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A SARS-CoV-2 Surveillance System in Sub-Saharan Africa: Modeling Study for Persistence and Transmission to Inform Policy.
J Med Internet Res. 2020 11 19; 22(11):e24248.JM

Abstract

BACKGROUND

Since the novel coronavirus emerged in late 2019, the scientific and public health community around the world have sought to better understand, surveil, treat, and prevent the disease, COVID-19. In sub-Saharan Africa (SSA), many countries responded aggressively and decisively with lockdown measures and border closures. Such actions may have helped prevent large outbreaks throughout much of the region, though there is substantial variation in caseloads and mortality between nations. Additionally, the health system infrastructure remains a concern throughout much of SSA, and the lockdown measures threaten to increase poverty and food insecurity for the subcontinent's poorest residents. The lack of sufficient testing, asymptomatic infections, and poor reporting practices in many countries limit our understanding of the virus's impact, creating a need for better and more accurate surveillance metrics that account for underreporting and data contamination.

OBJECTIVE

The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with new and decomposable surveillance metrics of COVID-19 that overcome data limitations and contamination inherent in public health surveillance systems. In addition to prevalence of observed daily and cumulative testing, testing positivity rates, morbidity, and mortality, we derived COVID-19 transmission in terms of speed, acceleration or deceleration, change in acceleration or deceleration (jerk), and 7-day transmission rate persistence, which explains where and how rapidly COVID-19 is transmitting and quantifies shifts in the rate of acceleration or deceleration to inform policies to mitigate and prevent COVID-19 and food insecurity in SSA.

METHODS

We extracted 60 days of COVID-19 data from public health registries and employed an empirical difference equation to measure daily case numbers in 47 sub-Saharan countries as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.

RESULTS

Kenya, Ghana, Nigeria, Ethiopia, and South Africa have the most observed cases of COVID-19, and the Seychelles, Eritrea, Mauritius, Comoros, and Burundi have the fewest. In contrast, the speed, acceleration, jerk, and 7-day persistence indicate rates of COVID-19 transmissions differ from observed cases. In September 2020, Cape Verde, Namibia, Eswatini, and South Africa had the highest speed of COVID-19 transmissions at 13.1, 7.1, 3.6, and 3 infections per 100,0000, respectively; Zimbabwe had an acceleration rate of transmission, while Zambia had the largest rate of deceleration this week compared to last week, referred to as a jerk. Finally, the 7-day persistence rate indicates the number of cases on September 15, 2020, which are a function of new infections from September 8, 2020, decreased in South Africa from 216.7 to 173.2 and Ethiopia from 136.7 to 106.3 per 100,000. The statistical approach was validated based on the regression results; they determined recent changes in the pattern of infection, and during the weeks of September 1-8 and September 9-15, there were substantial country differences in the evolution of the SSA pandemic. This change represents a decrease in the transmission model R value for that week and is consistent with a de-escalation in the pandemic for the sub-Saharan African continent in general.

CONCLUSIONS

Standard surveillance metrics such as daily observed new COVID-19 cases or deaths are necessary but insufficient to mitigate and prevent COVID-19 transmission. Public health leaders also need to know where COVID-19 transmission rates are accelerating or decelerating, whether those rates increase or decrease over short time frames because the pandemic can quickly escalate, and how many cases today are a function of new infections 7 days ago. Even though SSA is home to some of the poorest countries in the world, development and population size are not necessarily predictive of COVID-19 transmission, meaning higher income countries like the United States can learn from African countries on how best to implement mitigation and prevention efforts.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/21955.

Authors+Show Affiliations

Buehler Center for Health Policy & Economics and Departments of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.Division of Infectious Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL, United States.International Food Policy Research Institute, Washington, DC, United States.Buehler Center for Health Policy & Economics and Departments of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.Institute for Global Health, Northwestern University, Chicago, IL, United States.Division of Infectious Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.Buehler Center for Health Policy & Economics and Departments of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.Buehler Center for Health Policy & Economics and Departments of Emergency Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

33211026

Citation

Post, Lori Ann, et al. "A SARS-CoV-2 Surveillance System in Sub-Saharan Africa: Modeling Study for Persistence and Transmission to Inform Policy." Journal of Medical Internet Research, vol. 22, no. 11, 2020, pp. e24248.
Post LA, Argaw ST, Jones C, et al. A SARS-CoV-2 Surveillance System in Sub-Saharan Africa: Modeling Study for Persistence and Transmission to Inform Policy. J Med Internet Res. 2020;22(11):e24248.
Post, L. A., Argaw, S. T., Jones, C., Moss, C. B., Resnick, D., Singh, L. N., Murphy, R. L., Achenbach, C. J., White, J., Issa, T. Z., Boctor, M. J., & Oehmke, J. F. (2020). A SARS-CoV-2 Surveillance System in Sub-Saharan Africa: Modeling Study for Persistence and Transmission to Inform Policy. Journal of Medical Internet Research, 22(11), e24248. https://doi.org/10.2196/24248
Post LA, et al. A SARS-CoV-2 Surveillance System in Sub-Saharan Africa: Modeling Study for Persistence and Transmission to Inform Policy. J Med Internet Res. 2020 11 19;22(11):e24248. PubMed PMID: 33211026.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A SARS-CoV-2 Surveillance System in Sub-Saharan Africa: Modeling Study for Persistence and Transmission to Inform Policy. AU - Post,Lori Ann, AU - Argaw,Salem T, AU - Jones,Cameron, AU - Moss,Charles B, AU - Resnick,Danielle, AU - Singh,Lauren Nadya, AU - Murphy,Robert Leo, AU - Achenbach,Chad J, AU - White,Janine, AU - Issa,Tariq Ziad, AU - Boctor,Michael J, AU - Oehmke,James Francis, Y1 - 2020/11/19/ PY - 2020/09/28/received PY - 2020/10/22/accepted PY - 2020/10/20/revised PY - 2020/11/19/entrez PY - 2020/11/20/pubmed PY - 2020/11/27/medline KW - African COVID-19 surveillance system KW - African COVID-19 transmission acceleration KW - African COVID-19 transmission speed KW - African SARS-CoV-2 KW - African econometrics KW - African public health surveillance KW - African surveillance metrics KW - Angola KW - Benin KW - Botswana KW - Burkina Faso KW - Burundi KW - COVID-19 7-day persistence KW - COVID-19 transmission deceleration KW - COVID-19 transmission jerk KW - Cameroon KW - Central African Republic KW - Chad KW - Comoros KW - Congo KW - Cote d'Ivoire KW - Democratic Republic of Congo KW - Equatorial Guinea KW - Eritrea KW - Ethiopia KW - Gabon KW - Gambia KW - Ghana KW - Guinea KW - Guinea-Bissau KW - Kenya KW - Lesotho KW - Liberia KW - Madagascar KW - Malawi KW - Mali KW - Mauritania KW - Mauritius KW - Mozambique KW - Namibia KW - Niger KW - Nigeria KW - Rwanda KW - Sao Tome and Principe KW - Senegal KW - Seychelles KW - Sierra Leone KW - Somalia KW - South Africa KW - South Sudan KW - Sudan KW - Suriname KW - Swaziland KW - Tanzania KW - Togo KW - Uganda KW - Zambia KW - Zimbabwe KW - dynamic panel data KW - generalized method of the moments KW - global COVID-19 surveillance KW - sub-Saharan African COVID-19 SP - e24248 EP - e24248 JF - Journal of medical Internet research JO - J Med Internet Res VL - 22 IS - 11 N2 - BACKGROUND: Since the novel coronavirus emerged in late 2019, the scientific and public health community around the world have sought to better understand, surveil, treat, and prevent the disease, COVID-19. In sub-Saharan Africa (SSA), many countries responded aggressively and decisively with lockdown measures and border closures. Such actions may have helped prevent large outbreaks throughout much of the region, though there is substantial variation in caseloads and mortality between nations. Additionally, the health system infrastructure remains a concern throughout much of SSA, and the lockdown measures threaten to increase poverty and food insecurity for the subcontinent's poorest residents. The lack of sufficient testing, asymptomatic infections, and poor reporting practices in many countries limit our understanding of the virus's impact, creating a need for better and more accurate surveillance metrics that account for underreporting and data contamination. OBJECTIVE: The goal of this study is to improve infectious disease surveillance by complementing standardized metrics with new and decomposable surveillance metrics of COVID-19 that overcome data limitations and contamination inherent in public health surveillance systems. In addition to prevalence of observed daily and cumulative testing, testing positivity rates, morbidity, and mortality, we derived COVID-19 transmission in terms of speed, acceleration or deceleration, change in acceleration or deceleration (jerk), and 7-day transmission rate persistence, which explains where and how rapidly COVID-19 is transmitting and quantifies shifts in the rate of acceleration or deceleration to inform policies to mitigate and prevent COVID-19 and food insecurity in SSA. METHODS: We extracted 60 days of COVID-19 data from public health registries and employed an empirical difference equation to measure daily case numbers in 47 sub-Saharan countries as a function of the prior number of cases, the level of testing, and weekly shift variables based on a dynamic panel model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R. RESULTS: Kenya, Ghana, Nigeria, Ethiopia, and South Africa have the most observed cases of COVID-19, and the Seychelles, Eritrea, Mauritius, Comoros, and Burundi have the fewest. In contrast, the speed, acceleration, jerk, and 7-day persistence indicate rates of COVID-19 transmissions differ from observed cases. In September 2020, Cape Verde, Namibia, Eswatini, and South Africa had the highest speed of COVID-19 transmissions at 13.1, 7.1, 3.6, and 3 infections per 100,0000, respectively; Zimbabwe had an acceleration rate of transmission, while Zambia had the largest rate of deceleration this week compared to last week, referred to as a jerk. Finally, the 7-day persistence rate indicates the number of cases on September 15, 2020, which are a function of new infections from September 8, 2020, decreased in South Africa from 216.7 to 173.2 and Ethiopia from 136.7 to 106.3 per 100,000. The statistical approach was validated based on the regression results; they determined recent changes in the pattern of infection, and during the weeks of September 1-8 and September 9-15, there were substantial country differences in the evolution of the SSA pandemic. This change represents a decrease in the transmission model R value for that week and is consistent with a de-escalation in the pandemic for the sub-Saharan African continent in general. CONCLUSIONS: Standard surveillance metrics such as daily observed new COVID-19 cases or deaths are necessary but insufficient to mitigate and prevent COVID-19 transmission. Public health leaders also need to know where COVID-19 transmission rates are accelerating or decelerating, whether those rates increase or decrease over short time frames because the pandemic can quickly escalate, and how many cases today are a function of new infections 7 days ago. Even though SSA is home to some of the poorest countries in the world, development and population size are not necessarily predictive of COVID-19 transmission, meaning higher income countries like the United States can learn from African countries on how best to implement mitigation and prevention efforts. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21955. SN - 1438-8871 UR - https://www.unboundmedicine.com/medline/citation/33211026/A_SARS_CoV_2_Surveillance_System_in_Sub_Saharan_Africa:_Modeling_Study_for_Persistence_and_Transmission_to_Inform_Policy_ L2 - https://www.jmir.org/2020/11/e24248/ DB - PRIME DP - Unbound Medicine ER -