It seems that everyone is an epidemiologist on social media these days, to the extent that "flattening the curve" has entered the popular lexicon. Predicting when the curve will begin coming down in any country is no easy task, however.
A team of experts in the UAE from TachyHealth, the deep technology R&D company, has turned to a rigorous methodology - a "time-dependent compartmental model" - to understand where the curve is headed in the UAE, Saudi Arabia, Egypt and Algeria.
According to TachyHealth, the peak is the point at which the healthcare system is overwhelmed by COVID-19. Understanding the likely timing of the peak in these Middle Eastern countries will help in the planning of healthcare resources to meet patients' needs, while minimising the number of deaths.
Their results presented the scenarios below, with the epidemic in the UAE peaking sooner than in other countries, and Egypt projected to peak later:
UAE
- Scenario 1: Peak date is expected at 29/April/2020
- Scenario 2: Peak date is expected at 16/May/2020
- Scenario 3: Peak date is expected at 28/May/2020
Egypt
- Scenario 1: Peak date is expected at 06/June/2020
- Scenario 2: Peak date is expected at 18/June/2020
- Scenario 3: Peak date is expected at 16/July/2020
Saudi Arabia
- Scenario 1: Peak date is expected at 20/May/2020
- Scenario 2: Peak date is expected at 11/June/2020
- Scenario 3: Peak date is expected at 25/June/2020
Algeria
- Scenario 1: Peak date is expected at 29/May/2020
- Scenario 2: Peak date is expected at 11/June/2020
- Scenario 3: Peak date is expected at 06/July/2020
TachyHealth caveated that the real representation of the epidemiological curve depends on many factors, including the availability and access to the COVID-19 testing, the population response to the public health interventions, and the magnitude and strength of mitigation actions.
Other factors relate 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.