. By analyzing the formulations, we establish key structural properties which we use to construct efficient and accurate solution techniques. We conduct a case study on COVID-19 in the United States using geographic-based data. Our results reveal that the considered proactive targeted schemes outperform commonly adopted practices by substantially reducing misclassifications. Our case study provides important managerial insights with regards to optimal allocation of tests, testing designs, and protocols that dictate the optimality of schemes. Such insights can inform policy-makers with tailored and implementable data-driven recommendations.
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Affiliation(s)
- Jiayi Lin
- Department of Industrial and Systems Engineering, Texas A &M University, College Station, 77843, TX, USA.
| | - Hrayer Aprahamian
- Department of Industrial and Systems Engineering, Texas A &M University, College Station, 77843, TX, USA
| | - George Golovko
- Department of Pharmacology and Toxicology, The University of Texas Medical Branch, Galveston, 77555, TX, USA
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4
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Bajiya VP, Tripathi JP, Kakkar V, Kang Y. Modeling the impacts of awareness and limited medical resources on the epidemic size of a multi-group SIR epidemic model. INT J BIOMATH 2022. [DOI: 10.1142/s1793524522500450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The pharmaceutical interventions of emerging infectious diseases are constrained by the available medical resources such as drugs, vaccines, hospital beds, isolation places and the efficiency of the treatment. The awareness of the population also plays an important role in reducing contacts and consequently, reducing the disease transmission rate. In this paper, we propose a multi-group Susceptible, Infected and Recovered (SIR) epidemic model incorporating the awareness of population and the saturated treatment function that describes the effects of the availability of medical resources for treatment. We assume that the treatment of the infected individuals of a group is affected by the medical resources for the treatment of each group. We calculate the basic reproduction number [Formula: see text] in the term of the awareness parameter using the next generation approach. We determine the local and global stabilities of equilibrium (disease free equilibrium and endemic equilibrium) in terms of [Formula: see text] and the availability of medical resources for treatment. We obtain that backward bifurcation occurs at [Formula: see text] along with the existence of multiple endemic equilibria when [Formula: see text] Further, we consider the special case with a single group epidemic system and ensure the existence of multiple endemic equilibria. We showed a necessary condition on the parameter related to the availability of medical resources when backward bifurcation occurs. This situation indicates that reducing the basic reproduction number below unity is not sufficient to remove the disease when the medical resources for treatment are scarce. We used numerical simulations to support and counterpart our theoretical results and discussed the impacts of the awareness of susceptible population and availability of medical resources for treatment in each group, on the epidemic size of each group. Our findings suggest that in the case of limited medical resources, the high treatment rate and awareness of the population are very helpful to control the disease (to reduce the prevalence of infection) and the eradication of disease also depends on initial population sizes. More importantly, it is also obtained that sufficient medical resources for every group are required to eradicate the disease from an entire population.
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Affiliation(s)
- Vijay Pal Bajiya
- Department of Mathematics, Central University of Rajasthan, Kishangarh 305817, Ajmer, Rajasthan, India
| | - Jai Prakash Tripathi
- Department of Mathematics, Central University of Rajasthan, Kishangarh 305817, Ajmer, Rajasthan, India
| | - Vipul Kakkar
- Department of Mathematics, Central University of Rajasthan, Kishangarh 305817, Ajmer, Rajasthan, India
| | - Yun Kang
- College of Integrative Sciences and Arts, Arizona State University, Mesa, AZ 85212, USA
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5
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Upadhyay RK, Chatterjee S, Roy P, Bhardwaj D. Combating COVID-19 crisis and predicting the second wave in Europe: an Age-structured modeling. JOURNAL OF APPLIED MATHEMATICS & COMPUTING 2022; 68:4669-4689. [PMID: 35340716 PMCID: PMC8935103 DOI: 10.1007/s12190-022-01723-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/14/2022] [Accepted: 02/21/2022] [Indexed: 06/01/2023]
Abstract
We employ an age-structured susceptible-infected-quarantined-recovered model to simulate the progression of COVID-19 in France, Spain, and Germany. In the absence of a vaccine or conventional treatment, non-pharmaceutical interventions become more valuable, so our model takes into account the efficacy of official social distancing and lockdown measures. Using data from February to July 2020, we make useful predictions for the upcoming months, and further simulate the effect of lifting the lockdown at a later stage. A control model is also proposed and conditions for optimality are also obtained using optimal control theory. Motivated by the recent surge in cases in France and Spain, we also examine the possibility of a second wave of the pandemic. We conclude that further measures need to be taken in these two countries, while Germany is on its way to mitigating the disease.
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Affiliation(s)
- Ranjit Kumar Upadhyay
- Department of Mathematics and Computing, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad, Jharkhand 826004 India
| | - Sourin Chatterjee
- Indian Institute of Science Education and Research Kolkata, Kolkata, West Bengal 741246 India
| | - Parimita Roy
- School of Mathematics, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004 India
| | - Dyuti Bhardwaj
- Department of Electrical Engineering, IIT Delhi, Hauz Khas, New Delhi 110016 India
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Guan J, Zhao Y, Wei Y, Shen S, You D, Zhang R, Lange T, Chen F. Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges. MEDICAL REVIEW (BERLIN, GERMANY) 2022; 2:89-109. [PMID: 35658113 PMCID: PMC9047651 DOI: 10.1515/mr-2021-0022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 01/03/2022] [Indexed: 12/20/2022]
Abstract
Since late 2019, the beginning of coronavirus disease 2019 (COVID-19) pandemic, transmission dynamics models have achieved great development and were widely used in predicting and policy making. Here, we provided an introduction to the history of disease transmission, summarized transmission dynamics models into three main types: compartment extension, parameter extension and population-stratified extension models, highlight the key contribution of transmission dynamics models in COVID-19 pandemic: estimating epidemiological parameters, predicting the future trend, evaluating the effectiveness of control measures and exploring different possibilities/scenarios. Finally, we pointed out the limitations and challenges lie ahead of transmission dynamics models.
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Affiliation(s)
- Jinxing Guan
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yang Zhao
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,China International Cooperation Center for Environment and Human Health, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China.,Center of Biomedical BigData, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yongyue Wei
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,China International Cooperation Center for Environment and Human Health, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Sipeng Shen
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Dongfang You
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ruyang Zhang
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Theis Lange
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Feng Chen
- Departments of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.,China International Cooperation Center for Environment and Human Health, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China
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Yang B, Yu Z, Cai Y. A spread model of COVID-19 with some strict anti-epidemic measures. NONLINEAR DYNAMICS 2022; 109:265-284. [PMID: 35283556 PMCID: PMC8900482 DOI: 10.1007/s11071-022-07244-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 01/17/2022] [Indexed: 05/09/2023]
Abstract
In the absence of specific drugs and vaccines, the best way to control the spread of COVID-19 is to adopt and diligently implement effective and strict anti-epidemic measures. In this paper, a mathematical spread model is proposed based on strict epidemic prevention measures and the known spreading characteristics of COVID-19. The equilibria (disease-free equilibrium and endemic equilibrium) and the basic regenerative number of the model are analyzed. In particular, we prove the asymptotic stability of the equilibria, including locally and globally asymptotic stability. In order to validate the effectiveness of this model, it is used to simulate the spread of COVID-19 in Hubei Province of China for a period of time. The model parameters are estimated by the real data related to COVID-19 in Hubei. To further verify the model effectiveness, it is employed to simulate the spread of COVID-19 in Hunan Province of China. The mean relative error serves to measure the effect of fitting and simulations. Simulation results show that the model can accurately describe the spread dynamics of COVID-19. Sensitivity analysis of the parameters is also done to provide the basis for formulating prevention and control measures. According to the sensitivity analysis and corresponding simulations, it is found that the most effective non-pharmaceutical intervention measures for controlling COVID-19 are to reduce the contact rate of the population and increase the quarantine rate of infected individuals.
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Affiliation(s)
- Bo Yang
- Department of Automation, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049 People’s Republic of China
| | - Zhenhua Yu
- Institute of Systems Security and Control, College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, 710054 People’s Republic of China
| | - Yuanli Cai
- Department of Automation, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049 People’s Republic of China
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8
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Guzmán-Merino M, Durán C, Marinescu MC, Delgado-Sanz C, Gomez-Barroso D, Carretero J, Singh DE. Assessing population-sampling strategies for reducing the COVID-19 incidence. Comput Biol Med 2021; 139:104938. [PMID: 34678482 PMCID: PMC8507586 DOI: 10.1016/j.compbiomed.2021.104938] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 10/08/2021] [Accepted: 10/09/2021] [Indexed: 01/04/2023]
Abstract
As long as critical levels of vaccination have not been reached to ensure heard immunity, and new SARS-CoV-2 strains are developing, the only realistic way to reduce the infection speed in a population is to track the infected individuals before they pass on the virus. Testing the population via sampling has shown good results in slowing the epidemic spread. Sampling can be implemented at different times during the epidemic and may be done either per individual or for combined groups of people at a time. The work we present here makes two main contributions. We first extend and refine our scalable agent-based COVID-19 simulator to incorporate an improved socio-demographic model which considers professions, as well as a more realistic population mixing model based on contact matrices per country. These extensions are necessary to develop and test various sampling strategies in a scenario including the 62 largest cities in Spain; this is our second contribution. As part of the evaluation, we also analyze the impact of different parameters, such as testing frequency, quarantine time, percentage of quarantine breakers, or group testing, on sampling efficacy. Our results show that the most effective strategies are pooling, rapid antigen test campaigns, and requiring negative testing for access to public areas. The effectiveness of all these strategies can be greatly increased by reducing the number of contacts for infected individual.
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Affiliation(s)
| | | | | | - Concepción Delgado-Sanz
- CIBER en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; National Centre for Epidemiology, Carlos III Institute of Health, Madrid, Spain
| | - Diana Gomez-Barroso
- CIBER en Epidemiología y Salud Pública (CIBERESP), Madrid, Spain; National Centre for Epidemiology, Carlos III Institute of Health, Madrid, Spain
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9
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Cheng X, Wang Y, Huang G. Global dynamics of a network-based SIQS epidemic model with nonmonotone incidence rate. CHAOS, SOLITONS, AND FRACTALS 2021; 153:111502. [PMID: 34744326 PMCID: PMC8556768 DOI: 10.1016/j.chaos.2021.111502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/05/2021] [Accepted: 09/24/2021] [Indexed: 05/09/2023]
Abstract
The risk of propagation of infectious diseases such as avian influenza and COVID-19 is generally controlled or reduced by quarantine measures. Considering this situation, a network-based SIQS (susceptible-infected-quarantined-susceptible) infectious disease model with nonmonotone incidence rate is established and analyzed in this paper. The psychological impact of the transmission of certain diseases in heterogeneous networks at high levels of infection may be characterized by the related nonmonotone incidence rate. The expressions of the basic reproduction number and equilibria of the model are determined analytically. We demonstrate in detail the uniform persistence of system and the global asymptotic stability of the disease-free equilibrium. The global attractivity of the unique endemic equilibrium is discussed by using monotone iteration technique. We obtain that the endemic equilibrium is globally asymptotically stable under certain conditions by constructing appropriate Lyapunov function. In addition, numerical simulations are performed to indicate the theoretical results.
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Affiliation(s)
- Xinxin Cheng
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
| | - Yi Wang
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
| | - Gang Huang
- School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
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Kapitsinis N. The underlying factors of excess mortality in 2020: a cross-country analysis of pre-pandemic healthcare conditions and strategies to cope with Covid-19. BMC Health Serv Res 2021; 21:1197. [PMID: 34736434 PMCID: PMC8568489 DOI: 10.1186/s12913-021-07169-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/11/2021] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Government responses to the pandemic varied in terms of timing, duration, and stringency, seeking to protect healthcare systems, whose pre-pandemic state varied significantly. Therefore, the severity of Covid-19 and, thus, excess mortality have been unequal across counties. This paper explores the geography of excess mortality and its underlying factors in 2020, highlighting the effects of health policies pre-pandemic and strategies devised by governments to cope with Covid-19. METHODS Excess mortality is estimated for 79 high, medium and low-income countries. The factors of excess mortality are examined employing median quantile regression analysis. RESULTS Health privatization, healthcare underfunding, and late implementation of containment and mitigation strategies were powerful drivers of excess mortality. By contrast, the results suggest a negative association of excess mortality with health expenditure, number of doctors and hospital beds, share of population covered by health insurance and test and trace capacity. CONCLUSIONS The evidence highlights the importance of sufficiently funded healthcare systems with universal access and strong primary healthcare in the battle against the pandemic. An early response to Covid-19, including borders' controls and a strong test and trace capacity, could improve epidemiological surveillance and minimize excess mortality, with stringent and lengthy lockdowns not providing a significant benefit.
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Affiliation(s)
- Nikolaos Kapitsinis
- Cardiff University, Business School, Aberconway Building, Colum Dr, Cardiff, CF10 3EU, UK.
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Rai RK, Khajanchi S, Tiwari PK, Venturino E, Misra AK. Impact of social media advertisements on the transmission dynamics of COVID-19 pandemic in India. JOURNAL OF APPLIED MATHEMATICS & COMPUTING 2021; 68:19-44. [PMID: 33679275 PMCID: PMC7910777 DOI: 10.1007/s12190-021-01507-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 01/22/2021] [Accepted: 02/01/2021] [Indexed: 05/04/2023]
Abstract
In this paper, we propose a mathematical model to assess the impact of social media advertisements in combating the coronavirus pandemic in India. We assume that dissemination of awareness among susceptible individuals modifies public attitudes and behaviours towards this contagious disease which results in reducing the chance of contact with the coronavirus and hence decreasing the disease transmission. Moreover, the individual's behavioral response in the presence of global information campaigns accelerate the rate of hospitalization of symptomatic individuals and also encourage the asymptomatic individuals for conducting health protocols, such as self-isolation, social distancing, etc. We calibrate the proposed model with the cumulative confirmed COVID-19 cases for the Republic of India. We estimate eight epidemiologically important parameters, and also the size of basic reproduction number for India. We find that the basic reproduction number for India is greater than unity, which represents the substantial outbreak of COVID-19 in the country. Sophisticated techniques of sensitivity analysis are employed to determine the impacts of model parameters on basic reproduction number and symptomatic infected population. Our results reveal that to reduce disease burden in India, non-pharmaceutical interventions strategies should be implemented effectively to decrease basic reproduction number below unity. Continuous propagation of awareness through the internet and social media platforms should be regularly circulated by the health authorities/government officials for hospitalization of symptomatic individuals and quarantine of asymptomatic individuals to control the prevalence of disease in India.
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Affiliation(s)
- Rajanish Kumar Rai
- Department of Mathematics, Institute of Engineering and Rural Technology, Prayagraj, 211002 India
| | - Subhas Khajanchi
- Department of Mathematics, Presidency University, Kolkata, 700073 India
| | - Pankaj Kumar Tiwari
- Department of Basic Science and Humanities, Indian Institute of Information Technology, Bhagalpur, 813210 India
| | - Ezio Venturino
- Dipartimento di Matematica “Giuseppe Peano”, Università di Torino, Via Carlo Alberto 10, 10123 Torino, Italy
| | - Arvind Kumar Misra
- Department of Mathematics, Institute of Science, Banaras Hindu University, Varanasi, 221005 India
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Yang J, Tang S, Cheke RA. Impacts of varying strengths of intervention measures on secondary outbreaks of COVID-19 in two different regions. NONLINEAR DYNAMICS 2021; 104:863-882. [PMID: 33642697 PMCID: PMC7897556 DOI: 10.1007/s11071-021-06294-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 02/05/2021] [Indexed: 05/02/2023]
Abstract
By March 2020, China and Singapore had achieved remarkable results in the prevention and control of COVID-19, but in April Singapore's outbreak began to deteriorate, while China's remained controlled. Using detailed data from Tianjin, China, and Singapore, a stochastic discrete COVID-19 epidemic model was constructed to depict the impact of the epidemic. Parameter estimation and sensitivity analysis were developed to study the probability of imported cases inducing an outbreak in relation to different prevention and control efforts. Results show that the resumption of work and the re-opening of schools will not lead to an outbreak if the effective reproduction number is lower than 1 and approaches 0 and tracking quarantine measures are strengthened. Once an outbreak occurs, if close contacts can be tracked and quarantined in time, the outbreak will be contained. If work is resumed and schools are re-opened with the effective reproduction number greater than 1, then it is more likely that a secondary outbreak will be generated. Also, the greater the number of undetected foreign imported cases and the weaker the prevention and control measures, the more serious the epidemic. Therefore, the key to prevention of a second outbreak is to return to work and to re-open schools only after the effective reproduction number is less than 1 for a period, and when tracking quarantine measures have been strengthened. Our model provides a qualitative and quantitative basis for decision-making for the prevention and control of COVID-19 epidemics and the prediction, early warning and risk assessment of secondary outbreaks.
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Affiliation(s)
- Jie Yang
- School of Mathematics and Information Science, Shaanxi Normal University, Xi’an, 710062 People’s Republic of China
| | - Sanyi Tang
- School of Mathematics and Information Science, Shaanxi Normal University, Xi’an, 710062 People’s Republic of China
| | - Robert A. Cheke
- Natural Resources Institute, University of Greenwich at Medway, Central Avenue, Chatham Maritime, Chatham, Kent ME4 4TB UK
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Das P, Upadhyay RK, Misra AK, Rihan FA, Das P, Ghosh D. Mathematical model of COVID-19 with comorbidity and controlling using non-pharmaceutical interventions and vaccination. NONLINEAR DYNAMICS 2021; 106:1213-1227. [PMID: 34031622 PMCID: PMC8133070 DOI: 10.1007/s11071-021-06517-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 05/04/2021] [Indexed: 05/06/2023]
Abstract
Pandemic is an unprecedented public health situation, especially for human beings with comorbidity. Vaccination and non-pharmaceutical interventions only remain extensive measures carrying a significant socioeconomic impact to defeating pandemic. Here, we formulate a mathematical model with comorbidity to study the transmission dynamics as well as an optimal control-based framework to diminish COVID-19. This encompasses modeling the dynamics of invaded population, parameter estimation of the model, study of qualitative dynamics, and optimal control problem for non-pharmaceutical interventions (NPIs) and vaccination events such that the cost of the combined measure is minimized. The investigation reveals that disease persists with the increase in exposed individuals having comorbidity in society. The extensive computational efforts show that mean fluctuations in the force of infection increase with corresponding entropy. This is a piece of evidence that the outbreak has reached a significant portion of the population. However, optimal control strategies with combined measures provide an assurance of effectively protecting our population from COVID-19 by minimizing social and economic costs.
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Affiliation(s)
- Parthasakha Das
- Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah India
| | - Ranjit Kumar Upadhyay
- Department of Mathematics and Computing, Indian Institute of Technology (Indian School of Mines), Dhanbad, India
| | - Arvind Kumar Misra
- Department of Mathematics, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Fathalla A. Rihan
- Department of Mathematical Sciences, United Arab Emirates University Al Ain, Abu Dhabi, UAE
| | - Pritha Das
- Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah India
| | - Dibakar Ghosh
- Physics and Applied Mathematics Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata, 700108 India
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