Mathematical modeling of the COVID-19 pandemic with intervention strategies

Abstract

Mathematical modeling play an important role to better understand the disease dynamics and designing strategies to manage quickly spreading infectious diseases in lack of an effective vaccine or specific antivirals. During this period, forecasting is of utmost priority for health care planning and to combat against COVID-19 epidemic. In this study, we proposed and extended classical SEIR compartment model refined by contact tracing and hospitalization strategies to explain the COVID-19 outbreak. We calibrated our model with daily COVID-19 data for the five provinces of India namely, Kerala, Karnataka, Andhra Pradesh, Maharashtra, West Bengal and the overall India. To identify the most effective parameters we conduct a sensitivity analysis by using the partial rank correlation coefficients techniques. The value of those sensitive parameters were estimated from the observed data by least square method. We perform sensitivity analysis for to investigate the relative importance of the system parameters. Also, we compute the sensitivity indices for to determine the robustness of the model predictions to parameter values. Our study demonstrates that a critically important strategy can be achieved by reducing the disease transmission coefficient and clinical outbreak rate to control the COVID-19 outbreaks. Performed short-term predictions for the daily and cumulative confirmed cases of COVID-19 outbreak for all the five provinces of India and the overall India exhibit the steady exponential growth of some states and other states showing decays of daily new cases. Long-term predictions for the Republic of India reveals that the COVID-19 cases will exhibit oscillatory dynamics. Our research thus leaves the option open that COVID-19 might become a seasonal occurrence. Our model simulation demonstrates that the COVID-19 cases across India at the end of September 2020 obey a power law.

Evidence for Complex Fixed Points in Pandemic Data

“…Data collected in various instances show that the number of infected people in a limited region grows exponentially at the beginning, while then subsiding after a period of time characteristic of each type of virus. This feature can be effectively described by various mathematical models, including compartmental ones [1,[18][19][20][21], complex network diffusion techniques [2][3][4], the eRG approach [5][6][7] and other infection models [22]. Going beyond the first wave is however a challenge [23].…”

COVID-19 Critical Care Simulations: An International Cross-Sectional Survey

“…Model simulation research suggests that COVID-19 might become a seasonal disease and persist for more extended periods, hence the importance of ongoing training for the HCWs (4,5). Mathematical modeling to better understand the disease dynamics in order to control its rapidly spreading infectious nature highlights the importance of infection control measures in order to control the basic reproduction number R 0 and maintain the endemic equilibrium (6), keeping in mind that local and global dynamics are determined by the threshold parameters R0 and R1 (7).…”

“…Previous models that addressed intensive care units and hospital preparations for an influenza epidemic or mass disaster suggested enforcing communication, coordination, and collaboration between the ICU and key interface departments (10), and emphasized the beneficial effects of proper training, including Simulation-Based Education (SBE), during various coronavirus outbreaks on HCWs' mental health and well-being (11)(12)(13). Model simulation research suggests that COVID-19 might become a seasonal disease and persist for more extended periods, hence the importance of ongoing training for the HCWs (4,5). Another mathematical modeling to better understand the disease dynamics to control the rapidly spreading infectious disease demonstrates the importance of infection control measures to control the basic reproduction number R 0 and maintain the endemic equilibrium (6), keeping in mind that local and global dynamics are determined by the threshold parameters R0 and R1, and that dynamics of any proposed model still deserve more work to be done in the future (4)(5)(6)(7)14).…”

“…Model simulation research suggests that COVID-19 might become a seasonal disease and persist for more extended periods, hence the importance of ongoing training for the HCWs (4,5). Another mathematical modeling to better understand the disease dynamics to control the rapidly spreading infectious disease demonstrates the importance of infection control measures to control the basic reproduction number R 0 and maintain the endemic equilibrium (6), keeping in mind that local and global dynamics are determined by the threshold parameters R0 and R1, and that dynamics of any proposed model still deserve more work to be done in the future (4)(5)(6)(7)14). Therefore (SBE), is an important training tool that could especially be used to test and boost preparedness for global pandemics or natural disasters or man-made mass casualties' incidents (15).…”

The Impact of Disease Control Measures on the Spread of COVID-19 in the Province of Sindh, Pakistan

“…Simultaneously, social events such as religious congregations and observances typical to the Islamic culture; such as Ramadhan and Eid festivities, were also notable in the spread of COVID-19. It is shown in this paper how these interventive measures were likely to be effective in mitigating the spread of COVID-19 in Sindh by predicting its short-term dynamics [ 10 ].…”