Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators

Valeri, Linda; Patterson-Lomba, Oscar; Gurmu, Yared; Ablorh, Akweley; Bobb, Jennifer F.; Townes, F. William; Harling, Guy

doi:10.1371/journal.pone.0163544

Cited by 23 publications

(36 citation statements)

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“…This study investigated region level predictors of Ebola risk in three West African countries of Guinea, Liberia, and Sierra Leone. Most similar to this study are studies by Fang and Valeri (Fang et al, 2016;Valeri et al, 2016). All three studies were conducted at the same spatial scale and relate similar predictor variables to slightly different outcome variables related to EVD during the 2014 outbreak.…”

Section: Discussionsupporting

confidence: 60%

“…All three studies were conducted at the same spatial scale and relate similar predictor variables to slightly different outcome variables related to EVD during the 2014 outbreak. While our study ultimately relied on results from a negative binomial model, the study by Fang and risk/epidemic size of EVD (Fang et al, 2016;Valeri et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Many studies were also conducted at the population level (Agua-Agum et al, 2016;Fang et al, 2016;Francesconi et al, 2003;Haaskjold et al, 2016;Lau et al, 2017;Moyen et al, 2015;Valeri et al, 2016;Walsh and Haseeb, 2015). Some findings agree on risk factors for contracting and dying from the disease as well as the likely presence of "superspreaders" in the population, while others have illustrated the need for a better understanding of population level spread of the disease (Lau et al, 2017;Richards et al, 2015;Valeri et al, 2016;Walsh and Haseeb, 2015). In this study, we investigated region level predictors of EVD to help guide future studies and disease control efforts.…”

mentioning

confidence: 81%

“…A number of studies have also analyzed how the disease was transmitted among humans, some of which considered the types of contacts associated with EVD transmission at the individual level (Agua-Agum et al, 2016;Brainard et al, 2016;Francesconi et al, 2003;Lau et al, 2017;Richards et al, 2015), while others investigated the characteristics of the within-host progression of the disease (Haaskjold et al, 2016;Reza et al, 2015). Many studies were also conducted at the population level (Agua-Agum et al, 2016;Fang et al, 2016;Francesconi et al, 2003;Haaskjold et al, 2016;Lau et al, 2017;Moyen et al, 2015;Valeri et al, 2016;Walsh and Haseeb, 2015). Some findings agree on risk factors for contracting and dying from the disease as well as the likely presence of "superspreaders" in the population, while others have illustrated the need for a better understanding of population level spread of the disease (Lau et al, 2017;Richards et al, 2015;Valeri et al, 2016;Walsh and Haseeb, 2015).…”

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confidence: 97%

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9Background. Ebola Virus Disease (EVD) is a highly infectious disease that has produced over 25,000 cases in the past 50 years. While many past outbreaks resulted in relatively few cases, the 2014 outbreak in West Africa was the most deadly occurrence of EVD to date, producing over 15,000 confirmed cases. Objective. In this study, we investigated population level predictors of EVD risk at the regional level in Sierra Leone, Liberia, and Guinea. Methods. Spatial and descriptive analyses were conducted to assess distribution of EVD cases. Choropleth maps showing the spatial distribution of EVD risk across the study area were generated in ArcGIS. Poisson and negative binomial models were then used to investigate population and regional predictors of EVD risk. Results. Results indicated that the risk of EVD was significantly lower in areas with higher proportions of: (a) the population living in urban areas, (b) households with a low quality or no toilets, and (c) married men working in blue collar jobs. However, risk of EVD was significantly higher in areas with high mean years of education. Conclusions. The identified significant predictors of high risk were associated with areas with higher levels of urbanization. This may be due to higher population densities in the more urban centers and hence higher potential of infectious contact. However, there is need to better understand the role of urbanization and individual contact structure in an Ebola outbreak. We discuss shortcomings in available data and emphasize the need to consider spatial scale in future data collection and epidemiological studies.

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Section: Discussionsupporting

confidence: 60%

Section: Discussionmentioning

confidence: 99%

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confidence: 81%

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confidence: 97%

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“…Population level research on Ebola has yielded interesting results. For a start, risk of Ebola infection has been associated with a higher level of education [73,111]. Lower risk for acquisition of Ebola at the population level has been associated with urban residence, households with no or low-quality sanitary system, and married men in blue-collar professions in the 2014 outbreak in West Africa [73].…”

Section: Human Population Expansionmentioning

confidence: 99%

The Emerging Threat of Ebola

LaBrunda

Amin

2020

Advanced Sciences and Technologies for Security Applications

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Comparing Short-Term Univariate and Multivariate Time-Series Forecasting Models in Infectious Disease Outbreak

2022

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Predicting infectious disease outbreak impacts on population, healthcare resources and economics and has received a special academic focus during coronavirus (COVID-19) pandemic. Focus on human disease outbreak prediction techniques in current literature, Marques et al. (Predictive models for decision support in the COVID-19 crisis. Springer, Switzerland, 2021) state that there are four main methods to address forecasting problem: compartmental models, classic statistical models, space-state models and machine learning models. We adopt their framework to compare our research with previous works. Besides being divided by methods, forecasting problems can also be divided by the number of variables that are considered to make predictions. Considering this number of variables, forecasting problems can be classified as univariate, causal and multivariate models. Multivariate approaches have been applied in less than 10% of research found. This research is the first attempt to evaluate, over real time-series data of 3 different countries with univariate and multivariate methods to provide a short-term prediction. In literature we found no research with that scope and aim. A comparison of univariate and multivariate methods has been conducted and we concluded that besides the strong potential of multivariate methods, in our research univariate models presented best results in almost all regions’ predictions.

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Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators

Cited by 23 publications

References 35 publications

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The Emerging Threat of Ebola

Comparing Short-Term Univariate and Multivariate Time-Series Forecasting Models in Infectious Disease Outbreak

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