scholarly journals Knowledge discovery and dengue forecasting applied in a four-year dataset collected at Natal, RN – Brazil

2021 ◽  
Author(s):  
Ignacio Sanchez-Gendriz ◽  
Gustavo de Souza ◽  
Ion de Andrade ◽  
Adrião Duarte Doria Neto ◽  
Alessandre Tavares ◽  
...  
Keyword(s):  
Public Health ◽  
Socioeconomic Status ◽  
Deep Learning ◽  
Aedes Aegypti ◽  
Health Interventions ◽  
Dengue Vector ◽  
Vector Surveillance ◽  
Public Health Interventions ◽  
Spatially Distributed ◽  
Deep Learning Model

Abstract Dengue, a disease recognized as a health problem, causes significant impacts on health and affects millions of people each year worldwide. A suitable method for dengue vector surveillance is to count eggs the mosquitoes Aedes aegypti have laid in spatially distributed ovitraps. In view of this approach, this study uses a database collected in 397 ovitraps distributed across the municipality of Natal, RN – Brazil. The number of eggs in each ovitrap was counted weekly, over four years (2016 - 2019), and simultaneously analyzed with the incidence of dengue. Our results confirm that dengue incidence seems to be related to socioeconomic status in Natal. Using a deep learning model, we then predict the incidence of new dengue cases based on data obtained from the previous week of dengue or the number of eggs present in the ovitraps. The analysis shows that ovitrap data allows earlier detection (four to six weeks) when compared to dengue cases themselves (one week). Furthermore, the results confirm that quantifying Aedes aegypti eggs may be valuable for planning actions and public health interventions.

scholarly journals Knowledge discovery and dengue forecasting applied in a four-year dataset collected at Natal/RN – Brazil

2021 ◽  
Author(s):  
Ignacio Sanchez-Gendriz ◽  
Gustavo Fountuora ◽  
Ion de Andrade ◽  
Adrião Duarte Doria Neto ◽  
Alessandre de Medeiros Tavares ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Deep Learning ◽  
Aedes Aegypti ◽  
Knowledge Discovery ◽  
Health Problem ◽  
Learning Model ◽  
Dengue Vector ◽  
Vector Surveillance ◽  
Spatially Distributed ◽  
Deep Learning Model

Abstract Dengue is recognized as a health problem, it causes significant impacts on health worldwide, affecting millions of people each year. A useful method of dengue vector surveillance is to count Aedes aegypti eggs deposited in spatially distributed ovitraps. The present work uses a database collected in 397 ovitraps distributed in the municipality of Natal/RN – Brazil. The number of eggs in each ovitrap was counted weekly, for four years (2016 - 2019) and was analyzed jointly with the dengue incidence in the same period. Our results confirms that dengue incidence seems to be related to socioeconomic status on Natal’s municipality. Using a deep learning model, we predict the incidence of new dengue cases based on data obtained from the previous week of dengue or in the number of eggs present in the ovitraps. The analysis shows that ovitrap data allows earlier detection (4-6 weeks) than dengue cases itself (1 week). The results confirm that quantifying Aedes aegypti eggs can be valuable for planning health actions.

scholarly journals An analysis of COVID-19 clusters in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pooja Sengupta ◽  
Bhaswati Ganguli ◽  
Sugata SenRoy ◽  
Aditya Chatterjee
Keyword(s):  
Public Health ◽  
Cluster Analysis ◽  
Population Density ◽  
Case Studies ◽  
Compartment Model ◽  
Health Interventions ◽  
Disease Spread ◽  
Second Phase ◽  
Public Health Interventions ◽  
Homogeneous Groups

Abstract Background In this study we cluster the districts of India in terms of the spread of COVID-19 and related variables such as population density and the number of specialty hospitals. Simulation using a compartment model is used to provide insight into differences in response to public health interventions. Two case studies of interest from Nizamuddin and Dharavi provide contrasting pictures of the success in curbing spread. Methods A cluster analysis of the worst affected districts in India provides insight about the similarities between them. The effects of public health interventions in flattening the curve in their respective states is studied using the individual contact SEIQHRF model, a stochastic individual compartment model which simulates disease prevalence in the susceptible, infected, recovered and fatal compartments. Results The clustering of hotspot districts provide homogeneous groups that can be discriminated in terms of number of cases and related covariates. The cluster analysis reveal that the distribution of number of COVID-19 hospitals in the districts does not correlate with the distribution of confirmed COVID-19 cases. From the SEIQHRF model for Nizamuddin we observe in the second phase the number of infected individuals had seen a multitudinous increase in the states where Nizamuddin attendees returned, increasing the risk of the disease spread. However, the simulations reveal that implementing administrative interventions, flatten the curve. In Dharavi, through tracing, tracking, testing and treating, massive breakout of COVID-19 was brought under control. Conclusions The cluster analysis performed on the districts reveal homogeneous groups of districts that can be ranked based on the burden placed on the healthcare system in terms of number of confirmed cases, population density and number of hospitals dedicated to COVID-19 treatment. The study rounds up with two important case studies on Nizamuddin basti and Dharavi to illustrate the growth curve of COVID-19 in two very densely populated regions in India. In the case of Nizamuddin, the study showed that there was a manifold increase in the risk of infection. In contrast it is seen that there was a rapid decline in the number of cases in Dharavi within a span of about one month.

scholarly journals Impact of reduction of susceptibility to SARS-CoV-2 on epidemic dynamics in four early-seeded metropolitan regions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thomas J. Barrett ◽  
Karen C. Patterson ◽  
Timothy M. James ◽  
Peter Krüger
Keyword(s):  
Public Health ◽  
Growth Phase ◽  
Chronic Phase ◽  
Health Interventions ◽  
Resource Utilisation ◽  
Policy Planning ◽  
Infection Rates ◽  
Public Health Interventions ◽  
Metropolitan Regions ◽  
Epidemic Dynamics

AbstractAs we enter a chronic phase of the SARS-CoV-2 pandemic, with uncontrolled infection rates in many places, relative regional susceptibilities are a critical unknown for policy planning. Tests for SARS-CoV-2 infection or antibodies are indicative but unreliable measures of exposure. Here instead, for four highly-affected countries, we determine population susceptibilities by directly comparing country-wide observed epidemic dynamics data with that of their main metropolitan regions. We find significant susceptibility reductions in the metropolitan regions as a result of earlier seeding, with a relatively longer phase of exponential growth before the introduction of public health interventions. During the post-growth phase, the lower susceptibility of these regions contributed to the decline in cases, independent of intervention effects. Forward projections indicate that non-metropolitan regions will be more affected during recurrent epidemic waves compared with the initially heavier-hit metropolitan regions. Our findings have consequences for disease forecasts and resource utilisation.

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