Forecast of New and Deceased Cases of COVID-19 in Cuba with an Advance of 105 Days
Ricardo Osés Rodríguez1, Rigoberto Fimia Duarte2,3*, Claudia Osés Llanes4, María Patricia Zambrano Gavilanes5, Thaináh Bruna Santos Zambrano6 and Alfredo González Meneses7
1Provincial Meteorological Center of Villa Clara, Cuba
2Faculty of Health Technology and Nursing, University of Medical Sciences of Villa Clara (UMC-VC), Cuba
3Veterinary Medicine and Zootechnic Career, Faculty of Agrocultural Sciences, Central University “Marta Abreu” of Las Villas, Villa Clara, Cuba
4Department of Hygiene and Epidemiology, XX Anniversary Policlinical, Santa Clara, Villa Clara, Cuba
5Veterinary Medicine Career, Faculty of Veterinary Medicine and Zootechnic, Technical University of Manabí, Manabí, Ecuador
6Pathology Department, St. Gregory's University of Portoviejo, Portoviejo, Ecuador and Postgraduate Department in Biomedicine, Italian University Institute of Rosario (IUNIR), Argentine
7Construction Enterprise of the Electric Industry of Villa Clara, Cuba
*Corresponding Author: Rigoberto Fimia Duarte, Hygiene and Epidemiology Career, Faculty of Health Technology and Nursing, University of Medical Sciences of Villa Clara (UMC-VC), Cuba.
Received:
May 10, 2021; Published: June 23, 2021
Abstract
In this work, new cases and daily deaths of the COVID-19 pandemic affecting Cuba so far in the year 2021 were modeled. Mathematical models were obtained by means of the methodology of Regressive Objective Regression (ROR), which explains their behavior, depending on 105 days in advance, a parameter which is related to a delay of 4 months. A long-term prognosis was performed, which allows taking measures in clinical services to reduce deaths and complications in patients with COVID-19. It is concluded that COVID-19, despite being a new disease in the world, can be predicted 105 days in advance by means of ROR mathematical modeling, which allows reducing the number of deceased, severe, and critical patients for better management of the pandemic. The tendency of the disease is on the rise, so the management, monitoring, and control of the disease should continue to be taken to the extreme, as established in the medical protocols. An accumulated number of 1000 deaths could be reached by August 19, 2021, and 500 deaths by April 15, 2021, according to the models run.
Keywords: Cuba; COVID-19; New Cases; Deaths; Long Term Prognosis; Regressive Objective Regression
References
- Haines A and Patz JA. “Health effects of climate Change”. American Medical Association 291 (2004): 32-38.
- Anonymous J. American Journal of Respiratory and Critical Care Medicine (2009).
- Macklis R., et al. “Manual of introductory clinical medicine”. Library of Congress Catalogo, USA 83 (1987): 80296.
- Blackall J. “Infectious Coryza: Overview of the disease and new diagnostic options”. Clinical Microbiology Reviews 12 (1999): 627-632.
- Nicholson KG., et al. “Influenza”. Lancet 362 (2003): 1733-1745.
- Sánchez TN. “Infecciones respiratorias agudas”. Reporte Técnico de Vigilancia 1 (1996).
- Gilbert M., et al. “Climate change and avian influenza”. Revue Scientifique Et Technique 27 (2008): 459-466.
- Jaakkola K., et al. “Decline in temperature and humidity increases the occurrence of influenza in cold climate”. Environmental Health 13 (2014): 17-22.
- Artiles SJM. “Caracterización epidemiológica y climatológica de las atenciones por Infecciones Respiratorias Agudas. Villa Clara 2015-2016” [Tesis para optar por el título de Especialista de Primer Grado en Higiene y Epidemiología]. Centro Provincial de Higiene, Epidemiología y Microbiología (CPHEM), Villa Clara, Cuba (2017).
- Sánchez ÁML., et al. “La Regresión Objetiva Regresiva más allá de un ruido blanco para los virus que circulan en la provincia Villa Clara, Cuba”. The Biologist (Lima) 15 (2017): 127.
- Fimia DR., et al. “Factores antropogénicos y ambientales sobre la fauna de culícidos (Diptera: Culicidae) de la provincia Sancti Spíritus, Cuba”. The Biologist (Lima) 13 (2015): 41-51.
- Fimia DR., et al. “Asociación de algunas variables climáticas con la fasciolosis, angiostrongilosis y la malacofauna fluvial de la provincia Villa Clara, Cuba”. Neotropical Helminthology (aphia) 10 (2016): 259-273.
- Prem K., et al. “The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modeling study” (2020).
- Salas AR., et al. “Coronavirus COVID-19: Conociendo al causante de la pandemia”. The Biologist (Lima) 18 (2020): 9-27.
- Sun Z., et al. “Potential Factors Influencing Repeated SARS Outbreaks in China”. International Journal of Environmental Research and Public Health 17 (2020): 1633.
- Fan Y., et al. “Bat Coronaviruses in China”. Viruses 11 (2019): 1-14.
- Wang LS., et al. “A review of the 2019 Novel Coronavirus (COVID-19) based on current evidence”. International Journal of Antimicrobial Agents (2020).
- Woo PCY and Lau SKP. “Viruses and Bats”. Viruses 11 (2019): 880-884.
- Rue H., et al. “Bayesian computing with INLA: a Review”. Annual Reviews of Statistics and its Applications 4 (2017): 395-421.
- Simpson DP., et al. “Penalising model component complexity: a principled, practical approach to constructing priors”. Statistics Sciences 32 (2017): 1-46.
- Anastassopoulou C., et al. “Data-based analysis, modeling and forecasting of the COVID-19 outbreak”. PLOS ONE 15 (2020): 1-21.
- Roosa K., et al. “Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020”. Infectious Disease Modelling 5 (2020): 256-263.
- Zhao S and Chen H. “Modeling the epidemic dynamics and control of COVID-19 outbreak in China”. Quantitative Biology 8 (2020): 11-19.
- Zhao S., et al. “Preliminary estimation of the basic reproduction number of novel coronavirus (2019-n CoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak”. International Journal of Infectious Diseases 92 (2020a): 214-217.
- Zhao S., et al. “Estimating the Unreported Number of Novel Coronavirus (2019-n CoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak”. Journal of Clinical Medicine 9 (2020b): 388.
- Saez M., et al. “Effectiveness of the measures to flatten the epidemic curve of COVID-19. The case of Spain”. Science of the Total Environment 727 (2020): 138761.
- Fimia DR. “Mathematical modeling of population dynamics of the Aedes aegypti (Diptera: Culicidae) mosquito with some climatic variables in Villa Clara, Cuba”. International Journal of Zoology and Animal Biology (IZAB) 3 (2020): 16000233.
- Osés RR., et al. “The ROR’s methodology an it’s possibility to find information in a White noise”. International Journal of Current Research 9 (2017): 47378-47382.
- Osés RR., et al. “Prediction of latitude and longitude of earthquakes at global level using the Regressive Objective Regression method”. Advances in Theoretical and Computational Physics 1 (2018): 1-5.
- Osés RR., et al. “Modelación ROR aplicada a pronósticos”. Editorial Académica Española (eae). Editorial de OmniScriptum Publishing KS. Brivibas gatve 197, Riga, LV-1039 Latvia, European Union. (2019a).
- Osés RR., et al. “Estudio del consumo eléctrico provincial de Villa Clara y su pronóstico 2019-2023 Cuba”. Revista ECOSOLAR 65 (2019b): 32-43.
- Hernández CN., et al. “Determinación de la ictiofauna que participa en el control de culícidos en sistemas acuáticos del municipio Guamá, Santiago de Cuba”. Revista Cubana Medicina Tropical 58 (2006): 32-36.
- Osés RR., et al. “Modelación matemática del cólera por medio de la Regresión Objetiva Regresiva y su relación con las variables climáticas. Caibarién, Villa Clara, Cuba”. The Biologist (Lima) 15 (2017): 128-134.
- Fimia DR., et al. “La entomofauna de culícidos y los copépodos abordados desde las alternativas de control biológico hasta la modelación matemática en dos provincias centrales de Cuba”. Anales de la Academia de Ciencias de Cuba 10 (2020a): 1-11.
- Osés RR., et al. “Pronostico a muy largo plazo de casos graves de COVID-19 para Cuba”. En: Libro de Ponencias: VI Taller Cambio Climático y Salud. Sagua la Grande, Villa Clara, Cuba (2020): 7-15.
- Osés RR., et al. “Pronóstico de la COVID-19 por medio de la metodología de Regresión Objetiva Regresiva en Villa Clara, Cuba”. The Biologist (Lima) 18 (2020): 171-184.
- Osés R, et al. “Age prediction for COVID-19 suspects and contacts in Villa Clara province, Cuba”. EC Veterinary Science 6 (2021): 41-51.
- Osés RR., et al. “How to predict the Climatic Bultó Index with a year in advance?”. International Journal of Agriculture Innovations and Research 4 (2015a): 11-17.
- Osés RR., et al. “Climatic impact of the temperature in the presence of cold avian infections in Cuba”. International Journal of Development Research 5 (2015b): 14-19.
- Osés RR., et al. “Regressive methodology (ROR) VERSUS Genetic Code in mutations of VIH”. International Journal of Agriculture Innovations and Research 3 (2015a): 2319-1473.
- Osés RR., et al. “Climatic impact of the temperature in the presence of cold avian infections in Cuba”. International Journal of Development Research 5 (2015b).
- Osés RR., et al. “Modelación de la temperatura efectiva equivalente para la estación del Yabú y para la densidad larval total de mosquitos en Caibarién, provincia Villa Clara, Cuba”. Rev Peruana de Entomología 51 (2016): 1-7.
- Fimia DR., et al. “Modeling of Equivalent Effective Temperature and its possible incidence on larval density of Anopheles mosquitoes (Diptera: Culicidae) in Villa Clara province, Cuba”. Revista de Biología Tropical (RBT) 65 (2017): 565-573.
- Fimia DR., et al. “Modelación matemática del efecto de la presión atmosférica sobre la densidad poblacional de los mosquitos (Diptera: Culicidae) en Villa Clara, Cuba”. Revista de la Facultad de Medicina 68 (2020b): 541-549.
- Fimia DR. “Points of Interest for the New Coronavirus SARS-CoV-2 Causing COVID-19”. EC Veterinary Science 5 (2020c): 1-2.
- Osés RR., et al. “Modelación de la densidad larvaria total de mosquitos (Diptera: Culicidae) utilizando tres modelos en la provincia de Villa Clara, Cuba”. REDVET 15 (2014).
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