Potential for large outbreaks of Ebola virus disease

Camacho, Anton; Kucharski, Adam J.; Funk, Sebastian; Breman, Joel G.; Piot, Peter; Edmunds, W. John

doi:10.1016/j.epidem.2014.09.003

Cited by 139 publications

(114 citation statements)

References 20 publications

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“…To model the effect of treatment beds on Ebola transmission, we used a susceptible-exposed-infectious-removed (SEIR) framework that incorporated ETUs and EHCs/CCCs (8). We used a relatively simple framework as we were fitting to only a single case timeseries for each district (14). In the model, individuals started off susceptible to infection (S).…”

Section: Methodsmentioning

confidence: 99%

“…The model was stratified by district, and incorporated available data on bed capacity in ETUs, EHCs, and CCCs (13). As beds were not the only control measure in place, we also included a time-varying transmission rate in the model (4,14) to capture any variation in transmission which was not explained by the introduction of beds.…”

Section: T He 2013-2015 Ebola Virus Disease (Evd) Epidemic In Westmentioning

confidence: 99%

“…To allow for a time-varying community transmission rate, we used a flexible sigmoid function (14,17); depending on parameter values, transmission could be constant over time, or increase or decline. Our model structure therefore made it possible to separate the reduction in infection as a result of additional treatment beds and variation resulting from other effects, such as behavior changes and implementation of safe burials.…”

Section: T He 2013-2015 Ebola Virus Disease (Evd) Epidemic In Westmentioning

confidence: 99%

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Measuring the impact of Ebola control measures in Sierra Leone

Kucharski

Camacho

Glover

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

100

101

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Between September 2014 and February 2015, the number of Ebola virus disease (EVD) cases reported in Sierra Leone declined in many districts. During this period, a major international response was put in place, with thousands of treatment beds introduced alongside other infection control measures. However, assessing the impact of the response is challenging, as several factors could have influenced the decline in infections, including behavior changes and other community interventions. We developed a mathematical model of EVD transmission, and measured how transmission changed over time in the 12 districts of Sierra Leone with sustained transmission between June 2014 and February 2015. We used the model to estimate how many cases were averted as a result of the introduction of additional treatment beds in each area. Examining epidemic dynamics at the district level, we estimated that 56,600 (95% credible interval: 48,300–84,500) Ebola cases (both reported and unreported) were averted in Sierra Leone up to February 2, 2015 as a direct result of additional treatment beds being introduced. We also found that if beds had been introduced 1 month earlier, a further 12,500 cases could have been averted. Our results suggest the unprecedented local and international response led to a substantial decline in EVD transmission during 2014–2015. In particular, the introduction of beds had a direct impact on reducing EVD cases in Sierra Leone, although the effect varied considerably between districts.

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Section: Methodsmentioning

confidence: 99%

Section: T He 2013-2015 Ebola Virus Disease (Evd) Epidemic In Westmentioning

confidence: 99%

Section: T He 2013-2015 Ebola Virus Disease (Evd) Epidemic In Westmentioning

confidence: 99%

See 1 more Smart Citation

Measuring the impact of Ebola control measures in Sierra Leone

Kucharski

Camacho

Glover

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

100

101

View full text Add to dashboard Cite

show abstract

“…The aim of control interventions is to reduce the net reproduction number R t during an outbreak (also called the effective or instantaneous reproduction number) below unity so that the outbreak eventually ends. Studying the change in R t during the course of an outbreak provides useful information on the effectiveness of the control measures that were implemented (Chowell et al, 2004;Althaus, 2014;Camacho et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Ebola virus disease outbreak in Nigeria: transmission dynamics and rapid control

Althaus

Low

Musa

et al. 2015

Preprint

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International air travel has already spread Ebola virus disease (EVD) to major cities as part of the unprecedented epidemic that started in Guinea in December 2013. An infected airline passenger arrived in Nigeria on July 20, 2014 and caused an outbreak in Lagos and then Port Harcourt. After a total of 20 reported cases, including 8 deaths, Nigeria was declared EVD free on October 20, 2014. We quantified the impact of early control measures in preventing further spread of EVD in Nigeria and calculated the risk that a single undetected case will cause a new outbreak. We fitted an EVD transmission model to data from the outbreak in Nigeria and estimated the individual reproduction number of the index case at 9.0 (95% confidence interval [CI]: 5.2-15.6). We also found that the net reproduction number fell below unity 15 days (95% CI: 11-21 days) after the arrival of the index case. Hence, our study illustrates the time window for successful containment of EVD outbreaks caused by infected air travelers.

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“…The aim of control interventions is to reduce the net reproduction number R t during an outbreak (also called effective or instantaneous reproduction number) below unity so the outbreak eventually ends. Studying the change in R t during the course of an outbreak provides useful information on the effectiveness of the control measures that were implemented (Chowell et al, 2004;Althaus, 2014;Camacho et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Ebola virus disease outbreak in Nigeria: lessons to learn

Althaus

Gsteiger

Low

2014

Preprint

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International air travel has already spread Ebola virus disease (EVD) to major cities as part of the unprecedented epidemic that started in Guinea in December 2013. An infected airline passenger arrived in Nigeria on July 20, 2014 and caused an outbreak in Lagos and then Port Harcourt. After a total of 20 reported cases, including 8 deaths, Nigeria was declared EVD free on October 20, 2014. We quantified the impact of early control measures in preventing further spread of EVD in Nigeria and calculated the risk that a single undetected case will cause a new outbreak. We fitted an EVD transmission model to data from the outbreak in Nigeria and estimated the basic reproduction number R 0 = 9.0 (95% confidence interval [CI]: 5.2-15.6). We also found that the net reproduction number R t fell below unity 15 days (95% CI: 11-21 days) after the arrival of the index case. Using the estimated value of R 0 in Nigeria, we calculated that the risk of an outbreak from a single undetected case was 89% (95% CI: 81-94%). Even though R 0 in Nigeria was high, EVD outbreaks caused by infected air travelers can be successfully contained if control measures are rapidly implemented.

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Potential for large outbreaks of Ebola virus disease

Cited by 139 publications

References 20 publications

Measuring the impact of Ebola control measures in Sierra Leone

Measuring the impact of Ebola control measures in Sierra Leone

Ebola virus disease outbreak in Nigeria: transmission dynamics and rapid control

Ebola virus disease outbreak in Nigeria: lessons to learn

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