%0 Journal Article %1 KELLY2019100354 %A Kelly, J. Daniel %A Park, Junhyung %A Harrigan, Ryan J. %A Hoff, Nicole A. %A Lee, Sarita D. %A Wannier, Rae %A Selo, Bernice %A Mossoko, Mathias %A Njoloko, Bathe %A Okitolonda-Wemakoy, Emile %A Mbala-Kingebeni, Placide %A Rutherford, George W. %A Smith, Thomas B. %A Ahuka-Mundeke, Steve %A Muyembe-Tamfum, Jean Jacques %A Rimoin, Anne W. %A Schoenberg, Frederic Paik %D 2019 %J Epidemics %K cancerstemcell %P 100354 %R https://doi.org/10.1016/j.epidem.2019.100354 %T Real-time predictions of the 2018–2019 Ebola virus disease outbreak in the Democratic Republic of the Congo using Hawkes point process models %U http://www.sciencedirect.com/science/article/pii/S1755436519300258 %V 28 %X As of June 16, 2019, an Ebola virus disease (EVD) outbreak has led to 2136 reported cases in the northeastern region of the Democratic Republic of the Congo (DRC). As this outbreak continues to threaten the lives and livelihoods of people already suffering from civil strife and armed conflict, relatively simple mathematical models and their short-term predictions have the potential to inform Ebola response efforts in real time. We applied recently developed non-parametrically estimated Hawkes point processes to model the expected cumulative case count using daily case counts from May 3, 2018, to June 16, 2019, initially reported by the Ministry of Health of DRC and later confirmed in World Health Organization situation reports. We generated probabilistic estimates of the ongoing EVD outbreak in DRC extending both before and after June 16, 2019, and evaluated their accuracy by comparing forecasted vs. actual outbreak sizes, out-of-sample log-likelihood scores and the error per day in the median forecast. The median estimated outbreak sizes for the prospective thee-, six-, and nine-week projections made using data up to June 16, 2019, were, respectively, 2317 (95% PI: 2222, 2464); 2440 (95% PI: 2250, 2790); and 2544 (95% PI: 2273, 3205). The nine-week projection experienced some degradation with a daily error in the median forecast of 6.73 cases, while the six- and three-week projections were more reliable, with corresponding errors of 4.96 and 4.85 cases per day, respectively. Our findings suggest the Hawkes point process may serve as an easily-applied statistical model to predict EVD outbreak trajectories in near real-time to better inform decision-making and resource allocation during Ebola response efforts.

@article{KELLY2019100354, abstract = {As of June 16, 2019, an Ebola virus disease (EVD) outbreak has led to 2136 reported cases in the northeastern region of the Democratic Republic of the Congo (DRC). As this outbreak continues to threaten the lives and livelihoods of people already suffering from civil strife and armed conflict, relatively simple mathematical models and their short-term predictions have the potential to inform Ebola response efforts in real time. We applied recently developed non-parametrically estimated Hawkes point processes to model the expected cumulative case count using daily case counts from May 3, 2018, to June 16, 2019, initially reported by the Ministry of Health of DRC and later confirmed in World Health Organization situation reports. We generated probabilistic estimates of the ongoing EVD outbreak in DRC extending both before and after June 16, 2019, and evaluated their accuracy by comparing forecasted vs. actual outbreak sizes, out-of-sample log-likelihood scores and the error per day in the median forecast. The median estimated outbreak sizes for the prospective thee-, six-, and nine-week projections made using data up to June 16, 2019, were, respectively, 2317 (95% PI: 2222, 2464); 2440 (95% PI: 2250, 2790); and 2544 (95% PI: 2273, 3205). The nine-week projection experienced some degradation with a daily error in the median forecast of 6.73 cases, while the six- and three-week projections were more reliable, with corresponding errors of 4.96 and 4.85 cases per day, respectively. Our findings suggest the Hawkes point process may serve as an easily-applied statistical model to predict EVD outbreak trajectories in near real-time to better inform decision-making and resource allocation during Ebola response efforts.}, added-at = {2020-05-06T19:55:50.000+0200}, author = {Kelly, J. Daniel and Park, Junhyung and Harrigan, Ryan J. and Hoff, Nicole A. and Lee, Sarita D. and Wannier, Rae and Selo, Bernice and Mossoko, Mathias and Njoloko, Bathe and Okitolonda-Wemakoy, Emile and Mbala-Kingebeni, Placide and Rutherford, George W. and Smith, Thomas B. and Ahuka-Mundeke, Steve and Muyembe-Tamfum, Jean Jacques and Rimoin, Anne W. and Schoenberg, Frederic Paik}, biburl = {https://www.bibsonomy.org/bibtex/2fca42ba776b2c65a04cb6b3dde82fe23/sam-kelly}, doi = {https://doi.org/10.1016/j.epidem.2019.100354}, interhash = {040560fdbda255b024cdeaec496f8224}, intrahash = {fca42ba776b2c65a04cb6b3dde82fe23}, issn = {1755-4365}, journal = {Epidemics}, keywords = {cancerstemcell}, pages = 100354, timestamp = {2020-05-06T19:55:50.000+0200}, title = {Real-time predictions of the 2018–2019 Ebola virus disease outbreak in the Democratic Republic of the Congo using Hawkes point process models}, url = {http://www.sciencedirect.com/science/article/pii/S1755436519300258}, volume = 28, year = 2019 }