Epidemic model analyzed via particle swarm optimization based homotopy perturbation method

COVID-19 and Chikungunya: an optimal control model with consideration of social and environmental factors

Chikungunya is one of the Aedes aegypti diseases that mosquito transmits to humans and that are common in tropical countries like Yemen. In this work, we formulated a novel dynamic mathematical model framework, which integrates COVID-19 and Chikungunya outbreaks. The proposed model is governed by a system of dynamic ordinary differential equations (ODEs). Particle swarm optimization was employed to solve the parameters estimation problem of the outbreaks of COVID-19 and Chikungunya in Yemen (March 1, 2020, to May 30, 2020). Besides, a bi-objective optimal control model was formulated, which minimizes the number of affected individuals and minimizes the total cost associated with the intervention strategies. The bi-objective optimal control was also solved using PSO. Five preventive measures were considered to curb the environmental and social factors that trigger the emergence of these viruses. Several strategies were simulated to evaluate the best possible strategy under the conditions and available resources in Yemen. The results obtained confirm that the strategy, which provides resources to prevent the transmission of Chikungunya and provides sufficient resources for testing, applying average social distancing, and quarrying the affected individuals, has a significant effect on flattening the epidemic curves and is the most suitable strategy in Yemen.

A Generalized Mechanistic Model for Assessing and Forecasting the Spread of the COVID-19 Pandemic

Since early 2020, the world has been afflicted with an unprecedented global pandemic. The SARS-CoV-19 (COVID-19) has levied massive economic and public health costs across many countries. Due to its virulence, the pathogen is rapidly propagating throughout the world in such a way that makes it incredibly challenging for officials to contain its spread. Therefore, there is a pressing need for national and local authorities to have tools that aid in their ability to assess and extrapolate the future trends of the spread of COVID-19, so they may make rational and informed decisions that minimize public harm. Mechanistic models are prominent mathematical tools that are used to characterize epidemics. In this paper, we propose a generalized mechanistic model with eight states characterizing the COVID-19 pandemic evolution from a susceptible state to discharged states while passing by quarantined and hospitalized states. The parameters of the model are determined by solving a fitting optimization problem with three observed inputs: the number of infected, deceased, and reported cases. The model's objective function is weighted over the training days so as to guide the fitting algorithm towards the latest pandemic period and lead to more accurate trend predictions for a stronger forecast. We solve the fitting problem with the Levenberg-Marquardt algorithm; we compare the performance of the model generated from this algorithm to the one of another state-of-the-art fitting algorithm as well as to the one of another compartmental model widely used in literature. We test the model on the COVID-19 data from four highly afflicted countries. The fitting algorithm has been validated graphically and through numerical metrics, and results show significantly accurate results for most of the countries. Once the model's parameters are estimated, forecasting results are derived and uncertainty regions of the expected scenarios are provided.