The impact of super-spreader cities, highways, and intensive care availability in the early stages of the COVID-19 epidemic in Brazil

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Abstract

Although international airports served as main entry points for SARS-CoV-2, the factors driving the uneven geographic spread of COVID-19 cases and deaths in Brazil remain mostly unknown. Here we show that three major factors influenced the early macro-geographical dynamics of COVID-19 in Brazil. Mathematical modeling revealed that the “super-spreading city” of São Paulo initially accounted for more than 85% of the case spread in the entire country. By adding only 16 other spreading cities, we accounted for 98–99% of the cases reported during the first 3 months of the pandemic in Brazil. Moreover, 26 federal highways accounted for about 30% of SARS-CoV-2’s case spread. As cases increased in the Brazilian interior, the distribution of COVID-19 deaths began to correlate with the allocation of the country’s intensive care units (ICUs), which is heavily weighted towards state capitals. Thus, severely ill patients living in the countryside had to be transported to state capitals to access ICU beds, creating a “boomerang effect” that contributed to skew the distribution of COVID-19 deaths. Therefore, if (i) a lockdown had been imposed earlier on in spreader-capitals, (ii) mandatory road traffic restrictions had been enforced, and (iii) a more equitable geographic distribution of ICU beds existed, the impact of COVID-19 in Brazil would be significantly lower.

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Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

Seth Flaxman, Swapnil Mishra, Axel Gandy … (2020)

Following the detection of the new coronavirus1 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics of coronavirus disease 2019 (COVID-19). In response, many European countries have implemented non-pharmaceutical interventions, such as the closure of schools and national lockdowns. Here we study the effect of major interventions across 11 European countries for the period from the start of the COVID-19 epidemics in February 2020 until 4 May 2020, when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks previously, allowing for the time lag between infection and death. We use partial pooling of information between countries, with both individual and shared effects on the time-varying reproduction number (Rt). Pooling allows for more information to be used, helps to overcome idiosyncrasies in the data and enables more-timely estimates. Our model relies on fixed estimates of some epidemiological parameters (such as the infection fatality rate), does not include importation or subnational variation and assumes that changes in Rt are an immediate response to interventions rather than gradual changes in behaviour. Amidst the ongoing pandemic, we rely on death data that are incomplete, show systematic biases in reporting and are subject to future consolidation. We estimate that-for all of the countries we consider here-current interventions have been sufficient to drive Rt below 1 (probability Rt < 1.0 is greater than 99%) and achieve control of the epidemic. We estimate that across all 11 countries combined, between 12 and 15 million individuals were infected with SARS-CoV-2 up to 4 May 2020, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions-and lockdowns in particular-have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.

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The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak

Matteo Chinazzi, Jessica Davis, Marco Ajelli … (2020)

Motivated by the rapid spread of COVID-19 in Mainland China, we use a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic. The model is calibrated based on internationally reported cases, and shows that at the start of the travel ban from Wuhan on 23 January 2020, most Chinese cities had already received many infected travelers. The travel quarantine of Wuhan delayed the overall epidemic progression by only 3 to 5 days in Mainland China, but has a more marked effect at the international scale, where case importations were reduced by nearly 80% until mid February. Modeling results also indicate that sustained 90% travel restrictions to and from Mainland China only modestly affect the epidemic trajectory unless combined with a 50% or higher reduction of transmission in the community.

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Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy

Giulia Giordano, Franco Blanchini, Raffaele Bruno … (2020)

In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. Ending the global SARS-CoV-2 pandemic requires implementation of multiple population-wide strategies, including social distancing, testing and contact tracing. We propose a new model that predicts the course of the epidemic to help plan an effective control strategy. The model considers eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE. Our SIDARTHE model discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed individuals is important because the former are typically isolated and hence less likely to spread the infection. This delineation also helps to explain misperceptions of the case fatality rate and of the epidemic spread. We compare simulation results with real data on the COVID-19 epidemic in Italy, and we model possible scenarios of implementation of countermeasures. Our results demonstrate that restrictive social-distancing measures will need to be combined with widespread testing and contact tracing to end the ongoing COVID-19 pandemic.

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Author and article information

Contributors

Miguel A. L. Nicolelis: nicoleli@neuro.duke.edu

Journal

Journal ID (nlm-ta): Sci Rep

Journal ID (iso-abbrev): Sci Rep

Title: Scientific Reports

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2045-2322

Publication date (Electronic): 21 June 2021

Publication date PMC-release: 21 June 2021

Publication date Collection: 2021

Volume: 11

Electronic Location Identifier: 13001

Affiliations

[1 ]GRID grid.189509.c, ISNI 0000000100241216, Department of Neurobiology, , Duke University Medical Center, ; Box 103905, Durham, NC 27710 USA

[2 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Department of Biomedical Engineering, , Duke University, ; Durham, NC USA

[3 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Department of Neurology, , Duke University, ; Durham, NC USA

[4 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Department of Neurosurgery, , Duke University, ; Durham, NC USA

[5 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Department of Psychology and Neuroscience, , Duke University, ; Durham, NC USA

[6 ]Edmond and Lily Safra International Institute of Neurosciences, Natal, Brazil

[7 ]GRID grid.411216.1, ISNI 0000 0004 0397 5145, Department of Engineering and Environment and Postgraduate Program in Ecology and Environmental Monitoring (PPGEMA), Center for Applied Sciences and Education, , Federal University of Paraíba-Campus IV, ; Rio Tinto, Paraíba, Brazil

[8 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Department of Applied Mathematics, Institute of Mathematics and Statistics, , University of São Paulo, ; São Paulo, Brazil

[9 ]GRID grid.418068.3, ISNI 0000 0001 0723 0931, Laboratory of Biology and Parasitology of Wild Reservoir Mammals, IOC, , Oswaldo Cruz Foundation, ; Rio de Janeiro, Brazil

Article

Publisher ID: 92263

DOI: 10.1038/s41598-021-92263-3

PMC ID: 8217556

PubMed ID: 34155241

SO-VID: cc7dd578-23df-4792-9439-190a3bb5824c

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 21 October 2020

Date accepted : 26 May 2021

Funding

Funded by: Duke University Medical Center Distinguished Professor Endowed Chair

Funded by: Brazilian Synthesis Center on Biodiversity and Ecosystem Services (SinBiose/CNPQ)

Funded by: Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP 16/18445-7)

Funded by: CNPq (301778/2017-5)

Custom metadata

ScienceOpen disciplines: Uncategorized

Keywords: diseases,infectious diseases

Data availability:

ScienceOpen disciplines: Uncategorized

Keywords: diseases, infectious diseases

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