Dataset Information

Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City.

ABSTRACT: The present analysis uses the data of confirmed incidence of dengue cases in the metropolitan region of Panama from 1999 to 2017 and climatic variables (air temperature, precipitation, and relative humidity) during the same period to determine if there exists a correlation between these variables. In addition, we compare the predictive performance of two regression models (SARIMA, SARIMAX) and a recurrent neural network model (RNN-LSTM) on the dengue incidence series. For this data from 1999-2014 was used for training and the three subsequent years of incidence 2015-2017 were used for prediction. The results show a correlation coefficient between the climatic variables and the incidence of dengue were low but statistical significant. The RMSE and MAPE obtained for the SARIMAX and RNN-LSTM models were 25.76, 108.44 and 26.16, 59.68, which suggest that any of these models can be used to predict new outbreaks. Although, it can be said that there is a limited role of climatic variables in the outputs the models. The value of this work is that it helps understand the behaviour of cases in a tropical setting as is the Metropolitan Region of Panama City, and provides the basis needed for a much needed early alert system for the region.

SUBMITTER: Navarro Valencia V

PROVIDER: S-EPMC8619576 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City.

Navarro Valencia Vicente V Díaz Yamilka Y Pascale Juan Miguel JM Boni Maciej F MF Sanchez-Galan Javier E JE

International journal of environmental research and public health 20211118 22

The present analysis uses the data of confirmed incidence of dengue cases in the metropolitan region of Panama from 1999 to 2017 and climatic variables (air temperature, precipitation, and relative humidity) during the same period to determine if there exists a correlation between these variables. In addition, we compare the predictive performance of two regression models (SARIMA, SARIMAX) and a recurrent neural network model (RNN-LSTM) on the dengue incidence series. For this data from 1999-201 ...[more]

PMID: 34831862

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Project description:The 3-5year epidemic cycle of dengue fever in Thailand makes it a major re-emerging public health problem resulting in being a burden in endemic areas. Although the Thai Ministry of Public Health adopted the WHO dengue control strategy, all dengue virus serotypes continue to circulate. Health officers and village health volunteers implement some intervention options but there is a need to ascertain most appropriate (or a combination of) interventions regarding the environment and contextual factors that may undermine the effectiveness of such interventions. This study aims to understand the dengue-climate relationship patterns at the district level in the southern region of Thailand from 2002 to 2018 by examining the statistical association between dengue incidence rate and eight environmental patterns, testing the hypothesis of equal incidence of these. Data on environmental variables and dengue reported cases in Nakhon Si Thammarat province situated in the south of Thailand from 2002 to 2018 were analysed to (1) detect the environmental factors that affect the risk of dengue infectious disease; to (2) determine if disease risk is increasing or decreasing over time; and to (3) identify the high-risk district areas for dengue cases that need to be targeted for interventions. To identify the predictors that have a high and significant impact on reported dengue infection, three steps of analysis were used. First, we used Partial Least Squares (PLS) Regression and Poisson Regression, a variant of the Generalized Linear Model (GLM). Negative co-efficient in correspondence with the PLS components suggests that sea-level pressure, wind speed, and pan evaporation are associated with dengue occurrence rate, while other variables were positively associated. Using the Akaike information criterion in the stepwise GLM, the filtered predictors were temperature, precipitation, cloudiness, and sea level pressure with the standardized coefficients showing that the most influential variable is cloud cover (three times more than temperature and precipitation). Also, dengue occurrence showed a constant negative response to the average increase in sea-level pressure values. In southern Thailand, the predictors that have been locally determined to drive dengue occurrence are temperature, rainfall, cloud cover, and sea-level pressure. These explanatory variables should have important future implications for epidemiological studies of mosquito-borne diseases, particularly at the district level. Predictive indicators guide effective and dynamic risk assessments, targeting pre-emptive interventions.

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Project description:Evolutionary analyses of viral sequences can provide insights into transmission dynamics, which in turn can optimize prevention interventions. Here, we characterized the dynamics of HIV transmission within the Mexico City metropolitan area. HIV pol sequences from persons recently diagnosed at the largest HIV clinic in Mexico City (between 2016 and 2021) were annotated with demographic/geographic metadata. A multistep phylogenetic approach was applied to identify putative transmission clades. A data set of publicly available sequences was used to assess international introductions. Clades were analyzed with a discrete phylogeographic model to evaluate the timing and intensity of HIV introductions and transmission dynamics among municipalities in the region. A total of 6,802 sequences across 96 municipalities (5,192 from Mexico City and 1,610 from the neighboring State of Mexico) were included (93.6% cisgender men, 5.0% cisgender women, and 1.3% transgender women); 3,971 of these sequences formed 1,206 clusters, involving 78 municipalities, including 89 clusters of ≥10 sequences. Discrete phylogeographic analysis revealed (i) 1,032 viral introductions into the region, over one-half of which were from the United States, and (ii) 354 migration events between municipalities with high support (adjusted Bayes factor of ≥3). The most frequent viral migrations occurred between northern municipalities within Mexico City, i.e., Cuauhtémoc to Iztapalapa (5.2% of events), Iztapalapa to Gustavo A. Madero (5.4%), and Gustavo A. Madero to Cuauhtémoc (6.5%). Our analysis illustrates the complexity of HIV transmission within the Mexico City metropolitan area but also identifies a spatially active transmission area involving a few municipalities in the north of the city, where targeted interventions could have a more pronounced effect on the entire regional epidemic. IMPORTANCE Phylogeographic investigation of the Mexico City HIV epidemic illustrates the complexity of HIV transmission in the region. An active transmission area involving a few municipalities in the north of the city, with transmission links throughout the region, is identified and could be a location where targeted interventions could have a more pronounced effect on the entire regional epidemic, compared with those dispersed in other manners.

Dataset Information

Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City.

Publications

Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City.

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