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1.
Epidemiol Infect ; 149: e261, 2021 05 14.
Article in English | MEDLINE | ID: covidwho-1647899

ABSTRACT

Epidemic intelligence activities are undertaken by the WHO Regional Office for Africa to support member states in early detection and response to outbreaks to prevent the international spread of diseases. We reviewed epidemic intelligence activities conducted by the organisation from 2017 to 2020, processes used, key results and how lessons learned can be used to strengthen preparedness, early detection and rapid response to outbreaks that may constitute a public health event of international concern. A total of 415 outbreaks were detected and notified to WHO, using both indicator-based and event-based surveillance. Media monitoring contributed to the initial detection of a quarter of all events reported. The most frequent outbreaks detected were vaccine-preventable diseases, followed by food-and-water-borne diseases, vector-borne diseases and viral haemorrhagic fevers. Rapid risk assessments generated evidence and provided the basis for WHO to trigger operational processes to provide rapid support to member states to respond to outbreaks with a potential for international spread. This is crucial in assisting member states in their obligations under the International Health Regulations (IHR) (2005). Member states in the region require scaled-up support, particularly in preventing recurrent outbreaks of infectious diseases and enhancing their event-based surveillance capacities with automated tools and processes.


Subject(s)
Epidemics/prevention & control , Public Health Surveillance/methods , World Health Organization/organization & administration , Africa/epidemiology , Communicable Disease Control , Communicable Diseases/epidemiology , Disease Outbreaks/prevention & control , Disease Outbreaks/statistics & numerical data , Global Health , Humans , Risk Assessment
3.
BMJ Glob Health ; 7(3)2022 03.
Article in English | MEDLINE | ID: covidwho-1741617

ABSTRACT

The geographic and economic characteristics unique to island nations create a different set of conditions for, and responses to, the spread of a pandemic compared with those of mainland countries. Here, we aimed to describe the initial period of the COVID-19 pandemic, along with the potential conditions and responses affecting variation in the burden of infections and severe disease burden, across the six island nations of the WHO's Africa region: Cabo Verde, Comoros, Madagascar, Mauritius, São Tomé e Príncipe and Seychelles. We analysed the publicly available COVID-19 data on confirmed cases and deaths from the beginning of the pandemic through 29 November 2020. To understand variation in the course of the pandemic in these nations, we explored differences in their economic statuses, healthcare expenditures and facilities, age and sex distributions, leading health risk factors, densities of the overall and urban populations and the main industries in these countries. We also reviewed the non-pharmaceutical response measures implemented nationally. We found that the burden of SARS-CoV-2 infection was reduced by strict early limitations on movement and biased towards nations where detection capacity was higher, while the burden of severe COVID-19 was skewed towards countries that invested less in healthcare and those that had older populations and greater prevalence of key underlying health risk factors. These findings highlight the need for Africa's island nations to invest more in healthcare and in local testing capacity to reduce the need for reliance on border closures that have dire consequences for their economies.


Subject(s)
COVID-19 , Influenza, Human , Delivery of Health Care , Humans , Influenza, Human/epidemiology , Pandemics , SARS-CoV-2
4.
Epidemiol Infect ; 149: e264, 2021 11 04.
Article in English | MEDLINE | ID: covidwho-1594301

ABSTRACT

As of 03 January 2021, the WHO African region is the least affected by the coronavirus disease-2019 (COVID-19) pandemic, accounting for only 2.4% of cases and deaths reported globally. However, concerns abound about whether the number of cases and deaths reported from the region reflect the true burden of the disease and how the monitoring of the pandemic trajectory can inform response measures.We retrospectively estimated four key epidemiological parameters (the total number of cases, the number of missed cases, the detection rate and the cumulative incidence) using the COVID-19 prevalence calculator tool developed by Resolve to Save Lives. We used cumulative cases and deaths reported during the period 25 February to 31 December 2020 for each WHO Member State in the region as well as population data to estimate the four parameters of interest. The estimated number of confirmed cases in 42 countries out of 47 of the WHO African region included in this study was 13 947 631 [95% confidence interval (CI): 13 334 620-14 635 502] against 1 889 512 cases reported, representing 13.5% of overall detection rate (range: 4.2% in Chad, 43.9% in Guinea). The cumulative incidence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was estimated at 1.38% (95% CI: 1.31%-1.44%), with South Africa the highest [14.5% (95% CI: 13.9%-15.2%)] and Mauritius [0.1% (95% CI: 0.099%-0.11%)] the lowest. The low detection rate found in most countries of the WHO African region suggests the need to strengthen SARS-CoV-2 testing capacities and adjusting testing strategies.


Subject(s)
COVID-19/diagnosis , COVID-19/epidemiology , SARS-CoV-2 , World Health Organization/organization & administration , Africa/epidemiology , Aged , COVID-19/mortality , COVID-19/virology , Humans , Incidence , Middle Aged , Retrospective Studies , Time Factors
5.
Epidemiol Infect ; 149: e256, 2021 08 16.
Article in English | MEDLINE | ID: covidwho-1586104

ABSTRACT

This study analysed the reported incidence of COVID-19 and associated epidemiological and socio-economic factors in the WHO African region. Data from COVID-19 confirmed cases and SARS-CoV-2 tests reported to the WHO by Member States between 25 February and 31 December 2020 and publicly available health and socio-economic data were analysed using univariate and multivariate binomial regression models. The overall cumulative incidence was 1846 cases per million population. Cape Verde (21 350 per million), South Africa (18 060 per million), Namibia (9840 per million), Eswatini (8151 per million) and Botswana (6044 per million) recorded the highest cumulative incidence, while Benin (260 per million), Democratic Republic of Congo (203 per million), Niger (141 cases per million), Chad (133 per million) and Burundi (62 per million) recorded the lowest. Increasing percentage of urban population (ß = -0.011, P = 0.04) was associated with low cumulative incidence, while increasing number of cumulative SARS-CoV-2 tests performed per 10 000 population (ß = 0.0006, P = 0.006) and the proportion of population aged 15-64 years (adjusted ß = 0.174, P < 0.0001) were associated with high COVID-19 cumulative incidence. With limited testing capacities and overwhelmed health systems, these findings highlight the need for countries to increase and decentralise testing capacities and adjust testing strategies to target most at-risk populations.


Subject(s)
COVID-19/epidemiology , SARS-CoV-2 , World Health Organization , Adolescent , Adult , Africa/epidemiology , Humans , Incidence , Logistic Models , Middle Aged , Multivariate Analysis , Retrospective Studies , Time Factors , Young Adult
6.
Epidemiol Infect ; 149: e259, 2021 05 10.
Article in English | MEDLINE | ID: covidwho-1586100

ABSTRACT

Successive waves of COVID-19 transmission have led to exponential increases in new infections globally. In this study, we have applied a decision-making tool to assess the risk of continuing transmission to inform decisions on tailored public health and social measures (PHSM) using data on cases and deaths reported by Member States to the WHO Regional Office for Africa as of 31 December 2020. Transmission classification and health system capacity were used to assess the risk level of each country to guide implementation and adjustments to PHSM. Two countries out of 46 assessed met the criteria for sporadic transmission, one for clusters of cases, and 43 (93.5%) for community transmission (CT) including three with uncontrolled disease incidence (Eswatini, Namibia and South Africa). Health system response's capacities were assessed as adequate in two countries (4.3%), moderate in 13 countries (28.3%) and limited in 31 countries (64.4%). The risk level, calculated as a combination of transmission classification and health system response's capacities, was assessed at level 0 in one country (2.1%), level 1 in two countries (4.3%), level 2 in 11 countries (23.9%) and level 3 in 32 (69.6%) countries. The scale of severity ranged from 0 to 4, with 0 the lowest. CT coupled with limited response capacity resulted in a level 3 risk assessment in most countries. Countries at level 3 should be considered as priority focus for additional assistance, in order to prevent the risk rising to level 4, which may necessitate enforcing hard and costly lockdown measures. The large number of countries at level 3 indicates the need for an effective risk management system to be used as a basis for adjusting PHSM at national and sub-national levels.


Subject(s)
COVID-19/epidemiology , Decision Making , SARS-CoV-2 , World Health Organization , Africa/epidemiology , Delivery of Health Care , Humans , Public Health Administration , Risk Assessment
7.
Epidemiol Infect ; 149: e258, 2021 09 08.
Article in English | MEDLINE | ID: covidwho-1586098

ABSTRACT

Experience gained from responding to major outbreaks may have influenced the early coronavirus disease-2019 (COVID-19) pandemic response in several countries across Africa. We retrospectively assessed whether Guinea, Liberia and Sierra Leone, the three West African countries at the epicentre of the 2014-2016 Ebola virus disease outbreak, leveraged the lessons learned in responding to COVID-19 following the World Health Organization's (WHO) declaration of a public health emergency of international concern (PHEIC). We found relatively lower incidence rates across the three countries compared to many parts of the globe. Time to case reporting and laboratory confirmation also varied, with Guinea and Liberia reporting significant delays compared to Sierra Leone. Most of the selected readiness measures were instituted before confirmation of the first case and response measures were initiated rapidly after the outbreak confirmation. We conclude that the rapid readiness and response measures instituted by the three countries can be attributed to their lessons learned from the devastating Ebola outbreak, although persistent health systems weaknesses and the unique nature of COVID-19 continue to challenge control efforts.


Subject(s)
COVID-19/epidemiology , Ebolavirus , Hemorrhagic Fever, Ebola/epidemiology , Africa, Western/epidemiology , Delivery of Health Care , Humans , Incidence , SARS-CoV-2 , Time Factors
8.
Emerg Infect Dis ; 26(11): 2555-2564, 2020 11.
Article in English | MEDLINE | ID: covidwho-928226

ABSTRACT

Large-scale protracted outbreaks can be prevented through early detection, notification, and rapid control. We assessed trends in timeliness of detecting and responding to outbreaks in the African Region reported to the World Health Organization during 2017-2019. We computed the median time to each outbreak milestone and assessed the rates of change over time using univariable and multivariable Cox proportional hazard regression analyses. We selected 296 outbreaks from 348 public reported health events and evaluated 184 for time to detection, 232 for time to notification, and 201 for time to end. Time to detection and end decreased over time, whereas time to notification increased. Multiple factors can account for these findings, including scaling up support to member states after the World Health Organization established its Health Emergencies Programme and support given to countries from donors and partners to strengthen their core capacities for meeting International Health Regulations.


Subject(s)
Disease Outbreaks , Public Health , Africa/epidemiology , Global Health , Humans , Population Surveillance , Time Factors , World Health Organization
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