Corrigendum to "A decision support system for multi-target disease diagnosis: A bioinformatics approach"[Heliyon 6 (3), (March 2020) Article e03657].

[This corrects the article DOI: 10.1016/j.heliyon.2020.e03657.].

Design of a FAIR digital data health infrastructure in Africa for COVID-19 reporting and research.

The limited volume of COVID-19 data from Africa raises concerns for global genome research, which requires a diversity of genotypes for accurate disease prediction, including on the provenance of the new SARS-CoV-2 mutations. The Virus Outbreak Data Network (VODAN)-Africa studied the possibility of increasing the production of clinical data, finding concerns about data ownership, and the limited use of health data for quality treatment at point of care. To address this, VODAN Africa developed an architecture to record clinical health data and research data collected on the incidence of COVID-19, producing these as human- and machine-readable data objects in a distributed architecture of locally governed, linked, human- and machine-readable data. This architecture supports analytics at the point of care and-through data visiting, across facilities-for generic analytics. An algorithm was run across FAIR Data Points to visit the distributed data and produce aggregate findings. The FAIR data architecture is deployed in Uganda, Ethiopia, Liberia, Nigeria, Kenya, Somalia, Tanzania, Zimbabwe, and Tunisia.

Geographical trend analysis of COVID-19 pandemic onset in Africa.

Little has been documented in literature concerning the manner of occurrence and spread of COVID-19 in Africa. Understanding the geographic nature of the corona virus pandemic may offer critical response signals for Africa. This paper employed analysis of variance (ANOVA) to show that significant variations exist among African countries', particularly total population as well as those using basic drinking water services, gross national income, expenditure on health, number of physicians and air transport passengers. Although we have only considered the number of confirmed corona virus infections noting that the fatality may be too early to discuss, we have relied on data from the European Centre for Disease Prevention and Control (ECDC) to establish a significant association between international mobility based on average annual air passenger carried (r â€‹= â€‹0.6) which also successfully predicted (R 2 â€‹= â€‹0.501) the number of COVID-19 cases reported in each country along with the population density (R 2 â€‹= â€‹0.418). We also detected that COVID-19 cases report y geometrically increased daily x (R 2 â€‹= â€‹0.860) with a 2nd order polynomial equation in the form of y â€‹= â€‹0.3993 â€‹× â€‹2-8.7569 x and a clustered spatial pattern with a nearest neighbour ratio of 0.025 significant at 0.05 α-level. African countries have responded to the pandemic in different ways including partial lockdown, closure of borders and airports as well as providing test centres. We concluded that 40% of Africa are categorized as emerging hot spots while responses differ significantly across regions.

An exploratory assessment of a multidimensional healthcare and economic data on COVID-19 in Nigeria.

The coronavirus disease of 2019 (COVID-19) is a pandemic that is ravaging Nigeria and the world at large. This data article provides a dataset of daily updates of COVID-19 as reported online by the Nigeria Centre for Disease Control (NCDC) from February 27, 2020 to September 29, 2020. The data were obtained through web scraping from different sources and it includes some economic variables such as the Nigeria budget for each state in 2020, population estimate, healthcare facilities, and the COVID-19 laboratories in Nigeria. The dataset has been processed using the standard of the FAIR data principle which encourages its findability, accessibility, interoperability, and reusability and will be relevant to researchers in different fields such as Data Science, Epidemiology, Earth Modelling, and Health Informatics.

Malaria patients in Nigeria: Data exploration approach.

Malaria is a life threatening disease which is usually transmitted to people through the bite of infected female anopheles mosquitoes. However, this article deals with the data exploration of malaria symptoms reported by 337 patients attended to at Federal Polytechnic Ilaro Medical centre, Ogun State Nigeria. The study covers a period of four (4) weeks monitoring of patients attendance, their consultation with physician and malaria test results as compared to their claims of malaria infection. Logistic regression was used for the basic analysis of the dataset and it was discovered that people in the age range 38-47 years are mostly affected with malaria and that females are the most infected gender species with headache being the most significant symptom based on its Wald statistic value. This study strongly recommends the introduction of a long lasting malaria prevention scheme that cut across all categories of ages and genders within the Nigerian community, and that self-medication should be seriously warned against as most claims of malaria were not actually found to be true upon verification.

A decision support system for multi-target disease diagnosis: A bioinformatics approach.

Malaria and typhoid fever are revered for their ability to individually or jointly cause high mortality rate. Both malaria and typhoid fever have similar symptoms and are famous for their co-existence in the human body, hence, causes problem of under-diagnosis when doctors tries to determine the exact disease out of the two diseases. This paper proposes a Bioinformatics Based Decision Support System (BBDSS) for malaria, typhoid and malaria typhoid diagnosis. The system is a hybrid of expert system and global alignment with constant penalty. The architecture of the proposed system takes input diagnosis sequence and benchmark diagnosis sequences through the browser, store these diagnosis sequences in the Knowledge base and set up the IF-THEN rules guiding the diagnosis decisions for malaria, typhoid and malaria typhoid respectively. The matching engine component of the system receives as input the input sequence and applies global alignment technique with constant penalty for the matching between the input sequence and the three benchmark sequences in turns. The global alignment technique with constant penalty applies its pre-defined process to generate optimal alignment and determine the disease condition of the patient through alignment scores comparison for the three benchmark diagnosis sequences. In order to evaluate the proposed system, ANOVA was used to compare the means of the three independent groups (malaria, typhoid and malaria typhoid) to determine whether there is statistical evidence that the associated values on the diagnosis variables means are significantly different. The ANOVA results indicated that the mean of the values on diagnosis variables is significantly different for at least one of the disease status groups. Similarly, multiple comparisons tests was further used to explicitly identify which means were different from one another. The multiple comparisons results showed that there is a statistically significant difference in the values on the diagnosis variables to diagnose the disease conditions between the groups of malaria and malaria typhoid. Conversely, there were no differences between the groups of malaria and typhoid fever as well as between the groups of typhoid fever and malaria typhoid. In order to show mean difference in the diagnosis scores between the orthodox and the proposed diagnosis system, t-test statistics was used. The results of the t-test statistics indicates that the mean values of diagnosis from the orthodox system differ from those of the proposed system. Finally, the evaluation of the proposed diagnosis system is most efficient at providing diagnosis for malaria and malaria typhoid at 97% accuracy.