Forecasting Ebola with a regression transmission model

Asher, Jason

doi:10.1016/j.epidem.2017.02.009

Cited by 34 publications

(30 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiple mathematical modeling analyses related to Ebola hemorrhagic fever have been undertaken to forecast peak incidence and size of outbreaks [15]. They have evaluated shifts in disease transmission dynamics during epidemics [16], identified factors contributing to the recurrence and persistence of outbreaks [17], assessed the population-level impact of quarantine on disease transmission dynamics [18], estimated size and duration of outbreaks with and without vaccine use [19], assessed the role of sexual transmission in spread of infection during outbreaks [20], captured real-time disease dynamics in the midst of outbreaks [21], projected the short-and long-term course of outbreaks [22], evaluated the effectiveness of control were employed by Janssen Vaccines & Prevention B.V., Leiden, The Netherlands and RP, AK, VM and HB were employed by SmartAnalyst Inc, New York, NY, USA or its subsidiaries.…”

Section: Introductionmentioning

confidence: 99%

Impact of prophylactic vaccination strategies on Ebola virus transmission: A modeling analysis

Potluri¹,

Kumar²,

Maheshwari³

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

Ebola epidemics constitute serious public health emergencies. Multiple vaccines are under development to prevent these epidemics and avoid the associated morbidity and mortality. Assessing the potential impact of these vaccines on morbidity and mortality of Ebola is essential for devising prevention strategies. A mean-field compartmental stochastic model was developed for this purpose and validated by simulating the 2014 Sierra Leone epidemic. We assessed the impacts of prophylactic vaccination of healthcare workers (HCW) both alone and in combination with the vaccination of the general population (entire susceptible population other than HCW). The model simulated 8,706 (95% confidence intervals [CI]: 478-21,942) cases and 3,575 (95%CI: 179-9,031) deaths in Sierra Leone, in line with WHO-reported statistics for the 2014 epidemic (8,704 cases and 3,587 deaths). Relative to this base case, the model then estimated that prophylactic vaccination of only 10% of HCW will avert 12% (95% CI: 6%-14%) of overall cases and deaths, while vaccination of 30% of HCW will avert 34% of overall cases (95% CI: 30%-64%) and deaths (95% CI: 30%-65%). Prophylactic vaccination of 1% and 5% of the general population in addition to vaccinating 30% of HCW was estimated to result in reduction in cases by 44% (95% CI: 39%-61%) and 72% (95% CI: 68%-84%) respectively, and deaths by 45% (95% CI: 40%-61%) and 74% (95% CI: 70%-85%) respectively. Prophylactic vaccination of even small proportions of HCW is estimated to significantly reduce incidence of Ebola and associated mortality. The effect is greatly enhanced by the additional vaccination even of small percentages of the general population. These findings could be used to inform the planning of prevention strategies.

show abstract

Section: Introductionmentioning

confidence: 99%

Impact of prophylactic vaccination strategies on Ebola virus transmission: A modeling analysis

Potluri¹,

Kumar²,

Maheshwari³

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The inclusion of different compartments is based on the nature of the diseases ( Hethcote, 2000 ). The models have been applied to many emerging infectious diseases, for example, avian influenza ( De Jong & Hagenaars, 2009 ), Ebola ( Browne, Gulbudak & Webb, 2015 ; Khan et al, 2015 ; Santermans et al, 2016 ; Asher, 2017 ), HIV/AIDS ( Akpa & Oyejola, 2010 ; Luo et al, 2015 ), and many others.…”

Section: Resultsmentioning

confidence: 99%

Dynamics of Zika virus outbreaks: an overview of mathematical modeling approaches

Wiratsudakul

Suparit

Modchang

2018

PeerJ

View full text Add to dashboard Cite

BackgroundThe Zika virus was first discovered in 1947. It was neglected until a major outbreak occurred on Yap Island, Micronesia, in 2007. Teratogenic effects resulting in microcephaly in newborn infants is the greatest public health threat. In 2016, the Zika virus epidemic was declared as a Public Health Emergency of International Concern (PHEIC). Consequently, mathematical models were constructed to explicitly elucidate related transmission dynamics.Survey MethodologyIn this review article, two steps of journal article searching were performed. First, we attempted to identify mathematical models previously applied to the study of vector-borne diseases using the search terms “dynamics,” “mathematical model,” “modeling,” and “vector-borne” together with the names of vector-borne diseases including chikungunya, dengue, malaria, West Nile, and Zika. Then the identified types of model were further investigated. Second, we narrowed down our survey to focus on only Zika virus research. The terms we searched for were “compartmental,” “spatial,” “metapopulation,” “network,” “individual-based,” “agent-based” AND “Zika.” All relevant studies were included regardless of the year of publication. We have collected research articles that were published before August 2017 based on our search criteria. In this publication survey, we explored the Google Scholar and PubMed databases.ResultsWe found five basic model architectures previously applied to vector-borne virus studies, particularly in Zika virus simulations. These include compartmental, spatial, metapopulation, network, and individual-based models. We found that Zika models carried out for early epidemics were mostly fit into compartmental structures and were less complicated compared to the more recent ones. Simple models are still commonly used for the timely assessment of epidemics. Nevertheless, due to the availability of large-scale real-world data and computational power, recently there has been growing interest in more complex modeling frameworks.DiscussionMathematical models are employed to explore and predict how an infectious disease spreads in the real world, evaluate the disease importation risk, and assess the effectiveness of intervention strategies. As the trends in modeling of infectious diseases have been shifting towards data-driven approaches, simple and complex models should be exploited differently. Simple models can be produced in a timely fashion to provide an estimation of the possible impacts. In contrast, complex models integrating real-world data require more time to develop but are far more realistic. The preparation of complicated modeling frameworks prior to the outbreaks is recommended, including the case of future Zika epidemic preparation.

show abstract

“…4.1.1 Population-level. There has been extensive research on disease outbreaks for many different types of diseases and epidemics, including seasonal flu [3], Zika [200], H1N1 [158], Ebola [13], and COVID-19 [162]. These predictions target both the location and time of future events, while the disease type is usually fixed to a specific type for each model.…”

Section: Healthcarementioning

confidence: 99%

Event Prediction in Big Data Era: A Systematic Survey

Zhao¹

2020

Preprint

View full text Add to dashboard Cite

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as earthquakes, civil unrest, system failures, pandemics, and crimes. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth, also thanks to advances in high performance computers and new Artificial Intelligence techniques. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex (e.g., spatial, temporal, and semantic) dependencies, and streaming data feeds. Due to the strong interdisciplinary nature of event prediction problems, most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts’ searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided to introduce wider applications to model developers to help them expand the impacts of their research. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

show abstract

Forecasting Ebola with a regression transmission model

Cited by 34 publications

References 14 publications

Impact of prophylactic vaccination strategies on Ebola virus transmission: A modeling analysis

Impact of prophylactic vaccination strategies on Ebola virus transmission: A modeling analysis

Dynamics of Zika virus outbreaks: an overview of mathematical modeling approaches

Event Prediction in Big Data Era: A Systematic Survey

Contact Info

Product

Resources

About