A simple electrical-circuit analogous phenomenological COVID-19 model valid for all observed pandemic phases
Source abstract
Abstract
In physics and engineering, circuit modeling together with simple element models has been used to study concurrent physical phenomena. By using simple calculations, the underlying mechanisms that determine certain patterns can be understood. Here, we present a simple mathematical model to describe the COVID-19 pandemic time evolution. The model accounts for three phases occurring at the same pandemic wave, which are influenced by different mechanisms represented by a linear, an exponential, and a power law term, corresponding to an early stage of the contagious spread, an unconstrained spread, and a power-law increase defined by the effectiveness of the social distancing, respectively. This approach is based on parallel and series natural phenomena occurring in electrical circuits. The generality of the present tool is demonstrated using empirical data of nine countries from different continents.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: COVID-19 epidemiological studies · SARS-CoV-2 and COVID-19 Research · Viral Infections and Outbreaks Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Micro and Nanotechnology Research Center (MICRONA), Veracruz University 1 , Boca del Río, Veracruz 94294, Mexico
Master Program in Applied Engineering, Faculty of Construction Engineering and Habitat, Veracruz University 2 , Boca del Río, Veracruz 94294, Mexico
International Laboratory of Environmental Electron Devices (LAIDEA), National School of Higher Studies Unit Morelia, National Autonomous University of Mexico (ENES Morelia UNAM) 3 , 58190 Morelia, Mexico
Semiconductor Optics, Institute of Solid-State Physics, University of Bremen 4 , 28359 Bremen, Germany
MAPEX Center of Materials and Processes, University of Bremen 5 , 28359 Bremen, Germany
Funding
Consejo Nacional de Ciencia y Tecnología · Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México
References (42)
Show source-provided references
1. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak10.1016/j.jaut.2020.102433 ↗
2. WHO · 2020
3. Coronavirus Resource Center, Johns Hopkins University, Baltimore, Maryland, https://coronavirus.jhu.edu/map.html, as of January 15, 2021, updated daily.
4. A contribution to the mathematical theory of epidemics10.1098/rspa.1927.0118 ↗
5. The challenges of modeling and forecasting the spread of COVID-1910.1073/pnas.2006520117 ↗
6. Can we predict the occurrence of COVID-19 cases? Considerations using a simple model of growth10.1016/j.scitotenv.2020.138834 ↗
7. Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan10.1016/j.chaos.2020.109846 ↗
8. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy10.1038/s41591-020-0883-7 ↗
9. Inferring the effectiveness of government interventions against COVID-1910.1126/science.abd9338 ↗
10. Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing10.1126/science.abb6936 ↗
11. Effective containment explains subexponential growth in recent confirmed COVID-19 cases in China10.1126/science.abb4557 ↗
12. Mechanisms of recurrent outbreak of COVID-19: A model-based study10.1007/s11071-021-06371-w ↗
13. Standard and anomalous waves of COVID-19: A multiple-wave growth model for epidemics10.1007/s13538-021-00996-3 ↗
14. Multiwave pandemic dynamics explained: How to tame the next wave of infectious diseases10.1038/s41598-021-85875-2 ↗
15. Renormalization group approach to pandemics: The COVID-19 case10.3389/fphy.2020.00144 ↗
16. COVID-19: Mechanistic model calibration subject to active and varying non-pharmaceutical interventions10.1016/j.ces.2020.116330 ↗
17. 1998
18. Understanding the open circuit voltage in organic solar cells on the basis of a donor-acceptor abrupt (p-n++) heterojunction10.1016/j.solener.2019.04.031 ↗
19. Extraction of poly(3-hexylthiophene) (P3HT) properties from dark current voltage-characteristics in a P3HT/n-crystalline-silicon solar cell10.1063/1.3296294 ↗
20. Analytical energy-barrier-dependent Voc model for amorphous silicon solar cells10.1063/1.4959939 ↗
21. 1979
22. Maximum entropy analysis of flow networks: Theoretical foundation and applications10.3390/e21080776 ↗
23. 2010
24. Johns Hopkins University, COVID-19 data repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University from https://github.com/CSSEGISandData/COVID-19; accessed 13 July 2020.
25. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)10.1126/science.abb3221 ↗
26. See https://stochastik-tu-ilmenau.github.io/COVID-19/index.html for more information about the effective reproduction number values (R0) used for the analysis of Phase II.
27. Estimating individual and household reproduction numbers in an emerging epidemic10.1371/journal.pone.0000758 ↗
28. Complexity of the basic reproduction number (R0)10.3201/eid2501.171901 ↗
29. COVID-19 pandemic planning scenarios, available at https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html.
30. Estimate of the basic reproduction number for COVID-19: A systematic review and meta-analysis10.3961/jpmph.20.076 ↗
31. Modified Arrhenius equation in materials science, chemistry and biology10.3390/molecules26237162 ↗
32. 2020
33. Population density and basic reproductive number of COVID-19 across United States counties10.1371/journal.pone.0249271 ↗
34. The scaling of contact rates with population density for the infectious disease models10.1016/j.mbs.2013.04.013 ↗
35. USA, https://www.cnbc.com/2020/03/20/new-york-gov-cuomo-orders-100percent-of-non-essential-businesses-to-work-from-home.html.
36. DEU, https://pbs.twimg.com/media/ETjc738WoAI5zAt?format=jpg&name=large.
37. ESP, https://www.elmundo.es/espana/2020/03/13/5e6b844e21efa0dd258b45a5.html.
38. GBR, https://www.theguardian.com/world/2020/mar/23/boris-johnson-orders-uk-lockdown-to-be-enforced-by-police.
39. P. S. Grinchuk and S. P.Fisenko, “Power-law multi-wave model for COVID-19 propagation in countries with nonuniform population density,” arXiv:2012.06383 (2020).
40. SARS-CoV cumulative cases in China (2002): data recuperated from WHO situation reports. Dataset available at https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset.
41. Influenza A(H1N1) cumulative cases in Japan (2009): data recuperated from WHO situation reports. Dataset available at https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset.
42. Ebola cumulative cases in West Africa (2014–2015): data recuperated from WHO situation reports. Dataset available at https://apps.who.int/gho/data/node.ebola-sitrep.