ObjectivesAs of 13 January 2021, there have been 3 113 963 confirmed cases of SARS-CoV-2 and 74 619 deaths across the African continent. Despite relatively lower numbers of cases initially, many African countries are now experiencing an exponential increase in case numbers. Estimates of the progression of disease and potential impact of different interventions are needed to inform policymaking decisions. Herein, we model the possible trajectory of SARS-CoV-2 in 52 African countries under different intervention scenarios.DesignWe developed a compartmental model of SARS-CoV-2 transmission to estimate the COVID-19 case burden for all African countries while considering four scenarios: no intervention, moderate lockdown, hard lockdown and hard lockdown with continued restrictions once lockdown is lifted. We further analysed the potential impact of COVID-19 on vulnerable populations affected by HIV/AIDS and tuberculosis (TB).ResultsIn the absence of an intervention, the most populous countries had the highest peaks in active projected number of infections with Nigeria having an estimated 645 081 severe infections. The scenario with a hard lockdown and continued post-lockdown interventions to reduce transmission was the most efficacious strategy for delaying the time to the peak and reducing the number of cases. In South Africa, projected peak severe infections increase from 162 977 to 2 03 261, when vulnerable populations with HIV/AIDS and TB are included in the analysis.ConclusionThe COVID-19 pandemic is rapidly spreading across the African continent. Estimates of the potential impact of interventions and burden of disease are essential for policymakers to make evidence-based decisions on the distribution of limited resources and to balance the economic costs of interventions with the potential for saving lives.
ObjectiveThe purpose of this analysis was to describe national critical care capacity shortages for 52 African countries and to outline needs for each country to adequately respond to the COVID-19 pandemic.MethodsA modified SECIR compartment model was used to estimate the number of severe COVID-19 cases at the peak of the outbreak. Projections of the number of hospital beds, ICU beds, and ventilators needed at outbreak peak were generated for four scenarios – if 30, 50, 70, or 100% of patients with severe COVID-19 symptoms seek health services—assuming that all people with severe infections would require hospitalization, that 4.72% would require ICU admission, and that 2.3% would require mechanical ventilation.FindingsAcross the 52 countries included in this analysis, the average number of severe COVID-19 cases projected at outbreak peak was 138 per 100,000 (SD: 9.6). Comparing current national capacities to estimated needs at outbreak peak, we found that 31of 50 countries (62%) do not have a sufficient number of hospital beds per 100,000 people if 100% of patients with severe infections seek out health services and assuming that all hospital beds are empty and available for use by patients with COVID-19. If only 30% of patients seek out health services then 10 of 50 countries (20%) do not have sufficient hospital bed capacity. The average number of ICU beds needed at outbreak peak across the 52 included countries ranged from 2 per 100,000 people (SD: 0.1) when 30% of people with severe COVID-19 infections access health services to 6.5 per 100,000 (SD: 0.5) assuming 100% of people seek out health services. Even if only 30% of severely infected patients seek health services at outbreak peak, then 34 of 48 countries (71%) do not have a sufficient number of ICU beds per 100,000 people to handle projected need. Only four countries (Cabo Verde, Egypt, Gabon, and South Africa) have a sufficient number of ventilators to meet projected national needs if 100% of severely infected individuals seek health services assuming all ventilators are functioning and available for COVID-19 patients, while 35 other countries require two or more additional ventilators per 100,000 people.ConclusionThe majority of countries lack sufficient ICU bed and ventilator capacity to care for the projected number of patients with severe COVID-19 infections at outbreak peak even if only 30% of severely infected patients seek health services.This analysis reveals there is an urgent need to allocate resources and increase critical care capacity in these countries.
Objectives As of August 24th 2020, there have been 1,084,904 confirmed cases of SARS-CoV-2 and 24,683 deaths across the African continent. Despite relatively lower numbers of cases initially, many African countries are now experiencing an exponential increase in case numbers. Estimates of the progression of disease and potential impact of different interventions are needed to inform policy making decisions. Herein, we model the possible trajectory of SARS-CoV-2 in 52 African countries under different intervention scenarios. Design We developed a compartmental model of SARS-CoV-2 transmission to estimate the COVID-19 case burden for all African countries while considering four scenarios: no intervention, moderate lockdown, hard lockdown, and hard lockdown with continued restrictions once lockdown is lifted. We further analyzed the potential impact of COVID-19 on vulnerable populations affected by HIV/AIDS and TB. Results In the absence of an intervention, the most populous countries had the highest peaks in active projected number of infections with Nigeria having an estimated 645,081 severe infections. The scenario with a hard lockdown and continued post-lockdown interventions to reduce transmission was the most efficacious strategy for delaying the time to the peak and reducing the number of cases. In South Africa projected peak severe infections increase from 162,977 to 203,261, when vulnerable populations with HIV/AIDS and TB are included in the analysis. Conclusion The COVID-19 pandemic is rapidly spreading across the African continent. Estimates of the potential impact of interventions and burden of disease are essential for policy makers to make evidence-based decisions on the distribution of limited resources and to balance the economic costs of interventions with the potential for saving lives.
Background Antimicrobial resistance (AMR) is one of the greatest global health challenges today, but burden assessment is hindered by uncertainty of AMR prevalence estimates. Geographical representation of AMR estimates typically pools data collected from several laboratories; however, these aggregations may introduce bias by not accounting for the heterogeneity of the population that each laboratory represents. Methods We used AMR data from up to 381 laboratories in the United States from The Surveillance Network to evaluate methods for estimating uncertainty of AMR prevalence estimates. We constructed confidence intervals for the proportion of resistant isolates using (1) methods that account for the clustered structure of the data, and (2) standard methods that assume data independence. Using samples of the full dataset with increasing facility coverage levels, we examined how likely the estimated confidence intervals were to include the population mean. Results Methods constructing 95% confidence intervals while accounting for possible within-cluster correlations (Survey and standard methods adjusted to employ cluster-robust errors), were more likely to include the sample mean than standard methods (Logit, Wilson score and Jeffreys interval) operating under the assumption of independence. While increased geographical coverage improved the probability of encompassing the mean for all methods, large samples still did not compensate for the bias introduced from the violation of the data independence assumption. Conclusion General methods for estimating the confidence intervals of AMR rates that assume data are independent, are likely to produce biased results. When feasible, the clustered structure of the data and any possible intra-cluster variation should be accounted for when calculating confidence intervals around AMR estimates, in order to better capture the uncertainty of prevalence estimates.
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