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ObjectivesThis study aimed to develop and validate a symptom prediction tool for COVID-19 test positivity in Nigeria.DesignPredictive modelling study.SettingAll Nigeria States and the Federal Capital Territory.ParticipantsA cohort of 43 221 individuals within the national COVID-19 surveillance dataset from 27 February to 27 August 2020. Complete dataset was randomly split into two equal halves: derivation and validation datasets. Using the derivation dataset (n=21 477), backward multivariable logistic regression approach was used to identify symptoms positively associated with COVID-19 positivity (by real-time PCR) in children (≤17 years), adults (18–64 years) and elderly (≥65 years) patients separately.Outcome measuresWeighted statistical and clinical scores based on beta regression coefficients and clinicians’ judgements, respectively. Using the validation dataset (n=21 744), area under the receiver operating characteristic curve (AUROC) values were used to assess the predictive capacity of individual symptoms, unweighted score and the two weighted scores.ResultsOverall, 27.6% of children (4415/15 988), 34.6% of adults (9154/26 441) and 40.0% of elderly (317/792) that had been tested were positive for COVID-19. Best individual symptom predictor of COVID-19 positivity was loss of smell in children (AUROC 0.56, 95% CI 0.55 to 0.56), either fever or cough in adults (AUROC 0.57, 95% CI 0.56 to 0.58) and difficulty in breathing in the elderly (AUROC 0.53, 95% CI 0.48 to 0.58) patients. In children, adults and the elderly patients, all scoring approaches showed similar predictive performance.ConclusionsThe predictive capacity of various symptom scores for COVID-19 positivity was poor overall. However, the findings could serve as an advocacy tool for more investments in resources for capacity strengthening of molecular testing for COVID-19 in Nigeria.

BackgroundWith reports of surges in COVID-19 case numbers across over 50 countries, country-level epidemiological analysis is required to inform context-appropriate response strategies for containment and mitigation of the outbreak. We aimed to compare the epidemiological features of the first and second waves of COVID-19 in Nigeria.MethodsWe conducted a retrospective analysis of the Surveillance Outbreak Response Management and Analysis System data of the first and second epidemiological waves, which were between 27 February and 24 October 2020, and 25 October 2020 to 3 April 2021, respectively. Descriptive statistical measures including frequencies and percentages, test positivity rate (TPR), cumulative incidence (CI) and case fatality rates (CFRs) were compared. A p value of ResultsThere were 802 143 tests recorded during the study period (362 550 and 439 593 in the first and second waves, respectively). Of these, 66 121 (18.2%) and 91 644 (20.8%) tested positive in the first and second waves, respectively. There was a 21.3% increase in the number of tests conducted in the second wave with TPR increasing by 14.3%. CI during the first and second waves were 30.3/100 000 and 42.0/100 000 respectively. During the second wave, confirmed COVID-19 cases increased among females and people 30 years old or younger and decreased among urban residents and individuals with travel history within 14 days of sample collection (p value ConclusionNigeria experienced a larger but less severe second wave of COVID-19. Continued implementation of public health and social measures is needed to mitigate the resurgence of another wave.

Background: COVID-19 mortality rate has not been formally assessed in Nigeria. We therefore aimed to address this gap and identify associated mortality risk factors during the first and second waves in Nigeria. Methods: We conducted a retrospective cohort study using the national surveillance database between February 27, 2020, and April 3, 2021. The outcome was deaths amongst persons with a laboratory diagnosis of COVID-19. Incidence rates of COVID-19 death per 100,000 person-days were estimated. Adjusted negative binomial regression was used to identify factors associated with COVID-19 death, and presented as adjusted Incidence Rate Ratios (aIRR) with 95% confidence intervals (CI). Results: The first wave included 65,790 COVID-19 patients, of whom 994 (1∙51%) died; the second wave included 91,089 patients, of whom 513 (0∙56%) died. The incidence rate of deaths related to COVID-19 was higher in the first wave [54∙25 (95% CI: 50∙98-57∙73)] than in the second wave [19∙19 (17∙60-20∙93)]. Factors independently associated with increased risk of death in both waves were: age ≥45 years, male gender [first wave aIRR 1∙65 (1∙35-2∙02) and second wave 1∙52 (1∙11-2∙06)], being symptomatic [aIRR 3∙17 (2∙59-3∙89) and 3∙04 (2∙20-4∙21)], and being hospitalised [aIRR 4∙19 (3∙26-5∙39) and 7∙84 (4∙90-12∙54)]. Interpretation: The incidence rate of COVID-19 death in Nigeria was higher in the first wave, suggesting improved public health response and care during the second wave. Regional mortality differences suggest that policy makers focus on regional equity in access to testing and quality of care to mitigate the impact of another COVID-19 wave in Nigeria. Funding: None to declare. Declaration of Interest: None to declare. Ethical Approval: Ethical approval for the study was given by the Nigeria National Health Research Ethics Committee (NHREC/01/01/2007-22/06/2020).