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Background: The COVID-19 pandemic has ravaged economies, health systems, and lives globally. Concerns surrounding near total economic collapse, loss of livelihood and emotional complications ensuing from lockdowns and commercial inactivity, resulted in governments loosening economic restrictions. These concerns were further exacerbated by the absence of vaccines and drugs to combat the disease, with the fear that the next wave of the pandemic would be more fatal. Consequently, integrating disease surveillance mechanism into public healthcare systems is gaining traction, to reduce the spread of community and cross-border infections and offer informed medical decisions. Methods: Publicly available datasets of coronavirus cases around the globe deposited between December, 2019 and March 15, 2021 were retrieved from GISAID EpiFluTM and processed. Also retrieved from GISAID were data on the different SARS-CoV-2 variant types since inception of the pandemic. Results: Epidemiological analysis offered interesting statistics for understanding the demography of SARS-CoV-2 and helped the elucidation of local and foreign transmission through a history of contact travels. Results of genome pattern visualization and cognitive knowledge mining revealed the emergence of high intra-country viral sub-strains with localized transmission routes traceable to immediate countries, for enhanced contact tracing protocol. Variant surveillance analysis indicates increased need for continuous monitoring of SARS-CoV-2 variants.  A collaborative Internet of Health Things (IoHT) framework was finally proposed to impact the public health system, for robust and intelligent support for modelling, characterizing, diagnosing and real-time contact tracing of infectious diseases. Conclusions: Localizing healthcare disease surveillance is crucial in emerging disease situations and will support real-time/updated disease case definitions for suspected and probable cases. The IoHT framework proposed in this paper will assist early syndromic assessments of emerging infectious diseases and support healthcare/medical countermeasures as well as useful strategies for making informed policy decisions to drive a cost effective, smart healthcare system.

We provide in this Data Note the details of maternal, neonatal and child health (MNCH) datasets curated directly from patients’ medical records; comprising 538 maternal, 720 neonatal and 425 child records, captured at St Luke’s General Hospital, Anua, Uyo, Nigeria, from 2014 to 2019. Variables included in the datasets are gender, age, class of patient (mother/infant/child), LGA (local government area), diagnosis, symptoms, prescription, blood pressure (mm Hg), temperature (degree centigrade), and weight (Kg). The purpose of this publication is to describe the datasets for researchers who may be interested in its reuse (for analysis, research, quality assurance, policy formulation/decision, patient safety, and more). The curated datasets also involved the capturing of location information (GPS: global positioning system data) from the study area, to aid spatiotemporal and informed demographic analysis. We detail the methods used to curate the datasets and describe the protocol of variables selection and processing. For reasons of data privacy, some patients’ personal information such as names were replaced with patient numbers (a sequence generated using Microsoft Excel). Furthermore, the addresses/locations of the patients, date of visit, latitude, longitude, elevation, and GPS accuracy are restricted. Restricted data can be made available to readers after a formal request to the corresponding author (see data restriction statement). The curated datasets are available at the Open Science Framework.