ABSTRACT
Till date the comprehensive clinical pictures, comorbid conditions, and long-term complications of COVID-19 are not known. Recently using a multi-omics-based strategy, we have predicted the drugs for COVID-19 management with [~]70% accuracy. Here, using a similar multi-omics-based bioinformatics approach and three-ways of analysis, we identified the symptoms, comorbid conditions, and short, mid and possible long-term complications of COVID-19 with [~]90% precision. In our analysis (i) we identified 27 parent, 170 child, and 403 specific conditions associated with COVID-19. (ii) Among the specific conditions, 36 are viral and 53 short-term, 62 short to mid to long-term, 194 mid to long-term, and 57 are congenital conditions. (iii) At a cut off "count of occurrence" of 4, we found [~] 90% of the enriched conditions are associated with COVID-19. (iv) Except the dry cough and loss of taste, all other COVID-19 associated mild and severe symptoms are enriched. (v) Cardiovascular, pulmonary, metabolic, musculoskeletal, neuropsychiatric, kidney, liver, and immune system disorders are found as top comorbid conditions. (vi) Specific diseases such as myocardial infarction, hypertension, COPD, lung injury, diabetes, cirrhosis, mood disorders, dementia, macular degeneration, chronic kidney disease, lupus, arthritis etc. along with several other diseases are also enriched as top candidates. (vii) Interestingly, many cancers and congenital disorders associated with COVID-19 severity are also identified. (viii) Arthritis, dermatomyositis, glioma, diabetes, psychiatric disorder, cardiovascular diseases having bidirectional relationship with COVID-19 are also found as top ranked conditions. Based on the accuracy ([~]90%) of this analysis, long presence of SARS-CoV-2 RNA in human, and our previously proposed "genetic remittance" assumption, we hypothesize that all the identified comorbid conditions including the short-long-mid and mid-long non-communicable diseases (NCDs) could also be long-term consequences in COVID-19 survivors and warrants long-term observational studies.
ABSTRACT
BackgroundWe investigated a likely scenario of COVID-19 spreading in Brazil through the complex airport network of the country, for the 90 days after the first national occurrence of the disease. After the confirmation of the first imported cases, the lack of a proper airport entrance control resulted in the infection spreading in a manner directly proportional to the amount of flights reaching each city, following first occurrence of the virus coming from abroad. MethodologyWe developed a SIR (Susceptible-Infected-Recovered) model divided in a metapopulation structure, where cities with airports were demes connected by the number of flights. Subsequently, we further explored the role of Manaus airport for a rapid entrance of the pandemic into indigenous territories situated in remote places of the Amazon region. ResultsThe expansion of the SARS-CoV-2 virus between cities was fast, directly proportional to the airport closeness centrality within the Brazilian air transportation network. There was a clear pattern in the expansion of the pandemic, with a stiff exponential expansion of cases for all cities. The more an airport showed closeness centrality, the greater was its vulnerability to SARS-CoV-2. ConclusionsWe discussed the weak pandemic control performance of Brazil in comparison with other tropical, developing countries, namely India and Nigeria. Finally, we proposed measures for containing virus spreading taking into consideration the scenario of high poverty.
