Severe airport sanitarian control could slow down the spreading of COVID-19 pandemics in Brazil

Servio Pontes Ribeiro; Wesley Dattilo; Alcides Castro e Silva; Alexandre Barbosa Reis; Aristoteles Goes-Neto; Luiz Alcantara; Marta Giovanetti; Wendel Coura-vital; Geraldo Wilson Fernandes; Vasco Ariston Azevedo

1 Abstract 2 Background. We investigated a likely scenario of COVID-19 spreading in Brazil 3 through the complex airport network of the country, for the 90 days after the first 4 national occurrence of the disease. After the confirmation of the first imported cases, the 5 lack of a proper airport entrance control resulted in the infection spreading in a manner 6 directly proportional to the amount of flights reaching each city, following first 7 occurrence of the virus coming from abroad. 8 Methodology. We developed a SIR (Susceptible-Infected-Recovered) model divided in 9 a metapopulation structure, where cities with airports were demes connected by the 10 number of flights. Subsequently, we further explored the role of Manaus airport for a 11 rapid entrance of the pandemic into indigenous territories situated in remote places of 12 the Amazon region. 13 Results. The expansion of the SARS-CoV-2 virus between cities was fast, directly 14 proportional to the airport closeness centrality within the Brazilian air transportation 15 network. There was a clear pattern in the expansion of the pandemic, with a stiff 16 exponential expansion of cases for all cities. The more an airport showed closeness 17 centrality, the greater was its vulnerability to SARS-CoV-2. 18 Conclusions. We discussed the weak pandemic control performance of Brazil 34 From 17 th to 18 th March, Brazil had an increase of 31% in one day, with only four 35 capitals exhibiting community transmission, which was the same to India. However, a 36 very distinct pattern in the ascending starting point for the reported disease exponential 37 curve was observed in each country. By enlarging the comparison to another 38 developing tropical country in the Southern Hemisphere (thus in the same season), we 39 selected Nigeria, since it was the first country to detect a COVID-19 case in Africa. 40 Nigeria displayed less than 10 confirmed cases during the same period of time. 41 Furthermore, Nigeria has a population (206 million) similar to that of Brazil (209 million) . 42 Both India and Nigeria claim they imposed severe entrance control, and close 43 following up of each confirmed case, as well as their living and working area, and 44 people in contact with them. In Brazil, the Ministry of Health has developed a good 45 monitoring network and a comprehensive preparation of the health system for the worst-46 case scenario. Nonetheless, apparently, the decisions from the Ministry of Health did 47 not cover airport control, and only on March 19 th , eventually too late, the government 48 decided to control the airports, avoiding the entrance of people coming from Europe or 49 Asia. Hence, the entrance of diseased people in Brazil has been occurring with no 50 control, at least until the aforementioned date. Moreover, after confirming that a person 51 is infected with SARS-CoV-2, his/her monitoring is initiated but there is no monitoring of 52 his/her living network. 53 For pandemic situations, such as that with which we are living with SARS-CoV-2, 54 the classical algebraic ecological models of species population growth from Verhulst, 55 and species interaction models from Lotka-Volterra, are theoretical frameworks capable 56 to describe the phenomenon and to propose actions to stop it (Pianka 2000) . In many 57 aspects social isolation is a way to severely reduce carrying capacity, i.e., the resources 58 available for the virus dissemination. This is the best action for within-city pande