Background: We 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 the first occurrence of the virus coming from abroad. Methodology: We developed a 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 the Manaus airport for a rapid entrance of the pandemic into indigenous territories situated in remote places of the Amazon region. Results: The expansion of the SARS-CoV-2 virus between cities was fast, directly proportional to the city 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 the cities. The more a city showed closeness centrality, the greater was its vulnerability to SARS-CoV-2. Conclusions: We 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.

The spread of SARS-CoV-2 and the distribution of cases worldwide followed no clear biogeographic, climatic, or cultural trend. Conversely, the internationally busiest cities in all countries tended to be the hardest hit, suggesting a basic, mathematically neutral pattern of the new coronavirus early dissemination. We tested whether the number of flight passengers per time and the number of international frontiers could explain the number of cases of COVID-19 worldwide by a stepwise regression. Analysis were taken by 22 May 2020, a period when one would claim that early patterns of the pandemic establishment were still detectable, despite of community transmission in various places. The number of passengers arriving in a country and the number of international borders explained significantly 49% of the variance in the distribution of the number of cases of COVID-19, and number of passengers explained significantly 14.2% of data variance for cases per million inhabitants. Ecological neutral theory may explain a considerable part of the early distribution of SARS-CoV-2 and should be taken into consideration to define preventive international actions before a next pandemic.

This paper aims the use of Social Network Analysis (SNA) as an assessment tool to answer the following research questions: (i) Who are the actors in Humanitarian Operations Networks (HON)?; and (ii) Which actors exert influence on HON?

One hundred years after the flu pandemic of 1918, the world faces an outbreak of a new severe acute respiratory syndrome, caused by a novel coronavirus. With a high transmissibility, the pandemic has spread worldwide, creating a scenario of devastation in many countries. By the middle of 2021, about 3% of the world population had been infected and more than 4 million people had died. Different from the H1N1 pandemic, which had a deadly wave and ceased, the new disease is maintained by successive waves, mainly produced by new virus variants and the small number of vaccinated people. In the present work, we create a version of the SIR model using the spatial localization of persons, their movements, and considering social isolation probabilities. We discuss the effects of virus variants, and the role of vaccination rate in the pandemic dynamics. We show that, unless a global vaccination is implemented, we will have continuous waves of infections.

Background We 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. Methodology We 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. Results The 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. Conclusions We 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.

A second deadlier wave of COVID-19 and the causes of the recent public health collapse of Manaus are compared with the Spanish flu events in that city, and Brazil. Historic sanitarian problems, and its hub position in the Brazilian airway network are combined drivers of deadly events related to COVID-19. These drivers were amplified by misleading governance, highly transmissible variants, and relaxation of social distancing. Several of these same factors may also have contributed to the dramatically severe outbreak of H1N1 in 1918, which caused the death of 10% of the population in seven months. We modelled Manaus parameters for the present pandemic and confirmed that lack of a proper social distancing might select the most transmissible variants. We succeeded to reproduce a first severe wave followed by a second stronger wave. The model also predicted that outbreaks may last for up to five and half years, slowing down gradually before the disease disappear. We validated the model by adjusting it to the Spanish Flu data for the city, and confirmed the pattern experienced by that time, of a first stronger wave in October-November 1918, followed by a second less intense wave in February-March 1919.