Predicting the peak of the epidemic curve of COVID-19 in selected Arab countries

Our research team at TachyHealth used time-dependent compartmental epidemiological model, SIR model, to study the epidemic curve in four of the Middle East and North Africa (MENA) countries; UAE, Egypt, Saudi Arabia, and Algeria

The epidemic peak represents the point with the highest pressure on the healthcare system. Predicting it’s likely timing represents one of the most important questions to answer for proactive healthcare resources planning.

Background

The Novel Coronavirus, COVID-19 represents one of the greatest challenges that the global healthcare system has faced in the last few centuries. The current pandemic started with a cluster of cases of unknown pneumonia in Wuhan, Hubei Province in China in December 2019. It wasn’t until the end of January when the World Health Organization (WHO) declared it a public health emergency of international concern (PHEIC). With more spread in different countries, the WHO declared it a pandemic on the 11th of March 2020. Currently, there are more than 2,300,000 confirmed COVID-19 cases worldwide, with the US, China, Iran, and many European countries represent the most impacted countries. We have identified limited studies that are discussing the epidemiological curves in the region and wanted to contribute to the understanding of this unique pandemic and to drive a better-informed decision to all the public health stakeholders.

SARS COV-2 Virus which is causing the COVID-19 disease under microscopy

The situation in the MENA region:

In the Middle East and North Africa countries, the COVID-19 impact has been slower with the peak of the curve still yet to come for all countries except Iran which had more than 80,000 confirmed cases with a peak that was reached in the 1st week of April 2020. Till the time of writing this study, Saudi Arabia (9,362), United Arab Emirates (6,781), Qatar (5,448), Egypt (3,144), Morocco (2,855), and Algeria (2,629) are having the highest confirmed COVID-19 cases in the Arab region. We have also studied relevant healthcare indicators (resources and burden of diseases) in the selected countries; United Arab Emirates, Saudi Arabia, Egypt, and Algeria to understand the resources available to meet the demand generated by the COVID-19 with data collected from the World Bank as well as the WHO.

Healthcare resources and burden of diseases for selected MENA countries, UAE, Egypt, Saudi Arabia, and Algeria that are likely to correlate to the COVID-19 impact

The study of the epidemiological model

An epidemiological curve (epidemic curve) is a statistical chart to visualize the frequency of new cases compared to the date of disease onset which eventually showcases the disease’s magnitude. The curve would typically have a steep up-slope, a peak, and a more gradual down-slope. One of the most important components to understand in the epidemic curve is the peak, in which the healthcare systems is overwhelmed by the impact of the disease while the healthcare resources are facing the highest demand. Understanding the likely timing of the peak will help in the planning of the healthcare resources to meet the patients’ need while minimizing the morbidity and associated mortality.

The peak prediction using epidemiological compartmental SIR model

In our study, we used one type of compartmental models to research the likely peaks of the epidemic curve in the United Arab Emirates, Egypt, Saudi Arabia, and Algeria. Compartmental models are used for the mathematical modelling of communicable diseases in which the population is divided into groups (compartments) with the assumption that individuals in the same compartment have the same attributes. The SIR model is one of the compartmental models which consists of three compartments: S which represents the number of susceptible, I which represents the number of infectious, and R which represents the number of recovered or deceased or immune individuals. Real-time data query was performed and visualized, then the queried data is used for building the SIR predictive model based on daily observations of confirmed, death, and recovered cases from COVID-19. To apply the SIR model, the number of susceptible, infected and recovered individuals may vary over time (even if the total population size remains constant). For that reason, we assumed the values of t (time/day): S(t), I(t) and R(t).

Epidemiological and statistical data such as the recovery period is assumed to be from 12 to 14 days. Also, the basic reproduction number R0 is assumed to be between 2 and 2.6 persons as referenced by the WHO in 6th of March 2020 report, as well as the population of each country as referenced by the World Bank. The basic reproduction number R0, defined as the number of additional infections by an infected person before it recovers, is one of the commonly used metrics to check whether the disease will become an outbreak. In the SIR model, R0 is shown in formula: 𝑅0= 𝛽 / 𝛾. As shown in the formula, the infected person takes (on average) 1/𝛾 days to recover, while during that period, it will be in contact with (on average) 𝛽 persons. In our time-dependent SIR model, the basic reproduction number R0(t) is a function of time, it is defined as 𝛽(t)/𝛾(t). If R0(t) > 1, the disease will spread exponentially and infects a certain fraction of the total population. The transmission rate 𝛽 means that each individual has on average contacts with randomly chosen others per unit time. On the other hand, the recovering rate 𝛾 indicates that individuals in the infected state get recovered or die at a fixed average rate. Both the transmission rate 𝛽 and the recovering rate 𝛾 are functions of time t. Such a time-dependent SIR model is much better to track the disease spread, control, and predict the future trend.

The results:

1- For the United Arab Emirates (UAE):

COVID-19 Epidemic Peak Detection in the United Arab Emirates, TachyHealth Credit

1. Peak at 29/April assuming 5% of the population is susceptible, reproduction number= 2.6, recovery rate= 1/14
2. Peak at 16/May assuming 2.5% of the population is susceptible, reproduction number= 2.3 and the recovery rate= 1/13
3. Peak at 28/May assuming 1% of the population is susceptible, reproduction number= 2.6 and the recovery rate= 1/12

2- For Egypt:

COVID-19 Epidemic Peak Detection in the Egypt, TachyHealth Credit

1. Peak at 06/June assuming 5% of the population is susceptible, reproduction number= 2.6, recovery rate= 1/14
2. Peak at 18/June assuming 2.5% of the population is susceptible, reproduction number= 2.3 and the recovery rate= 1/13
3. Peak at 16/July assuming 1% of the population is susceptible, reproduction number= 2.6 and the recovery rate= 1/12

3- For Saudi Arabia:

COVID-19 Epidemic Peak Detection in Saudi Arabia (KSA), TachyHealth Credit

1. Peak at 20/May assuming 5% of the population is susceptible, reproduction number= 2.6, recovery rate= 1/14
2. Peak at 11/June assuming 2.5% of the population is susceptible, reproduction number= 2.3 and the recovery rate= 1/13
3. Peak at 25/June assuming 1% of the population is susceptible, reproduction number= 2.6 and the recovery rate= 1/12

4- For Algeria:

COVID-19 Epidemic Peak Detection in Algeria, TachyHealth Credit

1. Peak at 29/May assuming 5% of the population is susceptible, reproduction number= 2.6, recovery rate= 1/14
2. Peak at 11/June assuming 2.5% of the population is susceptible, reproduction number= 2.3 and the recovery rate= 1/13
3. Peak at 06/July assuming 1% of the population is susceptible, reproduction number= 2.6 and the recovery rate= 1/12

We want to emphasize that real representation of the epidemiological curve depend on many factors including, the availability and access to the COVID-19 testing, the population response to the public health interventions, the magnitude and the strength of the mitigation actions. There are other factors that are related to the virus itself; severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) including the response of the immune system, the mutations of the virus, and the seasonality of the spread of the infection.

In the future, we would like to explore further parameters of compartmental models including the seasonality of the pandemic, the infection rate of the population who are not showing typical symptoms of COVID-19 infection, the natural response of the body immunity following the 1st infection with COVID-19, the natural deaths due to other causes, and the new births. Also, we might apply stochastic models, such as the non-homogeneous Markov chain, to further improve the precision of the prediction results.

TachyHealth is a leading Dubai based Artificial Intelligence and Data Science healthcare technology company working on cutting-edge deep learning solutions for value-based healthcare.

Reach out for more information about this study to info@tachyhealth.com