Outbreak analytics: a developing data science for informing the response to emerging pathogens

Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control‘. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

[1]  Xinxin Zhang,et al.  Estimation of reproduction numbers of COVID-19 in typical countries and epidemic trends under different prevention and control scenarios , 2020, Frontiers of Medicine.

[2]  Erica L. Thompson,et al.  Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation , 2020, Chaos.

[3]  Paul E. Johnson,et al.  R Markdown: The Definitive Guide , 2020, The American Statistician.

[4]  Thomas W Scott,et al.  Optimizing the deployment of ultra-low volume and targeted indoor residual spraying for dengue outbreak response , 2020, PLoS Comput. Biol..

[5]  G. Leung,et al.  Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study , 2020, The Lancet.

[6]  Robert Moss,et al.  Infectious disease pandemic planning and response: Incorporating decision analysis , 2020, PLoS medicine.

[7]  R. Irizarry ggplot2 , 2019, Introduction to Data Science.

[8]  Joshua S Weitz,et al.  A practical generation-interval-based approach to inferring the strength of epidemics from their speed. , 2019, Epidemics.

[9]  J. Durgan Yellow , 2018, Art, Race, and Fantastic Color Change in the Victorian Novel.

[10]  Ali S Khan,et al.  World Health Organization Early Warning, Alert and Response System in the Rohingya Crisis, Bangladesh, 2017–2018 , 2018, Emerging infectious diseases.

[11]  Catherine A. Freije,et al.  Genomic analysis of Lassa virus from the 2018 surge in Nigeria , 2018, The New England journal of medicine.

[12]  Thibaut Jombart,et al.  epicontacts: Handling, visualisation and analysis of epidemiological contacts , 2018, F1000Research.

[13]  Zhian N. Kamvar,et al.  epiflows: an R package for risk assessment of travel-related spread of disease , 2018, F1000Research.

[14]  W. Edmunds,et al.  Real-time analysis of the diphtheria outbreak in forcibly displaced Myanmar nationals in Bangladesh , 2018, bioRxiv.

[15]  Khumbo Kalua,et al.  Quality Assurance and Quality Control in the Global Trachoma Mapping Project , 2018, The American journal of tropical medicine and hygiene.

[16]  J. Lewnard Ebola virus disease: 11 323 deaths later, how far have we come? , 2018, The Lancet.

[17]  Yihui Xie,et al.  R Markdown , 2018 .

[18]  Michael J. Ryan,et al.  Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April–May, 2018: an epidemiological study , 2018, The Lancet.

[19]  E. Holmes,et al.  Pandemics: spend on surveillance, not prediction , 2018, Nature.

[20]  J. Edmunds,et al.  Real-Time Modeling Should Be Routinely Integrated into Outbreak Response , 2018, The American journal of tropical medicine and hygiene.

[21]  K. Porten,et al.  Risk factors for measles mortality and the importance of decentralized case management during an unusually large measles epidemic in eastern Democratic Republic of Congo in 2013 , 2018, PloS one.

[22]  Kevin Mortimer,et al.  A comparison of smartphone and paper data-collection tools in the Burden of Obstructive Lung Disease (BOLD) study in Gezira state, Sudan , 2018, PloS one.

[23]  O. Pybus,et al.  Genomic Insights into Zika Virus Emergence and Spread , 2018, Cell.

[24]  Thibaut Jombart,et al.  When are pathogen genome sequences informative of transmission events? , 2018, PLoS pathogens.

[25]  Molly H. Biehl,et al.  Local, national, and regional viral haemorrhagic fever pandemic potential in Africa: a multistage analysis , 2017, The Lancet.

[26]  Trevor Bedford,et al.  Nextstrain: real-time tracking of pathogen evolution , 2017, bioRxiv.

[27]  Leonhard Held,et al.  Probabilistic forecasting in infectious disease epidemiology: the 13th Armitage lecture , 2017, Statistics in medicine.

[28]  Sebastian Funk,et al.  Assessing the performance of real-time epidemic forecasts: A case study of the 2013-16 Ebola epidemic , 2017 .

[29]  Gerardo Chowell,et al.  The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt. , 2017, Epidemics.

[30]  Christl A Donnelly,et al.  International risk of yellow fever spread from the ongoing outbreak in Brazil, December 2016 to May 2017 , 2017, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[31]  A Cori,et al.  International risk of yellow fever spread from the ongoing outbreak in Brazil , 2017, bioRxiv.

[32]  A. Merkoçi,et al.  Mobile phone-based biosensing: An emerging "diagnostic and communication" technology. , 2017, Biosensors & bioelectronics.

[33]  Paul J. Birrell,et al.  Evidence Synthesis for Stochastic Epidemic Models. , 2017, Statistical science : a review journal of the Institute of Mathematical Statistics.

[34]  Derek Tseng,et al.  Evaluation of a Mobile Phone-Based Microscope for Screening of Schistosoma haematobium Infection in Rural Ghana. , 2017, The American journal of tropical medicine and hygiene.

[35]  Mohamed A. Vandi,et al.  Contact tracing performance during the Ebola virus disease outbreak in Kenema district, Sierra Leone , 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

[36]  Shrivastava Saurabh,et al.  Role of contact tracing in containing the 2014 Ebola outbreak: a review. , 2017, African health sciences.

[37]  Xavier Didelot,et al.  Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks , 2017, PLoS Comput. Biol..

[38]  Trevor Bedford,et al.  Virus genomes reveal factors that spread and sustained the Ebola epidemic , 2017, Nature.

[39]  Gerardo Chowell,et al.  Perspectives on model forecasts of the 2014–2015 Ebola epidemic in West Africa: lessons and the way forward , 2017, BMC Medicine.

[40]  Thibaut Jombart,et al.  Key data for outbreak evaluation: building on the Ebola experience , 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

[41]  Simon Cauchemez,et al.  Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study , 2017, The Lancet. Infectious diseases.

[42]  Wes Hinsley,et al.  A simple approach to measure transmissibility and forecast incidence , 2017, Epidemics.

[43]  N. R. Faria,et al.  Establishment and cryptic transmission of Zika virus in Brazil and the Americas , 2017, Nature.

[44]  S. Funk,et al.  Real-time dynamic modelling for the design of a cluster-randomized phase 3 Ebola vaccine trial in Sierra Leone. , 2017, Vaccine.

[45]  Derek Tseng,et al.  Targeted DNA sequencing and in situ mutation analysis using mobile phone microscopy , 2017, Nature Communications.

[46]  R. Eggo,et al.  Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model , 2016, Epidemics.

[47]  Yihui Xie,et al.  bookdown: Authoring Books and Technical Documents with R Markdown , 2016 .

[48]  P. Daszak,et al.  The Global Virome Project , 2018, Science.

[49]  Jantien A. Backer,et al.  Spatiotemporal Analysis of the 2014 Ebola Epidemic in West Africa , 2016, PLoS Comput. Biol..

[50]  Mosoka P. Fallah,et al.  Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study , 2016, PLoS Medicine.

[51]  Khalil Abudahab,et al.  Microreact: visualizing and sharing data for genomic epidemiology and phylogeography , 2016, Microbial genomics.

[52]  Thibaut Jombart,et al.  Phylogenetic structure of European Salmonella Enteritidis outbreak correlates with national and international egg distribution network , 2016, Microbial genomics.

[53]  Christl A. Donnelly,et al.  Unraveling the drivers of MERS-CoV transmission , 2016, Proceedings of the National Academy of Sciences.

[54]  J. Cox,et al.  Factors associated with high heterogeneity of malaria at fine spatial scale in the Western Kenyan highlands , 2016, Malaria Journal.

[55]  C. Murray,et al.  Mapping global environmental suitability for Zika virus , 2016, eLife.

[56]  O. Doumbo,et al.  The Impact of Hotspot-Targeted Interventions on Malaria Transmission in Rachuonyo South District in the Western Kenyan Highlands: A Cluster-Randomized Controlled Trial , 2016, PLoS medicine.

[57]  Nicola De Maio,et al.  SCOTTI: Efficient Reconstruction of Transmission within Outbreaks with the Structured Coalescent , 2016, PLoS Comput. Biol..

[58]  J. Gardy,et al.  Declaring a tuberculosis outbreak over with genomic epidemiology , 2016, bioRxiv.

[59]  David A. Matthews,et al.  Real-time, portable genome sequencing for Ebola surveillance , 2016, Nature.

[60]  W. Team,et al.  Ebola Virus Disease among Male and Female Persons in West Africa , 2016 .

[61]  Hiroshi Nishiura,et al.  Objective Determination of End of MERS Outbreak, South Korea, 2015 , 2016, Emerging infectious diseases.

[62]  Christl A. Donnelly,et al.  The role of rapid diagnostics in managing Ebola epidemics , 2015, Nature.

[63]  Anne Njoroge,et al.  Enhancing data security in open data kit as an mHealth application , 2015, 2015 International Conference on Computing, Communication and Security (ICCCS).

[64]  Sebastian Funk,et al.  Estimating the probability of demonstrating vaccine efficacy in the declining Ebola epidemic: a Bayesian modelling approach , 2015, BMJ Open.

[65]  Lawrence O Gostin,et al.  Will Ebola change the game? Ten essential reforms before the next pandemic. The report of the Harvard-LSHTM Independent Panel on the Global Response to Ebola , 2015, The Lancet.

[66]  Sebastian Funk,et al.  Measuring the impact of Ebola control measures in Sierra Leone , 2015, Proceedings of the National Academy of Sciences.

[67]  S. Sardi,et al.  Zika Virus Outbreak, Bahia, Brazil , 2015, Emerging infectious diseases.

[68]  John-Arne Røttingen,et al.  The ring vaccination trial: a novel cluster randomised controlled trial design to evaluate vaccine efficacy and effectiveness during outbreaks, with special reference to Ebola , 2015, BMJ : British Medical Journal.

[69]  D. Bausch,et al.  Ebola Virus: Sensationalism, Science, and Human Rights , 2015, The Journal of infectious diseases.

[70]  G. Chowell,et al.  Assessing the risk of observing multiple generations of Middle East respiratory syndrome (MERS) cases given an imported case. , 2015, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[71]  Hao Hu,et al.  Extracting transmission networks from phylogeographic data for epidemic and endemic diseases: Ebola virus in Sierra Leone, 2009 H1N1 pandemic influenza and polio in Nigeria , 2015, International health.

[72]  F. Shuaib,et al.  Innovative Technological Approach to Ebola Virus Disease Outbreak Response in Nigeria Using the Open Data Kit and Form Hub Technology , 2015, PloS one.

[73]  Dan J Stein,et al.  Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013 , 2015, The Lancet.

[74]  Christl A. Donnelly,et al.  A review of epidemiological parameters from Ebola outbreaks to inform early public health decision-making , 2015, Scientific Data.

[75]  S. Hay,et al.  Emergence and potential for spread of Chikungunya virus in Brazil , 2015, BMC Medicine.

[76]  T. Dallman,et al.  A multi-country Salmonella Enteritidis phage type 14b outbreak associated with eggs from a German producer: 'near real-time' application of whole genome sequencing and food chain investigations, United Kingdom, May to September 2014. , 2015, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[77]  T. Dallman,et al.  Public Health Investigation of Two Outbreaks of Shiga Toxin-Producing Escherichia coli O157 Associated with Consumption of Watercress , 2015, Applied and Environmental Microbiology.

[78]  K Denecke,et al.  Surveillance and Outbreak Response Management System (SORMAS) to support the control of the Ebola virus disease outbreak in West Africa. , 2015, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[79]  W. Team Ebola virus disease among children in West Africa. , 2015 .

[80]  F. Luquero,et al.  Geographic Distribution and Mortality Risk Factors during the Cholera Outbreak in a Rural Region of Haiti, 2010-2011 , 2015, PLoS neglected tropical diseases.

[81]  Hao Hu,et al.  Extracting transmission networks from phylogeographic data for epidemic and endemic diseases: Ebola virus in Sierra Leone, 2009 H1N1 pandemic influenza and polio in Nigeria , 2015, International health.

[82]  W. Team,et al.  West African Ebola Epidemic after One Year — Slowing but Not Yet under Control , 2015 .

[83]  Eli P. Fenichel,et al.  Accounting for behavioral responses during a flu epidemic using home television viewing , 2015, BMC Infectious Diseases.

[84]  Geoffrey O. Arunga,et al.  A comparison of smartphones to paper-based questionnaires for routine influenza sentinel surveillance, Kenya, 2011–2012 , 2014, BMC Medical Informatics and Decision Making.

[85]  F. Luquero,et al.  Descriptive Epidemiology of Typhoid Fever during an Epidemic in Harare, Zimbabwe, 2012 , 2014, PloS one.

[86]  Pejman Rohani,et al.  Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola , 2014, Proceedings of the Royal Society B: Biological Sciences.

[87]  S. Shrivastava,et al.  Utility of contact tracing in reducing the magnitude of Ebola disease. , 2014, Germs.

[88]  W. Team Ebola Virus Disease in West Africa — The First 9 Months of the Epidemic and Forward Projections , 2014 .

[89]  Rachel S. G. Sealfon,et al.  Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak , 2014, Science.

[90]  Kris Sankaran,et al.  structSSI: Simultaneous and Selective Inference for Grouped or Hierarchically Structured Data. , 2014, Journal of statistical software.

[91]  Kosuke Imai,et al.  mediation: R Package for Causal Mediation Analysis , 2014 .

[92]  David L. Smith,et al.  Mapping the zoonotic niche of Ebola virus disease in Africa , 2014, eLife.

[93]  Samuel Soubeyrand,et al.  OutbreakTools: A new platform for disease outbreak analysis using the R software , 2014, Epidemics.

[94]  Rosamund F. Lewis,et al.  Yellow Fever in Africa: Estimating the Burden of Disease and Impact of Mass Vaccination from Outbreak and Serological Data , 2014, PLoS medicine.

[95]  Xavier Didelot,et al.  Bayesian Inference of Infectious Disease Transmission from Whole-Genome Sequence Data , 2014, Molecular biology and evolution.

[96]  Dong Xie,et al.  BEAST 2: A Software Platform for Bayesian Evolutionary Analysis , 2014, PLoS Comput. Biol..

[97]  Thibaut Jombart,et al.  outbreaker2: Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data , 2018 .

[98]  Simon Cauchemez,et al.  Edinburgh Research Explorer Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, surveillance biases, and transmissibility , 2022 .

[99]  Jacco Wallinga,et al.  Relating Phylogenetic Trees to Transmission Trees of Infectious Disease Outbreaks , 2013, Genetics.

[100]  F. Luquero,et al.  Risk factors for cholera transmission in Haiti during inter-peak periods: insights to improve current control strategies from two case-control studies , 2013, Epidemiology and Infection.

[101]  Astrid Gall,et al.  Transmission and evolution of the Middle East respiratory syndrome coronavirus in Saudi Arabia: a descriptive genomic study , 2013, The Lancet.

[102]  Joy Buolamwini,et al.  A Novel Electronic Data Collection System for Large-Scale Surveys of Neglected Tropical Diseases , 2013, PloS one.

[103]  C. Fraser,et al.  A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics , 2013, American journal of epidemiology.

[104]  D. Cummings,et al.  Hospital outbreak of Middle East respiratory syndrome coronavirus. , 2013, The New England journal of medicine.

[105]  S Cauchemez,et al.  Transmission scenarios for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and how to tell them apart. , 2013, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[106]  M. Pallen,et al.  Genomics and outbreak investigation: from sequence to consequence , 2013, Genome Medicine.

[107]  Gaetano Borriello,et al.  Open data kit 2.0: expanding and refining information services for developing regions , 2013, HotMobile '13.

[108]  Julian Parkhill,et al.  Whole-genome sequencing for analysis of an outbreak of meticillin-resistant Staphylococcus aureus: a descriptive study , 2013, The Lancet. Infectious Diseases.

[109]  Pierre-Yves Boëlle,et al.  The R0 package: a toolbox to estimate reproduction numbers for epidemic outbreaks , 2012, BMC Medical Informatics and Decision Making.

[110]  Bernadette A. Thomas,et al.  Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010 , 2012, The Lancet.

[111]  Michael J. Crawley The R Book: Crawley/The R Book , 2012 .

[112]  K. Khan,et al.  The Impact of Infection on Population Health: Results of the Ontario Burden of Infectious Diseases Study , 2012, PloS one.

[113]  Katharina T. Huber,et al.  ape 3.0: New tools for distance-based phylogenetics and evolutionary analysis in R , 2012, Bioinform..

[114]  J Wallinga,et al.  Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data , 2012, Proceedings of the Royal Society B: Biological Sciences.

[115]  John P. Woodall,et al.  Atlas of Human Infectious Diseases: Wertheim/Atlas of Human Infectious Diseases , 2012 .

[116]  Matthias Greiner,et al.  German outbreak of Escherichia coli O104:H4 associated with sprouts. , 2011, The New England journal of medicine.

[117]  N. Ferguson,et al.  Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza , 2011, Proceedings of the National Academy of Sciences.

[118]  Klaus Peter Schliep,et al.  phangorn: phylogenetic analysis in R , 2010, Bioinform..

[119]  Gaetano Borriello,et al.  Open data kit: tools to build information services for developing regions , 2010, ICTD.

[120]  Nicholas J Loman,et al.  High-throughput sequencing and clinical microbiology: progress, opportunities and challenges. , 2010, Current opinion in microbiology.

[121]  T Jombart,et al.  Reconstructing disease outbreaks from genetic data: a graph approach , 2010, Heredity.

[122]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[123]  David M. Aanensen,et al.  EpiCollect: Linking Smartphones to Web Applications for Epidemiology, Ecology and Community Data Collection , 2009, PloS one.

[124]  Alex R Cook,et al.  The International Journal of Biostatistics Inference in Epidemic Models without Likelihoods , 2011 .

[125]  Gavin J. D. Smith,et al.  Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic , 2009, Nature.

[126]  E. Lyons,et al.  Pandemic Potential of a Strain of Influenza A (H1N1): Early Findings , 2009, Science.

[127]  Babak Pourbohloul,et al.  Modeling Infectious Diseases in Humans and Animals By Matthew James Keeling and Pejman Rohani Princeton, NJ: Princeton University Press, 2008. 408 pp., Illustrated. $65.00 (hardcover) , 2008 .

[128]  Gaël Thébaud,et al.  Integrating genetic and epidemiological data to determine transmission pathways of foot-and-mouth disease virus , 2008, Proceedings of the Royal Society B: Biological Sciences.

[129]  A. Flahault,et al.  Estimating the impact of school closure on influenza transmission from Sentinel data , 2008, Nature.

[130]  Kenneth D. Mandl,et al.  HealthMap: Global Infectious Disease Monitoring through Automated Classification and Visualization of Internet Media Reports , 2008, Journal of the American Medical Informatics Association.

[131]  A. Le Menach,et al.  Time is of the essence: exploring a measles outbreak response vaccination in Niamey, Niger , 2008, Journal of The Royal Society Interface.

[132]  Michael Höhle,et al.  surveillance: An R package for the monitoring of infectious diseases , 2007, Comput. Stat..

[133]  M. Keeling,et al.  Modeling Infectious Diseases in Humans and Animals , 2007 .

[134]  M. Lipsitch,et al.  How generation intervals shape the relationship between growth rates and reproductive numbers , 2007, Proceedings of the Royal Society B: Biological Sciences.

[135]  R. Brookmeyer,et al.  A Hypothesis Test for the End of a Common Source Outbreak , 2006, Biometrics.

[136]  Edward J Feil,et al.  Displaying the relatedness among isolates of bacterial species -- the eBURST approach. , 2004, FEMS microbiology letters.

[137]  J. Wallinga,et al.  Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures , 2004, American journal of epidemiology.

[138]  C. Fraser,et al.  Epidemiology, transmission dynamics and control of SARS: the 2002-2003 epidemic. , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[139]  W. Hanage,et al.  eBURST: Inferring Patterns of Evolutionary Descent among Clusters of Related Bacterial Genotypes from Multilocus Sequence Typing Data , 2004, Journal of bacteriology.

[140]  O. Pybus,et al.  Unifying the Epidemiological and Evolutionary Dynamics of Pathogens , 2004, Science.

[141]  M. Maiden,et al.  Multi-locus sequence typing: a tool for global epidemiology. , 2003, Trends in microbiology.

[142]  John P. Huelsenbeck,et al.  MrBayes 3: Bayesian phylogenetic inference under mixed models , 2003, Bioinform..

[143]  C. Fraser,et al.  Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong , 2003, The Lancet.

[144]  Christopher G. Dowson,et al.  Development of a Multilocus Sequence Typing Scheme for the Pig Pathogen Streptococcus suis: Identification of Virulent Clones and Potential Capsular Serotype Exchange , 2002, Journal of Clinical Microbiology.

[145]  C. Walsh,et al.  The evolutionary history of methicillin-resistant Staphylococcus aureus (MRSA) , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[146]  Nick Andrews,et al.  A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease , 1996 .

[147]  A. J. Hall Infectious diseases of humans: R. M. Anderson & R. M. May. Oxford etc.: Oxford University Press, 1991. viii + 757 pp. Price £50. ISBN 0-19-854599-1 , 1992 .

[148]  J. Buring,et al.  Epidemiology in Medicine , 1987 .

[149]  M Gross,et al.  Oswego County revisited. , 1976, Public health reports.

[150]  Susan Buck-Morss,et al.  Exchange , 1919, The Indian medical gazette.

[151]  J. Snow On the Mode of Communication of Cholera , 1856, Edinburgh medical journal.

[152]  P. Salama Outbreak of Ebola virus disease in the Democratic Republic of the Congo , April – May , 2018 : an epidemiological study , 2019 .

[153]  T. Jombart,et al.  epicontacts: Handling, visualisation and analysis of epidemiological contacts , 2018, F1000Research.

[154]  Wes Hinsley,et al.  Ebola Virus Disease among Male and Female Persons in West Africa. , 2016, The New England journal of medicine.

[155]  C. Fraser,et al.  Ebola Virus Disease among Children in West Africa , 2015 .

[156]  Mikiko Senga,et al.  Ebola virus disease in West Africa--the first 9 months of the epidemic and forward projections. , 2014, The New England journal of medicine.

[157]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[158]  P. Horby,et al.  Atlas of human infectious diseases. , 2012 .

[159]  A. Molesworth Atlas of Human Infectious Diseases , 2012 .

[160]  J. Felsenstein Evolutionary trees from DNA sequences: A maximum likelihood approach , 2005, Journal of Molecular Evolution.

[161]  Alistair Woodward,et al.  Introduction and methods: assessing the environmental burden of disease at national and local levels. , 2003 .

[162]  Alan D. Lopez,et al.  The Global Burden of Disease Study , 2003 .

[163]  Benjamin S. Baumer,et al.  Tidy data , 2022, Modern Data Science with R.