An epidemic model SIPHERD and its application for prediction of the spread of COVID-19 infection in India

Mahajan, Ashutosh; Sivadas, Namitha; Solanki, Ravi

doi:10.1016/j.chaos.2020.110156

Cited by 68 publications

(38 citation statements)

References 17 publications

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“…Despite the scarce information on the virus and the availability of insufficient data, the mathematical modelling community did not fail to produce efforts to assist the health authorities [10] , [11] . Many significant papers have been published, ranging from statistical approaches [12] , [13] , [14] , [15] , to deterministic compartmental models [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] to stochastic procedures [26] , [27] . Some aim to estimate the transmission risk [28] , others to estimate the number of unreported cases [29] , or to consider both the age and social contact structure [30] .…”

Section: Introductionmentioning

confidence: 99%

Pandemic fatigue impact on COVID-19 spread: A mathematical modelling answer to the Italian scenario

Meacci

Primicerio

2021

Results in Physics

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The COVID-19 outbreak has generated, in addition to the dramatic sanitary consequences, severe psychological repercussions for the populations affected by the pandemic. Simultaneously, these consequences can have related effects on the spread of the virus. Pandemic fatigue occurs when stress rises beyond a threshold, leading a person to feel demotivated to follow recommended behaviours to protect themselves and others. In the present paper, we introduce a new susceptible-infected-quarantined-recovered-dead (SIQRD) model in terms of a system of ordinary differential equations (ODE). The model considers the countermeasures taken by sanitary authorities and the effect of pandemic fatigue. The latter can be mitigated by fear of the disease’s consequences modelled with the death rate in mind. The mathematical well-posedness of the model is proved. We show the numerical results to be consistent with the transmission dynamics data characterising the epidemic of the COVID-19 outbreak in Italy in 2020. We provide a measure of the possible pandemic fatigue impact. The model can be used to evaluate the public health interventions and prevent with specific actions the possible damages resulting from the social phenomenon of relaxation concerning the observance of the preventive rules imposed.

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Section: Introductionmentioning

confidence: 99%

Pandemic fatigue impact on COVID-19 spread: A mathematical modelling answer to the Italian scenario

Meacci

Primicerio

2021

Results in Physics

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“…Several models have recently been proposed and implemented in order to understand the dynamics of COVID-19. Most of these models are based on the traditional epidemiological model, called the susceptible-exposed-infectious-recovered (SEIR) model, which was introduced to study the pandemic transmission within a human community [4][5][6][7][8][9][10][11][12][13][14]. To date, very few attempts have been devoted to describe the interaction between the immune system and SARS-CoV-2 [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Immune Responses against SARS-CoV-2 Using an Ensemble Kalman Filter

et al. 2021

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In this paper, a mathematical model was developed to simulate SARS-CoV-2 dynamics in infected patients. The model considers both the innate and adaptive immune responses and consists of healthy cells, infected cells, viral load, cytokines, natural killer cells, cytotoxic T-lymphocytes, B-lymphocytes, plasma cells, and antibody levels. First, a mathematical analysis was performed to discuss the model’s equilibrium points and compute the basic reproduction number. The accuracy of such mathematical models may be affected by many sources of uncertainties due to the incomplete representation of the biological process and poorly known parameters. This may strongly limit their performance and prediction skills. A state-of-the-art data assimilation technique, the ensemble Kalman filter (EnKF), was then used to enhance the model’s behavior by incorporating available data to determine the best possible estimate of the model’s state and parameters. The proposed assimilation system was applied on the real viral load datasets of six COVID-19 patients. The results demonstrate the efficiency of the proposed assimilation system in improving the model predictions by up to 40%.

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“…Lopez et al [4] used the modified SEIR model to predict the COVID-19 outbreak in Spain and Italy. Ashutosh et al [5] estimated pure asymptomatic infection cases based on a SEIR model. The results showed that infection may last for a long time without a vaccine.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis

et al. 2021

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The outbreak of coronavirus disease 2019 (COVID-19) has caused a global disaster, seriously endangering human health and the stability of social order. The purpose of this study is to construct a nonlinear combinational dynamic transmission rate model with automatic selection based on forecasting effective measure (FEM) and support vector regression (SVR) to overcome the shortcomings of the difficulty in accurately estimating the basic infection number R0 and the low accuracy of single model predictions. We apply the model to analyze and predict the COVID-19 outbreak in different countries. First, the discrete values of the dynamic transmission rate are calculated. Second, the prediction abilities of all single models are comprehensively considered, and the best sliding window period is derived. Then, based on FEM, the optimal sub-model is selected, and the prediction results are nonlinearly combined. Finally, a nonlinear combinational dynamic transmission rate model is developed to analyze and predict the COVID-19 epidemic in the United States, Canada, Germany, Italy, France, Spain, South Korea, and Iran in the global pandemic. The experimental results show an the out-of-sample forecasting average error rate lower than 10.07% was achieved by our model, the prediction of COVID-19 epidemic inflection points in most countries shows good agreement with the real data. In addition, our model has good anti-noise ability and stability when dealing with data fluctuations.

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An epidemic model SIPHERD and its application for prediction of the spread of COVID-19 infection in India

Cited by 68 publications

References 17 publications

Pandemic fatigue impact on COVID-19 spread: A mathematical modelling answer to the Italian scenario

Pandemic fatigue impact on COVID-19 spread: A mathematical modelling answer to the Italian scenario

Mathematical Modeling of Immune Responses against SARS-CoV-2 Using an Ensemble Kalman Filter

Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis

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