ABSTRACT
OBJECTIVES: To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). METHODS: A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake. RESULTS: A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives. CONCLUSIONS: We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines.
Subject(s)
COVID-19 Vaccines , COVID-19 , Humans , Artificial Intelligence , COVID-19/epidemiology , COVID-19/prevention & control , Biological Transport , Data Analysis , VaccinationABSTRACT
BACKGROUND: COVID-19 is an infectious disease of variable severity caused by a new coronavirus. Clinical presentation ranges from asymptomatic cases to severe illness. Most cases in newborns appear to be asymptomatic or mild. OBJECTIVE: To conduct a systematic review of the literature on published studies of COVID-19 in newborns with a positive RT-PCR test. METHODS: The PubMed and EMBASE databases were searched for infection data in newborns from 1 December 2019-21 May 2021. The mesh terms included "SARS-CoV-2", "COVID-19", "novel coronavirus", "newborns" and "neonates". The selection criteria were as follows: original studies reporting clinical, radiological, laboratory, and outcome data in newborns with a positive RT-PCR test for SARS-CoV-2. Two independent investigators reviewed the studies. RESULTS: Seventy-two studies that involved 236 newborns were included. The main clinical manifestations were fever (43.2%), respiratory (46.6%), and gastrointestinal (35.2%) symptoms; 60.1% had mild/moderate disease. A total of 52.5% had a chest X-ray; 43.5% were normal, and 24.1% reported consolidation/infiltration images. The most frequent laboratory abnormalities were elevated C reactive protein and elevated procalcitonin and lymphopenia. Mortality was 1.7%. CONCLUSION: Symptoms of SARS-CoV-2 infection were mild to moderate in most of the newborns. The prognosis was good, and mortality was mainly associated with other comorbidities.
