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

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.26.20044370 doi: medRxiv preprint 154 increment rates, it is clear a first and high peak of infections in the hub cities, happening 155 around 50 days and, starting from 75 days, a new peripheric peak ( Figure 3B ). 156 The first ten cities to ascend infection rates (São Paulo, Rio de Janeiro, Salvador, 157 Recife, Brasília, Fortaleza, Belo Horizonte, Porto Alegre, Curitiba, and Florianópolis) will 158 actually reach this point about the same time, which is a concerning pattern for the 159 saturation of the public health services. Also, this peak in those cities will saturate all the 160 best hospitals in the country simultaneously. 161 Therefore, we defined the average proportion of infected people for the 90 days 162 as a measure of vulnerability to COVID-19 dissemination. Henceforth, we found that 163 more an airport shows closeness centrality within the air transportation network, the 164 greater was its vulnerability to disease transmission (Figure 4) . This scenario confirmed 165 the importance of a city connecting different cities within the Brazilian air transportation 166 network and, thus, acting as the main driver for the pandemic spreading across the 167 country. 168 169 Consequences for the Amazonian cities and indigenous people 170 Herein we showed that an uncontrolled complex airport system made a whole 171 country vulnerable in few weeks, allowing the virus to reach the most distant and remote 172 places, in the most pessimistic scenario. According to our model, any connected city will 173 be infected after three months. As the number of flights arriving in a city is the driver for 174 the proportion of infected people, Manaus, which is a relevant regional clustering, was 175 infected sooner. Indeed, on the 17 th of March, Manaus was the first Amazonian city with 176 confirmed cases (without community transmission yet), and it is a node that is one or 177 two steps to all the Amazonian cities. Thus, according to our model, Manaus may reach 178 1% of the infected population by the 44 th day, while, for instance, the far west 179 Amazonian Tabatinga will take 61 days to reach the same 1% of the population 180 infected. By day 60, Manaus may have an average of 50% of its population infected if 181 nothing is be done to prevent it. Tabatinga may also reach the aforementioned value by 182 day 78, if nothing is be done to avoid it. To sum up, within 46 days all the Amazonian 183 cities will have 1% of their population infected and a mean of 50% by day 70. 184 185 Discussion 186 Brazil has failed to contain COVID-19 in airports and failed to closely monitor those 187 infected people coming from abroad, as well as their living network. One main reason 188 for this is the difficult logistics required to produce such control in a continental country, 189 such as Brazil, which has a complex national flight network. According to the Brazilian 190 Airport Authority, Brazil has the second-largest flight network in the world (just after the 191 USA), with a total of 154 airports registered to commercial flights of which 31 are 192 considered international. In comparison, airport control may be much easier to set up in 193 Nigeria (31 airports of which only five are international). However, with a population 6.4 All rights reserved. No reuse allowed without permission.