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. 2021 Jan 15;23(1):e25830.
doi: 10.2196/25830.

SARS-CoV-2 Surveillance in the Middle East and North Africa: Longitudinal Trend Analysis

Lori Post  1 , Emily Marogi  2 , Charles B Moss  3 , Robert Leo Murphy  4 , Michael G Ison  5 , Chad J Achenbach  5 , Danielle Resnick  6 , Lauren Singh  1 , Janine White  1 , Michael J Boctor  2 , Sarah B Welch  1 , James Francis Oehmke  1
Affiliations

SARS-CoV-2 Surveillance in the Middle East and North Africa: Longitudinal Trend Analysis

Lori Post et al. J Med Internet Res. .

Abstract

Background: The COVID-19 pandemic has disrupted the lives of millions and forced countries to devise public health policies to reduce the pace of transmission. In the Middle East and North Africa (MENA), falling oil prices, disparities in wealth and public health infrastructure, and large refugee populations have significantly increased the disease burden of COVID-19. In light of these exacerbating factors, public health surveillance is particularly necessary to help leaders understand and implement effective disease control policies to reduce SARS-CoV-2 persistence and transmission.

Objective: The goal of this study is to provide advanced surveillance metrics, in combination with traditional surveillance, for COVID-19 transmission that account for weekly shifts in the pandemic speed, acceleration, jerk, and persistence to better understand a country's risk for explosive growth and to better inform those who are managing the pandemic. Existing surveillance coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until an effective vaccine is developed.

Methods: Using a longitudinal trend analysis study design, we extracted 30 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in MENA as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.

Results: The regression Wald statistic was significant (χ25=859.5, P<.001). The Sargan test was not significant, failing to reject the validity of overidentifying restrictions (χ2294=16, P=.99). Countries with the highest cumulative caseload of the novel coronavirus include Iran, Iraq, Saudi Arabia, and Israel with 530,380, 426,634, 342,202, and 303,109 cases, respectively. Many of the smaller countries in MENA have higher infection rates than those countries with the highest caseloads. Oman has 33.3 new infections per 100,000 population while Bahrain has 12.1, Libya has 14, and Lebanon has 14.6 per 100,000 people. In order of largest to smallest number of cumulative deaths since January 2020, Iran, Iraq, Egypt, and Saudi Arabia have 30,375, 10,254, 6120, and 5185, respectively. Israel, Bahrain, Lebanon, and Oman had the highest rates of COVID-19 persistence, which is the number of new infections statistically related to new infections in the prior week. Bahrain had positive speed, acceleration, and jerk, signaling the potential for explosive growth.

Conclusions: Static and dynamic public health surveillance metrics provide a more complete picture of pandemic progression across countries in MENA. Static measures capture data at a given point in time such as infection rates and death rates. By including speed, acceleration, jerk, and 7-day persistence, public health officials may design policies with an eye to the future. Iran, Iraq, Saudi Arabia, and Israel all demonstrated the highest rate of infections, acceleration, jerk, and 7-day persistence, prompting public health leaders to increase prevention efforts.

Keywords: Algeria; Arellano-Bond estimator; Bahrain; COVID-19; COVID-19 7-day lag; COVID-19 transmission deceleration; COVID-19 transmission jerk; Djibouti; Egypt; GMM; Iran; Iraq; Israel; Jordan; Kuwait; Lebanon; Libya; MENA COVID-19; MENA COVID-19 transmission acceleration; MENA COVID-19 transmission speed; MENA SARS-CoV-2; MENA econometrics; MENA public health surveillance; Middle East and North Africa COVID-19 surveillance system; Middle East and North Africa surveillance metrics; Morocco; Oman; Qatar; SARS-CoV-2; SARS-CoV-2 surveillance; Saudi Arabia; Syria; Tunisia; United Arab Emirates; Yemen; dynamic panel data; generalized method of moments; global COVID-19 surveillance; second wave; wave two.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Middle East and North Africa (MENA) timeline. WHO: World Health Organization; UAE: United Arab Emirates; EMRO: Regional Office for the Eastern Mediterranean.
Figure 2
Figure 2
Middle East and North Africa weekly trends [40].
See this image and copyright information in PMC

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