Spatiotemporal Analysis of Influenza Morbidity and Its Association with Climatic and Housing Conditions in Ecuador

Lobato-Cordero, Andrea; Quentin, Emmanuelle; Lobato-Cordero, Gina

doi:10.1155/2019/6741202

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…Some previous researches engaged in constructing the influenza transmission network by temporal and spatial statistics [11][12][13][14][15][16]. For example, Alonso WJ [16] used Fourier decomposition to find a seasonal southward traveling wave of influenza across Brazil originating from equatorial and low population regions in March-April and moving towards temperate and highly popular regions over a 3-month period.…”

Section: The Definition Of Spatio-temporal Routesmentioning

confidence: 99%

Spatial transmission network construction of influenza-like illness using Dynamic Bayesian Network and Vector-Autoregressive Moving Average Model

Qiu

Wang

et al. 2020

Preprint

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Background: Although vaccination is one of the main countermeasures against influenza epidemic, it is highly essential to make informed prevention decisions to guarantee that limited vaccination resources are allocated to the places where they are most needed. Hence, one of the fundamental steps for decision making in influenza prevention is to characterize its spatio-temporal trend, especially on the key problem about how influenza transmits among adjacent places and how much impact the influenza of one place could have on its neighbors. To solve this problem while avoiding too much additional time-consuming work on data collection, this study proposed a new concept of spatio-temporal route as well as its estimation methods to construct the influenza transmission network.Methods: The influenza-like illness (ILI) data of Sichuan province in 21 cities was collected from 2010 to 2016. A joint pattern based on the dynamic Bayesian network (DBN) model and the vector autoregressive moving average (VARMA) model was utilized to estimate the spatio-temporal routes, which were applied to the two stages of learning process respectively, namely structure learning and parameter learning. In structure learning, the first-order conditional dependencies approximation algorithm was used to generate the DBN, which could visualize the spatio-temporal routes of influenza among adjacent cities and infer which cities have impacts on others in influenza transmission. In parameter learning, the VARMA model was adopted to estimate the strength of these impacts. Finally, all the estimated spatio-temporal routes were put together to form the final influenza transmission network.Results: The results showed that the period of influenza transmission cycle was longer in Western Sichuan and Chengdu Plain than that in Northeastern Sichuan, and there would be potential spatio-temporal routes of influenza from bordering provinces or municipalities into Sichuan province. Furthermore, this study also pointed out several estimated spatio-temporal routes with relatively high strength of associations, which could serve as clues of hot spot areas detection for influenza surveillance.Conclusions: This study proposed a new framework for exploring the potentially stable spatio-temporal routes between different places and measuring specific the sizes of transmission effects. It could help making timely and reliable prediction of the spatio-temporal trend of infectious diseases, and further determining the possible key areas of the next epidemic by considering their neighbors’ incidence and the transmission relationships.

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Section: The Definition Of Spatio-temporal Routesmentioning

confidence: 99%

Spatial transmission network construction of influenza-like illness using Dynamic Bayesian Network and Vector-Autoregressive Moving Average Model

Qiu

Wang

et al. 2020

Preprint

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“…In this study, we analyzed spatiotemporal patterns, highlighting priority areas and showing their relationships with the displacement of COVID-19 epidemic outbreaks in Minas Gerais State. SARS-CoV-2 has a contagion process similar to the influenza virus that has been addressed in previous studies of spatial analysis 28 , 29 . For this reason, the use of geoprocessing in the surveillance and control of COVID-19 offers a potential resource for planning and evaluating actions in this area.…”

Section: Discussionmentioning

confidence: 97%

Spatiotemporal dynamics and risk estimates of COVID-19 epidemic in Minas Gerais State: analysis of an expanding process

Coura‐Vital

Cardoso

Ker

et al. 2021

Rev. Inst. Med. trop. S. Paulo

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COVID-19 is an infectious disease caused by the recently discovered coronavirus SARS-Cov-2. The disease became pandemic affecting many countries globally, including Brazil. Considering the expansion process and particularities during the initial stages of the epidemic, we aimed to analyze the spatial and spatiotemporal patterns of COVID-19 occurrence and to identify priority risk areas in Minas Gerais State, Southeast Brazil. An ecological study was performed considering all data from human cases of COVID-19 confirmed from the epidemiological week (EW) 11 (March 08, 2020) to EW 26 (June 27, 2020). Crude and smoothed incidence rates were used to analyze the distribution of disease patterns based on global and local indicators of spatial association and space-time risk assessment. Positive spatial autocorrelation and spatial dependence were found. Our results suggest that the metropolitan region of the State capital Belo Horizonte (MRBH) and Vale do Rio Doce mesoregions, as major epidemic foci in the beginning of the expansion process, have had important influence on the dispersion of SARS-CoV-2 in Minas Gerais State. Triangulo Mineiro/Alto Paranaiba region presented the highest risk of infection. In addition, six statistically significant spatiotemporal clusters were identified in the State, three at high risk and three at low risk. Our findings contribute to a greater understanding of the space-time disease dynamic and discuss strategies for identification of priority areas for COVID-19 surveillance and control.

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“…Some of the measures we developed to study HIV epidemics may be useful for other zoonotic infection epidemics as well [ 29 , 30 , 32 ]. For respiratory infections such as COVID-19, patterns of close proximity at workplaces (particularly indoor, poorly ventilated workplaces), housing (particularly crowded, poorly ventilated housing, including congregate settings such as shelters, homes for aged populations, prisons and jails), as well as the norms, institutions and laws that affect these, will be crucial measures [ 55 , 104 , 105 , 106 , 107 , 108 ]. For vector-borne infections or water-borne infections, still other pathways variables might need to be developed.…”

Section: Social Readiness Monitoring and Other Forms Of Preparation And Learningmentioning

confidence: 99%

Emerging Zoonotic Infections, Social Processes and Their Measurement and Enhanced Surveillance to Improve Zoonotic Epidemic Responses: A “Big Events” Perspective

Friedman

Jordan²,

Perlman

et al. 2022

IJERPH

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Zoonotic epidemics and pandemics have become frequent. From HIV/AIDS through COVID-19, they demonstrate that pandemics are social processes as well as health occurrences. The roots of these pandemics lie in changes in the socioeconomic interface between humanity and non-human host species that facilitate interspecies transmission. The degree to which zoonoses spread has been increased by the greater speed and extent of modern transportation and trade. Pre-existing sociopolitical and economic structures and conflicts in societies also affect pathogen propagation. As an epidemic develops, it can itself become a social and political factor, and change and interact with pre-existing sociobehavioral norms and institutional structures. This paper uses a “Big Events” approach to frame these processes. Based on this framework, we discuss how social readiness surveys implemented both before and during an outbreak might help public health predict how overall systems might react to an epidemic and/or to disease control measures, and thus might inform interventions to mitigate potential adverse outcomes or possibly preventing outbreaks from developing into epidemics. We conclude by considering what “pathways measures”, in addition to those we and others have already developed, might usefully be developed and validated to assist outbreak and epidemic disease responses.

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Spatiotemporal Analysis of Influenza Morbidity and Its Association with Climatic and Housing Conditions in Ecuador

Cited by 5 publications

References 36 publications

Spatial transmission network construction of influenza-like illness using Dynamic Bayesian Network and Vector-Autoregressive Moving Average Model

Spatial transmission network construction of influenza-like illness using Dynamic Bayesian Network and Vector-Autoregressive Moving Average Model

Spatiotemporal dynamics and risk estimates of COVID-19 epidemic in Minas Gerais State: analysis of an expanding process

Emerging Zoonotic Infections, Social Processes and Their Measurement and Enhanced Surveillance to Improve Zoonotic Epidemic Responses: A “Big Events” Perspective

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