Modelling the effect of non-pharmaceutical interventions on COVID-19 transmission from mobility maps.

The world has faced the COVID-19 pandemic for over two years now, and it is time to revisit the lessons learned from lockdown measures for theoretical and practical epidemiological improvements. The interlink between these measures and the resulting change in mobility (a predictor of the disease transmission contact rate) is uncertain. We thus propose a new method for assessing the efficacy of various non-pharmaceutical interventions (NPI) and examine the aptness of incorporating mobility data for epidemiological modelling. Facebook mobility maps for the United Arab Emirates are used as input datasets from the first infection in the country to mid-Oct 2020. Dataset was limited to the pre-vaccination period as this paper focuses on assessing the different NPIs at an early epidemic stage when no vaccines are available and NPIs are the only way to reduce the reproduction number ( R 0 ). We developed a travel network density parameter ß t to provide an estimate of NPI impact on mobility patterns. Given the infection-fatality ratio and time lag (onset-to-death), a Bayesian probabilistic model is adapted to calculate the change in epidemic development with ß t . Results showed that the change in ß t clearly impacted R 0 . The three lockdowns strongly affected the growth of transmission rate and collectively reduced R 0 by 78% before the restrictions were eased. The model forecasted daily infections and deaths by 2% and 3% fractional errors. It also projected what-if scenarios for different implementation protocols of each NPI. The developed model can be applied to identify the most efficient NPIs for confronting new COVID-19 waves and the spread of variants, as well as for future pandemics.

Modeling the Impact of the Imperfect Vaccination of the COVID-19 with Optimal Containment Strategy

Since the beginning of the COVID-19 pandemic, vaccination has been the main strategy to contain the spread of the coronavirus. However, with the administration of many types of vaccines and the constant mutation of viruses, the issue of how effective these vaccines are in protecting the population is raised. This work aimed to present a mathematical model that investigates the imperfect vaccine and finds the additional measures needed to help reduce the burden of disease. We determine the R0 threshold of disease spread and use stability analysis to determine the condition that will result in disease eradication. We also fitted our model to COVID-19 data from Morocco to estimate the parameters of the model. The sensitivity analysis of the basic reproduction number, with respect to the parameters of the model, is simulated for the four possible scenarios of the disease progress. Finally, we investigate the optimal containment measures that could be implemented with vaccination. To illustrate our results, we perform the numerical simulations of optimal control.

Evaluating COVID-19 control measures in mass gathering events with vaccine inequalities.

With the increasing global adoption of COVID-19 vaccines, limitations on mass gathering events have started to gradually loosen. However, the large vaccine inequality recorded among different countries is an important aspect that policymakers must address when implementing control measures for such events. In this paper, we propose a model for the assessment of different control measures with the consideration of vaccine inequality in the population. Two control measures are considered: selecting participants based on vaccine efficacy and restricting the event capacity. We build the model using agent-based modeling to capture the spatiotemporal crowd dynamics and utilize a genetic algorithm to assess the control strategies. This assessment is based on factors that are important for policymakers such as disease prevalence, vaccine diversity, and event capacity. A quantitative evaluation of vaccine diversity using the Simpson's Diversity Index is also provided. The Hajj ritual is used as a case study. We show that strategies that prioritized lowering the prevalence resulted in low event capacity but facilitated vaccine diversity. Moreover, strategies that prioritized diversity resulted in high infection rates. However, increasing the prioritization of participants with high vaccine efficacy significantly decreased the disease prevalence. Strategies that prioritized ritual capacity did not show clear trends.

Agent-Based Modeling of the Hajj Rituals with the Possible Spread of COVID-19

With the coronavirus (COVID-19) pandemic continuing to spread around the globe, there is an unprecedented need to develop different approaches to containing the pandemic from spreading further. One particular case of importance is mass-gathering events. Mass-gathering events have been shown to exhibit the possibility to be superspreader events;as such, the adoption of effective control strategies by policymakers is essential to curb the spread of the pandemic. This paper deals with modeling the possible spread of COVID-19 in the Hajj, the world’s largest religious gathering. We present an agent-based model (ABM) for two rituals of the Hajj: Tawaf and Ramy al-Jamarat. The model aims to investigate the effect of two control measures: buffers and face masks. We couple these control measures with a third control measure that can be adopted by policymakers, which is limiting the capacity of each ritual. Our findings show the impact of each control measure on the curbing of the spread of COVID-19 under the different crowd dynamics induced by the constraints of each ritual.

Modeling the Impact of Unreported Cases of the COVID-19 in the North African Countries.

In this paper, we study a mathematical model investigating the impact of unreported cases of the COVID-19 in three North African countries: Algeria, Egypt, and Morocco. To understand how the population respects the restriction of population mobility implemented in each country, we use Google and Apple's mobility reports. These mobility reports help to quantify the effect of the population movement restrictions on the evolution of the active infection cases. We also approximate the number of the population infected unreported, the proportion of those that need hospitalization, and estimate the end of the epidemic wave. Moreover, we use our model to estimate the second wave of the COVID-19 Algeria and Morocco and to project the end of the second wave. Finally, we suggest some additional measures that can be considered to reduce the burden of the COVID-19 and would lead to a second wave of the spread of the virus in these countries.

Optimal Containment Control Strategy of the Second Phase of the COVID-19 Lockdown in Morocco

This work investigates the optimal control of the second phase of the COVID-19 lockdown in Morocco. The model consists of susceptible, exposed, infected, recovered, and quarantine compartments (SEIRQD model), where we take into account contact tracing, social distancing, quarantine, and treatment measures during the nationwide lockdown in Morocco. First, we present different components of the model and their interactions. Second, to validate our model, the nonlinear least-squares method is used to estimate the model’s parameters by fitting the model outcomes to real data of the COVID-19 in Morocco. Next, to investigate the impact of optimal control strategies on this pandemic in the country. We also give numerical simulations to illustrate and compare the obtained results with the actual situation in Morocco.

Modeling the Impact of Unreported Cases of the COVID-19 in the North African Countries

In this paper, we study a mathematical model investigating the impact of unreported cases of the COVID-19 in three North African countries: Algeria, Egypt, and Morocco. To understand how the population respects the restriction of population mobility implemented in each country, we use Google and Apple’s mobility reports. These mobility reports help to quantify the effect of the population movement restrictions on the evolution of the active infection cases. We also approximate the number of the population infected unreported, the proportion of those that need hospitalization, and estimate the end of the epidemic wave. Moreover, we use our model to estimate the second wave of the COVID-19 Algeria and Morocco and to project the end of the second wave. Finally, we suggest some additional measures that can be considered to reduce the burden of the COVID-19 and would lead to a second wave of the spread of the virus in these countries.

Age-structured Modeling of COVID-19 Epidemic in the USA, UAE and Algeria

As the COVID-19 is still spreading in more than 180 countries, according to WHO. There is a need to understand the dynamics of this infection and predict its the impact on the public health capacity. This work aims to forecast the progress of the disease in three countries from different continents: The United States of America, the United Arab Emirates and Algeria. The existing data shows that the fatality of the disease is high in elderly people and people with comorbidity. Therefore, we consider an age-structured model. Our model also takes in to consider two main components of the COVID-19 (a) the number of Infected hospitalized people, therefore, we estimate the number of beds (acute and critical) needed (2) the possible infection of the healthcare personals (HCP). Hence, the model predict the peak time and the number of infectious cases at the peak before and after the implementation of non-pharmaceutical interventions (NPI), and we also compare this finding with case of full lockdown. Finally, we investigate the impact of the shortage of proper personal protective equipment (PPE) on the spread of the disease.