Estimation of epidemiological parameters and ascertainment rate from early transmission of COVID-19 across Africa

Han, Qing; Bragazzi, Nicola; Asgary, Ali; Orbinski, James; Wu, Jianhong; Kong, Jude Dzevela

doi:10.1098/rsos.230316

Cited by 5 publications

(8 citation statements)

References 40 publications

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“…It is important to underscore that the estimated basic and time-varying reproduction numbers in this study align closely with findings from other research. Notably, early pandemic research studies in Chad ( ), Central Afican African Republic ( ), Congo( ), Tanzania( ), Angola( ), Malawi( ) and Mozambique( ) by Han et al [ 23 ], employing similar methodologies, yielded estimates that were consistent or in close agreement with our basic reproduction estimates (see Table 1 ). In comparison to our study, very close estimates of the basic reproduction were also observed in Ivory Coast ( ), Ghana ( ), Kenya, ( ), and Nigeria ( ) from a study conducted by Oshinubi et al [ 24 ] and Demongeot et al [ 25 ].…”

Section: Resultssupporting

confidence: 85%

SARS-CoV-2 incidence monitoring and statistical estimation of the basic and time-varying reproduction number at the early onset of the pandemic in 45 sub-Saharan African countries

Oduro,

Arhin-Donkor,

Asiedu

et al. 2024

BMC Public Health

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The world battled to defeat a novel coronavirus 2019 (SARS-CoV-2 or COVID-19), a respiratory illness that is transmitted from person to person through contacts with droplets from infected persons. Despite efforts to disseminate preventable messages and adoption of mitigation strategies by governments and the World Health Organization (WHO), transmission spread globally. An accurate assessment of the transmissibility of the coronavirus remained a public health priority for many countries across the world to fight this pandemic, especially at the early onset. In this paper, we estimated the transmission potential of COVID-19 across 45 countries in sub-Saharan Africa using three approaches, namely, $$R_{0}$$ R 0 based on (i) an exponential growth model (ii) maximum likelihood (ML) estimation and (iii) a time-varying basic reproduction number at the early onset of the pandemic. Using data from March 14, 2020, to May 10, 2020, sub-Saharan African countries were still grappling with COVID-19 at that point in the pandemic. The region’s basic reproduction number ($$R_{0}$$ R 0 ) was 1.89 (95% CI: 1.767 to 2.026) using the growth model and 1.513 (95% CI: 1.491 to 1.535) with the maximum likelihood method, indicating that, on average, infected individuals transmitted the virus to less than two secondary persons. Several countries, including Sudan ($$R_{0}$$ R 0 : 2.03), Ghana ($$R_{0}$$ R 0 : 1.87), and Somalia ($$R_{0}$$ R 0 : 1.85), exhibited high transmission rates. These findings highlighted the need for continued vigilance and the implementation of effective control measures to combat the pandemic in the region. It is anticipated that the findings in this study would not only function as a historical record of reproduction numbers during the COVID-19 pandemic in African countries, but can serve as a blueprint for addressing future pandemics of a similar nature.

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Section: Resultssupporting

confidence: 85%

SARS-CoV-2 incidence monitoring and statistical estimation of the basic and time-varying reproduction number at the early onset of the pandemic in 45 sub-Saharan African countries

Oduro,

Arhin-Donkor,

Asiedu

et al. 2024

BMC Public Health

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“…The ascertainment rate, α, estimates were very low for Mozambique and Zimbabwe provinces, matching our prior assumption. Our estimates in both countries is in line with the studies by Han et al [18] and Evans et al [17]. This indicates that the majority of infections were never documented as cases; by extension, many severe cases requiring hospitalization or resulting in death may have been missed due to limited access to healthcare and testing facilities.…”

Section: Zimbabwe Parameter Estimatessupporting

confidence: 91%

“…The initial prior range of α i = [0.004 − 0.1] (which is low, corresponding to a maximum of one reported case for every ten infected) was motivated by the study of Han et al [18], which estimated an overall daily report rate (ascertainment rate) ranging from α = [0.0002 − 0.3], among all African countries. Furthermore, the study by Evans et al [17] highlighted that the lower-than-expected case burdens in Madagascar were explained solely by detection rates of 0.1-1% (or α = [0.001 − 0.01]).…”

Section: Initial Priorsmentioning

confidence: 99%

“…Understanding the level of ascertainment and the impact of undocumented cases on transmission is crucial to plan and implement control strategies. Models coupled with Bayesian inference methods have been used to investigate the role of undocumented infections in China [22], as well as for various regions (countries and US states) [7], 54 African countries [18], and 3 North African countries: Algeria, Egypt, and Morocco [11]. Russel et al [36] fitted a Bayesian Gaussian process model to estimate under-ascertainment in 210 countries and territories.…”

Section: Introductionmentioning

confidence: 99%

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SARS-CoV-2 transmission dynamics in Mozambique and Zimbabwe during the First Three Years of the Pandemic

Kaondera-Shava,

Galanti,

Perini

et al. 2024

Preprint

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The 2019 emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its rapid spread created a public health emergency of international concern. However, the impact of the pandemic in Sub-Saharan Africa, as documented in cases, hospitalizations and deaths, appears far lower than in the Americas, Europe, and Asia. Characterization of the transmission dynamics is critical for understanding how SARS-CoV-2 spread and the true scale of the pandemic. Here, to better understand SARS-CoV-2 transmission dynamics in two southern African countries, Mozambique and Zimbabwe, we developed a dynamic model-Bayesian inference system to estimate key epidemiologic parameters, namely the transmission and ascertainment rates. Total infection burdens (reported and unreported) during the first three years of the pandemic were reconstructed using a model-inference approach. Transmission rates rose with each successive wave, which aligns with observations in other continents. Ascertainment rates were found to be low matching prior assumption and consistent with other African countries. Overall, the estimated disease burden was higher than the documented cases, indicating need for improved reporting and surveillance. These findings aid understanding of COVID-19 disease and respiratory virus transmission dynamics in two African countries little investigated to date, and can help guide future public health planning and control strategies.

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“…The WHO estimates that only one in seven cases was being detected in Africa ( 8 ), highlighting a substantial gap in data accuracy and completeness. Our previous investigation into the early phase of the COVID-19 outbreak across 54 African nations also revealed a significant under-reporting trend ( 9 ). Specifically, an average of only about 5.37% of all COVID-19 cases was duly reported.…”

Section: Introductionmentioning

confidence: 93%

The determinants of COVID-19 case reporting across Africa

Han,

Rutayisire,

Mbogning Fonkou

et al. 2024

Front. Public Health

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BackgroundAccording to study on the under-estimation of COVID-19 cases in African countries, the average daily case reporting rate was only 5.37% in the initial phase of the outbreak when there was little or no control measures. In this work, we aimed to identify the determinants of the case reporting and classify the African countries using the case reporting rates and the significant determinants.MethodsWe used the COVID-19 daily case reporting rate estimated in the previous paper for 54 African countries as the response variable and 34 variables from demographics, socioeconomic, religion, education, and public health categories as the predictors. We adopted a generalized additive model with cubic spline for continuous predictors and linear relationship for categorical predictors to identify the significant covariates. In addition, we performed Hierarchical Clustering on Principal Components (HCPC) analysis on the reporting rates and significant continuous covariates of all countries.Results21 covariates were identified as significantly associated with COVID-19 case detection: total population, urban population, median age, life expectancy, GDP, democracy index, corruption, voice accountability, social media, internet filtering, air transport, human development index, literacy, Islam population, number of physicians, number of nurses, global health security, malaria incidence, diabetes incidence, lower respiratory and cardiovascular diseases prevalence. HCPC resulted in three major clusters for the 54 African countries: northern, southern and central essentially, with the northern having the best early case detection, followed by the southern and the central.ConclusionOverall, northern and southern Africa had better early COVID-19 case identification compared to the central. There are a number of demographics, socioeconomic, public health factors that exhibited significant association with the early case detection.

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Estimation of epidemiological parameters and ascertainment rate from early transmission of COVID-19 across Africa

Cited by 5 publications

References 40 publications

SARS-CoV-2 incidence monitoring and statistical estimation of the basic and time-varying reproduction number at the early onset of the pandemic in 45 sub-Saharan African countries

SARS-CoV-2 incidence monitoring and statistical estimation of the basic and time-varying reproduction number at the early onset of the pandemic in 45 sub-Saharan African countries

SARS-CoV-2 transmission dynamics in Mozambique and Zimbabwe during the First Three Years of the Pandemic

The determinants of COVID-19 case reporting across Africa

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