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

Abstract

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.

Exploring Household Food Dynamics During the COVID-19 Pandemic in Morocco

“…The scholarly literature on the effects of the COVID-19 pandemic on food systems and consumption patterns has been so far mostly geographically biased; it focuses on Western and Southern Europe, North America, and China (30), while developing countries in general and those of the NENA region in particular, such as Morocco, have been overlooked. The analysis of the scholarly literature shows that most of the papers dealing with the COVID-19 emergency in Morocco focus on the dynamics of the spread of the virus as well as its health impacts (44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58). Other articles analyze the pandemic's socio-economic impacts in the kingdom (45,59).…”

Analyzing the Spread of COVID-19 Disease Using An Age-Structured Model: Application to Italy, Spain, France, UK, and Algeria Using Early Data

“…1/γ is the average of the infection period which is estimated for COVID-19 as 10 to 14 days. In [13,6] it is considered that γ = 0.1, which means that the average of the infection period is 10 days. +∞ 0 I(t, a)da stands for the total fraction of the infected individuals at time t. To mention that the boundary conditions S(t, 0) = 0, I(t, 0) = 0 represents that there are no newborns (or no immigrations), these assumptions are put due to stopping all kind of immigrations for the most part of countries in our planet and neglecting the number of the new borne.…”

“…It seems that this is not a high rate of death but in Italy, during the recent outbreak 83% of the death are aged over the 60s [12]. Many new models study the outbreak of the coronavirus as the papers [9,8,5,6], but they neglect this case of death, and focus on the number of the infected individuals only. To mention that the declared cases are mostly the old individuals, where the researches show that a human can have COVID-19 with non-well visual symptoms.…”

Mathematical modeling and multi-agents approach for the evolution of the Coronavirus pandemic

“…We should also notice that we have taken into consideration the fact that an important number of cases is not reported by estimating the real number of cases from reported ones. The reader is referred to [10] for a detailed study investigating the impact of unreported cases in Algeria, Egypt, and Morocco. The value of β here is closed to 0.000005 and the initial population size is as in figure 2, the system converges to a " zero infection" steady state.…”