First COVID-19 case in Zambia — Comparative phylogenomic analyses of SARS-CoV-2 detected in African countries

Since its first discovery in December 2019 in Wuhan, China, COVID-19, caused by the novel coronavirus SARS-CoV-2, has spread rapidly worldwide. While African countries were relatively spared initially, the initial low incidence of COVID-19 cases was not sustained for long due to continuing travel links between China, Europe and Africa. In preparation, Zambia had applied a multisectoral national epidemic disease surveillance and response system resulting in the identification of the first case within 48 h of the individual entering the country by air travel from a trip to France. Contact tracing showed that SARS-CoV-2 infection was contained within the patient’s household, with no further spread to attending health care workers or community members. Phylogenomic analysis of the patient’s SARS-CoV-2 strain showed that it belonged to lineage B.1.1., sharing the last common ancestor with SARS-CoV-2 strains recovered from South Africa. At the African continental level, our analysis showed that B.1 and B.1.1 lineages appear to be predominant in Africa. Whole genome sequence analysis should be part of all surveillance and case detection activities in order to monitor the origin and evolution of SARS-CoV-2 lineages across Africa

Establishing an epidemiosurveillance centre in a resource-constrained setting: A Zambian experience

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Establishing an epidemiosurveillance centre in a resource-constrained setting: A Zambian experience

This presentation outlines the creation of a Provincial Epidemiological and Information Centre (PEIC) in Zambia’s Luapula province. This is only the second epidemiosurveillance centre in the country. Luapula province in the northern part of Zambia being one of only 3 provinces out of a total of 10 provinces that are free of theilleriosis in Zambia has the potential of being Zambia’s largest disease free zone. The challenges as well as lessons learnt from setting up this epidemiosurveillance centre are presented

Predicting Mortality in Hospitalized COVID-19 Patients in Zambia: An Application of Machine Learning

The coronavirus disease 2019 (COVID-19) has wreaked havoc globally, resulting in millions of cases and deaths. The objective of this study was to predict mortality in hospitalized COVID-19 patients in Zambia using machine learning (ML) methods based on factors that have been shown to be predictive of mortality and thereby improve pandemic preparedness. This research employed seven powerful ML models that included decision tree (DT), random forest (RF), support vector machines (SVM), logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and XGBoost (XGB). These classifiers were trained on 1,433 hospitalized COVID-19 patients from various health facilities in Zambia. The performances achieved by these models were checked using accuracy, recall, F1-Score, area under the receiver operating characteristic curve (ROC_AUC), area under the precision-recall curve (PRC_AUC), and other metrics. The best-performing model was the XGB which had an accuracy of 92.3%, recall of 94.2%, F1-Score of 92.4%, and ROC_AUC of 97.5%. The pairwise Mann–Whitney U-test analysis showed that the second-best model (GB) and the third-best model (RF) did not perform significantly worse than the best model (XGB) and had the following: GB had an accuracy of 91.7%, recall of 94.2%, F1-Score of 91.9%, and ROC_AUC of 97.1%. RF had an accuracy of 90.8%, recall of 93.6%, F1-Score of 91.0%, and ROC_AUC of 96.8%. Other models showed similar results for the same metrics checked. The study successfully derived and validated the selected ML models and predicted mortality effectively with reasonably high performance in the stated metrics. The feature importance analysis found that knowledge of underlying health conditions about patients’ hospital length of stay (LOS), white blood cell count, age, and other factors can help healthcare providers offer lifesaving services on time, improve pandemic preparedness, and decongest health facilities in Zambia and other countries with similar settings

Euvichol-plus vaccine campaign coverage during the 2017/2018 cholera outbreak in Lusaka district, Zambia: a cross-sectional descriptive study

Objective To determine the coverage for the oral cholera vaccine (OCV) campaign conducted during the 2017/2018 cholera outbreak in Lusaka, Zambia.Study design A descriptive cross-sectional study employing survey method conducted among 1691 respondents from 369 households following the second round of the 2018 OCV campaign.Study setting Four primary healthcare facilities and their catchment areas in Lusaka city (Kanyama, Chawama, Chipata and Matero subdistricts).Participants A total of 1691 respondents 12 months and older sampled from 369 households where the campaign was conducted. A satellite map-based sampling technique was used to randomly select households.Data management and analysis A pretested electronic questionnaire uploaded on an electronic tablet (ODK V.1.12.2) was used for data collection. Descriptive statistics were computed to summarise respondents’ characteristics and OCV coverage per dose. Bivariate analysis (χ2 test) was conducted to stratify OCV coverage according to age and sex for each round (p<0.05).Results The overall coverage for the first, second and two doses were 81.3% (95% CI 79.24% to 83.36%), 72.1% (95% CI 69.58% to 74.62%) and 66% (95% CI 63.22% to 68.78%), respectively. The drop-out rate was 18.8% (95% CI 14.51% to 23.09%). Of the 81.3% who received the first dose, 58.8% were female. Among those who received the second dose, the majority (61.0%) were females aged between 5 and 14 years (42.6%) and 15 and 35 years (27.7%). Only 15.5% of the participants aged between 36 and 65 and 2.5% among those aged above 65 years received the second dose.Conclusion These findings confirm the 2018 OCV campaign coverage and highlight the need for follow-up surveys to validate administrative coverage estimates using population-based methods. Reliance on health facility data alone may mask low coverage and prevent measures to improve programming. Future public health interventions should consider sociodemographic factors in order to achieve optimal vaccine coverage