Estimating healthcare resource needs for COVID-19 patients in Nigeria

Ogunbameru, Adeteju; Barrett, Kali; Joda, A.E.; Khan, Yasín A.; Pechlivanoglou, Petros; Mac, Stephen; Naimark, David; Ximenes, Raphael; Sander, Beate

doi:10.11604/pamj.2020.37.293.26017

Cited by 3 publications

(2 citation statements)

References 9 publications

(8 reference statements)

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“…While there has been no official data on the number of personal protective equipment, ventilators, and ICUs, that are available in Nigeria, it is estimated that there was 450 ventilators and 350 ICU beds at the start of the pandemic (Ogunbameru et al 2020 ). For a country with a population of about 200 million persons, an extrapolation using the figure would mean that there were just about 2 ventilators per 1 million persons in Nigeria.…”

Section: Covid-19 Response: Clinical Intervention and Implementationmentioning

confidence: 99%

A critical evaluation of Nigeria’s response to the first wave of COVID-19

Jacobs

Okeke

2022

Bull Natl Res Cent

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Background The first wave of the Coronavirus Disease 2019 (COVID-19) pandemic began when the first index case was reported in Nigeria on the 27th of February 2020, and since then, more than 68,000 cases of the disease were confirmed, with 1173 deaths as of November 30, 2020. Main body Daily situation reports from the Nigeria Centre for Disease Control spanning February 27–November 30, 2020, were fully considered in this review. Further literature search was performed using PubMed and Google Scholar databases for articles related to response measures adopted by Nigeria. The instantaneous reproduction number (R) was then estimated as a metric to investigate the non-pharmaceutical intervention measures. Nigeria responded to COVID-19 pandemic by implementing anti-COVID-19 mitigation strategies in travel restrictions, social distancing, source control, contact tracing, self-isolation, and quarantine, as well as in clinical interventions. Our epidemiological model estimated the R-value of more than 1.0 in Nigeria and in each of all the 36 states and the Federal Capital Territory. Conclusion Nigeria implemented containment and mitigation measures in response to the first wave of COVID-19 and these measures may have contributed to the mild COVID-19 outcome in Nigeria compared to the global trend. However, inadequate PCR testing capacity, lack or suboptimal utilization of epidemic metrics like the virus reproduction number (R) to inform decision making, and premature easing of lockdown measures among others were major challenges to the effective implementation of the COVID-19 response measures.

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Section: Covid-19 Response: Clinical Intervention and Implementationmentioning

confidence: 99%

A critical evaluation of Nigeria’s response to the first wave of COVID-19

Jacobs

Okeke

2022

Bull Natl Res Cent

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“…Denhard et al [18] defined an index for oxygen treatment capacity and facility treatment capacity to assess the preparedness of the Mozambican health system to respond to COVID-19 pandemic. In Nigeria, Ogunbameru et al [19] adapted the model used for Ontario, Canada, to estimate the healthcare resources depletion date and the corresponding number of excess deaths. For South Africa, studies have been conducted to discuss the spatial distribution of hospital beds across the country and the challenges of sharing the healthcare resources between provinces [8,20].…”

Section: Introductionmentioning

confidence: 99%

Management of hospital beds and ventilators in the Gauteng province, South Africa, during the COVID-19 pandemic

Alavinejad

Mellado²,

Asgary³

et al. 2022

PLOS Glob Public Health

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We conducted an observational retrospective study on patients hospitalized with COVID-19, during March 05, 2020, to October 28, 2021, and developed an agent-based model to evaluate effectiveness of recommended healthcare resources (hospital beds and ventilators) management strategies during the COVID-19 pandemic in Gauteng, South Africa. We measured the effectiveness of these strategies by calculating the number of deaths prevented by implementing them. We observed differ ences between the epidemic waves. The length of hospital stay (LOS) during the third wave was lower than the first two waves. The median of the LOS was 6.73 days, 6.63 days and 6.78 days for the first, second and third wave, respectively. A combination of public and private sector provided hospital care to COVID-19 patients requiring ward and Intensive Care Units (ICU) beds. The private sector provided 88.4% of High care (HC)/ICU beds and 49.4% of ward beds, 73.9% and 51.4%, 71.8% and 58.3% during the first, second and third wave, respectively. Our simulation results showed that with a high maximum capacity, i.e., 10,000 general and isolation ward beds, 4,000 high care and ICU beds and 1,200 ventilators, increasing the resource capacity allocated to COVID- 19 patients by 25% was enough to maintain bed availability throughout the epidemic waves. With a medium resource capacity (8,500 general and isolation ward beds, 3,000 high care and ICU beds and 1,000 ventilators) a combination of resource management strategies and their timing and criteria were very effective in maintaining bed availability and therefore preventing excess deaths. With a low number of maximum available resources (7,000 general and isolation ward beds, 2,000 high care and ICU beds and 800 ventilators) and a severe epidemic wave, these strategies were effective in maintaining the bed availability and minimizing the number of excess deaths throughout the epidemic wave.

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Assessing the Accuracy of Early COVID-19 Case and Fatality Model Projections in Africa

Mabuka¹,

Craig²,

Schueller³

et al. 2022

Preprint

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ObjectiveWe compared reported COVID-19 case, fatality, and peak date data for Africa Union (AU) member states with estimates and projections produced by various mathematical models to assess their accuracy in the context of an ongoing pandemic and identify key gaps to improve the utility of models in the future.MethodsWe conducted a systematic literature review to identify studies published in any language between January and December 2020 that reported results of COVID-19 modeling analyses for any AU member state. Reported COVID-19 case, fatality, peak date, and testing rate data were obtained. Descriptive, bivariate, and meta-analyses were conducted to compare reported data to model-generated estimates. FindingsFor included countries in the respective model simulation periods, model-predicted cumulative cases ranged from 2 to 76,213,155 while model-predicted cumulative deaths ranged from 8 to 700,000. The difference between reported and predicted cumulative COVID-19 cases was between -99.3 % to 1.44×106 % with most values being above 24.7%, and the difference between reported and predicted cumulative COVID-19 deaths for models reviewed was between -2.0 % to 2.73×105 % with most values being above 50.0%. The difference in the predicted and reported dates for the first epidemic wave peak was between -242 Days to 249 Days.ConclusionFor the first COVID-19 epidemic wave, epidemiological model results were observed to have high precision but low accuracy when compared to reported peak case date and cumulative cases and deaths indicating that these data were either under-reported or model-overestimated.

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Estimating healthcare resource needs for COVID-19 patients in Nigeria

Cited by 3 publications

References 9 publications

A critical evaluation of Nigeria’s response to the first wave of COVID-19

A critical evaluation of Nigeria’s response to the first wave of COVID-19

Management of hospital beds and ventilators in the Gauteng province, South Africa, during the COVID-19 pandemic

Assessing the Accuracy of Early COVID-19 Case and Fatality Model Projections in Africa

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