publication . Preprint . Article . 2021

An epidemic model for COVID-19 transmission in Argentina: Exploration of the alternating quarantine and massive testing strategies

Lautaro Vassallo; Ignacio A. Perez; Lucila G. Alvarez-Zuzek; Julián Amaya; Marcos F. Torres; Lucas D. Valdez; Cristian E. La Rocca; Lidia A. Braunstein;
Open Access English
  • Published: 01 Jul 2021
The COVID-19 pandemic has challenged authorities at different levels of government administration around the globe. When faced with diseases of this severity, it is useful for the authorities to have prediction tools to estimate in advance the impact on the health system as well as the human, material, and economic resources that will be necessary. In this paper, we construct an extended Susceptible-Exposed-Infected-Recovered model that incorporates the social structure of Mar del Plata, the 4°most inhabited city in Argentina and head of the Municipality of General Pueyrredón. Moreover, we consider detailed partitions of infected individuals according to the ill...
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free text keywords: Quantitative Biology - Populations and Evolution, Physics - Physics and Society, Applied Mathematics, General Agricultural and Biological Sciences, General Immunology and Microbiology, General Biochemistry, Genetics and Molecular Biology, Modelling and Simulation, General Medicine, Statistics and Probability, Original Research Article, Mathematical epidemiology, Compartmental models, COVID-19 in Mar del Plata, Data analysis
43 references, page 1 of 3

2. Vespignani A, Tian H, Dye C, Smith J, Eggo R, Shrestha M, et al. Modelling COVID-19. Nature Reviews Physics. 2020;2:279{281. doi:10.1038/s42254-020-0178-4.

3. Javan E, Fox SJ, Meyers LA. The unseen and pervasive threat of COVID-19 throughout the US. medRxiv. 2020;doi:10.1101/2020.04.06.20053561.

4. Wells CR, Sah P, Moghadas SM, Pandey A, Shoukat A, Wang Y, et al. Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak. Proceedings of the National Academy of Sciences. 2020;117(13):7504{7509. doi:10.1073/pnas.2002616117.

5. Oliver N, Lepri B, Sterly H, Lambiotte R, Deletaille S, De Nadai M, et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Science Advances. 2020;6(23). doi:10.1126/sciadv.abc0764.

6. Ganyani T, Kremer C, Chen D, Torneri A, Faes C, Wallinga J, et al. Estimating the generation interval for COVID-19 based on symptom onset data. Eurosurveillance. 2020;25(17):2000257. doi:10.2807/1560-7917.ES.2020.25.17.2000257. [OpenAIRE]

7. Tagliazucchi E, Balenzuela P, Travizano M, Mindlin GB, Mininni PD. Lessons from being challenged by COVID-19. Chaos, Solitons & Fractals. 2020;137:109923. doi:10.1016/j.chaos.2020.109923. [OpenAIRE]

8. Jimenez Romero C, Tisnes A, Linares S. Modelo de simulacion del COVID-19 basado en agentes. Aplicacion al caso argentino. Posicion. 2020;(3).

9. Neidhofer G, Neidhofer C. The effectiveness of school closures and other pre-lockdown COVID-19 mitigation strategies in Argentina, Italy, and South Korea. ZEW-Centre for European Economic Research Discussion Paper. 2020;(20-034).

10. Torrente F, Yoris AE, Low D, Lopez P, Bekinschtein P, Cetkovich M, et al. Sooner than you think: a very early affective reaction to the COVID-19 pandemic and quarantine in Argentina. medRxiv. 2020;doi:10.1101/2020.07.31.20166272.

11. Figar S, Pagotto V, Luna L, Salto J, Manslau MW, Mistchenko A, et al. Communitylevel SARS-CoV-2 Seroprevalence Survey in urban slum dwellers of Buenos Aires City, Argentina: a participatory research. medRxiv. 2020;doi:10.1101/2020.07.14.20153858.

12. Ahumada H, Espina S, Navajas FH. COVID-19 with uncertain phases: estimation issues with an illustration for Argentina. SSRN. 2020;doi:10.2139/ssrn.3633500. [OpenAIRE]

13. Ferguson N, Laydon D, Nedjati-Gilani G, Imai N, Ainslie K, Baguelin M, et al. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Imperial College London. 2020;doi:10.25561/77482.

14. Minnesota Population Center. Integrated Public Use Microdata Series, International: Version 7.2 [dataset]; Minneapolis, MN: IPUMS, 2019. doi:10.18128/D020.V7.2.

15. Mossong J, Hens N, Jit M, Beutels P, Auranen K, Mikolajczyk R, et al. Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases. PLOS Medicine. 2008;5(3):e74. doi:10.1371/journal.pmed.0050074.

16. Fumanelli L, Ajelli M, Manfredi P, Vespignani A, Merler S. Inferring the Structure of Social Contacts from Demographic Data in the Analysis of Infectious Diseases Spread. PLOS Computational Biology. 2012;8(9):e1002673. doi:10.1371/journal.pcbi.1002673. [OpenAIRE]

43 references, page 1 of 3
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