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Bayesian spatial modelling of Ebola outbreaks in Democratic Republic of Congo through the INLA-SPDE approach (lay summary)

This is a lay summary of the article published under the DOI:

Published onApr 06, 2023
Bayesian spatial modelling of Ebola outbreaks in Democratic Republic of Congo through the INLA-SPDE approach (lay summary)
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Abstract

Abstract Ebola virus (EBV) disease is globally acknowledged public health emergence, which is endemic in the West and equatorial Africa. To understand the epidemiology especially the dynamic pattern of EBV disease, we analyse the EBV case notification data for confirmed cases and reported deaths of the ongoing outbreak in Democratic Republic of Congo (DRC) between 2018 and 2019, and examined the impart of reported violence of the spread of the virus. Using fully Bayesian geo-statistical analysis through stochastic partial differential equations (SPDE) that allows us to quantify the spatial patterns at every point of the spatial domain. Parameter estimation based on the integrated nested Laplace approximation (INLA). Our findings reveal strong association between violent events in the affected areas and the reported EBV cases and deaths, and the presence of clusters for both cases and deaths both of which spread to neighbouring locations in similar manners. Findings from the study are therefore useful for hotspot identification, location-specific disease surveillance and intervention. Impacts In 2018, the Democratic Republic of Congo (DRC) confirmed their tenth Ebola epidemic in 40 years. The outbreak is the country’s largest Ebola outbreak and the second largest ever recorded after the West African epidemic of 2014-2016. The current outbreak is reported to be occurring in a longstanding conflict zone, this study focused investigating the spatial distribution of Ebola incidence in DRC and the role of violent events. Violent events in the affected areas was found to be significantly associated with reported Ebola cases, which is highly relevant for hotspot identification and location-specific disease surveillance and intervention.

Lay summary

Title

Violence predicts Ebola outbreaks in Democratic Republic of Congo

Brief summary of the what & so what (result and why it matters) [+- 20 words]

Ebola virus disease (EBV) is a highly infectious, deadly disease made worse by ongoing violence in the Democratic Republic of Congo (DRC). Researchers are now able to predict where EBV outbreaks will occur using data from violent events.

Why was this study done? (problem statement and background)

Ebola is a globally acknowledged public health emergency endemic in the West and equatorial regions of Africa. The recent outbreak in the DRC is the second largest ever recorded in West Africa.

In this country, armed conflicts provide fertile ground for infectious diseases to emerge and spread, because resources and health infrastructures are destroyed, and many people are displaced.

What was the purpose of this study? (Aims and objectives)

In this study, researchers searched for links between the spread of Ebola disease and the geographical areas where violence has been reported in DRC in 2018 and 2019.

What did the researchers do (summary or overview of methods, the big picture)

They used statistical methods based on Baye’s theorem, which uses sparse pieces of data to predict when or where another event will occur. They specifically used data about the breakout of violence in certain areas to predict where Ebola might breakout next.

What were the results of the study?

The study found that areas affected by violent events were strongly associated with reported cases of Ebola infections and deaths, which spread to neighboring areas as well. The researchers also accounted for Ebola cases and deaths where data had not been collected due to the ongoing violence. 

How do these findings add to what was already known? (impact on the current science)

For the very first time in the DRC, this study has shown that Bayesian spatial modelling can be used to identify Ebola hotspots where the data might not be available.

What are the potential weaknesses/uncertainties/controversies of the study? (If the paper talks about them at all, but most important for where they mention where future research is needed, or where the data was lacking)

However, researchers couldn’t distinguish between the types of violent events and how they specifically impact the health-care systems in the affected areas. They also did not capture their data on violent events in a way that can predict exactly when an Ebola outbreak will occur.

“How will these findings help solve a challenge in Africa?” (question speaking to the impact of the research on society as a whole, outside of the scientific community).

The researchers recommend that authorities in DRC  provide stronger disease surveillance and response systems in the affected areas, and indeed in all parts of the country to enhance early detection and control to slow the spread of Ebola.

Number of words:

Glossary terms for translation and coining:

  1. Baye’s theorem: In probability theory and statistics, Bayes' theorem, named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event.[2] For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately (by conditioning it on their age) than simply assuming that the individual is typical of the population as a whole.

  2. Endemic: An endemic is something that belongs to a particular people or country. For a virus or disease, it is endemic if it affects a particular country or region.

  3. Ebola: Ebola, also known as Ebola virus disease (EVD) and Ebola hemorrhagic fever (EHF), is a viral fever that causes bleeding in humans and other primates, caused by ebolaviruses. Symptoms typically start anywhere between two days and three weeks after becoming infected with the virus.

  4. Spatial modelling:  Spatial modelling is a method of using information from different parts of a geographical area to find the relationships between them.

  5. Epidemiology: Epidemiology is the branch of medicine to which deals with the incidence, distribution, and possible control of diseases and other factors relating to health.

Other keywords for ease of understanding:

  1. stochastic partial differential equations (SPDE) and integrated nested Laplace approximation (INLA): These are equations and mathematical models used in Bayesian spatial modelling.


Final summary for translation

Violence predicts Ebola outbreaks in Democratic Republic of Congo

Ebola virus disease (EBV) is a highly infectious, deadly disease made worse by ongoing violence in the Democratic Republic of Congo (DRC). Researchers are now able to predict where EBV outbreaks will occur using data from violent events.

Ebola is a globally acknowledged public health emergency endemic in the West and equatorial regions of Africa. The recent outbreak in the DRC is the second largest ever recorded in West Africa.

In this country, armed conflicts provide fertile ground for infectious diseases to emerge and spread, because resources and health infrastructures are destroyed, and many people are displaced.

In this study, researchers searched for links between the spread of Ebola disease and the geographical areas where violence has been reported in DRC in 2018 and 2019.

They used statistical methods based on Baye’s theorem, which uses sparse pieces of data to predict when or where another event will occur. They specifically used data about the breakout of violence in certain areas to predict where Ebola might breakout next.

The study found that areas affected by violent events were strongly associated with reported cases of Ebola infections and deaths, which spread to neighboring areas as well. The researchers also accounted for Ebola cases and deaths where data had not been collected due to the ongoing violence. 

For the very first time in the DRC, this study has shown that Bayesian spatial modelling can be used to identify Ebola hotspots where the data might not be available.

However, researchers couldn’t distinguish between the types of violent events and how they specifically impact the health-care systems in the affected areas. They also did not capture their data on violent events in a way that can predict exactly when an Ebola outbreak will occur.

The researchers recommend that authorities in DRC  provide stronger disease surveillance and response systems in the affected areas, and indeed in all parts of the country to enhance early detection and control to slow the spread of Ebola.

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Bayesian spatial modelling of Ebola outbreaks in Democratic Republic of Congo through the INLA-SPDE approach
Description

AbstractEbola virus (EBV) disease is globally acknowledged public health emergence, which is endemic in the West and equatorial Africa. To understand the epidemiology especially the dynamic pattern of EBV disease, we analyse the EBV case notification data for confirmed cases and reported deaths of the ongoing outbreak in Democratic Republic of Congo (DRC) between 2018 and 2019, and examined the impart of reported violence of the spread of the virus. Using fully Bayesian geo-statistical analysis through stochastic partial differential equations (SPDE) that allows us to quantify the spatial patterns at every point of the spatial domain. Parameter estimation based on the integrated nested Laplace approximation (INLA). Our findings reveal strong association between violent events in the affected areas and the reported EBV cases and deaths, and the presence of clusters for both cases and deaths both of which spread to neighbouring locations in similar manners. Findings from the study are therefore useful for hotspot identification, location-specific disease surveillance and intervention.ImpactsIn 2018, the Democratic Republic of Congo (DRC) confirmed their tenth Ebola epidemic in 40 years. The outbreak is the country’s largest Ebola outbreak and the second largest ever recorded after the West African epidemic of 2014-2016.The current outbreak is reported to be occurring in a longstanding conflict zone, this study focused investigating the spatial distribution of Ebola incidence in DRC and the role of violent events.Violent events in the affected areas was found to be significantly associated with reported Ebola cases, which is highly relevant for hotspot identification and location-specific disease surveillance and intervention.

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