Ruy Freitas Reis
Peter Congdon- 1Instituto de Ciências Exatas, Departamento de Ciência da Computação, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
- 2Pós-Graduação em Modelagem Computacional, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
- 3Queen Mary University of London, London, United Kingdom
Editorial on the Research Topic
Epidemiological considerations in COVID-19 forecasting
1. Epidemiological considerations in COVID-19 forecasting
In its initial epidemic phase from December 2019 to March 2020, Sars-CoV-2 infected about 800 thousand people worldwide and about 50 thousand of them died. The rapid spread of COVID-19 led the World Health Organization (WHO) to declare COVID-19 a global pandemic (1). Preliminary estimates suggest total global deaths attributable to COVID-19 throughout 2020 to be at least 3 million (2). The unusual rapid spread of a new disease led the scientific community to make a great effort to understand and represent the mechanisms underlying the spread of the pandemic.
Many mathematical and computational models have been adapted to describe the epidemiological behavior of COVID-19 spread, including predicting the dynamics to assist efforts to counter rapid dissemination of the disease (3–5). Different modeling strategies to describe the pandemic include stochastic/probabilistic (3, 6–9), and chaotic (10, 11), with many models using ODEs (Ordinary Differential Equations) adapting the compartmental SIR (Susceptible, Infected, and Recovered) model (5, 12–17). Many studies of COVID dynamics have been at national level, but spatially disaggregated approaches (e.g. spatio-temporal forecasts) have been proposed, raising questions about localized diffusion between nearby populations (18, 19).
Projecting possible scenarios of the pandemic’s duration, wave fluctuations and peaks provides valuable information for health public pandemic planning (20). Scenario planning is also relevant for economic reasons since many countries that have adopted circulation restrictions to reduce the spread of the disease still suffer from economic impacts and wider social ramifications (9). Furthermore, the use of computational tools for predicting potential high-risk areas to be monitored is also an important tool for health public strategies (21). On the other hand, following progress in developing effective vaccines many researchers have attempted to describe mathematically the impact of alternative vaccination strategies on viral spread dynamics (9, 14, 22).
2. Survey of papers in this research topic
About the time the COVID-19 pandemic started, the Global Health Security Index (GHSI) was published. The GHSI was proposed to score countries’ preparedness for a pandemic. A few months after the start of the pandemic, researchers began to analyze the validity of the GHSI. They correlated national COVID per capita death rates with GHSI scores. Surprisingly, they showed that the better prepared a country, the higher the death rate, i.e. a result that was counter to what would have been expected. Goldschmidt et al. takes another look at the GHSI by exploring the relationship in major European Union countries plus the United Kingdom.
Managing the COVID-19 pandemic continues to be a challenge due to poor adherence to COVID-19 prevention measures worldwide. The study of Eyeberu et al. aims to identify the determinants of community adherence to pandemic prevention among adults in the Harari Regional State of Eastern Ethiopia. They discovered that about half of the study participants showed poor adherence. On the other hand, pandemic management also requires appropriate and timely measures by government and non-governmental organizations.
Before applying diagnostic tests for screening purposes it is important to understand the baseline risk in the tested population. Particularly, in the COVID-19 pandemic, the incidence rate remains to change. The study of McAloon et al. uses incidence data to estimate the prevalence of community infection at two particular points in time. Their proposed methodology has the potential as a real-time estimation to support decision-making regarding control measures needed to allow mass gatherings while the pandemic is still to some degree extant. The WHO emphasize the importance of guidance for enabling mass gatherings (23).
The study of Lohia et al. analyses the epidemiological importance of testing the Indian population for COVID-19 during the pandemic. This research work is a retrospective analysis of the testing data collected by the Indian Council of Medical Research (about 170 million tests up to December 29, 2020). This study aimed to understand the probability of a person testing negative after an initial positive test and to evaluate the varied impact and duration of the disease in people of different age groups and genders.
Author contributions
All authors contributed to the article and approved the submitted version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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References
1. [Dataset] World Health Organization. WHO Timeline - COVID-19 - 27 April 2020. Available at: https://www.who.int/news/item/27-04-2020-who-timeline—covid-19. (Last accesses October 22, 2021).
2. Ritchie H, Mathieu E, Rodés-Guirao L, Appel C, Giattino C, Ortiz-Ospina E, et al. Coronavirus pandemic (COVID-19). Our World in Data (2020). Available at: https://ourworldindata.org/coronavirus
3. Hellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases, contacts. Lancet Glob Health. (2020) 8:e488–96. doi: 10.1016/S2214-109X(20)30074-7
4. Kantner M, Koprucki T. Beyond just ‘flattening the curve’: optimal control of epidemics with purely non-pharmaceutical interventions. J Math Ind. (2020) 10:1–23. doi: 10.1186/s13362-020-00091-3
5. Reis RF, de Melo Quintela B, de Oliveira Campos J, Gomes JM, Rocha BM, Lobosco M, et al. Characterization of the COVID-19 pandemic, the impact of uncertainties, mitigation strategies,, underreporting of cases in south Korea, Italy, and Brazil. Chaos Solitons Fractals. (2020) 136:109888. doi: 10.1016/j.chaos.2020.109888
6. Wu M, Li C, Shen Z, He S, Tang L, Zheng J, et al. Use of temporal contact graphs to understand the evolution of COVID-19 through contact tracing data. Commun Phys. (2022) 5:1–10. doi: 10.1038/s42005-022-01045-4
7. Congdon P. Mid-epidemic forecasts of COVID-19 cases and deaths: a bivariate model applied to the uk. Interdiscip Perspect Infect Dis. (2021) 2021:8847116. doi: 10.1155/2021/8847116
8. Coelho FC, Lana RM, Cruz OG, Villela DA, Bastos LS, Pastore y Piontti A, et al. Assessing the spread of COVID-19 in Brazil: mobility, morbidity, social vulnerability. PLoS ONE. (2020) 15:e0238214. doi: 10.1371/journal.pone.0238214
9. Cot C, Cacciapaglia G, Islind AS, Óskarsdóttir M, Sannino F. Impact of US vaccination strategy on COVID-19 wave dynamics. Sci Rep. (2021) 11:10960. doi: 10.1038/s41598-021-90539-2
10. Borah M, Gayan A, Sharma JS, Chen Y, Wei Z, Pham VT. Is fractional-order chaos theory the new tool to model chaotic pandemics as COVID-19? Nonlinear Dyn. (2022) 109:1–29.35698477
11. Mangiarotti S, Peyre M, Zhang Y, Huc M, Roger F, Kerr Y. Chaos theory applied to the outbreak of COVID-19: an ancillary approach to decision making in pandemic context. Epidemiol Infect. (2020) 148:e95. doi: 10.1017/S0950268820000990
12. Fanelli D, Piazza F. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos Solitons Fractals. (2020) 134:109761. doi: 10.1016/j.chaos.2020.109761
13. Pelinovsky E, Kurkin A, Kurkina O, Kokoulina M, Epifanova A. Logistic equation and COVID-19. Chaos Solitons Fractals. (2020) 140:110241. doi: 10.1016/j.chaos.2020.110241
14. Xavier CR, Oliveira RS, Rocha BM, Reis RF, de Melo Quintela B, et al. Timing the race of vaccination, new variants, and relaxing restrictions during COVID-19 pandemic. J Comput Sci. (2022) 61:101660. doi: 10.1016/j.jocs.2022.101660
15. Gonzalez-Parra G, Martínez-Rodríguez D, Villanueva-Micó RJ. Impact of a new SARS-CoV-2 variant on the population: a mathematical modeling approach. Math Comput Appl. (2021) 26:25. doi: 10.3390/mca26020025
16. Reis RF, Oliveira RS, Quintela BdM, Campos JdO, Gomes JM, Rocha BM, et al. The quixotic task of forecasting peaks of COVID-19: rather focus on forward and backward projections. Front Public Health. (2021) 9:623521. doi: 10.3389/fpubh.2021.623521
17. Beira MJ, Sebastião PJ. A differential equations model-fitting analysis of COVID-19 epidemiological data to explain multi-wave dynamics. Sci Rep. (2021) 11:1–13. doi: 10.1038/s41598-021-95494-6
18. Sartorius B, Lawson A, Pullan R. Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England. Sci Rep. (2021) 11:1–11. doi: 10.1038/s41598-021-83780-2
19. Congdon P. A model for highly fluctuating spatio-temporal infection data, with applications to the COVID epidemic. Int J Environ Res Public Health. (2022) 19:6669. doi: 10.3390/ijerph19116669
20. Xavier CR, Oliveira RS, Lobosco M, Dos Santos RW. Characterisation of omicron variant during COVID-19 pandemic and the impact of vaccination, transmission rate, mortality, and reinfection in South Africa, Germany, and Brazil. BioTech. (2022) 11:12.35822785
21. Alkhalifah A, Bukar UA. Examining the prediction of COVID-19 contact-tracing app adoption using an integrated model and hybrid approach analysis. Front Public Health. (2022) 10:847184. doi: 10.3389/fpubh.2022.847184
22. Borchering RK, Mullany LC, Howerton E, Chinazzi M, Smith CP, Qin M, et al. Impact of SARS-CoV-2 vaccination of children ages 5–11 years on COVID-19 disease burden and resilience to new variants in the united states, November 2021–March 2022: a multi-model study. Lancet Regional Health - Americas. (2023) 17:100398. doi: 10.1016/j.lana.2022.100398
Citation: Reis RF and Congdon P (2023) Editorial: Epidemiological considerations in COVID-19 forecasting. Front. Epidemiol. 2:1119559. doi: 10.3389/fepid.2022.1119559
Received: 8 December 2022; Accepted: 20 December 2022;
Published: 16 January 2023.
Edited and Reviewed by: Ruchi Singh, National Institute of Pathology (ICMR), India
© 2023 Reis and Congdon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ruy Freitas Reis cnV5LnJlaXNAdWZqZi5icg==
Specialty Section: This article was submitted to Infectious Disease Epidemiology, a section of the journal Frontiers in Epidemiology