Successive pandemic waves with more virulent strains, and the effects of vaccination for SARS-CoV-2

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 spreads worldwide, creating a scenario of devastation in many countries. By the middle of 2021, about 3% of the world population has been infected and more than 4 million people have died. Different from the H1N1 pandemic, which had a deadly wave and cessed, 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 with spatial localization of persons, their movements, and taking into account 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. HighlightsO_LIThe COVID-19 has infected more than 200 millions and has killed more than 4 million persons. C_LIO_LIWHO has not been successful in defining a global vaccination policy. C_LIO_LIMany epidemic scenarios arise when different countries apply different vaccination strategies. C_LIO_LIPresent model can show some insights on how vaccination programs can be managed. C_LI

COVID-19 most vulnerable Mexican cities lack the public health infrastructure to face the pandemic: a new temporally-explicit model

Recently, a wide array of epidemiological models have been developed to guide public health actors in containing the rapid dissemination of the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), cause of COVID-19. Despite their usefulness, many epidemiological models recently developed to understand the spread of SARS-CoV-2 and infection rates of COVID-19 fall short as they ignore human mobility, limiting our understanding of the spread of the disease, together with the vulnerability of population centers in a broad scale. We developed a new temporally-explicit model and simulated several social distancing scenarios to predict the vulnerability to COVID-19 of 50 Mexican cities that are interconnected by their air transportation network. Additionally, we assessed the sufficiency of the public health infrastructure in the focal cities to face the pandemic over time. Based on our model, we show that the most important cities within the Mexican air transportation network are the most vulnerable to COVID-19, with all assessed public health infrastructure being insufficient to face the modeled scenario for the pandemic after 100 days. Despite these alarming findings, our results show that social distancing could dramatically decrease the total number of infected people (77% drop-off for the 45% distancing scenario when contrasted with no distancing), flattening the growth of infection rate. Thus, we consider that this study provides useful information that may help decision-makers to timely implement health policies to anticipate and lessen the impact of the current pandemic in Mexico. Significance StatementWe used a new temporally-explicit model focused on air transportation networks to predict the vulnerability of 50 focal Mexican cities to COVID-19. We found that most vulnerable cities lack of the required public health infrastructure (i.e., number of inpatient and intensive care unit beds) to face this new pandemic, overloading in all cases after 100 days. However, our results show that a 45% social distancing scenario can reduce the number of infected people by up to 78.7%, flattening the growth rate of people with COVID-19 before infection rates soar exponentially countrywide.

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

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.