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Silva AT, Dorn RC, Tomás LR, Santos LB, Skalinski LM, Pinho ST. Spatial analysis of Dengue through the reproduction numbers relating to socioeconomic features: Case studies on two Brazilian urban centers. Infect Dis Model 2024; 9:142-157. [PMID: 38268698 PMCID: PMC10805647 DOI: 10.1016/j.idm.2023.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 12/08/2023] [Accepted: 12/16/2023] [Indexed: 01/26/2024] Open
Abstract
The study of the propagation of infectious diseases in urban centers finds a close connection with their population's social characteristics and behavior. This work performs a spatial analysis of dengue cases in urban centers based on the basic reproduction numbers, R0, and incidence by planning areas (PAs), as well as their correlations with the Human Development Index (HDI) and the number of trips. We analyzed dengue epidemics in 2002 at two Brazilian urban centers, Belo Horizonte (BH) and Rio de Janeiro (RJ), using PAs as spatial units. Our results reveal heterogeneous spatial scenarios for both cities, with very weak correlations between R0 and both the number of trips and the HDI; in BH, the values of R0 show a less spatial heterogeneous pattern than in RJ. For BH, there are moderate correlations between incidence and both the number of trips and the HDI; meanwhile, they weakly correlate for RJ. Finally, the absence of strong correlations between the considered measures indicates that the transmission process should be treated considering the city as a whole.
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Affiliation(s)
- Ana T.C. Silva
- Departamento de Física, Universidade Estadual de Feira de Santana, Av. Transnordestina, s/n. Novo Horizonte, Feira de Santana, 44036-900, BA, Brazil
- Instituto de Física, Universidade Federal da Bahia, Rua Barão de Jeremoabo s/n, Campus Universitário de Ondina, Salvador, 40170-115, BA, Brazil
| | - Rejane C. Dorn
- Instituto de Física, Universidade Federal da Bahia, Rua Barão de Jeremoabo s/n, Campus Universitário de Ondina, Salvador, 40170-115, BA, Brazil
| | - Lívia R. Tomás
- Centro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN), Estrada Dr. Altino Bondensan, 500, São José dos Campos, 12247-016, SP, Brazil
| | - Leonardo B.L. Santos
- Centro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN), Estrada Dr. Altino Bondensan, 500, São José dos Campos, 12247-016, SP, Brazil
| | - Lacita M. Skalinski
- Universidade Estadual de Santa Cruz, Campus Soane Nazaré de Andrade, Rodovia Jorge Amado, Km 16, Salobrinho, Ilhéus, 45662-900, BA, Brazil
- Instituto de Saúde Coletiva, Universidade Federal da Bahia, R. Basílio da Gama, s/n - Canela, Salvador, 40110-140, BA, Brazil
| | - Suani T.R. Pinho
- Instituto de Física, Universidade Federal da Bahia, Rua Barão de Jeremoabo s/n, Campus Universitário de Ondina, Salvador, 40170-115, BA, Brazil
- Instituto Nacional de Ciência e Tecnologia - Sistemas Complexos, Virtual Institution, Brazil
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Perramon-Malavez A, Bravo M, de Rioja VL, Català M, Alonso S, Álvarez-Lacalle E, López D, Soriano-Arandes A, Prats C. A semi-empirical risk panel to monitor epidemics: multi-faceted tool to assist healthcare and public health professionals. Front Public Health 2024; 11:1307425. [PMID: 38259774 PMCID: PMC10801172 DOI: 10.3389/fpubh.2023.1307425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction Bronchiolitis, mostly caused by Respiratory Syncytial Virus (RSV), and influenza among other respiratory infections, lead to seasonal saturation at healthcare centers in temperate areas. There is no gold standard to characterize the stages of epidemics, nor the risk of respiratory infections growing. We aimed to define a set of indicators to assess the risk level of respiratory viral epidemics, based on both incidence and their short-term dynamics, and considering epidemical thresholds. Methods We used publicly available data on daily cases of influenza for the whole population and bronchiolitis in children <2 years from the Information System for Infection Surveillance in Catalonia (SIVIC). We included a Moving Epidemic Method (MEM) variation to define epidemic threshold and levels. We pre-processed the data with two different nowcasting approaches and performed a 7-day moving average. Weekly incidences (cases per 105 population) were computed and the 5-day growth rate was defined to create the effective potential growth (EPG) indicator. We performed a correlation analysis to define the forecasting ability of this index. Results Our adaptation of the MEM method allowed us to define epidemic weekly incidence levels and epidemic thresholds for bronchiolitis and influenza. EPG was able to anticipate daily 7-day cumulative incidence by 4-5 (bronchiolitis) or 6-7 (influenza) days. Discussion We developed a semi-empirical risk panel incorporating the EPG index, which effectively anticipates surpassing epidemic thresholds for bronchiolitis and influenza. This panel could serve as a robust surveillance tool, applicable to respiratory infectious diseases characterized by seasonal epidemics, easy to handle for individuals lacking a mathematical background.
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Affiliation(s)
- Aida Perramon-Malavez
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Mario Bravo
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Víctor López de Rioja
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Martí Català
- Health Data Sciences, NDORMS, University of Oxford, Oxford, United Kingdom
| | - Sergio Alonso
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Enrique Álvarez-Lacalle
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Daniel López
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
| | - Antoni Soriano-Arandes
- Paediatric Infectious Diseases and Immunodeficiencies Unit, Children’s Hospital, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Catalonia, Spain
- Infection and Immunity in Paediatric Patients, Vall d’Hebron Research Institute, Barcelona, Catalonia, Spain
| | - Clara Prats
- Department of Physics, Computational Biology and Complex Systems (BIOCOM-SC) group, Barcelona School of Agri-Food and Biosystems Engineering, Universitat Politècnica de Catalunya, Castelldefels, Spain
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Marinov TT, Marinova RS, Marinov RT, Shelby N. Novel Approach for Identification of Basic and Effective Reproduction Numbers Illustrated with COVID-19. Viruses 2023; 15:1352. [PMID: 37376651 DOI: 10.3390/v15061352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
This paper presents a novel numerical technique for the identification of effective and basic reproduction numbers, Re and R0, for long-term epidemics, using an inverse problem approach. The method is based on the direct integration of the SIR (Susceptible-Infectious-Removed) system of ordinary differential equations and the least-squares method. Simulations were conducted using official COVID-19 data for the United States and Canada, and for the states of Georgia, Texas, and Louisiana, for a period of two years and ten months. The results demonstrate the applicability of the method in simulating the dynamics of the epidemic and reveal an interesting relationship between the number of currently infectious individuals and the effective reproduction number, which is a useful tool for predicting the epidemic dynamics. For all conducted experiments, the results show that the local maximum (and minimum) values of the time-dependent effective reproduction number occur approximately three weeks before the local maximum (and minimum) values of the number of currently infectious individuals. This work provides a novel and efficient approach for the identification of time-dependent epidemics parameters.
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Affiliation(s)
- Tchavdar T Marinov
- Department of Natural Sciences, Southern University at New Orleans, 6801 Press Drive, New Orleans, LA 70126, USA
| | - Rossitza S Marinova
- Department of Mathematical & Physical Sciences, Concordia University of Edmonton, 7128 Ada Boulevard, Edmonton, AB T5B 4E4, Canada
- Department Computer Science, Varna Free University, 9007 Varna, Bulgaria
| | - Radoslav T Marinov
- Rescale, 33 New Montgomery Street, Suite 950, San Francisco, CA 94105, USA
| | - Nicci Shelby
- Department of Natural Sciences, Southern University at New Orleans, 6801 Press Drive, New Orleans, LA 70126, USA
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Pinho STR. Some features on methodology of dengue modelling linked to data: Comment on "Mathematical modelling for dengue fever epidemiology: a 10-year systematic review" by M. Aguiar et al. Phys Life Rev 2023; 44:276-278. [PMID: 36821892 PMCID: PMC9916129 DOI: 10.1016/j.plrev.2023.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 01/30/2023] [Indexed: 02/12/2023]
Affiliation(s)
- Suani T R Pinho
- Instituto de Física, Universidade Federal da Bahia, 40170-115, Salvador, Brazil; Instituto Nacional de Ciência e Tecnologia - Sistemas Complexos, Brazil.
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Jorge DCP, Oliveira JF, Miranda JGV, Andrade RFS, Pinho STR. Estimating the effective reproduction number for heterogeneous models using incidence data. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220005. [PMID: 36133147 PMCID: PMC9449464 DOI: 10.1098/rsos.220005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 08/16/2022] [Indexed: 05/10/2023]
Abstract
The effective reproduction number, R ( t ) , plays a key role in the study of infectious diseases, indicating the current average number of new infections caused by an infected individual in an epidemic process. Estimation methods for the time evolution of R ( t ) , using incidence data, rely on the generation interval distribution, g(τ), which is usually obtained from empirical data or theoretical studies using simple epidemic models. However, for systems that present heterogeneity, either on the host population or in the expression of the disease, there is a lack of data and of a suitable general methodology to obtain g(τ). In this work, we use mathematical models to bridge this gap. We present a general methodology for obtaining explicit expressions of the reproduction numbers and the generation interval distributions, within and between model sub-compartments provided by an arbitrary compartmental model. Additionally, we present the appropriate expressions to evaluate those reproduction numbers using incidence data. To highlight the relevance of such methodology, we apply it to the spread of COVID-19 in municipalities of the state of Rio de Janeiro, Brazil. Using two meta-population models, we estimate the reproduction numbers and the contributions of each municipality in the generation of cases in all others.
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Affiliation(s)
- D. C. P. Jorge
- Instituto de Física Teórica, Universidade Estadual Paulista—UNESP, R. Dr. Teobaldo Ferraz 271, São Paulo 01140-070, Brazil
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - J. F. Oliveira
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil
| | - J. G. V. Miranda
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - R. F. S. Andrade
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - S. T. R. Pinho
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
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