Generalized models and the impacts of population density on COVID-19 transmission/ Modelos generalizados y los impactos de la densidad de población en la transmisión del COVID-19/ Modelos generalizados e os impactos da densidade populacional na transmissão da COVID-19

Autores

Palavras-chave:

COVID-19, Epidemiological Models, Health Policy

Resumo

Objective: to analyze epidemic curves based on mathematical models for the state of Mato Grosso do Sul and the impacts of population density on COVID-19 transmission. Method: the linear, polynomial and exponential regression model was used to make the numerical adjustment of the respective curves empirical. Result: it was found that the models used describe very well the empirical curves in which they were tested. In particular, the polynomial model is able to identify with reasonable reliability the appearance of the inflection point in the accumulated curves, which corresponds to the maximum point of the respective daily curves. The analysis indicates a weak positive correlation between infection, mortality, lethality and deaths from COVID-19 with population density, as revealed by the correlation and analysis of R2Conclusion: the models are very effective in describing the COVID-19 and epidemic curves in the estimation of important epidemiological parameters, such as peak case curves and daily deaths, allowing practical and efficient monitoring of the evolution of the epidemic.

Biografia do Autor

Amaury de Souza, Universidade Federal de Mato Grosso do Sul (UFMS)

Graduado em licenciatura em Fisica (UFSCAR), bacharelado em Fisica (USO-Sao Carlos), mestrado em meteorologia (UFV), doutorado em tecnologias ambientais (UFMS) e professor associado na UFMS

Referências

Anderson RM. Population Dynamics of Infectious Diseases. Theory and Applications.: Chapman & Hall; 1982.

Hethcote HW. Three basic epidemiological models. In: Springer, editor. Applied mathematical ecology. Berlin;1989.

Roda WC, Varughese M B, Han D, Li MY. Why is it difficult to accurately predict the COVID-19 epidemic? Infect Dis Model. 2020; 5 :271-281.

Fanelli D, Piazza F. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos Solitons Fractals. 2020; 134:109761.

Anastassopoulou C, Russo L, Tsakris A, Siettos C. Data-based analysis, modelling and forecasting of the COVID-19 outbreak. PLoS One. 2020; 15:e0230405.

Ghosal S, Sengupta S, Majumder M, Sinha B. Linear Regression Analysis to predict the number of deaths in India due to SARS-CoV-2 at 6 weeks from day 0 (100 cases - March 14th 2020). Diabetes Metabolic Syndrome. 2020; 14(4):311-315.

Aviv-Sharon E, Aharoni A. Generalized logistic growth modeling of the COVID-19 pandemic in Asia. Infect Dis Model. 2020; 5:502-509.

Ceylan Z. Estimation of COVID-19 prevalence in Italy, Spain, and France. Sci Total Environ. 2020; 10(729):138817.

Ayinde K, Lukman AF, Rauf IR, Alabi, OO, Okon CE, Ayinde OE. Modeling Nigerian COVID-19 cases: A comparative analysis of models and estimators. Chaos, Solitons & Fractals 2020; 138:109911.

Al-qaness MAA, Ewees AA, Fan H, El Aziz ABD, El MA. Optimization method for forecasting confirmed cases of COVID-19 in China. J Clin Med. 2020; 9(3):674.

Wang Q, Su M. A preliminary assessment of the impact of COVID-19 on environment - A case study of China. Sci Total Environ. 2020; 1(728):138915.

Li Q, Feng W, Quan YH. Trend and forecasting of the COVID-19 outbreak in China. J Infor Security. 2020; 80(4):469-496.

Wei W, Jiang J, Liang H, Gao L, Liang, Huang J, et al. Application of a combined model with autoregressive integrated moving average (ARIMA) and generalized regression neural network (GRNN) in forecasting hepatitis incidence in Heng County, China. PLoS One. 2016; 11:e0156768.

Brasil. DATASUS/TABNET [Internet]. Disponível em: https://datasus.saude.gov.br/informacoes-de-saude-tabnet/

Guerriero ICZ. Resolução nº 510, de 7 de abril de 2016, que trata das especificidades éticas das pesquisas nas ciências humanas e sociais e de outras que utilizam metodologias próprias dessas áreas. Ciênc Saúde Coletiva. 2016; 21(8):2619-29.

Yue S, Pilon P. A Comparison of the Power of the t Test, Mann-Kendall and Bootstrap Tests for Trend Detection. Hydrol Sci J. 2004; 49(1):21-37.

Blain GC. The modified Mann-Kendall test: on the performance of three variance correction approaches. Bragantia. 2013; 72 (4):416–425.

Sa’adi Z, Shahid S, Ismail T, Chung Es, Wang X J. Trends analysis of rainfall and rainfall extremes in Sarawak, Malaysia using modified Mann–Kendall test. Meteorol Atmos Phys. 2019; 131(3):263–277.

Noronha KVMS, Guedes GR, Turra CM, Andrade MV, Botega L, Nogueira D, et al. Pandemia por COVID-19 no Brasil: ana?lise da demanda e da oferta de leitos hospitalares e equipamentos de ventilac?a?o assistida segundo diferentes cena?rios. Cad Sau?de Pu?blica. 2020; 36(6):e00115320.

Moreira RS. COVID-19: unidades de terapia intensiva, ventiladores meca?nicos e perfis latentes de mortalidade associados a? letalidade no Brasil. Cad Sau?de Pu?blica. 2020; 36(5):e00080020.

Henriques CMP, Vasconcelos W. Crises dentro da crise: respostas, incertezas e desencontros no combate a? pandemia da COVID-19 no Brasil. Estud Av. 2020; 34(99):25-44.

Ministe?rio da Sau?de (BR). Centro de Operac?o?es de Emerge?ncias em Sau?de Pu?blica. Plano de Continge?ncia Nacional para Infecc?a?o Humana pelo novo Coronavi?rus COVID-19. Brasi?lia: Ministe?rio da Sau?de; 2020.

Ministe?rio da Sau?de (BR). Secretaria de Vigila?ncia em Sau?de. Doenc?a pelo Coronavi?rus COVID-19. Boletim Epidemiolo?gico Especial. Semana Epidemiolo?gica 30 (19 a 25/07), 2020. [online]. Disponi?vel em: <https://www.saude.gov.br/images/pdf/2020/July/30/Boletim- epidemiologico-COVID-24.pdf>

Índice de isolamento social: Mato Grosso do Sul. Disponível em: <https://www.inloco.com.br/COVID-19>

Marson FAL, Ortega MM. COVID-19 in Brazil. Pulmonology. 2020; 26(4): 241-244.

Candido DS, Watts A, Abade L, Kraemer MUG, Pybus OG, Croda J, et al. Routes for COVID-19 importation in Brazil Running. J Travel Med. 2020; 1:1-7.

Abdulkadir A. Is the lockdown important to prevent the COVID-19 pandemic? Effects on psychology, environment and economy-perspective. Ann Med Surg. 2020; 56:38-42.

Souza A, Abreu MC, Oliveira-Júnior JF. Spatio-temporal analysis between the incidence of COVID-19 and human development in Mato Grosso do Sul, Brazil. medRxiv. 2021; 1:1-25.

Andrade EO, Gouveia VV, D’Ávila RL, Carneiro MB, Massud M, Gallo JH. Índice de desenvolvimento em saúde: Conceituação e reflexões sobre sua necessidade. Rev Assoc Med Bras. 2012; 58(4):413-21.

Baqui P, Bica I, Marra V, Ercole A, Van Der Schaar M. Ethnic and regional variations in hospital mortality from COVID-19 in Brazil: a cross-sectional observational study. The Lancet Glob Health. 2020; 8(8):E1018-E1026.

Fortaleza CMCB, Guimarães RB, Almeida GB, Pronunciate M, Ferreira CP.Taking the inner route: spatial and demographic factors affecting vulnerability to COVID-19 among 604 cities from inner São Paulo State, Brazil. Epidemiol Infect. 2020; 148:e118.

Morata MM, Bastos SB, Cajueiro DO, Normey-Rico JE. An optimal predictive control strategy for COVID-19 (SARS-CoV-2) social distancing policies in Brazil. Annu Rev Control. 2020; 50:417-431.

Ribeiro MA, Albuquerque IMN, Paiva GM, Vasconcelos JPC, Araújo MAVF, Vasconcelos MIO. Georreferenciamento: ferramenta de análise do sistema de saúde de Sobral - Ceará. Sanare. 2014; 13(2):63-9.

Ribas RM, Campos PA, Brito CS, Gontijo-Filho PP. Coronavirus Disease 2019 (COVID-19) and healthcare-associated infections: Emerging and future challenges for public health in Brazil. Travel Med Infect Dis. 2020; 37:101675.

Rodriguez-Morales AJ, Gallego V, Escalera-Antezana JP, Mendéz CA, Zambrano LI, Franco-Paredes C, et al. COVID-19 in Latin America: The implications of the first confirmed case in Brazil. Travel Med Infect Dis. 2020; 35:101613.

Díaz-Pérez G. La pandemia de COVID-19 y sus violencias en América Latina. J Health NPEPS. 2020; 5(2):1-7.

Souza SS, Cunha AC, Suplici SER, Zamprogna KM, Laurindo DLP. Influência da cobertura da Atenção Primária no enfrentamento da COVID-19. J Health NPEPS. 2021; 6(1):1-21.

Ventura-Silva JMA, Ribeiro OMPL, Santos MR, Faria ACA, Monteiro MAJ, Vandresen L. Planejamento organizacional no contexto de pandemia por COVID-19: implicações para a gestão em enfermagem. J Health NPEPS. 2020; 5(1):e4626.

Mendonça FD, Rocha SS, Pinheiro DLP, Oliveira SV. Região Norte do Brasil e a pandemia de COVID-19: análise socioeconômica e epidemiológica. J Health NPEPS. 2020; 5(1):20-37.

Campos ACV, Leitão LPC. Letalidade da COVID-19 entre profissionais de saúde no Pará, Brasil. J Health NPEPS. 2021; 6(1):22-34.

Bhadra A, Mukherjee A, Sarkar K. Impact of population density on Covid?19 infected and mortality rate in India. Model Earth Syst Environ. 2021; 7:623–629.

Kodera S, Rashed E A, Hirata A. Correlation between COVID-19 Morbidity and Mortality Rates in Japan and Local Population Density, Temperature, and Absolute Humidity. Int J Environ Res Public Health. 2020; 17(15):5477.

Diao Y, Kodera S, Anzai D, Gomez-Tames, J, Rashed EA, Hirata A. Influence of population density, temperature, and absolute humidity on spread and decay durations of COVID-19: A comparative study of scenarios in China, England, Germany, and Japan. One Health. 2021; 12:100203.

Downloads

Publicado

24/08/2021

Como Citar

Souza, A. de, Abreu, M. C., Oliveira-Júnior, J. F. de, Alves Fernandes, W., Aristone, F., Martins de Souza, D., da Silva, S. D., & Barros da Silva, E. (2021). Generalized models and the impacts of population density on COVID-19 transmission/ Modelos generalizados y los impactos de la densidad de población en la transmisión del COVID-19/ Modelos generalizados e os impactos da densidade populacional na transmissão da COVID-19. Journal Health NPEPS, 6(2). Recuperado de https://periodicos.unemat.br/index.php/jhnpeps/article/view/5597

Edição

Seção

Artigo Original/ Original Article/ Artículo Originale