This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
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.
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.
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.
The regression Wald statistic was significant (χ25=859.5,
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.
SARS-CoV-2, the novel coronavirus that causes the disease COVID-19, first presented in December 2019 in Wuhan City, China, and was declared a public health emergency of international concern on January 30, 2020, as it spread quickly around the globe through human-to-human transmission [
Many countries use a combination of containment and mitigation strategies, including isolation of cases, contact tracing, social distancing, border closures, masking, hand and surface hygiene, and travel restrictions [
Responses to COVID-19 in MENA have ranged from restrictive temporary lockdowns to denial and lack of organization [
Middle East and North Africa (MENA) timeline. WHO: World Health Organization; UAE: United Arab Emirates; EMRO: Regional Office for the Eastern Mediterranean.
Countries in MENA face a dual crisis from the COVID-19 pandemic and the collapse of oil prices [
Violent conflicts significantly weakened the health infrastructure in several countries across MENA, resulting in poor health worker capacity [
Middle Eastern populations have a high disease burden, which suggests a reduction in routine health service utilization results in increased mortality, a trend expected to hold true for COVID-19 [
On August 4, 2020, a deadly warehouse explosion, caused by 2750 tons of ammonium nitrate stored at Beirut’s port, killed hundreds, wounded and displaced thousands, left 5 hospitals in the area either nonfunctional or only partially functional, and destroyed 17 containers of WHO essential medical supplies [
The objective of our research is to provide additional surveillance metrics to add to the public health arsenal using dynamic panel modeling and method of moments to minimize sampling bias by measuring significant negative or positive weekly shifts in the increase, decrease, or plateaued transmission of SARS-CoV-2. We will apply novel indicators derived specifically to inform policy makers about the COVID-19 pandemic [
This study relies on a longitudinal trend analysis of data collected from the Foundation for Innovative New Diagnostics (FIND) [
Regression results are presented for 17 MENA countries in
The regression Wald statistic was significant (
Arellano-Bond dynamic panel data modeling of the number of daily infections reported by country, October 5-18, 2020.
Variable | Statistic | |
L7Posa | <.001 | |
Cumulative tests | <.001 | |
Weekend | .95 | |
Wald statistic for regression | <.001 | |
Sargan statistic for validity | .99 |
aL7Pos: the statistical impact of the 7-day lag of speed on today’s value of speed. New cases per day tend to have an echo effect 7 days later, similar to the echo effect in the population pyramid caused by the baby boom. Reported as the weekly average number of new cases per day that are attributable to the weekly average of the 7-day lag of the number of new cases per day.
Static surveillance metrics for the week of October 5-11, 2020.
Country | New COVID-19 cases, n | Cumulative COVID-19 cases, n | 7-day moving average of new cases | Rate of infection | New deaths, n | Cumulative deaths, n | 7-day moving average of death rate | Rate of death |
Algeria | 132 | 53,072 | 133.71 | 0.31 | 6 | 1801 | 5.86 | 0.01 |
Bahrain | 327 | 75,614 | 421.71 | 19.92 | 2 | 275 | 2.14 | 0.12 |
Djibouti | 0 | 5423 | 0.57 | 0.00 | 0 | 61 | 0.00 | 0.00 |
Egypt | 129 | 104,516 | 119.00 | 0.13 | 12 | 6052 | 10.14 | 0.01 |
Iran | 3822 | 500,075 | 4043.29 | 4.61 | 251 | 28,544 | 226.71 | 0.30 |
Iraq | 2206 | 402,330 | 3312.71 | 5.61 | 62 | 9852 | 64.71 | 0.16 |
Israel | 618 | 290,493 | 3388.29 | 6.83 | 39 | 1980 | 37.29 | 0.43 |
Jordan | 928 | 24,926 | 1326.57 | 9.19 | 10 | 191 | 12.86 | 0.10 |
Lebanon | 1010 | 53,568 | 1298.00 | 14.73 | 4 | 459 | 7.57 | 0.06 |
Libya | 1026 | 42,712 | 843.29 | 15.14 | 8 | 631 | 5.57 | 0.12 |
Morocco | 2563 | 152,404 | 2733.14 | 7.03 | 33 | 2605 | 39.29 | 0.09 |
Oman | 1761 | 105,890 | 660.00 | 35.40 | 29 | 1038 | 8.71 | 0.58 |
Qatar | 207 | 127,985 | 212.43 | 7.31 | 1 | 220 | 0.57 | 0.04 |
Saudi Arabia | 323 | 339,267 | 411.43 | 0.94 | 25 | 5043 | 24.00 | 0.07 |
Tunisia | 1297 | 32,556 | 1475.14 | 11.09 | 22 | 478 | 22.43 | 0.19 |
United Arab Emirates | 1096 | 106,229 | 1061.14 | 11.22 | 2 | 445 | 2.71 | 0.02 |
Region | 17,445 | 2,417,060 | 18,569.14 | 4.35 | 506 | 59,675 | 470.57 | 0.13 |
Static surveillance metrics for the week of October 12-18, 2020.
Country | New weekly COVID-19 cases, n | Cumulative COVID-19 cases, n | 7-day moving average of new cases | Rate of infection | New weekly deaths, n | Cumulative deaths, n | 7-day moving average of death rate | Rate of deaths per 100k |
Algeria | 199 | 54,402 | 190 | 0.46 | 10 | 1856 | 7.86 | 0.02 |
Bahrain | 331 | 77,902 | 326.86 | 20.17 | 7 | 300 | 3.57 | 0.43 |
Djibouti | 7 | 5459 | 5.14 | 0.72 | 0 | 61 | 0.00 | 0.00 |
Egypt | 127 | 105,424 | 129.71 | 0.13 | 11 | 6120 | 9.71 | 0.01 |
Iran | 3890 | 530,380 | 4329.29 | 4.69 | 252 | 30,375 | 261.57 | 0.30 |
Iraq | 3110 | 426,634 | 3472 | 7.91 | 56 | 10,254 | 57.43 | 0.14 |
Israel | 339 | 303,109 | 1802.29 | 3.74 | 19 | 2209 | 32.71 | 0.21 |
Jordan | 1520 | 37,573 | 1806.71 | 15.05 | 15 | 345 | 22.00 | 0.15 |
Lebanon | 1002 | 62,286 | 1245.43 | 14.62 | 3 | 520 | 8.71 | 0.04 |
Libya | 945 | 48,790 | 868.29 | 13.94 | 26 | 725 | 13.43 | 0.38 |
Morocco | 2721 | 173,632 | 3032.57 | 7.46 | 50 | 2928 | 46.14 | 0.14 |
Oman | 1657 | 109,953 | 580.43 | 33.31 | 30 | 1101 | 9.00 | 0.60 |
Qatar | 204 | 129,431 | 206.57 | 7.20 | 1 | 224 | 0.57 | 0.04 |
Saudi Arabia | 348 | 342,202 | 419.29 | 1.02 | 20 | 5185 | 20.29 | 0.06 |
Tunisia | 0 | 40,542 | 1140.86 | 0.00 | 0 | 626 | 21.14 | 0.00 |
United Arab Emirates | 1215 | 115,602 | 1339.00 | 12.44 | 4 | 463 | 2.57 | 0.04 |
Region | 17,615 | 2,563,321 | 18,397.14 | 4.39 | 504 | 63,292 | 516.71 | 0.13 |
In
The novel surveillance metrics are presented in
Novel surveillance metrics for the week of October 5-11, 2020.
Country | Speeda | Accelerationb | Jerkc | 7-day persistence effect on speedd |
Algeria | 0.31 | 0.00 | 0.00 | 0.20 |
Bahrain | 25.70 | –0.22 | 0.48 | 16.37 |
Djibouti | 0.06 | –0.01 | 0.00 | 0.08 |
Egypt | 0.12 | 0.00 | 0.00 | 0.07 |
Iran | 4.88 | 0.03 | –0.03 | 2.49 |
Iraq | 8.43 | –0.36 | 0.12 | 6.15 |
Israel | 37.43 | –2.70 | 2.83 | 32.14 |
Jordan | 13.13 | 0.05 | –0.14 | 5.76 |
Lebanon | 18.93 | 0.05 | –0.09 | 9.77 |
Libya | 12.44 | 0.64 | 0.75 | 5.34 |
Morocco | 7.49 | 0.20 | –0.10 | 3.48 |
Oman | 13.27 | –2.65 | –2.65 | 6.25 |
Qatar | 7.50 | 0.24 | 0.23 | 4.06 |
Saudi Arabia | 1.20 | –0.03 | –0.02 | 0.76 |
Tunisia | 12.61 | 0.01 | –3.82 | 4.26 |
United Arab Emirates | 10.86 | 0.08 | 0.23 | 6.11 |
Region | 5.35 | –0.09 | –0.07 | 3.11 |
aDaily positives per 100k (weekly average of new daily cases per 100k).
bDay-to-day change in the number of positives per day, weekly average, per 100k.
cWeek-over-week change in acceleration, per 100k.
dNew cases per day per 100k attributed to new cases 7 days ago.
Novel surveillance metrics for the week of October 12-18, 2020.
Country | Speeda | Accelerationb | Jerkc | 7-day persistence effect on speedd |
Algeria | 0.44 | 0.02 | 0.00 | 0.18 |
Bahrain | 19.92 | 0.03 | 1.61 | 14.64 |
Djibouti | 0.53 | 0.10 | 0.06 | 0.03 |
Egypt | 0.13 | 0.00 | 0.00 | 0.07 |
Iran | 5.22 | 0.01 | –0.03 | 2.78 |
Iraq | 8.83 | 0.33 | 0.01 | 4.80 |
Israel | 19.91 | –0.44 | 1.36 | 21.32 |
Jordan | 17.89 | 0.84 | 0.46 | 7.48 |
Lebanon | 18.17 | –0.02 | 0.44 | 10.79 |
Libya | 12.81 | –0.17 | 0.50 | 7.09 |
Morocco | 8.31 | 0.06 | –0.06 | 4.27 |
Oman | 11.67 | –0.30 | –0.30 | 7.56 |
Qatar | 7.29 | –0.02 | –0.30 | 4.27 |
Region | 5.21 | 0.01 | –0.06 | 3.05 |
Saudi Arabia | 1.22 | 0.01 | 0.03 | 0.68 |
Tunisia | 9.76 | –1.58 | –3.28 | 7.19 |
United Arab Emirates | 13.70 | 0.17 | –0.42 | 6.19 |
Region | 5.21 | 0.01 | –0.06 | 3.05 |
aDaily positives per 100k (weekly average of new daily cases per 100k).
bDay-to-day change in the number of positives per day, weekly average, per 100k.
cWeek-over-week change in acceleration, per 100k.
dNew cases per day per 100k attributed to new cases 7 days ago.
Middle East and North Africa weekly trends [
Overall, the MENA region was more stable than other global regions, but metrics between October 5-18 indicate pending growth. The speed of new infections decelerated during the week of October 5-11 and remained stable during the week of October 11-18. In addition, in the latter week, there was a slight negative jerk. The persistence rate slightly decreased from 3.11 to 3.05 per 100,000 population over the study period, which directly measures those new cases that are statistically related to the number of new infections 7 days earlier. While the surveillance metrics for the MENA region as a whole are promising, they are averages and thus we must look to those countries with increasing rates of speed, acceleration, jerk, and persistence to understand which countries have outbreaks and less control of the pandemic. In
Seven-day persistence difference.
Country | 7-day persistence | |
|
October 5-11, 2020 | October 12-18, 2020 |
Israel | 32.14 | 21.32 |
Bahrain | 16.37 | 14.64 |
Lebanon | 9.77 | 10.79 |
Oman | 6.25 | 7.56 |
Iraq | 6.15 | —a |
Jordan | — | 7.48 |
aNot applicable.
Israel, Bahrain, Lebanon, and Oman had the highest rate of COVID-19 persistence, which is the number of new infections statistically related to new infections from 7 days ago. These 3 countries had higher persistence rates 2 weeks in a row. Finally, Egypt has the highest population in MENA (
Most populous countries in the Middle East and North Africa.
Country | Population as of 2020, n |
Egypt | 102,334,404 |
Iran | 83,992,949 |
Algeria | 43,851,044 |
Iraq | 40,222,493 |
Morocco | 36,910,560 |
Analysis at the country level indicates there are some nations that should increase their public health efforts to gain control of the COVID-19 pandemic. Iran, Israel, Iraq, and Morocco had the highest reported 7-day average per 100,000 population, which is significantly higher than other nations in the region. Iran, Iraq, Saudi Arabia, and Israel had the highest caseload at the end of October 18. Looking toward the future, Jordan, Iraq, and the United Arab Emirates have the fastest acceleration in new COVID-19 infections while Bahrain and Israel have the largest upwards jerk in infections, which can lead to explosive growth. Iran began the pandemic with explosive growth but during the last week of this study ending on October 18, Iran’s acceleration rate has leveled off and its jerk has reversed lower; however, given the number of new cases and population size, the country could easily flare up in new outbreaks, especially considering the second wave of COVID-19 infections has just begun.
Data are limited by granularity and collection method. Data were collected at the country level, which precludes local analysis of surveillance trends. Moreover, data collection mechanisms differ by country and may even differ by region within a given country. These different methods lead to week-end effects, missing data points, and other contamination.
This study is part of a broader research program at Northwestern University Feinberg School of Medicine,
Static and dynamic public health surveillance tools provide a more complete picture of pandemic progression across countries and regions. While static measures capture data at a given point in time, like infection rates and death rates, they are less successful at assessing population health over a period of weeks or months. By including speed, acceleration, jerk, and 7-day persistence, public health officials may design policies with an eye to the future.
MENA countries with the highest risk all shared a number of characteristics according to the surveillance data. There was a definite positive shift between October 5-11 and October 12-18. Iran, Iraq, Saudi Arabia, and Israel all demonstrated the highest numbers of cumulative infections, acceleration, jerk, and 7-day persistence rates. Looking ahead, policy makers in these countries and the region at large should be concerned about growth in the already substantial number of cases over the short term. Given the substantial 7-day persistence rates of Israel, Bahrain, and Lebanon, it is imperative that efforts be made to target super spreader events. Analysis of subsequent surveillance data using both static and dynamic tools can help confirm the efficaciousness of new policies.
Weekly MENA country statistics.
Weekly MENA SARS-CoV-2 trends.
Weekly MENA acceleration and jerk map.
Weekly MENA 7-day persistence map.
Weekly MENA speed map.
Foundation for Innovative New Diagnostics
generalized method of moments
Middle East and North Africa
Middle East respiratory syndrome
severe acute respiratory syndrome
World Health Organization
Partial support for this publication was provided by Feed the Future through the US Agency for International Development, under the terms of contract #7200LA1800003. The opinions expressed herein are those of the author(s) and do not necessarily reflect the views of the US Agency for International Development.
None declared.